essays in natural resources and development economics · 2018. 10. 10. · essays in natural...

Essays in Natural Resources and Development Economics

by

Danamona Holinirina Andrianarimanana

A dissertation submitted in partial satisfaction of the

requirements for the degree of

Doctor of Philosophy

in

Agricultural and Resource Economics

in the

Graduate Division

of the

University of California, Berkeley

Committee in charge:

Professor Maximilian Au↵hammer, Chair

Professor Alain de Janvry

Associate Professor Solomon Hsiang

Spring 2018


Copyright 2018

by


Abstract


by


Doctor of Philosophy in Agricultural and Resource Economics

University of California, Berkeley

Professor Maximilian Au↵hammer, Chair

There is great geographical overlap between key areas of natural resources, global biodi-versity and regions of acute poverty. The world’s poorest people, including 59% of thepopulation of Asia, Africa and Latin America live in rural areas alongside great naturalresources on which they heavily rely for food source and income generation. However,proximity does not imply free unlimited access and often involves a great deal of trade-o↵s and risk ranging from natural weather and catastrophic shocks a↵ecting resourceavailability, productivity and even human lives, to changes in governance and resourceuse regulations. In this dissertation, I study the linkages between natural resources use,livelihoods, governance and the environment, using the case study of Madagascar, a low-income country with great biodiversity and natural resources endowment. In particular, Istudy how di↵erent types of regulations and restrictions a↵ect household resource use andwell-being. In Chapter 1, I evaluate the health and wealth trade-o↵s of the widely prac-ticed fire use in agriculture in Madagascar, using high-frequency satellite data to modelpollution exposure taking advantage of random variation in wind direction. In Chapter2, I study how poor households cope with natural disasters using the quasi-experimentsetting of high frequency cyclones in Madagascar. In Chapter 3, I take advantage of aunique dataset coupled with the staggered rollout of a biodiversity conservation policy tostudy the impacts of community-based conservation on bushmeat hunting in northeasternMadagascar.

In the first chapter, I study the impacts of agricultural fires on local health and on agri-cultural productivity in Madagascar. Every year, despite agricultural fires being illegal,25% to 50% of grasslands and 7% to 10% of forests are set on fire due to slash-and-burnagriculture and livestock farming. This leads to great pollution throughout the island,yet there is limited empirical evidence on the health impacts of fires in the island. I firstestimate the health impacts of fires by using high frequency and high resolution satel-lite data on fire location and wind speed on the day of fire to model pollution exposurearound population centers. Identification comes from the random variation in wind di-rection and the frequent change in pollution source. I find that agricultural fires greatlyimpact birth outcomes and respiratory health of infants and that fires are responsible forover 4,000 “missing infants”, or 0.7% of all births across the island every year. To identifythe agricultural impacts of fires, I use an instrumental variable strategy taking advantageof a rapid expansion of protected areas in Madagascar that led to tripling of protectedareas and delimitation of numerous potential parks. I use proposed parks, areas thatwere physically delimited as potential o�cial protected areas, as an instrument for fires.Delimitation of proposed parks led to reduced fire activity, however, since parks were notactually implemented, surrounding populations were una↵ected by potential economicreturns or changes in behavior that would raise concerns regarding the validity of the

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exclusion restriction. Grassland fires led to increased livestock production and yields forcassava and corn, whereas forest fires increased corn farming land and harvest, leadingto decreased food prices. These quantity and price e↵ects increased consumer surplus byUSD1.884 billion per year, implying that, for the output gains to outweigh the mortalityimpacts, one would have to assume a value of statistical life of less than USD440,000,whereas typical values for VSL range from 4 million to 9 million USD. Therefore themortality costs of fires alone, excluding hospitalization costs and morbidity, exceed thebenefits from increased agricultural production. Given that land use rights are ambigu-ous and government resources in regulating forest fires are limited, a more cooperativeand integrative approach such as payments for ecosystem services might be e↵ective inincentivizing farmers to engage in less frequent more sustainable fire activity. In the sec-ond chapter, I use cyclone track data and hourly wind direction data to model cycloneexposure and study the impact of tropical storms in Madagascar. Madagascar is thesecond most exposed country to multi-disaster risks in Africa, and experiences multipleepisodes of droughts, floods, locust invasions and cyclones every year. On average, theisland yearly experiences three to five cyclones that claim 10% to 30% of annual GDPin post-disaster losses and damages. Indeed, 74% of total labor is employed in agricul-ture, furthermore, agricultural products including exports amount to 45% of GDP. Yet,there is little government e↵ort in terms of risk mitigation, resilience building and evendisaster relief. Looking at the impact of cyclones on household well-being along multipledimensions, I find that both rural and urban households are negatively impacted by cy-clones in Madagascar despite better infrastructure and less reliance on natural resourcesin urban areas. While rural areas experience more physical losses than urban areas asmeasured by cyclone e↵ects on housing and access to electricity, rural households are ableto smooth consumption and are less prone to cyclone-driven poverty compared to theirurban counterparts. In this latter group, average cyclones have no significant impacton physical assets, but lead to lower consumption and higher rates of transient poverty.I show that this is the result of a strong informal safety net between rural and urbanfamilies through informal insurance and relief in the form inter-household transfers. Toprovide relief to rural families, urban households reduce expenditure in non-food expen-diture including education. This suggests that, while partially e↵ective in managing riskand achieving consumption smoothing along some key dimensions, lack of formal insur-ance diverts resources away from potentially productive investments such as educationand towards unequivocally necessary informal relief. In the third chapter, I use a uniquehousehold-level panel data to evaluate how community-based conservation impacts bush-meat or wildlife hunting and consumption in the northeastern rainforests of Madagascar,where lemurs, bats, carnivores, tenrecs and bush pigs are commonly consumed to sat-isfy nutritional needs. Taking advantage of the staggered rollout of the policy, I findthat community-based conservation has decreased overall hunting in the study area byreducing opportunistic hunting and hunting by less reliant, richer households. This e↵ectwas larger among relatively more educated households. Furthermore, community-basedconservation successfully modified consumption patterns among poorer households suchthat illegal hunting (hunting of lemurs and bats) was reduced and substituted by huntingpractices conforming with conservation practices (seasonal hunting of sustainable prey).While these results are encouraging given the increasing shift towards decentralization,it is important to note that, in my study setting, community-based conservation wasfound to have some limitations. First, e↵ects did not persist and faded over time. Sec-ond, not all types of hunting were successfully reduced and the policy led to increasedactive hunting through weapons and traps as households respond by retaliating and over-extracting resources in fear of completely losing access in the future. The e↵ectivenessof community-based conservation on opportunistic hunting and bushmeat purchase was

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found to be heterogeneous based on income and education. Better community integrationand dissemination of community conservation design principles is therefore recommendedas it has proven to e↵ectively reduce illegal hunting and also has the potential of solvingthe retaliation and fear-based extraction behavior. Furthermore, given that biodiversityis a global public good, local users should not be the only bearers of conservation costsand alternative livelihood strategies need to be introduced for the long-run success ofconservation e↵orts.

3

Contents

Contents i

List of Figures ii

List of Tables ii

Acknowledgments v

1 Fire as an agricultural input: are the output gains worth the mortality

impacts? A case study of Madagascar 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Background: Madagascar’s fire problem . . . . . . . . . . . . . . . . . . . . . 3

1.3 Health analysis data and methods . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 Health results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.5 Agricultural outcomes analysis data and methods . . . . . . . . . . . . . . . 16

1.6 Agricultural outcomes results and discussion . . . . . . . . . . . . . . . . . . 18

1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.8 Figures and tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2 The role of inter-household transfers in coping with post-disaster losses in

Madagascar 46

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

2.4 Estimation strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.5 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58


3 To hunt or not to hunt: evaluation of community-based biodiversity con-

servation in northeastern Madagascar 70

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3.4 Estimation strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

3.5 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

i

3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83


References 95

List of Figures

Figure 1.1: Study Area: Madagascar . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Figure 1.2: Average number of daily fires (January to March) . . . . . . . . . . . . 24

Figure 1.3: Average number of daily fires (April to December) . . . . . . . . . . . . 25

Figure 1.4: Area with observed fire (km2) . . . . . . . . . . . . . . . . . . . . . . . 26

Figure 1.5: Monthly average of area with observed fire (km2) . . . . . . . . . . . . 26

Figure 1.6: Fire pixels against yearly agricultural land . . . . . . . . . . . . . . . . 26

Figure 1.7: Fire pixels against yearly livestock . . . . . . . . . . . . . . . . . . . . . 26

Figure 1.8: Decomposition of fires by wind direction . . . . . . . . . . . . . . . . . 26

Figure 1.9: Protected areas and parks in Madagascar . . . . . . . . . . . . . . . . . 27

Figure 2.1: Madagascar’s full cyclone history . . . . . . . . . . . . . . . . . . . . . 60

Figure 2.2: Madagascar’s cyclones during study period (1995 - 2010) . . . . . . . . 61

