metadata: authors: 0: author: Khamis,Melanie
1: author: Maertens,Annemie
2: author: Sharma,Siddharth
count: India
docna: 0: docna: Absentee Landlords and Land Tenancy
docty: Policy Research Working Paper
abstracts: cdata!: Internal migration and structural
            transformation are strongly interrelated. This paper uses
            Indian data spanning 2001&#x2013;13 to examine a
            little-known aspect of this relationship: how migration
            affects agricultural land rental contracts. Building on
            anecdotal evidence and theory, the paper hypothesizes that
            as landlords migrate away, their choice of contract for
            tenant-cultivators changes from sharecropping to fixed rent.
            Using a shift-share instrument that exploits information on
            bilateral migration flows between districts, the paper shows
            that migration increased fixed rent tenancy and contract
            formalization. Given the continued importance of
            agricultural land rental markets, these findings have
            significant implications for rural efficiency and equity in
            developing countries.
display_title: Absentee Landlords and Land Tenancy
listing_relative_url: /research/2024/08/34376449/absentee-landlords-land-tenancy
topicv3: Agriculture and Food Security m1326205 435,Economic Growth m1326241 471,Labor Markets m1326488 855,Rural Development m1504505 2667
publishtoextweb_dt: 2024-08-12T00:00:00Z
dois: 10.1596/1813-9450-10865
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 82
author_keys: 0: author_key: 644582
1: author_key: 969967
2: author_key: 717230
model_extractions: Policy Research Working Paper 10865 Absentee Landlords and Land Tenancy _Melanie Khamis Annemie Maertens Siddharth Sharma_ South Asia Region Office of the Chief Economist August 2024
Policy Research Working Paper 10865

# **Abstract**

Internal migration and structural transformation are strongly interrelated. This paper uses Indian data spanning 2001–13 to examine a little-known aspect of this relationship: how migration affects agricultural land rental contracts. Building on anecdotal evidence and theory, the paper hypothesizes that as landlords migrate away, their choice of contract for tenant-cultivators changes from sharecropping to fixed rent.

Using a shift-share instrument that exploits information on bilateral migration flows between districts, the paper shows that migration increased fixed rent tenancy and contract formalization. Given the continued importance of agricultural land rental markets, these findings have significant implications for rural efficiency and equity in developing countries.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ssharma1@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
# Absentee Landlords and Land Tenancy

Melanie Khamis, Annemie Maertens and Siddharth Sharma<sup>∗</sup>

## **Keywords: migration, land tenancy, sharecropping, land rent, India JEL Classification: O1, J61, Q13, Q15**

> ∗Corresponding Author: Annemie Maertens (a.maertens@sussex.ac.uk) at Sussex University; Melanie Khamis (mkhamis@wesleyan.edu) at Wesleyan University and IZA; Siddharth Sharma (ssharma1@worldbank.org) at the World Bank. We acknowledge excellent research support from Deepika Pingali, Maika Schmidt, Sakshi Bhardwaj, Claudia Isabel Medina Lopez, Aditi Guha and Morgan Usen, and support in obtaining the data from Dipendra Das and Cyriac George, as well as Eswaran Jagadeesh and Anupama at ICRISAT. Michael Norton at the World Bank generated the travel time data set using scanned maps provided by Treb Allen. We are grateful to Sanjay Jain, AV Chari, Shingyi Wang, Laura Schechter, Anukriti, Rachel Heath, Adriana Kugler, Ira Gang, Cory Smith, Amrit Amirapu, Agyris Sakalis, Larry Blume and seminar participants at the 2019 AAEA, 2021 16th Annual Conference on Economic Growth and Development (ISI), 2022 97th Annual Conference WEAI, 2022 MLPortal 1st Workshop of Applied Microeconomics, 2022 100 Years of Economic Development Conference Cornell University, 2023 Southern Economic Association, Fordham University, CUNY Baruch and at Sussex University for excellent comments and suggestions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank or its Executive Directors.
# **1 Introduction**

The distribution of land ownership and structure of landlord-tenant relationships are central features of agricultural societies. In this paper, we theorize and empirically document how internal migration impacts this structure, thereby affecting the efficiency and equity of the rural sector.

