metadata: authors: 0: author: Behrer,Arnold Patrick
count: India
docna: 0: docna: Man or Machine ? Environmental Consequences
            of Wage Driven Mechanization in Indian Agriculture
docty: Policy Research Working Paper
abstracts: cdata!: This paper uses an exogenous shock to
            wages from the world&#x2019;s largest anti-poverty
            program to show that higher wages can lead to increased air
            pollution, likely by inducing farmers to shift into a
            labor-saving and mechanized production process. Using a
            difference-in-differences approach on the staggered roll-out
            of India&#x2019;s Mahatma Gandhi National Rural
            Employment Guarantee Act (MNREGA), combined with data on
            nearly 1 million fires, the paper shows that the frequency
            of agricultural fires increases by 21 percent after the
            shock. The increase in fires is concentrated in districts
            that appear more likely to mechanize the harvest. MNREGA did
            not lead to changes in area planted or tonnage produced in
            fire intensive crops. The estimates show that nationally,
            the shock increased the rate of particulate emissions from
            biomass burning by 30 to 50 percent. The results suggest
            that absent policies to correct for environmental
            externalities of mechanization at all stages of development,
            labor market shocks may lead to inefficient levels of mechanization.
display_title: Man or Machine ? Environmental
            Consequences of Wage Driven Mechanization in Indian Agriculture
listing_relative_url: /research/2023/03/34029330/man-or-machine-environmental-consequences-wage-driven-mechanization-indian-agriculture
topicv3: Pollution Management m1326210 601,Structural Transformation for Growth
            m1326245 1089,Air Pollution m1327122 1791,Poverty, Jobs and Development m1503888 3786,Agricultural Waste m1583753 4288
publishtoextweb_dt: 2023-03-27T00:00:00Z
dois: 10.1596/1813-9450-10376
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1561699
model_extractions: Policy Research Working Paper 10376

# Man or Machine?

Environmental Consequences of Wage Driven Mechanization in Indian Agriculture _A. Patrick Behrer_ Development Economics Development Research Group March 2023
Policy Research Working Paper 10376

# **Abstract**

This paper uses an exogenous shock to wages from the world’s largest anti-poverty program to show that higher wages can lead to increased air pollution, likely by inducing farmers to shift into a labor-saving and mechanized production process. Using a difference-in-differences approach on the staggered roll-out of India’s Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA), combined with data on nearly 1 million fires, the paper shows that the frequency of agricultural fires increases by 21 percent after the shock. The increase in fires is concentrated in districts that appear more likely to mechanize the harvest. MNREGA did not lead to changes in area planted or tonnage produced in fire intensive crops. The estimates show that nationally, the shock increased the rate of particulate emissions from biomass burning by 30 to 50 percent. The results suggest that absent policies to correct for environmental externalities of mechanization at all stages of development, labor market shocks may lead to inefficient levels of mechanization.

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

_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
# Man or Machine? Environmental of driven consequences wage mechanization in Indian agriculture

# A. Patrick Behrer

JEL Codes: Q1, Q53, D2, O13, O44, J43 Keywords: Air pollution, Mahatma Gandhi National Rural Employment Guarantee Act, poverty reduction, structural change, agriculture, India

> ∗I would like to thank Edward Glaeser, Rema Hanna, Gabriel Kreindler, Robert Stavins, Gernot Wagner, Joseph Aldy, Jisung Park, Teevrat Garg, Michael-David Mangini, Kristen McCormack, Kibrom Tafere, Gabriel Englander, Valentin Bolotnyy and numerous seminar participants at Harvard, Stanford, The University of Maryland, The World Bank, and the University of Chicago for comments and suggestions. Simon Schröder gave excellent research assistance and both Rakesh Kumar and Ryan Lee provided invaluable technical assistance collecting data. All remaining errors are my own. A previous version of this paper was circulated as “Earth, Wind and Fire: The impact of anti-poverty efforts on Indian agriculture and air pollution.” Funding from the Harvard Environmental Economics Program, the Environmental Protection Agency and the Harvard Climate Change Solutions Fund is gratefully acknowledged. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **1 Introduction**

Shifting labor from low-productivity, labor-intensive sectors to high-productivity, often capital intensive, sectors is an essential component of economic development (Huneeus and Rogerson, 2020; Kuznets, 1973; Arrow et al., 1995). This occurs as workers transition out of agriculture into industry (Gollin et al., 2002) and again as workers transition out of industry into services or higher value added industry. In both cases, higher wages in the labor-intensive sector, at least in part, help to drive the transition by encouraging firms to invest in labor-saving mechanization. But, unlike their impacts on structural transformation, the effects of higher wages on environmental damage and pollution are theoretically ambiguous (Jayachandran, 2021) and depend on which type of production has larger environmental externalities. In settings in which mechanization relies on fossil-fuel generated power – electrical or otherwise – this mechanization will be associated with a pollution externality.<sup>1</sup> If firms that rely on fossil-fuel generated power do not internalize those externalities, their investments in mechanization may be inefficiently high. There is a large literature on the potentially adverse impacts of the adoption of “inappropriate technology” on development, especially when the technology is labor-saving in a labor-rich setting (Stewart, 1978; Acemoglu and Zilibotti, 2001; Acemoglu, 2021). This adverse effect, in the context of mechanization of manufacturing, has even been implicated in premature deindustrialization in some contexts (Huneeus and Rogerson, 2020). The hypothesized relationship between wages and pollution here suggests that proper Pigouvian taxation of environmental externalities might lead to a more efficient transition from labor to capital intensive production and a healthier process of economic development.

I show empirically that an anti-poverty policy that exogenously raised wages had the unintended consequence of also increasing pollution. This increase in pollution appears to be driven by mechanization in response to the wage increases. A central challenge in demonstrating that increasing wages has a causal impact on pollution by increasing mechanization has been the simultaneity of wage growth and mechanization (Ebenstein et al., 2015). Rising wages could drive increases in

> 1The transition from human powered cotton production to steam powered production in England is a classic example: as firms replaced human labor with coal-fired powered cotton looms, production increased and shifted to Manchester which suffered notable declines in air quality (Rodgers, 1960; Longhurst and Conlan, 1970). Longhurst and Conlan (1970) quotes the Manchester police commission in the 1800s: “the increase of steam engines as well as smoak issuing from chimnies used over stoves, foundries, dressers, dyehouses and bakehouses are become a great nuisance to the town.”

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mechanization by raising labor wages and leading firms to invest in labor-saving mechanization.<sup>2</sup> However, exogenous innovation in production techniques also leads to labor-saving mechanization that raises labor productivity, with wage increases coming as a consequence of higher productivity (Solow, 1957).<sup>3</sup> I address the problem of simultaneity by measuring how air pollution changes after an exogenous shock to wages generated by India’s Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA), the world’s largest anti-poverty program. After the implementation of MNREGA, low-skill wages increased by between 4% and 8% (Imbert and Papp, 2015) in rural areas of India. I show, using a difference-in-differences framework taking advantage of the staggered roll-out of MNREGA coupled with data on nearly 1 million fires, that agricultural fires increased by between 9% and 21% after implementation of MNREGA.

To explain the mechanism, I outline a simple model showing that rising incomes could lead firms to invest in labor-saving, but polluting, mechanization. My model suggests that farmers responded to MNREGA by mechanizing harvest. This is consistent with both Hornbeck and Naidu (2014) and Clemens et al. (2018), who show that farmers mechanize after shocks to the low-skill labor market in the United States. Mechanization leads to an increase in agricultural fires in India because it leaves between 80% and 120% more biomass on a field relative to manual harvesting (Yang et al., 2008; Jitendra et al., 2017). Biomass must be removed prior to planting and fires are the least expensive way to remove biomass (Ministry of Finance, 2018). I show that fires increase by 27% in districts with the highest probability of mechanization compared to no statistically or economically significant change in districts with low probability of mechanization.

To further isolate the mechanization channel, I test for evidence of two alternative mechanisms: (1) an increase in production that may have led to more fires and (2) a shift in cropping patterns due to MNREGA’s role as implicit insurance. Increases in income driven by MNREGA may have led to changes in local demand that raised production. I show reduced form estimates of MNREGA’s impact on total hectares planted and total tonnage produced that suggest there were not large

> 2Allen (2011) argues that firms’ choice to invest in labor-saving production techniques in response to higher relative wages in England is the central reason the Industrial Revolution began there.

> 3Humphries (2013) and Kelly et al. (2014) argue that wages were not in fact higher in England at the time of the Industrial Revolution and adoption of labor-saving technology was driven by concerns other than wages. See also Voth et al. (2022). Higher subsequent wages were then a side-effect of increased labor productivity. Zheng and Kahn (2017) point out that the causality could also go in the opposite direction: firms in polluted cities in 19<sup>_th_</sup> England might have had to pay higher wages to attract workers _because_ of the pollution.

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changes in the area or tonnage of crops specifically associated with the use of fires. Alternatively, MNREGA provided implicit insurance that may have allowed farmers to shift into higher value but higher variance crops. While others have found that MNREGA induced shifts into higher value crops (Raghunathan and Hari, 2014), I do not find evidence of an increase in the overall volume of agricultural output due to MNREGA. Further, the crops that are shifted into are not associated with the use of fires.

I show that air emissions from biomass burning also increase after the implementation of MNREGA. I show that the rate of emissions from biomass burning of black carbon, organic carbon, and SO2 – pre-cursor pollutants to PM2 _._ 5 and PM10 – increased by between 30% and 50% after the implementation of MNREGA. I also show that the number of months in which the Indian ambient standard for PM2 _._ 5 is exceeded increases by 11% after implementation of MNREGA.<sup>4</sup> My results speak to the growing body of work on “envirodevonomics” outlined by Greenstone and Jack (2013) and Jayachandran (2021) and the general question of how economic development – and concurrent changes in incomes, consumption, and production – interacts with environmental quality. I contribute most directly to the work examining the environmental impacts of raising incomes and wages (Gertler et al., 2006; Alix-Garcia et al., 2013).<sup>5</sup> Pollution, in particular air pollution, tends to be substantially above recommended limits in developing countries (Alpert et al., 2012; Liu et al., 2018) and these elevated levels of pollution lead to meaningful negative impacts on health and other economic outcomes (Cropper et al., 2012; Ebenstein et al., 2017; Barrows et al., 2018). Work examining the causes of these elevated levels of pollution has focused on institutional failures (Greenstone and Hanna, 2014) and potentially lower willingness to pay for

> 4The contribution of agricultural fires to air pollution is an acute policy challenge in India. As much as 40% of the pollution in Delhi during the winter may be due to crop residue burning (Bikkina et al., 2019). Increases in agricultural fires are believed to be a major reason that winter air quality in Delhi is among the worst in the world (Shyamsundar et al., 2019) with levels of PM2 _._ 5 that exceed WHO standards by as much as 1,000% (Liu et al., 2018). Aside from the Indian context, the use of agricultural fires to clear residue after harvest is a long-standing practice globally and contributes substantially to local air pollution. Agricultural burning is widely used in Pakistan and China and is used more intensively in Africa than anywhere else (Korontzi et al., 2006). The drivers of agricultural fires appear to be similar across countries (Cassou, 2018; Andini et al., 2018) thus understanding these drivers is important beyond India.