Figure 2.3: Number of cyclones observed per month since 1851 . . . . . . . . . . . 62

Figure 2.4: Commune wind speed variation during cyclone Gafilo . . . . . . . . . . 62

Figure 2.5: Cyclone exposure of Malagasy communes during the period 1970 - 2010 63

Figure 3.1: Study area and villages . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Figure 3.2: Reported obtention method for bushmeat consumed . . . . . . . . . . . 86

Figure 3.3: Bushmeat consumption during study period . . . . . . . . . . . . . . . 86

List of Tables

Table 1.1: Aerosol and fire summary statistics . . . . . . . . . . . . . . . . . . . . 28

Table 1.2: Health summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . 28

Table 1.3: Regression of pollution on monthly count of fires . . . . . . . . . . . . . 29

Table 1.4: Regression of pollution on monthly count of confidence-weighted fires . 30

Table 1.5: Regression of pollution on monthly count of fires weighted by confidence,

angle and distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Table 1.6: Regression of pollution on monthly count of fires, quadratic specification 31

Table 1.7: Decomposing fires by di↵erent angles . . . . . . . . . . . . . . . . . . . 31

Table 1.8: Regression of birth outcomes on monthly count of confidence-weighted

fires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Table 1.9: Regression of birth outcomes on forest fires and grassland fires . . . . . 33

Table 1.10: E↵ects of fires on cases of respiratory and diarrheal diseases (All ages) . 34

ii

Table 1.11: E↵ects of fires on cases of respiratory and diarrheal diseases (Children 0

- 5years) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Table 1.12: E↵ect of fires on cases of respiratory and diarrheal diseases (Infants 0 -

12 months) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Table 1.13: E↵ect of fires on cases of respiratory and diarrheal diseases by type of

fire (All ages) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35


fire (Children 0 - 5 years) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36


fire (0 to 12 months) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Table 1.16: Agricultural production summary statistics . . . . . . . . . . . . . . . . 38

Table 1.17: Reduced form impacts of all fires on area cropped . . . . . . . . . . . . 38

Table 1.18: Reduced form impacts of all fires on quantity harvested . . . . . . . . . 39

Table 1.19: Reduced form impacts of all fires on crop yield . . . . . . . . . . . . . . 39

Table 1.20: Reduced form impacts of grassland and forest fires on area cropped . . 40

Table 1.21: Reduced form impacts of grassland and forest fires on quantity harvested 40

Table 1.22: Reduced form impacts of grassland fires and forest fires on crop yield . 41

Table 1.23: Reduced form impacts of grassland fires by trimester on yield and livestock 41

Table 1.24: First stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Table 1.25: IV impacts on cropped area (All fires) . . . . . . . . . . . . . . . . . . 42

Table 1.26: IV impacts on cropped quantity (All fires) . . . . . . . . . . . . . . . . 43

Table 1.27: IV impacts on crop yield (All fires) . . . . . . . . . . . . . . . . . . . . 43

Table 1.28: IV impacts on cropped area (Grassland and forest fires) . . . . . . . . . 44

Table 1.29: IV impacts on cropped quantity (Grassland and forest fires) . . . . . . 44

Table 1.30: IV impacts on crop yield (Grassland and forest fires) . . . . . . . . . . 45

Table 1.31: IV impacts on food prices (All fires) . . . . . . . . . . . . . . . . . . . . 45

Table 2.1: Cyclone summary statistics (IBTrACS) . . . . . . . . . . . . . . . . . . 63

Table 2.2: Cyclone summary statistics (constructed exposure) . . . . . . . . . . . 64

Table 2.3: Household summary statistics . . . . . . . . . . . . . . . . . . . . . . . 64

Table 2.4: Transfer summary statistics . . . . . . . . . . . . . . . . . . . . . . . . 65

Table 2.5: Balance of household characteristics based on cyclone exposure . . . . . 65

Table 2.6: Impact of cyclones on household well-being . . . . . . . . . . . . . . . . 66

Table 2.7: Impact of cyclones on per capita transfers and consumption . . . . . . 66

Table 2.8: Impact of cyclone dummy on household well-being . . . . . . . . . . . . 67

Table 2.9: Impact of cyclone dummy on per capita transfers and consumption (log) 67

Table 2.10: Impact of 90th percentile cyclones on household well-being . . . . . . . 68

iii

Table 2.11: Impact of 90th percentile cyclones on transfers and consumption per

capita (log) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Table 2.12: Impact of cyclones on transfers . . . . . . . . . . . . . . . . . . . . . . 69

Table 2.13: Indirect impact of cyclones on household well-being . . . . . . . . . . . 69

Table 3.1: Household summary statistics . . . . . . . . . . . . . . . . . . . . . . . 87

Table 3.2: Hunting behavior summary statistics . . . . . . . . . . . . . . . . . . . 87

Table 3.3: Relationship between GCF and ZOC with pre-policy hunting trends . . 88

Table 3.4: Impact of GCF and ZOC policies on hunting behavior . . . . . . . . . 88

Table 3.5: Household characteristics and bushmeat consumption . . . . . . . . . . 89

Table 3.6: Impact of GCF and ZOC policies on quantity of bushmeat obtained

opportunistically . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Table 3.7: Impact of GCF and ZOC policies on quantity of bushmeat purchased . 90

Table 3.8: Impact of GCF and ZOC policies on quantity of bushmeat consumed at

a friend’s house . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Table 3.9: Impact of GCF and ZOC policies on hunting with traps . . . . . . . . . 91

Table 3.10: Impact of GCF and ZOC policies on active hunting . . . . . . . . . . . 91

Table 3.11: Heterogeneity of impact of GCF on all hunting . . . . . . . . . . . . . . 92

Table 3.12: Heterogeneity of impact of GCF on opportunistic hunting . . . . . . . . 92

Table 3.13: Heterogeneity of impact of GCF on purchased bushmeat . . . . . . . . 93

Table 3.14: Heterogeneity of impact of GCF on hunting using trap . . . . . . . . . 93

Table 3.15: Heterogeneity of impact of GCF on active hunting . . . . . . . . . . . . 94

iv

Acknowledgments

This dissertation would not have been possible without the invaluable support and guid-ance of many people whom I would like to thank and recognize.

My three advisors, Maximilian Au↵hammer, Alain de Janvry and Solomon Hsiang, haveprovided invaluable support, both professionally and personally. They generously sharedtheir thoughts regarding my academic work, and continuously supported me through theentire process. I am also grateful to Elisabeth Sadoulet, David Zilberman and Je↵rey Perlo↵for their suggestions and feedback earlier in the process.

I am indebted to the participants in the Environmental and Resources Economics seminar(Department of Agricultural and Resource Economics) for their thoughtful feedback on mypresentations. Specifically, I am deeply grateful to Brian Wright and Peter Berck for theiruseful comments. Tamma Carleton, John Loeser, Edward Rubin and Deirdre Sutula alsoprovided useful suggestions at various stages.

The local data used in the first and second chapters were generated by the Ministry ofHealth in Madagascar and by the Malagasy National Institute of Statistics (INSTAT). Iam deeply thankful for their cooperation and exceptional communication. The local datain the third chapter was collected and generously shared by Christopher Golden and theMadagascar Health and Environmental Research organization (MAHERY). I am extremelygrateful for his support and mentorship throughout the years and for his helpful commentson the third chapter.

Financial support for the first chapter of this dissertation came from the University ofCalifornia’s Graduate Division, the Agricultural and Resources Economics Department andthe Ethan Ligon Family Foundation. I acknowledge their generous support.

Finally, I am deeply grateful to my family for their unwavering support and constantencouragement. This journey would not have been possible without their endless generosity,support and love. They have been and remain a source of inspiration and motivation for me.

v

Chapter 1

Fire as an agricultural input: are

the output gains worth the mortality

impacts? A case study of Madagascar

1.1 Introduction

Fire is regularly used in agricultural practices around the world and is a significant com-ponent of global fires (Korontzi et al., 2006). While present in all continents (Stohl et al.,2007; Chen et al., 2013; Zha et al., 2013), the highest volume of agricultural fires isfound in developing countries. India and China alone are responsible for forty percent ofglobal agricultural fires and for twelve percent of global greenhouse gas (GHG) emissions.This widely used agricultural practice has been sparsely researched in economics, partlydue to the traditional lack of reliable data on fires and partly due to the challenges inidentifying the e↵ects of this anthropogenic activity. The majority of existing studiesuse national-level data and focus on national and global ecological, climatic and envi-ronmental consequences of fires. Hence, to date, there is a paucity of evidence on thesubnational and individual-level consequences of fires as an agricultural input, an issuethat is important for at least two reasons.