In traditional two-sector growth models, the rural economy serves as a reservoir of labor. Rural-to-urban migration feeds a growing industrial sector, eventually pushing rural wages upwards (Lewis, 1954; Ranis and Fei, 1961). Later models explore other channels of interaction between migration and rural economies, such as rural asset markets (Taylor and Martin, 2001). Migration may affect access to insurance (Rosenzweig and Stark, 1989; Giles, 2006; Munshi and Rosenzweig, 2016; Morten, 2019).<sup>1</sup> Coupled with labor and land market frictions in villages, migration may affect agriculture through labor loss in migrant-sending households, causing changes in crop acreage, crop diversity and capital intensity (Rozelle, Taylor and De Brauw, 1999; De Brauw, 2020; Brewer, Larsen and Noack, 2024; Madhok et al., 2024). As such, migration can spur mechanization (Hornbeck and Naidu, 2014) and result in spatial reorganisation of agriculture (Madhok et al., 2024). It may further impact land markets as emigrating landlords resort to selling or renting their land (Reddy, 1996).

In this paper we contribute to this literature, and formalize and quantify the latter channel. The linkage between emigration of landlords and land rental markets has been somewhat overlooked by economists, but is of utmost importance to understanding the increase in inequality in low and middle income countries (Van Der Hoeven, 2019; The World Bank, 2021). Our study is set in India, a middle income country with increasing internal migration, an active land rental market and large agricultural labor force (Deininger, Jin and Nagarajan, 2008; Kone et al., 2018). The availability of several data sources makes India an ideal setting to study this linkage. We digitize, compile

> 1In turn, credit constraints and uninsured risk may limit migration (Bryan, Chowdhury and Mobarak, 2014; Angelucci, 2015; Gazeaud, Mvukiyehe and Sterck, 2023).

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and connect existing data, present a comprehensive picture of the land rental market, and test the implications of a model connecting internal landlord migration to the land market.

We start with the observation that the Indian land rental market has changed in the last few decades. Sharecropping, an arrangement in which the tenant pays the landlord a share of the harvest, used to be the most common arrangement. An estimated 50% of rented-out land was under sharecropping in the late 1970s in India, as compared to about 16% in Latin America, 12% in Europe and 31% in North America (Otsuka, Chuma and Hayami, 1992). This share has declined in the last 30 years, with the most drastic decline occurring after 2000. Sharecropping is being replaced by another arrangement: fixed rent. In this case, the tenant pays the landlord a fixed amount, often a cash amount at the start of the season or year.<sup>2</sup> These changes have a number of implications for economic efficiency as well as income inequality and economic vulnerability. Economists have long studied how the nature of the contractual arrangement determines efficiency. Sharecropping, when effort cannot be observed nor contracted, taxes effort, and may thereby reduce labor inputs, one of the most important inputs in agricultural production. This may reduce labor productivity and generate Marshallian inefficiency (Marshall, 1890).<sup>3</sup> This focus on the residual claimant and efficiency misses four advantages of sharecropping which emerge in richer models of land markets. First, share2 While we did not collect India-wide data on the period preceding 1981, local accounts note that fixed tenancy was also the norm prior to the 1950s. After independence, various Indian states introduced acts on land tenancy and land reform, which might have resulted in a shift away from both registered and fixed tenancy (Reddy, 1996).

> 3Estimates of the degree of this inefficiency vary by context and method. In India, Shaban (1987) shows that farmers generate 33% higher yields on own/fixed rent plots versus sharecropping plots (controlling for land quality) in six villages. Deininger, Jin and Yadav (2013), exploiting a land reform policy in West Bengal, note a reduction in longer term profits of 20% (compared to owner-cultivators). Burchardi et al. (2019) report sizable effects using a randomized controlled trial which varied the share of output among Ugandan farmers. See also Arcand, Ai and Ethier<sup> ́</sup> (2007) and Jacoby and Mansuri (2009). Jaynes (1982), Otsuka and Hayami (1988), Singh (1989 and 2004), Otsuka, Chuma and Hayami (1992), Lastarria-Cornhiel, Melmed-Sanjak and Phillips (1999) provide an overview of the theory and empirical work.