> 5This work has often focused explicitly on the impact of anti-poverty programs. Alix-Garcia et al. (2013) show evidence of an anti-poverty and environment trade-off in places with little market access operating via a consumption channel. I show evidence that the consumption mechanism does not appear to be operating in my context. The same low levels of market access that drive the negative effects they observe may generate positive environmental change in other settings (Barbier, 2010). Determining the extent of this trade-off is especially important because the poor disproportionately live in more environmentally degraded areas (Dogo et al., 2017).

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environmental quality (Kremer et al., 2011).<sup>6</sup> I move this work forward by showing, using an exogenous shock, that raising wages led to an increase in pollution and introducing evidence that wage increases can causally increase pollution by inducing mechanization.The opening example of England in the Industrial Revolution provides a non-agrarian example where increased labor costs led to increases in mechanization, and power demand, in manufacturing that results in higher pollution. Proper Pigouvian policies have long been recognized as an efficient mechanism for internalizing environmental or other externalities (Weitzman, 1974; Stavins, 1996; Blackman, 2010; Kremer and Willis, 2016). My results suggest that the implementation of anti-poverty programs that distort labor markets in settings without proper Pigouvian taxation can lead to externalities that reduce the gains of the program itself. MNREGA led to an increase in pollution that likely had negative health consequences for rural residents that were the target of the anti-poverty efforts. Proper Pigouvian taxation of pollution might have offset some of these negative effects. Further, and in line with existing work on the role of tax policy in indirectly driving automation (Acemoglu et al., 2020), this paper suggests that by forcing firms to internalize the environmental costs of mechanization, Pigouvian policies might also have implications for the adoption of “inappropriate technologies.” There is a large literature on the drivers of structural transformation and a smaller, but growing literature on the environmental consequences of this transformation. There are two recent papers in that literature that are closely related to the current work. Garg et al. (2022) examine the expansion of the roads network in India that they argue facilitates structural transformation by enabling the movement of labor out of agriculture and find that crop burning increases substantially in villages after they receive a road. Like this paper, they demonstrate a causal link between elements of structural transformation and environmental damage. Caunedo and Kala (2021) separately examine the impact of facilitating access to mechanization technology in an RCT among farmers in India and find that, consistent with the results here, when the cost of mechanization is lowered relative to that of labor, farmers substitute mechanization for labor and release labor to other sectors of the economy.

> 6In showing that MNREGA increases pollution emissions I contribute to the nascent literature on the health impacts of MNREGA (Thomas, 2015; Dasgupta, 2017; Nair et al., 2013). Emissions from agricultural fires have been shown to have negative health consequences (Rangel and Vogl, 2016; Pullabhotla, 2018) and so my results suggest that there may have been important health impacts from MNREGA, particularly in downwind districts, that are not captured by existing work.

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This work also contributes to the on-going examination of what has driven the increase in agricultural burning in North India over the last 20 years. In addition to Garg et al. (2022), recent work has shown that shifting the beginning of the cropping season to minimize groundwater extraction has contributed to the increase in burning (Kant et al., 2022). I show that wage changes driven by MNREGA appear to be an additional driver of the increase in burning on top of changes induced by regulations around the timing of planting and the construction of rural roads.<sup>7</sup>

# **2 Agricultural fires in India and MNREGA**

## **2.1 Fires in Indian agriculture**

While slash-and-burn agriculture is still widely used in some parts of the world (notably Africa and Indonesia) (Korontzi et al., 2006; Andini et al., 2018), the predominant use of fire in agriculture in India today is to clear harvest residue off of fields in order to prepare the field for the subsequent season’s planting (Jain et al., 2014; Bhuvaneshwari et al., 2019).<sup>8</sup> Fires are widely used despite being nominally illegal since the mid-1990s (Lohan et al., 2018). While governments in some states have begun to enforce these laws, the expected cost of violation during my period of study is small. In 2012 the state government of Haryana handed out a total of roughly $12,000 in fines (Anand, 2016). That works out to an expected fine of 0.75$USD per fire in the state. Farmers face an average marginal cost of roughly 50$USD to clear their fields of residue without using fire (Ministry of Finance, 2018).

While fires offer an inexpensive means of clearing crop residue, they have substantial negative effects. The primary negative effect is the increase of both local and global air pollutants. The largest source of carbonaceous particulate matter globally is crop residue burning (Cassou, 2018). More significant than their contribution to climate change is the impact that agricultural fires have on local air pollution and health. Source-apportionment studies have suggested that pollution from agricultural fires can raise local concentrations of PM2 _._ 5 to more than 1,000% above the WHO 24-hour guideline of 15 _μg/m_<sup>3</sup> (Bikkina et al., 2019; Balakrishnan et al., 2019; Liu et al.,

> 7While not examining the drivers of burning directly, Jack et al. (2022) show that an up-front payment for ecosystem services (PES) program that provide farmers payments not to burn their fields can reduce burning, underlining the fact that burning behavior is responsive to economic incentives.

> 8For a brief discussion of the history of fire use and details of its global use, see Appendix 1.1.

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2018). Exposure to these elevated levels of pollution leads to reductions in child height for age and weight for age scores (Singh et al., 2019; Rangel and Vogl, 2016) and increased infant mortality (Pullabhotla, 2018).<sup>9</sup> The use of fire is particularly prevalent in the parts of India that grow crops in a coupled ricewheat cropping system (Jain et al., 2014; Prasad et al., 1999) because of the short turn-around time between harvest of rice and planting of wheat. In this system farmers plant rice during the monsoon season (“kharif”), roughly from August to December, and wheat immediately following rice harvest during the pre-monsoon (“rabi”) season from January to March or April. This system of agriculture is particularly widespread in Punjab, Haryana, Uttar Pradesh and Uttarakhand (NAAS, 2017).<sup>10</sup> Figure 1 shows the general pattern of agricultural fires across India in the years from 2003-2005, prior to the implementation of MNREGA. Consistent with the expected pattern of fire use by crop type, fires are concentrated in the northwest and Indo-Gangetic plain. The sugarcane producing areas of the country also show some local hot spots. Areas that predominantly produce oilseeds, namely Rajasthan and Maharashtra, have low levels of fire.

Districts that plant more rice, wheat, and sugarcane have more fires in the pre-MNREGA period. Areas with more land in larger farms also appear to have more fires.<sup>11</sup> In Table 1, I show the correlation between district characteristics in the pre-MNREGA period and the level of fires over the same time period. Column one shows the univariate relationship while column two shows the results including all predictors in the same regression.

> 9There is a large body of literature that shows exposure to high levels of general particulate pollution – not solely pollution from crop burning – has negative economic consequences aside from health effects. Hanna and Oliva (2015) show reductions in pollution levels in Mexico City due to a refinery closure increased weekly hours worked. Chang et al. (2016) show substantial declines in worker productivity as exposure to PM2 _._ 5 increases in California and Deschenes et al. (2017) show that individuals make substantial defensive investments to avoid the consequences of air pollution. Aguilar-Gomez et al. (2022) provides a recent overview of the literature on human capital outcomes. There is also evidence that elevated levels of particulate pollution have direct negative consequences for agricultural yields (Burney and Ramanathan, 2014).

> 10In Appendix Figures A4a-A4c I show the distribution of wheat, rice and sugarcane production across India in the pre-MNREGA period. I also show maps of the average crop coverage on October 31<sup>_st_</sup> each year as an approximation of which areas most heavily engage in rice-wheat production in Appendix Figure A5.

> 11This may be due to the fact that larger farms enjoy more economies of scale from mechanizing the harvest. Evidence from both China (Wang et al., 2018) and Indonesia (Yamauchi, 2016) supports the claim that larger farms are more able to substitute mechanization for labor.

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# **2.2 MNREGA**

## **2.2.1 Roll-out of MNREGA**

The objective of MNREGA is explicitly to provide employment to rural households on projects that create public assets that “address causes of chronic poverty...so that the process of employment generation is on a sustainable basis” (GOI, 2007). The essential feature of the program is a guarantee of 100 days of employment for rural households in a given financial year. The first districts received MNREGA in February of 2006 (“phase 1”). The targeting formula used to select these first districts is unknown (Sukhtankar, 2016).<sup>12</sup> However, the government had an explicit goal of ensuring that poor districts were included in the first wave and every state had to have at least one district in the first wave (Shah and Steinberg, 2015). After the initial roll-out, another 130 districts received the program in April 2007 (“phase 2”) with the remaining roughly 270 districts receiving the program in April 2008 (“phase 3”).

Table 2 summarizes the pre-MNREGA (pre-2006) level of a number of measures of economic development and the primary outcome variables in this study by MNREGA phase. The table highlights that earlier districts were on average poorer, more rural, slightly more agricultural and had less land in cash crops than later districts. Differences between each phase are not equal however, with the largest differences between districts in the first two phases and those in phase three.

## **2.2.2 Impact of MNREGA in the literature**

Despite, or perhaps because of, the size of MNREGA, program implementation has been inconsistent. This inconsistency has resulted in heterogeneous impacts across states and difficulty in precisely assessing the true impact of MNREGA.<sup>13</sup> In the most comprehensive review of the research on MNREGA, Sukhtankar (2016) suggests that there are four aspects of MNREGA that the substantial literature agrees on: (1) heterogeneity in impact, (2) despite the statutory requirement that employment be provided on demand, there has been meaningful rationing of employment provision, (3) MNREGA has increased rural, private sector wages, and (4) the overall impact on rural

> 12For details on the history of workfare in India as well as background on the structure of MNREGA see Appendix 1.2.

> 13One reason for the heterogeneity in implementation has been substantial corruption and rent-seeking by implementing officials (Jha et al., 2009; Niehaus and Sukhtankar, 2013).

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productivity is ambiguous.