First, it is well-known that the agricultural sector is predominant in developing coun-tries, in terms of national share of labor, generation of income and livelihood reliance(Johnston and Mellor, 1961). In low-income countries, forty to sixty percent of nationalincome is typically produced in agriculture and fifty to eighty percent of labor force isengaged in agricultural production. However, it has also been shown that the large quan-tities of land and labor committed to agriculture are used at low levels of productivity.Maximizing profitability and increasing productivity in agriculture is therefore essen-tial because it is a key sector for development, thanks to its ability to enhance growththrough product, factor and market contributions, to reduce poverty through increasedfood security and better health and to provide environmental services (Hirschman, 1958;Mellor et al., 1966; Cipolla, 1976; Conway, 1998; Christiaensen et al., 2011; De Janvryand Sadoulet, 2009). As a result, there is a great deal of research tackling the issues ofimproving input e�ciency and technology adoption in agriculture (Besley et al., 1994;Foster and Rosenzweig, 1995; Duflo et al., 2008; Suri, 2011; Conley and Udry, 2010; Fos-ter and Rosenzweig, 2010; Cai et al., 2015). Yet, limited work has been conducted ineconomics on rigorously understanding and measuring the profitability and productivityof fire as agricultural input.

Second, as a controversial farming technique, a discussion of the use of agriculturalfires does not come without the recognition of its negative impacts. To date, the discus-sion is largely dominated by the important and relevant themes of loss of forest cover,biodiversity and biomass; as well as global climate change impacts (DeBano, 2000; Nasiet al., 2002; Pettus, 2009). There is limited research and evidence on the local impacts of

1

agricultural pollution, a significant and unavoidable consequences of agricultural fires. Itis naturally expected that agricultural fires will lead to increased pollution, which in turnis expected to have significant health impacts on surrounding localities. General pollutionresearch shows that pollution leads to increased in-utero mortality, lower birth rates andincreased infant mortality rates with larger impacts in poorer communities (Chay andGreenstone, 2003; Currie and Neidell, 2005; Arceo et al., 2016). However, the externalvalidity of these results with regards to developing countries is questionable, given thatlow-income countries typically have di↵erent baseline levels of pollution and health thanthe rich countries, where most studies are conducted.

Evidence on the impacts of general pollution on health in developing countries is stillsparse and results are mixed. Studies show that, in India, the most successful pollu-tion regulation has not led to any significant decline in infant mortality (Greenstone andHanna, 2014). As for agricultural pollution studies, the few existing studies seem to con-form with the general wisdom. A study of wildfires in the Australian monsoon tropicsshows that one unit increase in PM10 per cubic meter of air per 24-hour period due towildfires leads to a 26% increase in daily asthma presentations to the emergency depart-ment of the Royal Darwin Hospital, with a threshold at 40 mg/m3 PM10 (Bowman andJohnston, 2005). Two projects that are closest to my study use satellite data to investi-gate the impact of fires on infant health. The first takes advantage of the 1997 massivewildfires in Indonesia and concludes that fires contributed to 16,400 fewer survivals or a1% reduction of cohort size especially in poor areas as inferred to missing children in 2000census (Jayachandran, 2009). The second evaluates the impacts of smoke from sugarcaneharvest fires in Brazil on infant health and finds that late-pregnancy exposure to upwindfires decreases birth weight, gestational length and in utero survival (Rangel and Vogl,2016). Furthermore, they find that non-upwind fires are correlated with better infanthealth, highlighting the role of agricultural fires in driving both pollution and economicactivities.

Using the case study of Madagascar, a low-income country that heavily relies on firein agriculture, my paper contributes to the sparse literature on agricultural fires in devel-oping countries and adds to the literature by extending the analysis on health impacts tothe general population and by explicitly identifying the economic gains from agriculturalfires in order to evaluate the health versus wealth tradeo↵ of fires. While these e↵ects areimportant in and of themselves, assessing the trade-o↵ is essential in advising regulatorsin the appropriate direction and costs of future fire regulations. Using high-resolutiondaily fire and weather satellite data combined with monthly hospitalization data andyearly agricultural data, I evaluate the health consequences of agricultural fires on sur-rounding localities and identify the e↵ects of fires land area used for agriculture, harvestof main crops and yields. In order to identify the health e↵ects of fire, I decompose firesinto downwind and upwind fires and use wind direction on the day of fire as source ofexogenous variation. The underlying assumption is that while both fires can potentiallya↵ect health through income and liquidity e↵ects, upwind fires will di↵erentially impacta↵ected populations through pollution. To identify the impacts of agricultural fires onagricultural outcomes, I take advantage of an international conservation movement thatled to rapidly tripling the area of protected parks in Madagascar. In particular, I use “pro-posed parks”, areas that were physically delimited as potential o�cial protected areas,as an instrument for fires. The assumption is that the rapid creation of proposed parksled to a decrease in fires and that the choice of proposed park is not otherwise relatedto agricultural outcomes since they were driven by international conservation trends and

2

given that no other changes were implemented besides the physical delimitation of parkboundaries. To proceed with the analysis, the remainder of this document is organizedas follows. Section 1.2 describes the fire situation in my study area, Madagascar. Section1.3 describes the data and methods for the health analysis. Section 1.4 presents and dis-cusses the health results. Section 1.5 describes the data and methods for the agriculturaloutcomes analysis followed by a discussion of the results in Section 1.6. Finally, Section1.7 concludes.

1.2 Background: Madagascar’s fire problem

1.2.1 Fire use in agriculture

Agricultural fires account for around ten percent of all global fire activity (Korontzi et al.,2006) and can be used during the pre-planting, harvesting and/or post-harvesting peri-ods. These uses include clearing crop residue, fertilizing the soil, eliminating pests andweed, wildfire prevention and pastoral management. In Madagascar, one of many devel-oping countries with high seasonal fire activity (Figure 1.1a), slash-and-burn agricultureand pastoral management are the two main reasons for agricultural fire use. Madagas-car, located in the Indian Ocean east of Mozambique, is the fourth largest island in theworld, with a land mass of 587,000km2 and 24.24 million inhabitants. It is a renownedbiodiversity hotspot and a critical priority for development and conservation e↵orts dueto a large overlap of chronic poverty and unparalleled levels of endemism and species di-versity. Madagascar has large but declining forest cover spread across the island (Figure1.1b): every year, 25% to 50% of the island’s grasslands and 7% to 10% of rainforestsare set aflame, yet bushfires do not figure among the list of natural hazards as thosefires are usually set by farmers and cattle herders for agricultural purposes. Slash-and-burn agriculture is widely used in developing countries, and refers to the process of firstpartially clearing forests then burning the cut trees for the establishment of plantations.This second phase of burning the remaining plants leaves vegetable ashes that providenatural fertilizers leading to three or four years of abundant crops followed by decliningsoil fertility and yield. After this short period of high fertility, nutrients leach out ofthe soil so that it becomes too poor for agriculture. The cleared land is then left tofallow and farmers typically need to repeat the process of slash-and-burn on new forests(Gay-des Combes et al., 2017). While it is clear that slash-and-burn agro-ecosystemsare central to livelihoods in poor countries, there are mixed views on its sustainability.The most optimistic view regarding slash-and-burn claims that it is sustainable if used inan ecologically sound fashion (with appropriate burning frequencies and fallow lengths)because it does not require outside inputs but rather is based on natural elements forfertilizers, pesticides and irrigation (Kleinman et al., 1995). On the other end of thespectrum, some studies have suggested that soil fertility decreases rapidly and remark-ably after the initial release of plant nutrients and requires treatment with herbicides,insecticides and fungicides to reduce root competition and plant diseases. Furthermore,short-term success requires set up of crop specific shelter-wood systems (Brinkmann andDo Nascimento, 1973). That is, while both sides agree that repeated use is harmful inthe long run there is some disagreement in terms of how successful slash-and-burn is inthe short run and recommendations for appropriate cropping and fallow cycle lengthsvary from five to ten years (Juo and Manu, 1996). Other studies have shown that there is

3

significant variation in farmer e�ciency in slash-and-burn fires and that this variation canbe attributed to human and social capital, access to information and technology, credit,soil fertility and environmental policies that might lead to under or over exploitation ofa given plot (Binam et al., 2004).

Another common use of fire is in maintaining pasture for livestock. This is referredto as pastoral fires and consists of burning pastures just after (early dry season, EDSfires) or just before (late dry season, LDS fires) the rainy season for greener pasture andto prevent bush encroachment. Using fire for pasture management is also complex asthere are tradeo↵s in choosing how frequently to burn as well as when to burn. Frequentburning (yearly or two-yearly) can drastically increase pastoral area by rapidly depletingwoody cover but leads to poor pasture condition in the long run. As for the timing offires, livestock farmers face a di�cult trade-o↵ between short-term use of grass biomassfor grazing and longer term use as fuel to manage tree–grass balances with fire. Whileearly fires are recommended to reduce the chances of late season wildfires, EDS wereassociated with declining pasture condition due to the longer exposure to post fire grazingon early burnt sites. Late season fires were most e↵ective for managing woody cover whilstmaintaining higher pasture production and perennial grass composition (Dyer and Smith,2003; Cowley et al., 2014). Overall, it is clear that optimal use of fire depends on a set oflocality-specific characteristics which include vegetation type, land use, grazing intensityand the prevailing seasonal timing and frequency of fire.