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cropping allows the landlord and the tenant to share the inherent risk in agricultural production; in contrast, the tenant bears the entire risk under fixed rent (Cheung, 1969; Stiglitz, 1974; Ghatak and Pandey, 2000). Second, sharecropping can reduce the impacts of the tenant’s limited liability (Shetty, 1988; Basu, 1992; Laffont and Matoussi, 1995; Sengupta, 1997; Ray and Singh, 2001). Third, the control over the tenant in sharecropping might allow the landlord to ensure a more sustainable use of the land (Reddy, 1996). Fourth, sharecropping provides an incentive for the landlord to provide inputs, such as, pesticides, fertilizers and managerial support, to the tenant (Newbery and Stiglitz, 1979; Eswaran and Kotwal, 1985).

Motivated by anecdotal evidence and this last argument, we develop a theoretical framework which combines prior models (Eswaran and Kotwal, 1985) with the idea that when landlords migrate away from the village, the opportunity cost of providing managerial inputs increases. This results (for those who do not opt for land sales) in a preference for fixed over sharecropping arrangements.

To test this proposition we construct a district-level data set covering the first decade of the 21st century. The choice of time period is dictated by both data availability (we have detailed migration data only for the decades preceding 2001 and 2011) and contextual considerations: liberalization in the 1990s strengthened property rights, allowing for migration of the landed class (Giles and Mu, 2018). This period also saw an acceleration in the trend towards fixed rent arrangements: from 1992 to 2012, fixed rent agreements increased from 45% to 58% while sharecropping decreased from 45% to 31% (as a percentage of total rented in acreage). The largest drop happened in the last decade under consideration (Table 1).

Establishing a causal link from migration to rental contracts is challenging because of the presence of potential confounding factors. They include a major rural bank branch expansion program (Burgess and Pande, 2005) which could have simultaneously increased fixed rent arrangements (following Laffont and Matoussi (1995) and evidenced by Das, de Janvry and Sadoulet (2019) in Bangladesh) and migration (Shonchoy, Fujii and Raihan, 2018; Foltz, Guo 4
and Yao, 2020; Gazeaud, Mvukiyehe and Sterck, 2023). Other potential confounding factors include trade liberalization, climate change and a rural roads program.<sup>4</sup> To establish causality between migration and land rental contracts, we employ an instrumental variable strategy relying on arguably exogenous migration “pull” factors. Our main explanatory variable is the share of the high-skilled in total migrant outflows: lacking information on land ownership of migrants, we use this measure to capture the out-migration of the landholding classes. We instrument for it by using a shift-share strategy (Card, 2001; Anelli et al., 2023; Madhok et al., 2024). One unique feature of our paper is that the data allows us exploit migration networks to instrument for _internal_ migration using this strategy.

Our approach, which uses district-to-district migration flows from the 2001 and 2011 Population Censuses, can be explained as follows. For a given migrant-sending (or, “source”) district, every other district in India is considered a potential “destination” district. We proxy for the attractiveness of each destination district by considering the total immigration it from all other districts (barring the source district). This is the shift component. The share component is the historical share of each destination district in the total migrants from the source district. The share-weighted shift yields a measure of the migration pull factor for the source district. We construct two instruments, one for high-skilled migrants and one for low-skilled migrants, and use these to predict high-skilled out-migrants as a percentage of total out-migrants in each district. This distinction by education level is unique and contributes to 4Trade liberalization exposed farmers to increased volatility, affecting crop choices (Allen and Atkin, 2022) and agricultural investments (Tian, Xia and Yang, 2021). Changes in rainfall patterns (associated with climate change) may have impacted agricultural wages and migration directly (Rosenzweig and Udry, 2014; Colmer, 2018; Brey and Hertweck, 2019; Kaur, 2019), or indirectly through a declining groundwater tables (Blakeslee, Fishman and Srinivasan, 2020). A rural roads construction program increased educational investment and migration (Adukai, Asher and Nosovad, 2020), while affecting agricultural technology and crop choice (Aggarwal, 2018; Shamdasani, 2021). Note, however, that trade liberalization and climate change increase risk and should push land tenancy towards _more_ sharecropping. Hence they are not a competing explanation for the observed trends, although they can be a threat to establishing causality.