There is wide consensus in the published estimates that MNREGA increased unskilled wages by between 5% and 8%, increased labor participation in public works, and may have led to declines in the supply of labor to the private sector (Azam, 2011; Berg et al., 2012; Imbert and Papp, 2015). Imbert and Papp (2015) show that MNREGA increased wages of low-skill workers by 5% on average and this increase is concentrated in the dry season, when they show the bulk of MNREGA work occurs, and is accompanied by a decline in private sector labor supply of 1.3%. When they focus on the states in which the fraction of time spent on public works projects by rural, prime age adults was above 1 percent (“star” states), they find wages increased by 9% and private sector employment fell by 3%. Deininger and Liu (2013) show that MNREGA led to an increase in the accumulation of non-financial assets in the medium run. Raghunathan and Hari (2014) show that farmers plant riskier crops after the implementation of MNREGA which increases their incomes above the direct wage support of the program. Berg et al. (2012) suggests that the wage increase takes between 6 and 11 months after program implementation to materialize and is biased towards low-skill labor.

In the only large-scale RCT on MNREGA to-date (Muralidharan et al., 2016) (MNS) show that improving the implementation of MNREGA increases wages by 7%.<sup>14</sup> They focus only on Andhra Pradesh but note that the similarity in the size of their estimates on the impact of improving MNREGA to the impacts of initial implementation highlight the importance of implementation heterogeneity. They also do not find substantial evidence of impacts on migration but note that their migration data differs from the data used in previous studies.<sup>15</sup> The impact of MNREGA on education also suggests MNREGA tightened local labor markets. Shah and Steinberg (2015) show that MNREGA decreased the educational attainment of 13-16 yearolds by decreasing school enrollment. The effects are similar for boys and girls. They suggest that

> 14I describe a replication exercise of my main results using treatment in this RCT in Appendix 6.

> 15Other attempts to estimate the impact of MNREGA on migration have been limited but suggest it may have had important impacts. A government review of the impacts of MNREGA claims that the program led to a 27% decline in cross-district migration caused by economic distress. One of the few academic studies of the impact of MNREGA on migration finds a reduction in rural to urban migration of around 8% (Imbert and Papp, 2014). While not direct evidence of reductions in migration, several studies have estimated a positive spillover of MNREGA on wages in neighboring districts that is hypothesized to operate via reductions in inter-district migration (Prasann, 2016; Muralidharan et al., 2017). These estimates suggest that wages in neighboring districts may have increased by as much as 9% and that the estimated impact of MNREGA on wages in districts in which it was implemented may be substantially underestimated due to these spillovers.

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MNREGA induces these changes by tightening local labor markets which raises the opportunity cost of schooling. As a result, boys leave school to provide labor in the market and girls substitute into domestic work.

Overall the existing literature on MNREGA suggests that it had positive impacts on wages, incomes, and potentially health outcomes. It is clear that MNREGA had meaningful impacts on local labor markets. In particular it appears to have tightened labor markets, especially for unskilled labor, by providing an outside option for unskilled labor in the form of public works at a wage that may have been above the prevailing agricultural wage and therefore made unskilled labor more expensive.

# **3 Research design and implementation**

I follow much of the existing literature analyzing MNREGA and utilize a difference-in-differences framework that takes advantage of the phased roll-out of MNREGA across the country to examine its overall impact on the use of fires in agriculture. As a robustness check, I also utilize the treatment pattern from the MNS experiment to determine if improving the implementation of MNREGA leads to an increase in agricultural fires. The design and results of this robustness check are described in Appendix 6.

## **3.1 Data**

**Agricultural fires:** The source of the raw data on fire presence comes from NASA’s Fire Information for Resource Management System (FIRMS).<sup>16</sup> The FIRMS data provides the latitude and longitude and detection time of fires around the world using imagery from the MODIS and VIIRS imaging platforms. For all the primary analysis I use only data from MODIS because VIIRS does not provide sufficient temporal coverage.<sup>17</sup> MODIS Aqua imagery is available from mid-2002 to the present. Imagery from the satellites is collected every 6-12 hours around the world and processed using NASA’s image processing algorithm to identify fires based on the emissions of mid-range infrared radiation. The algorithm is designed to filter out spurious signals

(e.g. solar glare and gas flaring). NASA suggests that the imagery can detect fires as small as

> 16I primarily use data from MODIS, available here: https://earthdata.nasa.gov/firms

> 17See Appendix 1.5 for additional details on the difference between MODIS and VIIRS imagery.

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50m<sup>2</sup> if conditions are ideal and at sizes around 100m<sup>2</sup> under average conditions. They also report that fires are located at the correct location with a spatial margin of error of less than 100m on average. Unfortunately, the resolution of MODIS is such that the data available in FIRMS only measures whether at least one fire exists in a given square kilometer. As a result, MODIS does not provide any information about the size of the detected fire or the total burned area. Further, it does not distinguish between pixels with only one fire and those with multiple fires.

I combine the FIRMS data on fire presence with remotely sensed landcover data from the European Space Agency’s Copernicus system to determine the land uses on which fires occur.<sup>18</sup> Copernicus land cover data assigns each pixel to a land class based on imaging that measures its reflectivity. Classes include water/ice, urban, wetland, irrigated cropland, non-irrigated cropland, various classes of forest, and various classes of dry shrubland. I use the cropland classes to determine which fires occur on cropland and focus my analysis on these agricultural fires. I assign agricultural fires based on land use in 2006. I supplement the land use data from Copernicus with additional polygon layers from the Harvard Center for Geographic Analysis (CGA) that identify roads and urban areas in India. I verify that my agricultural fires do not include any fires that occur in locations that the CGA defines as urban. Finally, I aggregate the assigned agricultural fires to the district by month level.

Figure 1 shows the annual average number of monthly fires per subdistrict prior to MNREGA. Fires are concentrated in the northwest of the country and along the Indo-Gangetic Plain. However, it is clear that burning occurs on cropland throughout India. Both of these facts are confirmed by Figure 2 showing the distribution of states by mean monthly fires in subdistricts. While fires are used in most states in India, their use in Punjab and Haryana is far more widespread than in other states (see notes on Figure 2).

**Weather Controls:** I collect weather re-analysis data from ERA5. ERA5 is a weather re-analysis product produced by the European Commission’s Copernicus Climate Change Service.<sup>19</sup> When working with weather data, there is a trade-off between using re-analysis data, which combines observed data with a physics model to provide data at a fine resolution over long

> 18Copernicus data is available here:https://land.copernicus.eu/imagery-in-situ

> 19Data available here: https://cds.climate.copernicus.eu/cdsapp!/dataset/reanalysis-era5-land?tab=overview

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time periods, and data collected from weather stations. Station data has the advantage of being based only on observation and not including a modeled component. However, station networks often lack complete geographic coverage of a given area and station records may be incomplete, introducing temporal gaps in coverage as well. Re-analysis solves these problems but relies on models to do so. Despite the reliance on models, re-analysis data is broadly believed to provide a reasonable best estimate of weather variables (Auffhammer et al., 2013). As a result, it is widely used in both environmental and development economics (Schlenker and Lobell, 2010; Hsiang, 2016; Emerick, 2018). I chose re-analysis data for this project because comprehensive station data is not available. The ERA5 Land hourly product provides data at an hourly level on a grid of 0.1<sup>_◦_</sup> _×_ 0.1<sup>_◦_</sup> , which translates to a 9km resolution. I collect data on cloud cover, temperature, and precipitation over the full sample from 2003 to 2014. I aggregate these weather variables to the district level in the primary analysis.

**Agricultural Data:** I collect data on Indian agriculture from a number of sources. The first is the ICRISAT Meso data (Rao et al., 2012). This data is collected by the International Crops Research Institute for the Semi-Arid Tropics and measures the performance, structure, and behavior of the agricultural economy at the district level in India since 1966. I use data from 2003 to 2014. Because Indian district boundaries have changed over time, they provide both apportioned, where they adjust data for boundary changes, and unapportioned data in which data is not re-apportioned based on changes. I use the unapportioned data and manually re-apportion data to the district boundaries as they were recorded in the 2001 census to align with my other data sources. I use data on district level cropping patterns and land holdings from ICRISAT.

I supplement agricultural data from ICRISAT with data from the Indian Ministry of Agriculture. I scrape agricultural census data from 2001, 2005, and 2011 at the district and subdistrict levels. This provides additional data on characteristics of agricultural holdings and planting patterns at the district level to supplement ICRISAT data and provides resolution at the subdistrict level not available in the ICRISAT data. I also scrape the agricultural input survey data from 2001, 2006, and 2011 to collect data on machine inputs to production.<sup>20</sup> I also scrape data from the cost of

> 20The agricultural census can be found here: http://agcensus.dacnet.nic.in/districtsummarytype.aspx. The agricultural input survey can be found here: http://inputsurvey.dacnet.nic.in/districttables.aspx. Cost of cultivation data is here: https://eands.dacnet.nic.in/Cost_of_Cultivation.htm.

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cultivation survey to measure trends in input costs at the state level.

**SHRUG Data:** To measure baseline conditions in the districts in the primary analysis, I use data from the 2001 Census compiled in the new Socioeconomic High-resolution Rural-Urban Geographic panel for India (SHRUG) dataset (Asher et al., 2019). I use the night lights data provided in SHRUG in robustness checks as well.

**Pollution Data:** Previous work on the relationship between agricultural fires and pollution has relied on data from air quality monitoring stations (Pullabhotla, 2018). Like with weather data, this has the disadvantage of limiting the analysis of pollution to areas with monitors that have been active over the full time period. To get around this issue, I use satellite re-analysis data from the Modern-Era Retrospective analysis for Research and Applications (MERRA) database provided by NASA (Rienecker et al., 2011). This is a satellite based product used by economists to study air pollution from coal fired power in India (Barrows et al., 2018) that provides data on the monthly average emissions rates for black carbon, organic carbon, and sulfur dioxide (SO2) on a 0.5<sup>_◦_</sup> _×_ 0.625<sup>_◦_</sup> grid. Importantly for my study, MERRA separately identifies the emissions of the above pollutants by source, including biomass burning. I also collect concentrations of black carbon, organic carbon, sulfur dioxide, and sulfate, which allows me to calculate the concentration of PM2 _._ 5 (He et al., 2019).

# **3.2 Empirical framework**

Following the difference-in-differences approach of Shah and Steinberg (2015), I estimate the effect of MNREGA on fire use as the change in fires before and after implementation of MNREGA within a district, controlling for month by year and district fixed effects. In the primary framework, districts are treated when MNREGA becomes statutorily effective in the district. Appendix Figure A1 shows the pattern of MNREGA roll-out.