1.2.2 Fire regulations and politics

The century-old practice of burning has been a constant source of conflict and tensionin Madagascar. On the one hand, a broad group of conservationists and governmentalinstitutions believe that the island sees too much fire and that fires should be stoppedas they lead to deforestation, desertification, rangeland impoverishment, soil degrada-tion and accelerated erosion. Wary of these negative consequences, past colonial rulers,post-independence leaders, the Malagasy Forest Service and international environmentalagencies have used laws and information campaigns to ban fire throughout the island,with a few exceptions. Prior to colonization of Madagascar by the French in 1896, farm-ers and herders managed fires locally through mutual understanding, evolving traditions,and conflict resolution mechanisms overseen by elders or royalty. In the 19th century,after several wars of unification, Madagascar had its first recorded state-level regulationof fires, but only regarding forests, which were seen as a valuable source of timber (Dez,1968). These regulations became much stricter and larger in extent during colonizationas the French saw fires as a threat to natural resources (to protect productive assets andto avoid soil loss and deforestation) as well as to the stability of the colonial authority.Post-colonial bureaucratic perceptions of fire in Madagascar were then deeply influencedby these views so that strict anti-fire regulations remained. From independence in 1960until today, anti-fire regulations have evolved and hardened leading to more “theoretical”fire repression and criminalization of fire. In practice, however, there are still many moreillegal than authorized fires. According to the current fire legislation, preventive fires andcounter-season fires are authorized while burning is strictly prohibited during the drymonths (April to November). As per Ordinance 60-127, infractions would be punishedby a fine and/or six months to three years in prison (Kull, 2016).

On the other hand, Malagasy farmers and herders rely on fire to meet their livelihoodneeds, to manage resources, and are therefore trapped by anti-fire laws. Thanks to the

4

nature of fires (fires can be set from a distance and can be blamed on natural phenomena)and because of the weakness of the state and its inability to enforce anti-fire laws, a veryhigh volume of fire still persists across the island while pressures and tensions betweenboth sides remain.

1.2.3 Fire patterns

As shown in Figures 1.2 and 1.3, which represent the daily average number of fires percommune per month over the study period (2000 - 2015), there is high fire activity acrossthe island with considerable heterogeneity across space and time. The fire season typicallystarts in April, which also marks the end of the rainy season, in the south western regionsof Madagascar. These are early dry season fires that are used for pasture management.Fire activity increases as the year goes on and moves from south west to the north east.Late dry season pastoral fires typically happen during August and September and slash-and-burn fires are practiced around October and November. Other and less commoncauses of fire in Madagascar include charcoal production, hunting, customary causes,criminal causes and natural causes (Kull, 2012; Styger et al., 2007; Randriambelo et al.,1998). Fires in Madagascar are seasonal and are closely linked to agricultural seasons.This seasonality of fires is further illustrated in Figure 1.4 and Figure 1.5. The blue linerepresents slash-and-burn fires which are defined as a fire that occurs in a forested area.The orange line represents pastoral fires which are fires that occur in the grasslands. It canbe seen that both types of fires are seasonal but there are more pastoral fires than forestfires in terms of area burnt per month, which is consistent with Madagascar’s vegetationcover as forests only account for ten percent of total land area. Figure 1.5 shows thatboth early dry fires and late dry fires are practiced but the latter is more widely used.Finally, Figure 1.6 and Figure 1.7 plot fires along with the area of agricultural land usedfor production and livestock production and suggest that fires track economic activity,especially while looking at agricultural land area. This is at most a correlation and theexistence of any causal relationship between fires and agricultural outcomes is yet to beestablished in this paper.

Fire is evidently important for the Malagasy economy, both in terms of potential agri-cultural benefits, as well as in actual uses. The subject has been and still is extensivelyresearched in Madagascar, but through the lens of ecological and environmental studies(Kull, 2012, 2004), biophysical analyses (Styger et al., 2007; Clark, 2012) as well as geopo-litical essays (Randriamalala and Liu, 2010). These studies show that anthropologic firesin Madagascar are essential to farmers’ livelihoods but come at a high environmentaland biodiversity costs (Kull, 2004). They further argue that taking farmers’ reliance onfire into account while drafting fire regulations would lead to drastic improvements inboth farmers’ well-being and government’s resource use and that criminalizing fires willonly lead to suboptimal and unsustainable uses of fire. However, these policy recom-mendations are not evidence-based as traditional studies of fire in Madagascar rely onself-reported bureaucratic accounts that are plagued with missing data and measurementerror problems. This paper provides some of the first robust evidence assessing this claimfor Madagascar and even adds to the local literature given that the discussion of healthe↵ects of fires are virtually absent from the main debate. The next section highlights mydata and methodology for the first part of the analysis, which focuses on health.

5

1.3 Health analysis data and methods

1.3.1 Fire data

Data on wildfires and smoke are obtained from National Aeronautic and Space Ad-ministration (NASA)’s Moderate Resolution Imaging Spectro-radiometer (MODIS) At-mosphere Science Team. According to NASA, this fire data from MODIS is the mostadvanced global data product for fire monitoring but still builds on heritage algorithmsfor operational fire monitoring from NOAA’s Geostationary Operational EnvironmentalSatellites (GOES) and Advanced very-high-resolution radiometer (AVHRR) sensors. Inparticular, it consists of geo-spatial maps containing information on the location andcount of fires per one square-kilometer grid available at the daily level. Throughout thehealth analysis section, the administrative unit of analysis is the commune, which is thefourth administrative division in Madagascar. There are 1,433 communes in Madagascar.On any given day and for any given commune, I draw a 50km radius circle around thecommune’s population centroid and match every observed fire pixel to the correspondingwind data in order to di↵erentiate between upwind and downwind fires. Fire summarystatistics are reported in Table 1.1. On average, 12.61 fires per day and 72 fires per monthare observed within a 50km radius of communes.

1.3.2 Pollution data

Smoke is measured using the aerosol optical depth (AOD) variable, which comparesreflectance intensity in a particular band against a reference value and attribute thediscrepancy to particulates in the air. That is, it measures the degree to which aerosolsor airborne particles such as windblown dust, sea salts, volcanic ash, smoke from firesand pollution from factories, prevent the transmission of light by absorption or scatteringof light. AOD, as represented by MODIS’s maps takes on values between 0 and 1 wherea value of less than 0.1 indicates a crystal clear sky with maximum visibility, while themaximum value of 1 indicates the presence of aerosols so dense that the Sun would notbe visible even at noon. Despite caveats, AOD is the next best when ground measuresare not available (Jayachandran, 2009; Foster et al., 2009; Gendron-Carrier et al., 2018).This paper constructs monthly measures of pollution from daily observations of 3-kmgridded AOD data. Aerosol summary statistics are reported in Table1.1. The averagemean AOD is 0.09 compared to 0.20 for the whole continent of Africa (Gendron-Carrieret. al, 2016) and the average maximum AOD is 0.34.

1.3.3 Wind data and other weather controls

Wind data consist of daily-averaged 23⇥12 - degree wind vectors from NASA’s Modern-Era

Retrospective analysis for Research and Applications (MERRA) database. Northwardand eastward wind vectors are used to compute wind direction as angles in degrees andwind speed.

In all of my estimation, I control for temperature, rain and their interaction. I usedaily gridded (1km2 grids) daytime and nighttime temperature data from MODIS and14 ⇥

14 - degree rainfall data from NASA’s Tropical Rainfall Measuring Mission (TRMM)

satellite.

6

1.3.4 Decomposing fire by wind direction

To construct the treatment variable, I first count the daily number of 1km-by-1km gridcells containing a fire within a 50km radius of each commune’s population centroid.Population centroid data are obtained from the Gridded Population of the World (GPW,version 4) database. To be conservative, I omit fires that are within 5km as they arelikely to be within the urbanized areas and might not be related to agriculture. Instead,I include them as a control variable in all of my specifications. For each fire pixel withinthe 50km radius, I compute the di↵erence between the bearing of the pixel relative to thepopulation centroid and the wind direction itself. A fire is classified as “Upwind fire” ifthe absolute value of the di↵erence described above is less than 90 degrees, “Downwindfire” if the di↵erence is greater than 270 degrees and as “Other fire” otherwise. Figure1.8 illustrates this. Alternatively, I also group the two latter categories such that firesthat are not within the 90 degree quadrant are classified as a “Non-upwind fire.

1.3.5 Birth outcomes data and hospital records

Data on birth outcomes and pulmonary diseases are obtained from commune-level monthlyhospital records from the Malagasy Ministry of Health. This dataset include all communesin Madagascar and contains information on the number of consultations, diagnosis andhospitalizations per month, broken down by diseases and by age group. My primaryoutcomes of interest are birth outcomes (prenatal consultations, total number of births,number of live births, birth weight, and maternal deaths), respiratory diseases (asthma,respiratory infection, pneumonia, respiratory diseases, cough and cold), diarrheal diseasesand malnutrition. Unlike most studies on infant health, I do not have data on gestationalage at birth. Table 1.2 presents summary statistics for the health dataset. The averagerate of live births per month is 972.57 per 1,000 births. Among live births, 86 per 1,000infants are born with a low birth weight (

1.3.6.a Impact of fires on pollution

To test the key assumption that upwind fires raise pollution more than downwind fires,I run the following regression using a Spatial Fixed E↵ect model:

AODit = ↵Uupwindfires

it+↵Ddownwindfiresit +↵

Ootherfiresit +W0it�+ ⌧i + �t + uit (1)

The dependent variable AODit is the pollution concentration in commune i in month tas measured by Aerosol Optical Depth. I look at both average and maximum AOD sinceboth the average and the extreme values of pollution are known to significantly impacthealth. In this health specification, the main independent variables of interest are theupwindfires

itvariable, the count of upwind fires, downwindfiresit the count of downwind

and otherfiresit the count of other fires all measured during month t in commune i.Results from this estimation are reported in Table 1.3. The vector of covariates Witconsists of weather controls that include monthly average temperature, humidity andtheir interactions, minimum and maximum temperature and wind speed. These weathercovariates are associated with both fire incidence and pollutant concentrations but arenot di↵erentially associated with fires by wind direction. ⌧i and �t are commune andyear-month fixed e↵ects, respectively, so that this estimation captures within-commune,within-time variation. As robustness checks, I also use confidence-weighted, missingvalues adjusted and non-linear expressions of the fire count variables.

Spatial Fixed E↵ects models are appropriate and necessary for this paper’s analysisgiven the importance of spatial patterns of fires, weather and health. Furthermore, dueto the seasonality of fires and weather, it is also important to account for temporalcorrelations in the model as well as in the standard errors. To account for both spatialand temporal correlations, all specifications use Conley standard errors, computed usingcode from Hsiang (2010).

1.3.6.b Impact of fires on birth outcomes

My birth outcomes regression specification is similar to equation (1) in terms of indepen-dent variables and controls, but it uses a distributed lag-model since pollution exposurethroughout pregnancy is expected to impact birth outcomes. Therefore, I include ninemonth lags and run the following regression:

Birth outcomeit =9X

m=0

↵Umupwindfires

i,t�m +9X

m=0

↵Dmdownwindfiresi,t�m

+9X

m=0

↵Omotherfiresi,t�m +

9X

m=0

W 0i,t�m�m + ⌧i + �t + uit

(2)

where Birth outcomeit is the average of a specific birth outcome reported in com-mune i during the month t, ⌧i and �t are commune and year-month fixed e↵ects, and↵Um,↵D

mand ↵O

mare the distributed-lag versions of the coe�cients from equation (1). To

follow the literature on in utero exposure, I focus on the three trimesters of gestation andreport three-month coe�cients in Table 1.8. These coe�cients represent the impact ofincreasing fires by one occurrence per month for three months. Given that I do not havethe exact date of birth and that I am counting backward from the month of birth, these

8

coe�cients do not exactly correspond to the three gestation periods. An implication ofthis research design is that I implicitly assume that all born babies are born at the ninthmonth of pregnancy, that is, that no babies are prematurely born. This assumption leadsto selection biases on the first and second trimester coe�cients. The coe�cient on thelast trimester is more reliable and can be assumed to be not impacted by this bias since itis reasonable to expect that all babies were in utero for at least three months. A centralassumption for identification in this specification is that pregnant mothers living upwindfrom high-burn areas are selected on characteristics relevant to infant health. Given thefrequent change of fire source location and exogeneity of wind direction, and the absenceof within-commune and between commune migration, I argue that this is not a concernin this analysis.

1.3.6.c Impact of fires on birth outcomes

Finally, I also look at the impact of fires on pulmonary diseases incidence among thegeneral population as well as among infants. My general health outcomes regressionspecification is similar to equation (2) in terms of independent variables and controls,but more flexible in terms of lags as di↵erent diseases might have di↵erent incubationperiods and expected e↵ect:

Health outcomeait =MX

m=0

↵Umupwindfires

i,t�m +MX

m=0

↵Dmdownwindfiresi,t�m

+MX

m=0

↵Omotherfiresi,t�m +

MX

m=0

W 0i,t�m�m + ⌧i + �t + uit

(3)

where Health outcomeit is the average of a specific health outcome for the age groupa reported in commune i during the month t, ⌧i and �t are commune and year-monthfixed e↵ects, and ↵U

m,↵D

mand ↵O

mare the distributed-lag versions of the coe�cients from

equation (1).In both specifications from equations (2) and (3), I also use an independent variable

that captures the di↵erential impact of upwind fires relative to non-upwind fires. In this

setup, the reported coe�cient is ↵ =t̄P

m=t↵Um� ↵D

m� ↵O

mwhere t is the start month and

t̄ is the end month per aggregated period or trimester. Therefore, I run the followingestimation:

Health outcomeait = ↵Firesit +W0i,t�m�m + ⌧i + �t + uit

(4)

1.4 Health results and discussion

1.4.1 Impact of fires on air pollution

Table 1.3 reports estimations of equation (1), showing that upwind fires di↵erentiallyincrease pollution as measured by mean and maximum AOD. The results from this table

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use the simplest specification of fire, count of fires within a month within a 50km radiusof the commune centroid, and the model is augmented in later estimations by weightingby confidence, number of missing pixels, angle and distance to the centroid. Columns(1) and (2) report results for unweighted counts of fires, disregarding their orientation tothe wind and both specifications exclude weather controls. Columns (3) and (4) reportresults for unweighted counts of fires, disregarding their orientation to the wind andinclude weather controls. From column (1), one additional fire in the past month raisesmean AOD by 0.00022 and raises maximum AOD by 0.00104. In order to provide amore intuitive way of interpreting the results, I interpret all further results in terms ofstandard deviations rather than in terms of the regression coe�cients. That is, a onestandard deviation increase in fires within 5 to 50km of a commune centroid leads toa 0.45 standard deviation increase in average aerosol optical depth and a 0.59 standarddeviation increase in maximum aerosol optical depth. Surprisingly, fires within 5km ofthe population centroid have no significant impact on average AOD but significantlyincreases maximum AOD. From column (4), I find that a one standard deviation increasein fires within 5km of the population centroid raises maximum AOD by 0.011 standarddeviations. When including weather covariates in columns (3) and (4), I find that non-decomposed fires are no longer significant predictors of average aerosol optical depth,however the coe�cients in the regression of maximum aerosol optical depth are bothhighly significant. A one standard deviation increase in fires within 5 to 50km of acommune centroid increases maximum aerosol optical depth by 0.17 standard deviationsand a one standard deviation increase in fires within 5km increases maximum aerosoloptical depth by 0.013 standard deviations.

In columns (5) through (8), I decompose the sum of fires into upwind fires, downwindfires, and other fires within a 5 to 50km radius of population centroids in a given month.In columns (5) and (6), I also decompose the sum of fires within 5km of the commune intoupwind fires, downwind fires, and other fires, whereas in columns (7) and (8) they aresummed together since I am not treating them as agricultural fires but include them ascontrols. This di↵erence in specification does not influence the magnitude and significanceof the main coe�cients of interest therefore remaining results will use the specification incolumns (7) and (8). When looking at the impact of fires on average aerosol optical depth,in columns (5) and (7), I find that both upwind and downwind fires significantly predictpollution while other fires do not. Furthermore, the e↵ect of upwind fires on averageon aerosol optical depth is approximately four to five times larger than the e↵ects ofdownwind fires. A one standard deviation increase in upwind fire raises average aerosoloptical depth by 0.08 standard deviations or by 7.3 percent of the mean, whereas a onestandard deviation increase in downwind fires raises average aerosol optical depth by0.018 standard deviations or by 1.6 percent of the mean. The coe�cient of upwind firesis significant at the 1% level, whereas the coe�cient on downwind fires is significant onlyat the 10% level. I find a similar trend while analyzing the impact of downwind fires onmaximum aerosol optical index. A one standard deviation increase in upwind fires raisesmaximum aerosol optical depth by 0.11 standard deviations or by 9.0 percent of the mean,whereas a one standard deviation increase in downwind fires raises maximum aerosoloptical depth by 0.02 standard deviations or by 1.9 percent of the mean. Other fires alsosignificantly impact maximum aerosol optical depth. A one standard deviation increasein other fires increases maximum aerosol optical depth by 0.025 standard deviations. Allthree coe�cients of interest in columns (6) and (8) are statistically significant. Comparedto previous findings in the literature, Rangel and Vogl (2016) also find that upwind

10

fires are associated with a four to five times larger increase in PM10 concentration thandownwind fires, however the magnitude of impact they find is much larger (an upwindfire raises PM10 by 0.16 standard deviations).