I assume that the number of fires _Fimy_ in district _i_ in month _m_ of year _y_ follows a Poisson distribution. This is appropriate given both the count nature of the data and the skewness of the distribution of monthly fires. Many district-months do not have any fires as a result of the nature 13
of agricultural production. I estimate variants of: where _μ_ ( **X** _imy_ ) is the conditional mean of fires in district _i_ in month _m_ and year _y_ , _Nimy_ is an indicator that MNREGA had been implemented in district _i_ in month _m_ and year _y_ . **W** _imy_ is a vector of weather controls including minimum and maximum temperature, cloud cover, and total precipitation. _δmy_ is a month _×_ year fixed effect and _ψi_ is a district fixed effect. I estimate versions of equation 1 with and without **W** _imy_ . Because of the impact that cloud cover has on the ability of satellites to detect fires and the impact that temperatures and precipitation can have on the presence of fire, my preferred specification includes **W** _imy_ .<sup>21</sup> _β_ is the estimate of interest and measures the approximate percentage change in monthly fires after the implementation of MNREGA.I cluster standard errors at the district level (Abadie et al., 2017).

In any difference-in-differences study, the crucial identifying assumption is that the trends in the outcome variable, in this case agricultural fires, would have been similar across all groups without the treatment. This is a fundamentally untestable assumption. Instead, common practice is to test whether the trends were parallel prior to the implementation of the policy being evaluated. Here that requires that the trend in agricultural fires in the years leading up to treatment in phase 1 districts was similar to that in phase 2 and phase 3 districts. Like Imbert and Papp (2015) and Shah and Steinberg (2015), I am relying on the assumption that the assignment of districts to MNREGA phases was based on features of the district that do not include the trend in fires _and_ are not correlated with trends in fires.

I show evidence in Figure 3 that the trends in pre-MNREGA fires did not differ across the phases. Figure 3 shows the results of an event study on the year of MNREGA implementation where the outcome is monthly agricultural fires. There is a clear and significant increase in fires after the implementation of MNREGA but little evidence of trends in the number of fires prior to MNREGA’s implementation. I show event studies for a number of other outcomes (e.g. area

> 21 Cloud cover introduces non-classical measurement error into my estimates and failing to control for it may lead to attenuation bias (see appendix section Appendix 2). There is also evidence that large-scale burning induces cloud creation (Fromm et al., 2010; Gatebe et al., 2012; Jain et al., 2014; Liu et al., 2020). This will exacerbate the attenuation effect.

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planted in various crops) in the appendix.

# **4 Main Results**

I present the main results in two subsections. First, I discuss the impact of implementing MNREGA on fires across the entire country using my primary difference-in-differences specification and the full country sample. Second, I present results that show how the effect of MNREGA varies when districts are divided based on their score in an index measuring ease of mechanization.

## **4.1 Nationwide mean impacts**

Table 3 shows that MNREGA increased fires by approximately 21% after implementation (column 2 of Table 3). Although the confidence interval is wide – I cannot rule out an increase of between 11% and 30% at 95% confidence – I can easily reject a zero effect at 99% confidence. This suggests that MNREGA had a sizable impact on the frequency of agricultural fires. To put these estimates in perspective, the average number of monthly fires increased by approximately 40% from the beginning of my sample in 2003 to the end in 2014. The estimates here suggest that between 25% and 75% of that increase can be explained by MNREGA.

The estimates of MNREGA’s impact when I do not to control for the weather conditions in the district at the time of fire detection are substantially smaller than the estimates in the preferred specification (column 1 of Table 3). This is consistent with the expectation that failure to control for cloud cover biases the estimates towards zero because it introduces non-classical measurement error.<sup>22</sup> Even with this potential bias, when I do not control for weather, I estimate that MNREGA increased the frequency of agricultural fires by approximately 10% with a range from 1% to 18% at 95% confidence. While the estimated impact when I do not control for weather is smaller than my preferred specification, the expected impact is still meaningful and there is reason to believe that this estimate may be biased downwards.

> 22Because the measurement error is not classical, the standard result that measurement error in the dependent variable only reduces precision, and does not introduce attenuation bias (Hausman, 2001), does not apply. See Appendix 2 for a discussion of this measurement error.

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# **4.2 Robustness**

## **4.2.1 TWFE robustness**

There has been an enormous amount of recent econometric research on the use of differencein-differences in settings with staggered treatment where all units are eventually treated (see De Chaisemartin and D’Haultfoeuille (2022)), raising concerns about the validity of difference-indifferences estimators in these contexts. I summarize the most relevant portions of this literature and implement several of the robustness checks it describes in Appendix 3.1. I find that the results from my primary specification are not sensitive to varying weights in the aggregation of the individual 2x2 comparisons (de Chaisemartin and d’Haultfoeuille, 2020; Goodman-Bacon, 2021) nor to restricting comparisons to those between treated and untreated units (Callaway and Sant’Anna, 2019). Across a range of weights, I estimate treatment effects of between 0.18 and 0.23, compared to 0.21 in my primary specification. When I focus only on the treated to untreated comparison I estimate a treatment effect of 0.23 using the phase 1 to phase 2 and 3 comparison and one of 0.7 using the phase 2 to phase 3 comparison. In the latter comparison only 8% of the overall sample period is in the treated group.

## **4.2.2 Results in “star” states**

Imbert and Papp (2015) identify several states that were particularly effective at implementing MNREGA. In these states they find wage effects that are roughly double their average effects. If the impact of MNREGA on fires is driven by changes in wages, one would expect effects on fires to be larger in these states as well. In Appendix 3.2 I replicate their specification and show that effects are substantially larger in “star” states relative to both the average impact and effects in non-star states. I examine whether these differences simply reflect higher state capacity in these “star” states in Appendix 3.3. The RGGVY program is another government program, implemented by states, whose implementation timing may reflect general state capacity independent from capacity to implement MNREGA specifically. I do not find any difference in effects across states that were more proactive in implementing RGGVY compared to those that were less proactive.

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## **4.2.3 Other robustness**

I show in Appendix 3.4 that implementation of MNREGA only increased agricultural fires and had no detectable impact on shrubland fires, forest fires, or fires on plantations. This suggests that my results are not driven by secular trends in the incidence of fires or changes in fire detection technology. In Appendix 3.5, I examine the seasonality of MNREGA’s impact. Fires are used after harvest in both the late fall (post- _kharif_ ) and in the spring (post- _rabi_ ). MNREGA had larger impacts on labor markets in the spring (Sukhtankar, 2016) and consistent with this I find larger impacts on fires after the _rabi_ season. This also helps alleviate concerns that my estimation is simply picking up increases in fires that occur after the _kharif_ season due to changes in the timing of planting _kharif_ crops. While that mechanism surely explains some of the increase in fires after 2009, when the law driving changes in planting times was implemented (Kant et al., 2022), those changes cannot explain my overall results that are driven by changes prior to 2009 nor the effect I observe after the _rabi_ season.

In Appendix 3.6, I conduct a standard placebo test where I move treatment forward by one year for all phases. In this placebo treatment, I find that “MNREGA treatment” had no detectable impact on fires. In Appendix 3.7, I conduct something akin to a randomization inference test where I keep treatment timing the same but randomly assign districts to phases. This imposes a null hypothesis of no effects and provides an estimate of the likelihood that my results are due to random chance in the assignment of districts to treatment status. I find that my estimated effects are substantially different than the average effect estimated (clustered around a zero impact) of the randomization test.

# **5 Heterogeneity of MNREGA’s impact on agricultural fires**

Fires are used to manage crop residue differentially across India as the predominant crops vary with agricultural conditions. The ease of switching from manual harvesting to mechanical harvesting in response to wage changes also varies as average farm characteristics and the presence of the necessary mechanical equipment vary across districts. As a result, we should not expect the impact of MNREGA on fires to be consistent across districts. Rather, MNREGA should have had large impacts on the use of fires to clear crop residue in those districts that (a) tended to plant crops 17
whose harvest can be easily mechanized, (b) where combines were present to facilitate the hiring of these combines in place of manual labor, and (c) where, as I show in the model described in Appendix 4, farms tended to be larger.

This expected variation in the impact of MNREGA provides an opportunity to test the hypothesis that it was increases in mechanization that linked MNREGA to increases in agricultural fires. To do so, I construct an index of mechanization for each district. The construction of this index is driven by the features of my model and the relationship between fire use and mechanization in Indian agriculture as described in the existing literature (e.g. among others Jain et al. (2014) and Bhuvaneshwari et al. (2019)). I follow Asher and Novosad (2018) and use an index rather than testing multiple measures of mechanization to limit concerns of multiple hypothesis testing.<sup>23</sup> To construct the index, I consider the district averages of the following variables in the years prior to 2006: the share of agricultural land in both marginal and medium or large holdings (less than 1 HA or more than 4 HA), the number of combines in 2006, and the production areas of wheat, rice, and sugarcane (the ability to mechanize varies across crops in India (Solomon, 2016)).<sup>24</sup> To make the index, I turn these averages into Z scores. Each district receives a Z score for each individual measure. I then add them together to determine an index measuring the likelihood of mechanization in a given district. Higher index scores indicate a higher predicted level of mechanization. In the appendix, I show that this score is positively correlated with the best available measure of post-MNREGA mechanization – the number of combines in a district in 2011. I then divide districts into quartiles based on their mechanization index score and run my difference-in-differences regression from equation 1 on districts in each quartile.<sup>25</sup> I find in the full, nationwide sample, districts with the highest mechanization scores have sub-

> 23For details of each component of the index, as well as heterogeneity results by the individual components, see Appendix 3.8.

> 24Proposition 2 of my model indicates the impact of MNREGA on agricultural fires will vary across districts based on the median farm size, _AD_ . Specifically, given a distribution of _A_ that is single-peaked and where the threshold farm size for adoption of mechanization ( _A_<sup>ˆ</sup> ) is above _AD_ districts with initially higher _AD_ will see a larger increase in fires after the implementation of MNREGA. Figure A2 indicates that the average distribution of farm sizes across India does appear to be single-peaked. Further, Foster and Rosenzweig (2011) report that in the pre-MNREGA period (the late 1990s) fewer than 10% of farms were mechanized, suggesting a level of _A_<sup>ˆ</sup> substantially above _AD_ .

> 25Li (2017) suggests that areas with higher land concentrations, an important piece of my mechanization score, had lower pre-MNREGA agricultural wages than other districts, which suggests my mechanization score is not simply capturing districts with high wages prior to MNREGA. Using, imperfect, wage data from ICRISAT I find that the correlation between the Z score of district wages in 2005 and the mechanization index is -0.11 ( _p_ = 0 _._ 71). I directly measure the correlation between the Z score of per capita GDP and my mechanization score and find that it is -0.07 ( _p_ = 0 _._ 45) which suggests that I am also not simply identifying wealthier districts.

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stantially larger increases in the frequency of fire after the implementation of MNREGA. Districts in the highest quartile of the index see a 27% increase in monthly fires after the implementation of MNREGA (table 4). This is compared to an effect that is not different from zero in all other quartiles of the index. I show in Appendix 3.8 that this relationship is broadly consistent using each of the components individually as well. Further, in Appendix 3.9, I show that the importance of farm size in the heterogeneity analysis does not appear to be driven by differential likelihood of detecting fires in districts with larger farms.

This suggests that districts in which more farmers could feasibly respond to the agricultural labor market shock caused by MNREGA by mechanizing their harvests saw larger increases in fires. Larger increases in the use of fire in these districts is consistent with the predictions of my model and suggests that it was an increase in mechanization in response to the labor shock imposed by MNREGA that drove the increase in fires. However, it is possible that MNREGA impacted the use of fires through other channels. I explore two of these in the next section.

# **6 MNREGA’s impact on agricultural output**

MNREGA’s shock to agricultural labor markets may have done more than simply changing low skill labor wages. Changing the cost and availability of labor may have caused farmers to change which crops they planted as well. Alternatively, by increasing incomes in local markets, MNREGA may have increased demand, and prices, for agricultural products and incentivized farmers to increase their production of existing crops. If this increased production occurred in crops that used fire as part of the production process the increase in fires may have been driven by the increased crop production as opposed to being driven directly by changes in the labor market. I call this a consumption effect.

MNREGA may have also acted as an implicit insurance program for farmers (Sukhtankar, 2016). By guaranteeing the availability of outside employment in the event of a crop failure, MNREGA may have encouraged farmers to plant higher value but higher risk crops. If these crops are associated with more fire use than the previously planted, lower value crops, it could be that the insurance aspects of the MNREGA program are the driving force behind the change in fire frequency. I explore both of these potential explanations now.

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# **6.1 MNREGA had little impact on production**

I find that MNREGA had little impact on the area planted (in 000s of HA) or total tonnage produced of crops most associated with fire production (wheat, rice, and sugarcane) (Jain et al., 2014). When I use the full, national sample I find no effect on total area planted or total tonnage of fire associated crops or in total area planted and tonnage in non-fire associated crops. In the appendix I show event studies for each of these crop outcomes. In all cases the trend after the implementation of MNREGA is flat or declining and in no case is it different from zero.

Using the same difference-in-differences approach as described by equation 1, replacing agricultural fires ( _Fimy_ ) with the area in each crop ( _Aimy_ ) and tonnage produced ( _Timy_ ) in each crop as the outcome, I confirm the lack of impact suggested by the event studies (see Table 5). In all crops I examine (total crops, other non-fire associated crops, wheat, and sugarcane), except rice, I estimate an effect of MNREGA that is not statistically different from zero and is, in most cases, estimated to be close to zero with relatively high precision. The only crop for which I estimate a statistically significant effect at standard levels is rice, which sees a small (approximately 3%-4%) increase in area under production and total tons produced. This increase is small relative to the estimated change in fires and relatively imprecisely estimated. Based on these estimates I can reject an increase in fires of more than 0.40 % due to increasing area in wheat production at 95% confidence. Similarly I can reject changes greater than 1.60 % and 0.27 % for changes in the area in rice and sugarcane production.<sup>26</sup> MNREGA may have led to small increases in the area planted and total tons produced of rice. I find no evidence that it led to meaningful increases in area planted or tonnage of other crops. The estimated increase in fires due to the changes in area produced is small both in absolute terms and relative to the estimated overall impact of MNREGA on the frequency of fires. No more than 30% of the smallest estimated increase in fires is estimated to be due to changes in area under production and the estimated changes in production would account for no more than approximately 10% of

> 26To determine these bounds, I take the estimated change in area planted due to MNREGA from Table 5 and convert that estimated change in area into standard deviation units based on the distribution of area planted in each crop across all of India. This makes the units comparable to the units in Table 1 where I estimate the correlation between area planted in wheat, rice, and sugarcane and the frequency of fires. Based on the correlations in Table 1, I estimate the predicted change in fires for each crop based on the predicted change in area planted from Table 5. I use the delta method to calculate standard errors for these estimates. I report the upper end of the 95% confidence interval for the estimated change in fires.

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the estimated change in fires in the preferred specification.

## **6.2 Changes in crop choice induced by MNREGA’s role as insurance**

Table 5 provides no evidence that the overall production of non-fire associated crops, which includes higher value, higher variance crops, increases as measured either by area in production or total tons. Existing work (Gehrke, 2013; Raghunathan and Hari, 2014) has suggested that farmers do in fact shift into higher variance, higher value crops such as cotton after the implementation of MNREGA. I find no evidence of such a change but more importantly, the production process of the crops that farmers may have shifted into (Gehrke, 2013) does not typically include fire (Jain et al., 2014; Bhuvaneshwari et al., 2019). It is therefore difficult to explain the observed increase in fires as being driven by MNREGA’s implicit insurance provision leading to transitions in crop type. In Appendix 3.11, I show that the absence of an impact of MNREGA on crop production in the average persists when examining outcomes by quartiles of the mechanization index. That is, there is no differential impact of MNREGA on crop production in different quartiles of the mechanization index.

# **7 Implications for air pollution**

The primary concern regarding the use of fires to clear crop residue stems from concern that this practice may increase air pollution. Previous estimates of the contribution of crop burning to pollution in Delhi suggest that substantial amounts, from 17% to 60% of particulate emissions in Delhi in the winter months are the result of upwind crop burning (Liu et al., 2018; Bikkina et al., 2019). In a direct examination of the impact of upwind crop burning on infant mortality in India, Pullabhotla (2018) suggests that an increase of five upwind fires in a given year increases the infant mortality rate by approximately 10%.

The availability of satellite measures of emissions due specifically to biomass burning enables me to directly examine how the increase in agricultural fires caused by MNREGA translates to an increase in the emissions of three precursor pollutants of PM10 and PM2 _._ 5: black carbon, organic carbon, and sulfur dioxide (SO2). I use data from the MERRA-2 satellite platform that measures the monthly emissions rates from biomass burning of these three pollutants by district to determine the impact of MNREGA on emissions. To do so I replace agricultural fires ( _Fimy_ ) as the outcome of 21
equation 1 with the monthly average emissions rate of each of these three pollutants and re-estimate the same difference-in-differences model on the national sample.

# **7.1 Direct effect of MNREGA on pollutant emissions**

To begin I present the event study for the emissions rate of each of the three pollutants in Figure 4. Each panel shows the trend in emissions rates for black carbon, organic carbon, and SO2 before and after the implementation of MNREGA. In all three panels there is a clear increase in emissions rates in the year of MNREGA implementation that continues to grow in the year after implementation before leveling off and remaining above pre-MNREGA levels. For both black carbon and organic carbon the pre-trends are relatively flat and not-distinguishable from zero. SO2 shows more of an increasing trend but this is driven primarily by the estimates on the earliest years in the sample.<sup>27</sup> For all three pollutants that I examine, the implementation of MNREGA significantly increases the emissions rate from biomass burning. Beginning with black carbon, column 1 of Table 6 indicates that MNREGA increased the emissions rate from biomass burning by 38%. Organic carbon emissions also increase by 37% while SO2 emissions increase by an estimated 49%.

These estimated effects indicate substantial increases in the average monthly emissions rates as a result of the implementation of MNREGA. They provide some confirmatory evidence that MNREGA increased the number of fires; if fires increase one would expect to see a corresponding increase in emissions from biomass burning.

The size of the change in emissions rates is substantially larger than the estimated change in the number of fires. There are two explanations for this. The first is that the quantity of biomass burned per fire may have increased. This would be consistent with the model explored below of increased fires being driven by increased combine use. Previous estimates suggest fields cleared with combines have roughly 100% more biomass than fields cleared manually (Yang et al., 2008). To the extent that fires that consume more biomass have higher emissions (Smil, 1999) this increase in biomass is comparable to the difference between the estimated increase in emissions rates and the increase in fires.

> 27I pool the early and late years of the sample because I do not have a balanced sample in event time. In other words, the estimates of the coefficient on event time -4 are only identified by districts in Phase 2 or Phase 3. As a result, I pool event time -5 and -4 with -3 in Figure 4. If I do not do this pooling, and drop event time -4 and -5 instead, the slight trend disappears.

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Fires being used more intensively following MNREGA would also explain a larger increase in emissions than raw fires. While the MODIS satellite can detect fires as small as 100m<sup>2</sup> it does not distinguish between pixels that have one fire and those that have many fires (Korontzi et al., 2006). As a result, if the implementation of MNREGA induces the use of fires in areas that already had frequent fires, MODIS will underestimate the increase in fires. Using more finely resolved data on fires in the period after 2014 from the VIIRS satellite platform, I show in the appendix that MODIS does indeed under-count fires relative to VIIRS (appendix Figure A14a and A14b).

Translating these increases in emissions rates into precise changes in pollutant concentrations is difficult and would require a model of pollution dispersion. I do not create such a model here. Rather, I estimate the impact of the change in emissions on pollution concentrations in two ways. First, I estimate the correlation between emissions rates of black carbon, organic carbon, and SO2 using data on emissions concentrations from MERRA at the district _×_ month level.<sup>28</sup> These estimated correlations suggest that in months with emissions rates that are higher than average, PM2 _._ 5 concentrations are also higher than average.

Consistent with this correlational evidence, I show that the implementation of MNREGA is associated with an increase of approximately 11% in the number of months in which PM2 _._ 5 levels exceed the annual ambient standard set by the Indian government.<sup>29</sup> I look specifically at PM2 _._ 5 as it has the largest negative impacts on health (Behrer and Mauter, 2017; Muller and Mendelsohn, 2007; Nel, 2005; Chen et al., 2016). The annual threshold for ambient concentrations is set by the Central Pollution Control Board at 40 _μg/m_<sup>3</sup> . I calculate the share of district _×_ months that have an average PM2 _._ 5 level that exceeds this threshold and use a linear fixed effects regression in the difference-in-differences framework to determine if the implementation of MNREGA increased the share of months in which the threshold was exceeded.

Column 4 of Table 6 shows that implementation of MNREGA increases share of months that exceed the threshold by 0.014 percentage points. This represents an 11% increase relative to the preMNREGA baseline rate.<sup>30</sup> The magnitude of this effect is consistent with pollution levels exceeding

> 28 MERRA provides estimates of the monthly concentration of black carbon, organic carbon and SO4. I convert these into a measure of PM2 _._ 5 using the formula described in He et al. (2019).