In the next set of regressions, I use confidence-weighted fire counts. That is, each firepixel is weighted by the probability or confidence that it is an actual fire as is documentedin the MODIS dataset. Table 1.4 reports the results from this new fire specification.Results are generally similar to what I have found in the first set of regressions. Fromcolumn (1), I find that a one standard deviation increase in fires within 5 to 50km ofthe population centroid raises average aerosol optical depth by 0.01 standard deviationsand raises maximum aerosol optical depth by 0.18 standard deviations. A one standarddeviation increase in fires within 5km of the population centroid raises maximum AOD by0.043 standard deviations but has no significant impact on average aerosol optical depth.In columns (3) through (6), I decompose the sum of fires within 5 to 50km of the communeinto upwind fires, downwind fires, and other fires and I again find that decomposing thefires within 5km or leaving them together does not change the magnitude and significanceof my independent variables of interest.

While my results on maximum aerosol optical depth are unchanged in this second setof regressions, I find that downwind fires are no longer a significant predictor of averageaerosol optical depth, unlike other fires. From columns (3) and (5), I find that a onestandard deviation increase in upwind fires raises average aerosol optical depth by 0.07standard deviations and raises maximum aerosol optical depth by 0.10 standard devia-tions, compared to 0.08 standard deviations and 0.11 standard deviations respectively inTable 1.3. A one standard deviation increase in downwind fires raises maximum aerosoloptical depth by 0.025 standard deviations compared to 0.02 standard deviations in Table1.3. Finally, a one standard deviation increase in other fires raises average aerosol opticaldepth by 0.025 standard deviations and raises maximum aerosol optical depth by 0.08standard deviations.

After weighting by confidence, I also weight the count of fires by angle and distance ofthe fire to the population centroid. That is, before summing up individual fires in orderto compute the monthly count of fires, I first multiply each fire by the cosine of the angleof its bearing to the population centroid and the wind direction at the fire location (a firethat has the exact same bearing to the commune as wind direction will be multiplied by 1,whereas a fire whose bearing is in the opposite direction of wind direction will be appliedan angle weight of -1) and then divide by distance squared. Results are reported in Table1.5 and are generally consistent with earlier findings. From column (1), I find that a onestandard deviation increase in fires within 5 to 50km of the population centroid raisesaverage aerosol optical depth by 0.02 standard deviations and raises maximum aerosoloptical depth by 0.03 standard deviations. In columns (3) and (4), I decompose the sum offires within 5 to 50km of the commune into upwind fires, downwind fires, and other fires.The interpretation of coe�cients here is slightly tricky, since now, all weighted upwindfires have a positive sign (the smaller the angle between bearing and wind direction, thecloser to 1 the weight is), all weighted downwind fires have a negative sign (the smaller theangle between bearing and the opposite of wind direction, the more negative the weightis) and other fires can be either negative or positive. In columns (5) and (6), I recodeangles to be positive and I again find that results are consistent with earlier estimates. Ifind that a one standard deviation increase in upwind fires raises average aerosol opticaldepth by 0.065 standard deviations and raises maximum aerosol optical depth by 0.11standard deviations, compared to 0.08 standard deviations and 0.11 standard deviations

11

respectively in Table 1.3. A one standard deviation increase in downwind fires raisesaverage maximum optical depth by 0.04 standard deviations. A one standard deviationincrease in other fires raises average aerosol optical depth by 0.027 standard deviationsand raises maximum aerosol optical depth by 0.05 standard deviations compared to 0.025standard deviations and 0.08 standard deviations respectively in Table 1.4.

Finally, in Table 1.6, I include quadratic terms to test for a quadratic relationshipbetween fires and pollution. I find that the magnitude and coe�cients of the linear termsare unchanged and quadratic terms are significant but very close to zero, suggestinga linear relationship between fires and pollution. A one standard deviation increasein upwind fires raises average aerosol optical depth by 0.06 standard deviations andraises maximum aerosol optical depth by 0.08 standard deviations. A one standarddeviation increase in other fires raises maximum aerosol optical depth by 0.02 standarddeviations and has no impact on average aerosol optical depth. Downwind fires do notsignificantly predict average and maximum aerosol optical depth. As a final robustnesscheck, I decompose fires by di↵erent central angles and I find that my results are generallyunchanged (Table 1.7).

These sets of estimation help me conclude that my results are robust to di↵erentspecifications of fire and wind. Going forward, I use the specification in Table 1.4, whichuses confidence-weighted count of fires, as my preferred specification.

1.4.2 Impact of fires on birth outcomes

I now investigate the impact of fires on birth outcomes by estimating equation (2). Resultsare reported in Table 1.8. In particular, I look at the impact of exposure to upwindfires, downwind fires and other fires during the three semesters of pregnancy on cohortsize, fetal deaths, stillbirths, birthweight and maternal deaths. I find that upwind fireslead to smaller birth cohorts, fewer livebirths, more fetal deaths and more maternaldeaths. Upwind fires in all trimesters significantly reduce cohort size, and, surprisingly,the earlier the fire, the larger the coe�cient. One additional upwind fire in the lasttrimester of pregnancy decreases cohort size by 0.023%, whereas an upwind fire duringthe first trimester of pregnancy reduces cohort size by 0.049%, with the largest and mostconsistent e↵ect being that on cohort size. That is, a one standard deviation increase infirst trimester upwind fires leads to a 0.86% decrease in monthly births per commune, aone standard deviation increase in second trimester upwind fires leads to a 1.57% decreasein monthly births per commune and a one standard deviation increase in third trimesterupwind fires leads to a 1.83% decrease in monthly births per commune. Other fires alsoreduce cohort size, whereas downwind fires lead to the opposite e↵ect. A one standarddeviation increase in other fires during the last trimester leads to a 1.78% smaller birthcohort, whereas a one standard deviation increase in downwind fires during the lasttrimester leads to a 0.94% larger birth cohort. A one standard deviation increase in“other” fires during the first trimester leads to a 1.17% smaller birth cohort, whereas aone standard deviation increase in downwind fires during the first trimester leads to a1.18% larger birth cohort. When applying these coe�cients to the a↵ected cohorts in mydataset, I find that, on average, agricultural fires (all fires within 5 to 50km) lead to 12.62fewer births per commune per month. The e↵ect of fires on birth size was negative for94.5% of commune-months and positive for the remaining 5.5%. For commune-monthsin the latter category, these occurrences correspond to months when there are manymore downwind fires than upwind fires and other fires. On average, aggregating for

12

a↵ected cohorts in the entire island, agricultural fires lead to 4,290 fewer births everyyear (CI = [3, 870 � 4, 710]). The underlying assumption here is that pollution leadsto fewer pregnancies and more fetal deaths. Column (2) reports the impact of fires onfetal deaths and, while it shows that upwind fires increase fetal deaths, all coe�cientsare extremely small in magnitude. This is due to the very small mean value of fetaldeaths recorded at clinics (µfetal

m= 0.0002, sdfetal

m= 0.16). Anecdotal evidence suggests

that most fetal deaths are likely to be unreported or even unknown if they happen earlyenough in the pregnancy.

In column (3), I investigate the impact of fires on livebirths per 1,000 births. Ifind that exposure during only the last trimester significantly impacts livebirths. A onestandard deviation increase in last trimester upwind fires leads to 1.11 fewer livebirthsper 1,000 births per month and a one standard deviation increase in other fires duringthe last trimester reduces livebirths per 1,000 births by 0.93. Applying this to a↵ectedcohorts across the nation, I find that, across the island, agricultural fires lead to 98.87fewer livebirths per year (CI = [47.96 � 149.78]). Therefore, on average, agriculturalfires in Madagascar lead to 4,389 “missing infants” per year or 1.95% of a↵ected cohortsand 0.7% of all births in Madagascar. These results are in line with Jayachandran (2009)who finds that wildfires in Indonesia lead to a 1.1% reduction in birth cohort size ofa↵ected populations. I find no significant impact on newborns’ probability of having abirth weight lower than 2,500g, of being hospitalized after birth and on maternal deaths.

Next, I look at the impact of fires on birth outcomes based on the type of vegetationthat is being burnt (forest or grassland). Results are reported in Table 1.9 and I alsofind that fires impact cohort size and livebirths but conditional on surviving, there are noimpacts on birthweight, hospitalization after birth and maternal deaths. However, forestfires and grassland fires di↵erentially impact cohort size. Forest fires during the lasttrimester of pregnancy negatively impact birth outcomes, whereas grassland fires in thefirst trimester of pregnancy positively predict health. A one standard deviation increase inupwind forest fires relative to non-upwind fires during the last trimester leads to a 0.54%decrease in monthly births per commune, to a 0.006 standard deviation increase in fetaldeaths and to 1.35 fewer livebirths per 1,000 births. A one standard deviation increasein upwind forest fires relative to non-upwind fires during the first trimester leads to 1.17fewer livebirths per 1,000 births. Grassland fires in the two last trimesters of pregnancyhave no significant impact on birth outcomes, whereas a one standard deviation increasein upwind grassland fires relative to non-upwind fires lead to 0.52% larger cohort sizeand 1.15 more livebirths per 1,000 births. These results suggest that forest fires are morepolluting than grassland fires, as would be expected due to the higher mass of vegetationburnt and the longer duration of forest fires, and that the income e↵ect from grasslandfires might outweigh the pollution e↵ects as shown by their positive impact on birthoutcomes. In the next section, I investigate the relationship between fires and an arrayof diseases related to respiratory health and nutrition.

1.4.3 Impact of fires on pulmonary disease

1.4.3.a Impact on disease prevalence

Results from the estimation of equation (4) are reported in Tables 10 through 15. In Table1.10, I investigate the di↵erential e↵ect of current and lagged fires on the prevalenceof several diseases that might be associated with fires and smoke among the generalpopulation. For this, I construct a variable defined as the di↵erence between upwind fires

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and non-upwind fires. Since both upwind and non-upwind fires can be associated witheconomic activity, which might also impact health, taking the di↵erence between the twowill isolate the impact of pollution. I find that fires lead to higher prevalence of respiratoryinfections, diarrheal diseases and malnutrition. Note that I include diarrheal diseases asfires might also be associated with water pollution if particles from fires are brought towater sources by wind. I look at the impact on malnutrition to test for nutrition e↵ects offires through increased production of food. I find that a one standard deviation increasein upwind fires relative to downwind fires in the current month leads to a 1.19% increasein cases of respiratory diseases (asthma, pneumonia, respiratory infections and cough)diagnosed per month per commune which is mostly driven by the impact of fires onrespiratory infections (columns (1) and (4)). Indeed, a one standard deviation increasein upwind fires relative to downwind fires in the current month leads to a 1.00% increasein cases of respiratory infections diagnosed per month per commune. Column (3) showsthat a one standard deviation increase in upwind fires relative to downwind fires in theprevious month leads to a 0.85% increase in cases of pneumonia diagnosed in the currentmonth. There does not seem to be any di↵erential impacts of upwind fires on asthmaand cough. From column (6), I find that a one standard deviation increase in upwindfires relative to downwind fires in the current month (in the previous month) leads to a1.33% (0.68%) increase in diarrheal cases diagnosed. This suggests that fires also leadto water pollution as particles carried from fires might end up in water sources. Finally,I also find that a one standard deviation increase in upwind fires relative to downwindfires in the current month leads to a 0.55% increase in malnutrition cases diagnosed.

While these e↵ects are significant both statistically and economically speaking, I ex-pect an even larger magnitude among vulnerable populations as supported by the lit-erature on pollution and health. To test this, I look at the impact of fires on diseaseprevalence among children that are under five years of age and report the results in Table1.11. Results are generally similar to what was previously discussed, and as expected,children are found to be more sensitive to pollution and e↵ects are more persistent. I findthat a one standard deviation increase in upwind fires relative to downwind fires in thecurrent (previous) month leads to a 1.33% (0.85%) increase in cases of respiratory dis-eases diagnosed. A one standard deviation increase in upwind fires relative to downwindfires in the current (previous) month leads to a 0.26% (0.36%) increase in cases of asthmadiagnosed. A one standard deviation increase in upwind fires relative to downwind firesin the current (previous) month leads to a 1.10% (0.85%) increase in cases of respiratoryinfections diagnosed. A one standard deviation increase in upwind fires relative to down-wind fires in the current month leads to a 0.71% increase in cases of common coughsbut fires during the previous month do not seem to matter. In terms of non-respiratorydiseases, I find that a one standard deviation increase in upwind fires relative to down-wind fires in the current (previous) month leads to a 1.36% (0.65%) increase in cases ofdiarrhea diagnosed. Finally, a one standard deviation increase in upwind fires relativeto downwind fires in the current month leads to a 0.65% increase in cases of malnutri-tion. The magnitudes of impact here are slightly larger than those found for the entirepopulation. I find no impact of fires on pneumonia.

Finally, I look at the impact on infant health as reported in Table 1.12. Resultshere are somewhere in between the two cases discussed above. A one standard deviationincrease in upwind fires relative to downwind fires in the current (previous) month leadsto a 0.78% (0.59%) increase in cases of respiratory diseases diagnosed. Fires are no longera significant predictor of asthma prevalence. A one standard deviation increase in upwind

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fires relative to downwind fires in the current month leads to a 0.52% increase in casesof pneumonia diagnosed. A one standard deviation increase in upwind fires relative todownwind fires in the current (previous) month leads to a 0.97% (0.59%) increase incases of respiratory infections diagnosed. A one standard deviation increase in upwindfires relative to downwind fires in the current month leads to a 0.71% increase in casesof common coughs, a 0.84% increase in diarrheal cases and a 0.39% increase in cases ofmalnutrition diagnosed. E↵ects on infants seem to be smaller in magnitude to those onchildren under five, this might be due to lower smoke exposure of infants compared to olderchildren who can wander near pollution sources. I no longer find a significant impact onasthma, which might be in part due to the di�culty of diagnosing asthma among infants.Indeed, mothers might keep infants indoors during high smoke season, whereas childrenare more likely to be outside even during high smoke season. Furthermore, compared toan infant, a five year old would have a longer exposure to pollution due to age, hencee↵ects might be larger in magnitude.

1.4.3.b Impact on disease prevalence by type of fire

In this section, I replicate the estimation above while distinguishing between forest firesand grassland fires. Table 1.13 reports the impact of fires among populations of allages and finds that most of the e↵ects found in the previous section were driven byforest fires. A one standard deviation increase in forest fires in the current (previous)month increases prevalence of pneumonia by 0.94% (1.30%), respiratory infections by0.84% (0.88%), diarrhea by 1.19 % and malnutrition 1.13% (1.14%). E↵ects of fire onmalnutrition are large in magnitude and persistent. I find no impact of grassland fires onany of the diseases. These results are not surprising given that, all else equal in terms ofduration and distance of fires, a larger mass of vegetation is burnt during forest fires. Theimpact on diarrhea is expected to occur through pollution of surrounding water sources.

In Table 1.14, I report the results on the impact of fires on children that are un-der five years of age. I find that both forest fires and grassland fires impact childrenhealth, however, impacts of forest fires are larger in magnitude and more persistent, es-pecially on asthma. This is not surprising given that forest fires are more polluting andis consistent with several studies in the health and pollution literature documenting in-creases in asthma incidence as a result of increased pollution(Lee et al., 2002; Trasandeand Thurston, 2005; Halonen et al., 2008; Pénard-Morand et al., 2010). A one standarddeviation increase in forest fires in the current (previous) month increases prevalence ofasthma by 0.55% (0.81%), respiratory infections by 1.94% (1.20%), cough by 0.97%, diar-rhea by 1.16 % and malnutrition 0.90% (1.31%). E↵ects of fire on malnutrition are large inmagnitude and persistent. As for grassland fires, they only impact respiratory infectionsand malnutrition. Furthermore, negative e↵ects are only found for the contemporaneousmonth. A one standard deviation increase in grassland fires in the current month in-creases prevalence of respiratory infections by 0.84% and malnutrition by 0.55%. A onestandard deviation increase in grassland fires three months earlier decreases prevalenceof malnutrition by 0.62%.

In Table 1.15, I report the results on the impact of fires on infants. I find significant andpersistent impact of forest fires on the diseases of interest except for asthma. Grasslandfires only negatively and significantly impact diarrheal diseases and malnutrition. Aone standard deviation increase in forest fires in the current (previous) month increasesprevalence of pneumonia by 0.74% (1.43%), respiratory infections by 0.84% (1.14%),

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cough by 0.71%. Lagged forest fires from three months earlier to the previous month leadto increases in diarrhea diagnoses. Finally, a one standard deviation increase in forestfires in the previous month leads to a 0.65% increase in malnutrition among infants.Overall, these results show that both forest fires and grassland fires lead to increasedprevalence of pollution-related diseases but forest fires have a larger e↵ect. Adults seemto be impacted by forest fires only, whereas children and infants are also sensitive tograssland fires, which is expected to lead to less pollution, consistent with the healthliterature regarding the vulnerability of children and infants.

Now that it has been established that agricultural fires negatively predict healththrough their e↵ects on fetal and infant deaths as well as on pulmonary and nutritionrelated diseases, I will investigate the flip side which predicts that agricultural fires willincrease output in the long run and yield in the short run.

1.5 Agricultural outcomes analysis data and methods

1.5.1 Agricultural production data

Data on agricultural production is obtained from the Ministry of Agriculture and fromthe National Institute of Statistics. Agricultural data mainly consists of yearly cropproduction data and monthly food prices which are available at the district level, thethird administrative division in Madagascar. Summary statistics for the main food cropsas well as for sugar, vanilla and cattle are reported in Table 1.16. The most commoncrops, by hectares planted are rice, cassava and corn and are planted in all districts.Rice is the staple food and cassava and corn are typically seen as inferior substitutes.On average, a given district uses twelve thousands hectares for rice, four thousands forcassava, three thousands hectares for corn every year and harvests forty thousands tonsof rice, thirty-one thousands tons of cassava and five thousands tons of corn. Livestockproduction is also a significant part of agriculture in Madagascar where the average yearlycount of cattle is 2.5 million with a maximum of eleven millions in a given district in ayear, or half of the Malagasy population.