> 29The Air (Prevention and Control of Pollution) Act 1981 set standards for Annual and 24hour concentrations in the ambient air of a number of pollutants. They are detailed here: http://www.arthapedia.in/index.php?title=Ambient_Air_Quality_Standards_in_India

> 30When I use the 24-hour threshold of 60 _μg/m_ 3 I observe a similar increase, 0.007 percentage points or roughly 10% on the pre-MNREGA baseline rate.

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the standard for one additional month - the month of harvest - after the implementation of NREGA. Figure 5 shows the event study of the share of district _×_ months that exceed the standard. There are no obvious pre-trends and a clear increase after the implementation of MNREGA.

This discussion of pollution concentration has focused on the level of pollutant concentrations in the districts in which MNREGA is implemented. That leaves out the change in concentration levels that are downwind of the implementing district. These downwind effects may be more severe than the impacts in implementing districts (Behrer and Mauter, 2017). Even if they are less severe, considering pollution only in the implementing district presents only a partial picture of the impact of MNREGA on pollutant concentrations.

# **7.2 Welfare consequences of increased pollution**

There are a variety of negative economic and health consequences associated with exposure to air pollution. Mani et al. (2022) review many of these consequences in the context of South Asia while Aguilar-Gomez et al. (2022) provide a more comprehensive review. While existing studies of MNREGA’s impact on health find generally positive effects, it is possible that these effects would have been larger absent the increase in pollution. Previous work has estimated substantial infant mortality and reductions in birth weight from exposure to cropland fire smoke (Pullabhotla, 2018; Rangel and Vogl, 2016; Singh et al., 2019; Garg et al., 2022) and forest fire smoke (Jayachandran, 2009; Pullabhotla et al., 2022).<sup>31</sup> Rangel and Vogl (2016) in particular find meaningful negative impacts on infant health based on changes in concentrations similar to what I observe after the implementation of MNREGA.

My results combined with previous literature raise a natural question: what was the magnitude of the monetized damages due to the increase in agricultural fires caused by MNREGA? The difference-in-differences framework I use cannot estimate the impact of the increase in agricultural fires on health outcomes separately from the total impact of MNREGA. MNREGA likely impacted both pollution and health outcomes through channels in addition to changing agricultural fires. Estimating the total health impacts of MNREGA is well beyond the scope of this paper. Instead, I can roughly benchmark the health costs against the total spending on MNREGA to provide a

> 31Additional work suggests that those same forest fires reduced the later in life wages of those children who were exposed but survived and that the pollution exposure rates they experienced may be comparable to the exposure rates residents of Delhi experience from crop burning (Tan-Soo and Pattanayak, 2019).

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sense of the magnitude of the effects of MNREGA’s increase in crop burning.

Jack et al. (2022) report that the monetized damages from deaths due to crop burning was USD$66 billion in 2018.<sup>32</sup> The average quantity of dry matter burned annually from 2015-2018 was approximately 65% higher than in 2006 at the beginning of MNREGA implementation (Liu et al., 2020). That suggests that mortality costs in 2006 were roughly USD$27 billion prior to MNREGA implementation and my estimates indicate that MNREGA increased burning by 21%, implying an increase in mortality costs of around USD$5 billion. Cropper et al. (2019) calculate a much smaller VSL for India than used by Jack et al. (2022), of between USD$256,000 and $84,000 (USD$2015). Using the mid-point of these VSL estimates lowers the welfare costs to roughly USD$0.85 billion.

This is a very crude benchmarking exercise, intended not to provide a precise estimate of the welfare costs of the increase in pollution but rather to provide a rough benchmark of their magnitude. The estimated magnitude of the welfare losses is large but plausible given the scope of burning, the size of the populations impacted, and the well-documented and severe costs of exposure to high levels of air pollution. The scale of the impacts is of the same order of magnitude as the annual federal expenditures on MNREGA, which has averaged between USD$4 billon and USD$11 billion between 2014 and 2021 An important dimension of these potential health effects is their potentially unequal distribution. Previous work (Lipscomb and Mobarak, 2016; Rangel and Vogl, 2016; Behrer and Mauter, 2017) suggests that while the region containing the source of emissions suffers negative consequences from those emissions, the majority of the damages may occur outside the region containing the polluting source. This is particularly true if there are major cities downwind of the emitting region. Rangel and Vogl (2016) find clear evidence that crop burning has negative effects in downwind areas but, because crop burning is driven by economic activity in the burning areas, increased burning may be associated with slight improvements in health in the areas where burning occurs.

# **8 Conclusion**

In this paper, I examine whether wage increases led to corresponding increases in pollution using the exogenous shock to wages generated by MNREGA. Because farmers appear to have responded

> 32They arrive at this figure using a value of a statistical life (VSL) of USD$1 million. It is worth noting that this is roughly 1/10 of the value used by the U.S. EPA in cost benefit calculations.

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to the wage increases generated by MNREGA by mechanizing, an indirect consequence of the program was an increase in the frequency of agricultural fires of between 9% and 21%. It also led to large increases, between 30% and 50%, in the emissions rate of black carbon, organic carbon, and SO2 from biomass burning. These pollutants are important contributors to both PM2 _._ 5 and PM10 pollution. In districts that had higher levels of a number of indicators of mechanization prior to MNREGA, I find an increase in agricultural fires of 27%. I test empirically for alternative explanations for the increase in agricultural fires but find the results to be most consistent with a model that suggests districts with greater ability to mechanize the harvest saw larger effects on fires.

I cannot rule out that the increase in fires was driven by some other aspect of MNREGA or a feature of districts that is correlated with my mechanization index. However, higher pre-MNREGA wages are not highly correlated with higher scores in the mechanization index nor is district level wealth. I also observe low correlation between the mechanization index and pre-MNREGA fires, suggesting that the mechanization index is not simply identifying areas with high levels of fire prior to MNREGA. Despite this, it remains possible that the observed increase was driven by some factor other than changes in mechanization and these results should be interpreted with caution.

With that in mind, it is important to remember that this is not an analysis of MNREGA in its entirety. Estimating the welfare consequences of MNREGA inclusive of the measured increase in agricultural fires is beyond the scope of this paper. Others have found meaningful increases in income, consumption, and health as a result of MNREGA that may offset any negative effects of increased emissions from increases in the number of fires. How the distribution of these benefits compares to the distribution of negative impacts from the emissions increase I measure deserves the attention of future work.

The results presented here do not call for, nor justify, wholesale changes to MNREGA. Nor should they be interpreted as casting doubt on the value of anti-poverty programs generally. Rather, they suggest that policy makers should be cognizant of the potential consequences of large policy changes that raise wages and incomes for environmental quality and consider ways to mitigate negative impacts.

Another potential policy response is to expand existing programs to encourage the adoption of the agricultural practices that do not require residue removal or mechanize such removal (Shyam26
sundar et al., 2019). Burning has declined as an agricultural practice in much of North America and Europe as the result of the adoption of “no till” agricultural practices (Korontzi et al., 2006; Marlon et al., 2008). Recent work has suggested that re-allocation of existing subsidies to more promising technology might encourage more widespread adoption of capital that reduces the need to burn (Shyamsundar et al., 2019). Alternatively, PES programs that encourage farmers not to burn with up-front payments have shown some promise in reducing burning (Jack et al., 2022).

There are lessons here for policy makers beyond India as well. Many countries around the world have a goal of raising incomes with some form of a work guarantee scheme. The results here highlight that these kinds of interventions in labor markets, and programs to raise incomes generally, can have environmental impacts by changing production decisions. Governments may be able to reduce these environmental impacts by incorporating Pigouvian tax and subsidy policies, which encourage firms to internalize environmental externalities, into income raising policies. .

Most generally, the results here indicate that in many settings Pigouvian policies that correct for environmental externalities may have implications for the rate of structural transformation. Specifically, if labor-saving mechanization has environmental externalities, firms may over-invest in mechanization in response to wage shocks relative to the efficient level of investment with regard to the pollution externality. Policies that correct for these pollution externalities slow the rate of mechanization, which may have positive spillovers in the context of arguments about inappropriate technology adoption.

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36
# **9 Tables**

Table 1: What predicts average monthly fires over 2003-2005?

|Outcome: Average cropland fres by district|(1)|(2)|
|---|---|---|
|Avg. Per Capita GDP|.099<br>( .115)|.124<br>( .132)|
|Avg. Per Capita GDP, Ag|.323***<br>( .078)|.13<br>( .093)|
|Avg. Area Planted in Rice (000s HA)|.44***<br>( .099)|.237*<br>( .121)|
|Avg. Area Planted in Wheat (000s HA)|.389***<br>( .034)|.189***<br>( .07)|
|Avg. Area Planted in Sugarcane (000s HA)|.238***<br>( .046)|.23***<br>( .014)|
|Avg. Area Planted in Other Crops (000s HA)|-.025<br>( .062)|-.122<br>( .105)|
|Combines in 2006 (000s)|.119***<br>( 9.0e-03)|.018<br>( .015)|
|Share of holdings _>_4 HA|.5***<br>( .064)|.471***<br>( .091)|
|Covariates regressed separately|X||
|State FE|X|X|

Notes: In all columns the outcome is the average number of monthly fires in a district averaged over the years 2003-2005. In column 1 each row is a seperate regression. In column 2 all covariates listed down the left are included in the same regression. In all cases the specification is a fixed effects poisson regression. All independent variables are measured as Z scores so that units are comparable. Per Capita GDP (Ag) reports the average per capita value of (agricultural) GDP in the district in from 2003 to 2005 measured in Lakh |. Average Area Planted in Wheat, Rice, and Sugarcane measures the district average area in each crop from 2003-2005 in 1,000s of hectares as reported in the ICRISAT Meso data (Rao et al., 2012). Total area reports the total area in all other crops also in 000s of HA from the same data. Combines reports the number (in 000s) of self-propelled combines in the district as recorded in the 2006 Agricultural Input Survey. The Share of Holdings in each size class reports the share of acreage in the district in holdings within each size class in 2005 as reported in the ICRISAT Meso data. All columns include state fixed effects. Standard errors clustered at the state level are reported in parentheses.