1.5.2 Estimation strategy

In the absence of biases and causality challenges, estimating the reduced form in equation(5) below would provide the causal impact of fires on agricultural outcome.

Yict = ↵0 + ↵Fireit +Wit� + ⌧i + �t + uit (5)

The dependent variable Yit is a crop-specific outcome (area planted, quantity har-vested and yearly yield) for commune i in year t. The independent variable of interest isFireit, which is defined as the sum of fires inside commune i during year t. The vectorof covariates Wit consists of weather controls that include yearly average temperature,humidity and their interactions, minimum and maximum temperature and wind speed. Ialso control for pollution as pollution might impact agricultural outcome either directlyor through its health impacts on morbidity. ⌧i and �t are commune and year fixed e↵ects,respectively, so that this estimation captures within-commune, within-time variation.

However, there are many identification concerns with the above specification. Thefirst and obvious issue is reverse causality, given that these agricultural fires are moti-

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vated by expected agricultural output gains. Second, atmospheric and weather conditionsplay an important role in both agricultural productivity and ignition and spread of fire.Finally, seasonality in harvest, temperature and humidity may create spurious relation-ships between agricultural fires and output. To overcome these concerns, I use exogenousvariation from the process of rapid tripling the areas of protected parks in Madagascar,a commitment made during the 2003 Durban Action Plan, as part of an internationalconservation trend.

During the Fifth International Union for Conservation of Nature World Parks Congressheld in Durban South Africa in September 2003, the then president Marc Ravalomananaannounced that Madagascar was going to triple the amount of land under o�cial pro-tected status, from the existing 1.7 million hectares to a total of 6 million hectares or 10%of the country’s surface area by the year 2010. This benchmark of protecting 10 percentof a country’s biome is Target 11 of the Convention on Biological Diversity, a treaty thatwas signed by 150 government leaders at the 1992 Rio Earth Summit and is currentlyachieved in approximately 55 percent of all terrestrial eco-regions. While this ambitiousgoal was not achieved by 2010, Madagascar successfully secured 10% of its land areaby 2011. Figure 1.9 illustrates this expansive creation of new parks between 2003 and2011. In the year following the announcement in 2004, local and international conser-vation organizations physically mapped out and delimited potential new parks based onan ecosystem approach taking into account the overall and spatial distribution of biodi-versity throughout the island and their current conservation status. Out of the proposedlist, new parks were rolled out every year from 2005 to 2011 and not all proposed parkswere turned into o�cial protected areas. I use proposed parks as an instrument for firesas specified in equations (7) and (6) below. Actual parks are a good candidate for arelevant instrument, however the exclusion restriction might be violated as new parksmight impact agricultural output through both reduced fire activity and tourism-inducedincreased economic activity and access to information and technology.

Qit= �0 + �1 ˆFireit + �2Wit + ↵i + �t + uit (6)

Fireit = �0 + �1Proposed Parkit + �3Wit + ↵i + �t + vit (7)

The dependent variable in the second stage in equation (6) is Yit, a crop-specificoutcome (area planted, quantity harvested and yearly yield) for commune i in year tand Wit is the same vector of weather covariates defined in equation (5). ⌧i and �t arecommune and year fixed e↵ects, respectively. In the first stage described in equation (7),the instrumented variable is Fireit, which is the sum of fires inside commune i during yeart. The variable Proposed Park

itis the area of potential parks that were proposed but not

implemented as o�cial parks in commune i in year t. In such instances, park limits werephysically drawn with the intention of creating a new park leading to de facto protectionand reduction in fires but since there was no o�cial implementations, proposed parks areexpected to a↵ect agricultural outcomes only through fires and not through other factorsrelated to park creation such as better road infrastructure, higher volume of informationthrough tourists and higher economic activity.

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1.6 Agricultural outcomes results and discussion

1.6.1 Reduced form

First, I report the results from the reduced form specification in equation (5). Despitethe causality concerns discussed above, working with the reduced form is useful becauseit allows to understand crop-specific patterns and uses of fire. Tables 1.17, 1.18 and 1.19report the impact of all fires during the current and three previous years on area planted,quantity harvested and yield respectively for di↵erent crops. From the fire literature, Iexpect that more fires would lead to more land area available for planting hence moreagricultural output and a higher yield to due a more fertile land post-burning (Gay-des Combes et al., 2017; Kleinman et al., 1995). The coe�cients for the current yearand the year before are either negative (cassava and vanilla) or not significant, whereasthe two and three-year lag coe�cients are positive and significant for rice only (Table1.17). Unsurprisingly, this suggests a lag in the process of turning a newly cleared landinto a field, which is especially true for rice as it requires irrigation. Table 1.18 reportsthe impact of fires on quantity harvested and show no significant impact except for anegative impact of contemporaneous fire on quantity of vanilla harvested and a positiveimpact of previous year’s fire on livestock production. The results on vanilla are notsurprising given that vanilla is planted in the forest, therefore there is a tradeo↵ betweenclearing forest for land and planting vanilla. Contemporaneous fires might also decreasethe quantity of vanilla harvested by burning of the plant and crop destruction before itcan be harvested. Table 1.19 reports the impact of fires on yield and is consistent with thetwo previous tables. I find a negative association between recent fires and yield of vanillaand no significant impact of recent fires on other crops’ yield. Older fires are associatedwith increased corn yield. From looking at these first results, it is clear that timing offires matter and that impacts are di↵erent based on the nature of the crop. To furtherinvestigate this, I split fires into grassland fires and forest fires as I expect the earlier tomainly a↵ect yield and livestock production and the latter to a↵ect area cropped. Resultsare reported in Tables 1.20 through 1.22. From Table 1.20 column (1), which looks atthe impact of fires on total area cropped, I indeed find no significant impact of grasslandfires and a positive and persistent e↵ect of lagged forest fires. A forest fire in the currentyear does not lead to increased cropped area in the same year. Table 1.20 column (2),looks at the impact of fires on area used for rice plantation, and results are reversedthere. A grassland fire occurring two years before the year reported is associated withincreased area used for rice. None of the forest fire coe�cients are significant and thisis not surprising given that grasslands are much more likely to be turned into rice fieldthan forested areas. Table 1.20 column (3) looks at the impact of fires on land area usedfor corn production and shows that grassland fires are not associated with any increasedland planted unlike forest fires. Contemporaneous forest fires are associated with lessarea used for corn production, whereas forest fires occurring two years before the yearreported is associated with more land used for corn. Finally, contemporaneous forest firesdecrease the area used for cassava and vanilla production, whereas lagged grassland firesdecrease the area used for sugar and vanilla production.

Next, I look at the relationship between each type of fire and crop harvest, resultsare reported in Table 1.21. I find that lagged grassland fires are associated with a higherproduction of cattle and rice, and less vanilla harvested, which is consistent with theimpacts on area cropped discussed earlier. On the other hand, I find that lagged forest

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fires are associated with higher quantity of corn and cassava harvested but fewer cattleand vanilla. The negative impact of forest fires on cattle production is surprising, butwould be consistent with switching activities from herding to farming. Finally, I look atthe impact of fires on yield and find that forest fires have no significant impact on yield,whereas lagged grassland fires are associated with higher corn and bean yield (Table1.22). However, e↵ects are very small in magnitude. To further investigate the impactof grassland fires on yield, I split grassland fires into di↵erent trimesters of the year.Results are reported in Table 1.23. I find that, for livestock production, as predicted bythe pastoral fire literature, impacts are driven by late dry season burning (July to Augustbefore the rainy season) and that there is no significant impact of early dry season burning(April to June, which is immediately after the rainy season). For rice and corn, burningduring July to August and September to December both seem e↵ective.

To sum up this section, it is clear that agricultural outcomes are highly correlatedwith fires. Recent fires negatively impact crops and yields, whereas longer term firesincrease area cropped, quantity and yield. Area e↵ects are driven by forest fires, whereasyield e↵ects are more associated with grassland fires. In the next section, I report anddiscuss the results from the instrumental variable approach to see whether these observedpatterns are causal.

1.6.2 Instrumental variable

Table 1.24 reports the first stage described in equation (7). Both o�cial and proposedparks lead to fewer fires and unsurprisingly, the magnitude of e↵ects is larger for o�cialparks. On average, the creation of a new national park is associated with a 29% decreasein the number of all fires in the district, whereas having new land proposed for parkcreation is associated with a 17% decrease in all fire activity. For both o�cial andproposed parks, the e↵ects are larger for grassland fires than for forest fires. The creationof an o�cial park is associated with a 32% decrease in grassland fires but only a 16%decrease in forest fires. Proposing new land as a potential park is associated with a 23%reduction in grassland fires and 8.5% decrease in forest fires. There are two possibleexplanations that could be consistent with this. First, forest fires might be harder tomonitor and regulate than grassland fires if they are not located within park boundaries.Second, under the assumption that forest fires are primarily used for clearing land andthat grassland fires are used for livestock consumption and the occasional pest controlthen the smaller reduction in forest fires would imply that households have more inelasticdemand for

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