37
Table 2: Summary of Pre-MNREGA Economic, Agricultural and Fire data

|Phase 1<br>(1)|Phase 2<br>(2)|Phase 3<br>(3)|
|---|---|---|
|**Data from 2001 Census**<br>|||
|Total Population (000s)<br>1,712.12|1,772.15|1,693.61|
|Total Households (000s)<br>320.12|326.19|320.43|
|% Rural<br>0.84|0.83|0.73|
|% Urban<br>0.16|0.17|0.27|
|% Scheduled Castes<br>0.15|0.15|0.15|
|% Literate<br>0.48|0.52|0.57|
|% with domestic electricity<br>0.88|0.87|0.91|
|% with agricultural electricity<br>0.74|0.59|0.72|
|% with electricty<br>0.75|0.65|0.74|
|% Paved road<br>0.49|0.56|0.68|
|% Mud road<br>0.82|0.82|0.76|
|**Data from 2011 Census, 2012 SECC and VC**|**F Data**||
|Distance to nearest place with _>_100k population<br>50.78|41.87|40.74|
|Per capita consumption, rural<br>15.64|16.59|19.02|
|% HH with cultivation as main income<br>0.35|0.37|0.38|
|Avg. Forest Cover, 2002-05<br>11.31|15.15|13.34|
|Avg. Night Lights<br>29.50|27.93|61.63|
|**Data from NASA FIRMS**|||
|Monthly Fires prior to MNREGA<br>3.61|3.73|7.33|
|**Data from ICRISAT, Planning Commission and 200**|**6 Input **|**Survey**|
|Avg. Per Capita GDP, Ag<br>3.70|4.22|4.90|
|Avg. Per Capita GDP<br>14.39|15.13|21.26|
|Avg. Area Planted in Sugarcane (000s HA)<br>5.86|5.71|10.36|
|Avg. Area Planted in Rice (000s HA)<br>102.43|117.19|58.77|
|Avg. Area Planted in Wheat (000s HA)<br>39.76|57.85|61.63|
|Avg. Area Planted in Other Crops (000s HA)<br>172.67|165.39|207.96|
|Share of holdings _>_4 HA<br>0.26|0.28|0.33|
|Combines in 2006 (000s)<br>1.41|1.12|1.41|

Notes: Columns 1-3 report the mean of the named variable by district according to the MNREGA phase of that district. Data from the 2001 Census, the 2011 Census, the 2012 SECC and the VCF data come from the SHRUG dataset (Asher et al., 2019). Fire data is downloaded and assembled from the NASA FIRMS data and is derived from imagery from the MODIS satellite. ICRISAT data comes from the ICRISAT meso dataset (Rao et al., 2012). GDP data is scraped from the Indian Planning Commission website and covers the years 2003-2005 for most districts. Information on combines is scraped from the Indian Ministry of Agriculture website and comes from the 2006 Agricultural Input survey.

38
Table 3: Nationwide impact of MNREGA on monthly fires

||Cropla|nd Fires|
|---|---|---|
|Post-MNREGA|0.096<sup>_∗∗_</sup>|0.213<sup>_∗∗∗_</sup>|
||(0.044)|(0.051)|
|Districts|558|558|
|Months|144|144|
|N|80,352|80,352|
|Avg. monthly fres pre-MNREGA|5.46|5.46|
|District FE|X|X|
|Year _×_ Month FE|X|X|
|Weather Controls||X|

Notes: Each column represents separate regressions. In all columns the outcome is monthly cropland fires. In all columns the coefficient can be interpreted as the approximate percentage change in fires after MNREGA was statutorily implemented in a district. In all columns the base regression is a Poisson of fires in district _i_ in month _m_ in year _y_ that includes district fixed effects and year by month fixed effects. In column 2 I include controls for the monthly average cloud cover, precipitation and minimum and maximum temperature in district _i_ in month, year _y_ . N refers to the number of districts _×_ months included in each regression. The sample is a balanced, monthly panel of districts in India from 2003 to 2014. Heteroskedasticity robust standard errors clustered at the district level are in parentheses. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

39
# Table 4: Heterogeneity of treatment impact by ease of mechanization index

||All of India|
|---|---|
|(A)Quartile 1 of Ease of Mechanization Index||
|Post-MNREGA|-0.013<br>(0.087)|
|Districts|140|
|Months|144|
|N|20,160|
|Avg. monthly fres pre-MNREGA|2.82|
|(B)Quartile 2 of Ease of Mechanization Index||
|Post-MNREGA|-0.008<br>(0.075)|
|Districts|143|
|Months|144|
|N|20,592|
|Avg. monthly fres pre-MNREGA|2.7|
|(C)Quartile 3 of Ease of Mechanization Index||
|Post-MNREGA|0.144<br>(0.112)|
|Districts|135|
|Months|144|
|N|19,440|
|Avg. monthly fres pre-MNREGA|3.06|
|(D)Quartile 4 of Ease of Mechanization Index||
|Post-MNREGA|0.265<sup>_∗∗∗_</sup><br>(0.084)|
|Districts|140|
|Months|144|
|N|20,160|
|Avg. monthly fres pre-MNREGA|13.28|
|District FE|X|
|Month _×_ Year FE|X|
|Weather Controls|X|

Notes: Coefficients can be interpreted as the approximate percentage change in fires after MNREGA was statutorily implemented in a district. The sample is all districts in India. The specification is a fixed effects Poisson where the outcome is monthly cropland fires in district _i_ in month _m_ in year _y_ . The ease of mechanization index is the sum of a district’s Z score across measures of land concentration, combine presence and crop types. The mechanization index is calculated based on levels of each component variable in the district prior to 2006. I include district fixed effects, a year by month fixed effect, and weather controls. Each panel is a different quartile of the mechanization index with quartile 4 corresponding to the places mechanization is predicted to be easiest. N refers to the number of district _×_ months included in each regression. Districts reports districts in each sample. The average number of monthly fires (the outcome) in the pre-treatment period in each quartile are reported. The sample is a balanced, monthly panel of districts in India from 2003 to 2014. Heteroskedasticity robust standard errors clustered at the district level are in parentheses. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

40
Table 5: Effect of MNREGA on crop production

||All C<br>Area|rops<br>Tons|Wh<br>Area|eat<br>Tons|R<br>Area|ice<br>Tons|Sugar<br>Area|cane<br>Tons|Other<br>Area|Crops<br>Tons|
|---|---|---|---|---|---|---|---|---|---|---|
|Post-MNREGA|0.011|0.016|-0.001|0.014|0.025<sup>_∗∗_</sup>|0.039<sup>_∗∗_</sup>|0.034|-0.119|0.006|0.032|
||(0.007)|(0.022)|(0.019)|(0.025)|(0.010)|(0.019)|(0.040)|(0.102)|(0.008)|(0.060)|
|Districts|492|492|441|429|473|473|442|441|492|491|
|N|56,376|56,376|50,388|48,948|54,180|54,180|51,264|51,132|56,376|56,256|
|District FE|X|X|X|X|X|X|X|X|X|X|
|Year _×_ Month FE|X|X|X|X|X|X|X|X|X|X|
|Weather Controls|X|X|X|X|X|X|X|X|X|X|

Notes: Each column represents separate regressions. In all columns the outcome is identified in the column heading. Area is measured in 000s of hectares while quantity produced is measured in 000s of tons. In all columns the coefficient can be interpreted as the approximate percentage change in the outcome after MNREGA was statutorily implemented in a district. In all columns the base regression is a fixed effects Poisson of the form E [ _Cimy|_ **X** _imy_ ] = _exp_ ( _βPostimy_ + _γi_ + _δmy_ ) where _Cimy_ is the outcome in district _i_ in month _m_ in year _y_ . Post _imy_ is a dummy variable equal to one after MNREGA treatment takes effect in district _i_ . _γi_ are district fixed effects while _δmy_ is a year by month fixed effect. All columns include controls for the monthly average cloud cover, precipitation and minimum and maximum temperature in district _i_ in month _t_ . N refers to the number of districts _×_ months included in each regression. The sample is a balanced, monthly panel of districts in India from 2003 to 2014. Heteroskedasticity robust standard errors clustered at the district level are in parentheses. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).
Table 6: Effect of MNREGA on emission rates of pollutants from biomass burning and months exceeding ambient PM2 _._ 5 standard

||E|missions Rates||Ambient PM2_._5 Standard|
|---|---|---|---|---|
||Black Carbon|Organic Carbon|SO2|Share of months _>_ standard|
|Post-MNREGA|0.384<sup>_∗∗∗_</sup>|0.378<sup>_∗∗_</sup>|0.491<sup>_∗∗∗_</sup>|0.014<sup>_∗∗∗_</sup>|
||(0.143)|(0.163)|(0.176)|(0.003)|
|Districts|558|558|558|560|
|Months|144|144|144|144|
|N|80,352|80,352|73,656|80,640|
|Pre-MNREGA Mean|45.81|536.09|52.09|.12|
|District FE|X|X|X|X|
|Year _×_ Month FE|X|X|X|X|
|Weather Controls|X|X|X|X|

Notes: Each column represents separate regressions. In columns 1-3 the outcome is the monthly emissions rate of the pollutant named at the top of the column measured in ng/m<sup>2</sup> s. In column 4 the outcome is the share of months in which the measured PM2 _._ 5 concentration exceeds the annual ambient air quality standard for India set by the Air Prevention and Control of Pollution Act (1981). Concentrations are measured in _μg/m_<sup>3</sup> . All data comes from the MERRA-2 satellite system. In columns 1-3 the coefficient can be interpreted as the approximate percentage change in the outcome after MNREGA was statutorily implemented in a district. In column 4 the coefficient is the change in percentage points in the percent of months that exceed the Indian Ambient standard of 40 _μg/m_<sup>3</sup> . In columns 1-3 the base regression is a fixed effects Poisson of the form _log_ ( E [ _Eimy|_ **X** _imy_ ]<sup>)</sup> = _βPostimy_ + _γi_ + _δmy_ where _Eimy_ is the outcome in district _i_ in month _m_ in year _y_ . In column 4 I use a linear fixed effects specification of the form _Timy_ = _βPostimy_ + _γi_ + _δmy_ where _Timy_ is an indicator for whether district _i_ had PM2 _._ 5 levels that exceed ambient standards in month _m_ in year _y_ . Post _imy_ is a dummy variable equal to one after MNREGA treatment takes effect in district _i_ . _γi_ are district fixed effects while _δmy_ is a year by month fixed effect. In all columns I include controls for the monthly average cloud cover, precipitation and minimum and maximum temperature in district _i_ in month _t_ . N refers to the number of districts _×_ months included in each regression. The sample is a balanced, monthly panel of districts in India from 2003 to 2014. The mean of the outcomes prior to MNREGA for each is presented. Heteroskedasticity robust standard errors clustered at the district level are in parentheses. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

# **10 Figures**

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# **Appendices For Online Publication**

## **Appendix 1 Additional Background Discussion**

### **Appendix 1.1 Fires & Agriculture**

Fire has been used by humans to manage landscapes for at least 40,000 years (Pyne and Goldammer, 1997) and in the cultivation of corn for at least 5,000 years (Rue et al., 2002). Sediment cores indicate that fire was used to manage agricultural fields in India at least 600 years ago (Morrison, 1994). The use of fires in agriculture is still widespread today in both developed and developing countries. Historically fires were used to clear land for planting in a swidden (“slash-and-burn”) style of agriculture and its use evolved over time to include preparing fields for harvest and clearing residue to prepare fields for re-planting (Pyne, 2019).

Analysis of satellite imagery suggests that globally there are roughly 1.5 million fires annually with the largest number in Russia (Korontzi et al., 2006). The widespread use of fires in Russia, Eastern Europe (namely Ukraine) and North America mean that in absolute terms fire use in agriculture is more common in the developed world than the developing world (Cassou, 2018). On a per hectare basis, however, African countries are the most frequent users of fire. This is in part due to declines in recent years in the use of fires in North America and the European Union (Marlon et al., 2008), driven in part by increasing regulation around the practice. It should be noted, however, that the use of fires to clear crop residue was widespread in California and Western Canada until the mid-1990s when increasing concerns over air pollution led to regulation to eliminate the practice (Cassou, 2018).

Frequent and long-term use of crop residue burning may decrease the productivity of agricultural land (Smil, 1999; Vasilica et al., 2014; Sawyer, 2019; Prasad et al., 1999; Mandal et al., 2004). It does so by destroying micro-nutrients in the soil and removing valuable fertilizer including nitrogen and phosphorus. Others have argued, however, that the extent to which burning negatively impacts soil quality is highly dependent on the type of soil. Further, farmers who shift from a production process that includes burning to one that removes residue from the field without burning may suffer short-term yield losses if they fail to adjust their use of fertilizer as well (Jain et al., 2014; Bhargava, 2014).

### **Appendix 1.2 MNREGA Details**

The MNREGA program is the latest in a succession of work-based anti-poverty programs in India dating back to at least the British Raj (Imbert and Papp, 2015). The most notable program prior to MNREGA was the Maharashtra Employment Guarantee Scheme (MEGS) introduced in 1977 by the Government of Maharashtra (Shah and Mehta, 2008). MEGS was not a national program but much of the design of MEGS was incorporated into MNREGA. Like MNREGA the aim of MEGS was to provide employment to rural residents, focused on labor-intensive work, and targeted the formation of public goods. The number of person-days of work that it generated reached an early peak in 1980 and declined through the 1990s before climbing again through the early 2000s. Variation in the number of person-days supplied through MEGS appears to be tied to changes in the wage schedule and declines in the level of activist support for the program (Shah and Mehta, 2008). Of particular relevance to this study and MNREGA, estimates suggest that MEGS increased agricultural labor wages by around 18%(Gaiha, 1997).

After independence, the national government experimented with a number of national rural workfare programs. A series of small-scale and pilot programs in the 1960s and 1970s were rolled into 48
the national Food for Work Programme (FWP) in 1977. Despite receiving significant investment, there is little evidence that the FWP had a meaningful impact on reducing rural unemployment, due, at least in part, to poor implementation and exclusion of the poorest citizens (Deshingkar et al., 2005). The FWP became the National Rural Employment Program in the 1980s. In 2001, the Sampoorn Grameen Rozgar Yojana (SGRY) program combined this with several existing poverty alleviation programs and rural infrastructure programs to consolidate effort and provide additional employment, food security, and infrastructure in rural areas (GOI, 2007). Wages were paid in a combination of cash and food supplies. By 2008 the SGRY program had been fully merged with MNREGA.

The law creating MNREGA was passed in September 2005 and the program was implemented in the first districts in February 2006. Figure A1 shows the districts included in each phase. I map districts on 2001 geographies and apportion all data to the districts as they existed in 2001.

To participate in MNREGA households obtain job cards from their local districts and then are able to apply for work whenever they would like. The district office is to provide work within 5 km of their house within 15 days of receiving their applications. The district must pay an unemployment allowance in cash if they fail to provide employment. Wages are to be paid at a statutorily set minimum wage that is not less than 60 Rs/day.

MNREGA project lists are prepared at the district level and projects must be in one of the permitted categories. Those are: water conservation, drought proofing, flood protection, land development, minor irrigation, and rural connectivity (GOI, 2007).<sup>33</sup> All projects must have a ratio of labor expense to material expense of at least 60/40 and the use of contractors and machinery is not allowed.<sup>34</sup> The cost of MNREGA is split between the central government and state governments but, crucially, the full cost of unskilled labor is borne by the central government. State governments bear none of the cost of unskilled labor and 25% of the cost of skilled labor and materials, giving states an incentive to prioritize projects that use a greater share of unskilled labor.

The scale of the program is remarkable. It is generally agreed that MNREGA is the largest workfare/rural poverty reduction program in the world (Ambasta et al., 2008). By 2014, 121 million job cards had been registered. In 2009-2010 there were 2.8 billion person-days of work conducted under the program (Sukhtankar, 2016). Participation appears to have grown steadily from implementation in 2006 to around 2013. Roughly 11% of the world’s population is covered by the program(Niehaus and Sukhtankar, 2013). In principle, MNREGA marked a shift from existing anti-poverty programs by being demand, as opposed to supply, driven (GOI, 2007). In practice, implementation challenges and state capacity may have limited the extent to which it was able to fully meet demand (Sukhtankar, 2016; Niehaus and Sukhtankar, 2013). By March 2007, demand for work exceeded supply in at least 30% of the states with significant demand (GOI, 2007). Despite this, MNREGA provided an average of three times the number of person-days of employment in its first years as SGRY provided in its final years.

# **Appendix 1.3 Agricultural mechanization in India and fires**

Rice, wheat, and sugarcane are the three crops most associated with the use of fire on cropland in India. Of these, the harvest of rice and wheat can be mechanized using existing technology present

> 33This work can occur on private land if it is owned by a member of a scheduled caste or tribe. The operational guidelines of MNREGA were modified in 2009 to allow for work to also be conducted on private land if the total holdings of the owner placed them in the “small” or “marginal” categories and the owner participated in the work (GOI, 2009).

> 34The use of contractors and machinery was understood to be an obstacle to the effectiveness of previous programs in providing pro-poor benefits (Ambasta et al., 2008).

49
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in India (Yadav, 2007; Solomon, 2016). Mechanizing the rice and wheat harvest is done using combines, which data from the Agricultural Input Survey shows are present throughout India prior to the implementation of MNREGA but tend to be present at higher levels in areas with higher use of fire.

The relationship between mechanization and the use of fires is driven by the fact that harvesting with a combine leaves more residue on the field than harvesting by hand (Yang et al., 2008). Specifically, combine harvesting leaves stocks that tend to be around 30-40cm as opposed to the 10-15cm that harvesting by hand leaves. The higher stalks interfere with the ability of farmers to plant the following season’s crops and must be removed to facilitate planting (Jain et al., 2014; Smil, 1999; Cassou, 2018; Bhuvaneshwari et al., 2019).<sup>35</sup> Burning is the least expensive way of dealing with this residue (Cassou, 2018), as interviews with farmers indicate: “Ankit Choyal Jat...offers an answer. ‘If I can clear my farm using a one-rupee matchbox, why will I spend thousands? (Jitendra et al., 2017)’” While combines were used in harvesting prior to the implementation of MNREGA, the shock that MNREGA provided to agricultural labor markets led to a substantial increase in their use. The only existing study of the impact of MNREGA on agricultural production processes (Bhargava, 2014) shows that the use of labor saving animal-based production processes increased after the implementation of MNREGA. Data from the Agricultural Input Survey shows a substantial increase in the average number of combines from 2006 to 2011 (Figure A3) – an increase that is much larger both in absolute and percentage terms than the increase from 2001 to 2006. Such an increase is consistent with broad state level trends from the Cost of Cultivation survey that show a decline in the amount of labor used in agriculture and an increase in the amount spent on machine inputs to production over the period that MNREGA was implemented.

The notion that farmers responded to the impact of MNREGA on agricultural labor markets by mechanizing is supported by their own statements as well. Media interviews with farmers from areas that have seen the largest increase in burning frequently include quotes like the following (Jitendra et al., 2017): Hari Ram Karore, a 71-year-old farmer who owns more than 10 hectares (ha) in the same village, says, “We started using combine harvester machines to tide over the labour scarcity. The machine finishes the task of reaping, threshing and winnowing in a few hours and is also economical,” he adds.

and Residents of villages around Kota say that mechanisation has killed the practice of using wheat stalk and straw as fodder, and burning is the only way out. “The cuttings left by the machines are too sharp. Not only do they injure us, even animals find it difficult to graze on,” says Shital Devi.

These quotes are supported by more formal work interviewing farmers (FLA, 2012) that indicates farmers believe the supply of unskilled agricultural labor has declined as a result of MNREGA. This is consistent with survey data from the 2011 India Human Development Survey that suggest a large share ( _≈_ 33%) of surveyed villages believe there to be fewer agricultural laborers than in the past and individual households report a 30% decline in the hours worked in agriculture. Fewer households in the IHDS report traveling for work after MNREGA as well.

> 35There are alternatives to clearing the residue after a combine harvest. Namely tilling the standing residue back into the field. However this requires specialized planting equipment that is both expensive and not widely present in India (Jain et al., 2014; Bhuvaneshwari et al., 2019).

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# **Appendix 1.5 Relationship between MODIS and VIIRs fire detection**

VIIRs is similar to the MODIS platform in that it is a source for satellite based imagery. However, VIIRs is newer than MODIS, with the imagery available starting in 2012, and has higher resolution. The lack of data prior to 2012 means I cannot use VIIRs for the primary analysis.

VIIRs and MODIS are able to detect roughly the same size fires but VIIRs provides data at a much finer pixel resolution than MODIS. VIIRs resolution is roughly 375m compared to 1km for MODIS. Both classify a pixel as having a fire if at least one fire is detected in that pixel. However, the finer resolution of VIIRs means it is able to count more pixels that contain fires. For example, two fires located 750m apart within a given square km would be counted as only one fire by MODIS but would likely be distinguished as two separate fires by VIIRs. Figure A6 shows clearly that MODIS detects substantially fewer individual fires than VIIRs.

Korontzi et al. (2006) shows that MODIS and VIIRs are both highly accurate in counting a pixel that should contain a fire as containing a fire. So while MODIS underestimates fires it does not appear to mis-classify pixels that include low numbers of fires as non-fire.

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