5
instrument exogeneity.

Using this strategy, we find that a one point increase in high-skilled male out-migrants (as a percentage of total male out-migrants) is associated with a statistically significant increase of 2.6 percentage points in fixed rent arrangements, and a reduction of 1.8 percentage points in sharecropping. This represents an effect size of, respectively, 6% and 5%. This result is robust to including controls for bank branches, roads, weather and other agro-climatic factors.<sup>5</sup> A remaining concern arises from local inter-district connections in agricultural markets coupled with the tendency of Indian migrants to move to nearby districts (Kone et al., 2018). For example, the effects of a drought could reach across neighboring districts, which would be a concern for our approach if the historical migrant network too were strongly dominated by neighbouring districts. While we include controls for weather conditions, we also present a unique variation of instrumental variable strategy to address this concern. We deduct the variation that can be explained by distance (specifically, travel time) from the total variation in the historical migrant network, and utilize this residual variation as the new share in our instrument. Using this technique results in very comparable numbers: an increase in high-skilled male out-migrants by one percentage point increases fixed rent agreements by 2.7 percentage points and decreases sharecropping by 1.9 percentage points.

We further show that this increase in fixed rent represents a broader trend towards increased formalization in the land rental market (between 2002 and 2012), with an increase in written contracts and contracts of longer duration. Migration appears to play some role in this increased formalization. Finally, we note a mixed yield response to high-skilled male out-migrants, which may reflect two countervailing forces: greater Marshallian efficiency of fixed rent contracts versus the loss of the emigrant landlord’s labor and knowledge.

Our contribution to the literature is threefold. First, we document the 5Our results are also robust to controlling for the roll out of a rural employment guarantee program which might have altered the relationship between tenants and landlords (Imbert and Papp, 2015; Narayanan et al., 2017; Berg et al., 2018; Chari et al., 2019).

6
importance of an (by economists) understudied link between labor and land markets, thereby improving our understanding of the structural transformation process in developing economies. Migration, by driving contractual change, can alter the efficiency of the agricultural sector, and have implications for equity and access. Second, we present three decades of national-level descriptive statistics on the nature of the relationship between tenants and landlords for India, one of the most populous country in the world which have a significant share of the population in agriculture. Third, we introduce a novel instrumental variable strategy for emigration: stripping the pull instrument from distance-related components strengthens exogeneity and can be applied in other contexts.

The rest of the paper is structured as follows. In section 2, we provide background on migration in India. Section 3 introduces the data. Section 4 presents descriptive statistics. Section 5 outlines a theoretical framework. Section 6 presents the empirical identification strategy. Section 7 presents the results. Section 8 concludes.

# **2 Setting: Migration in India**

India is often thought of as a country with low internal migration. Several studies point to India’s low internal labor mobility and localised labor markets, which are associated with regional wage gaps, efficiency losses and high local vulnerability (Jayachandran, 2006; Topalova, 2010; Chari et al., 2021). In 2001, merely 2.8% of India’s population consisted of individuals who had moved permanently to a different district in the last five years. In contrast, 9% of Brazil’s population had moved permanently to a different municipality in the last five years, and 10% of China’s population had moved to a different prefecture in the last five years (Kone et al., 2018). Research into the barriers to migration and labor mobility pointed at the role of social networks (Munshi and Rosenzweig, 2016), political and cultural inter-state differences (Kone et al., 2018) and restrictive labor laws (Adhvaryu, Chari and Sharma, 2013).

However, internal migration has been on the rise in India. According to 7
the 2001 Population Census of India, 11% of India’s population consisted of individuals who had moved permanently to their current district from a different district at some point in the past. In the 2011 Census, this share had increased to 14 percent.<sup>6</sup> Using railway data, the Government of India’s 201617 Economic Survey Report estimates a doubling of internal migration rate from 1991-2001 to 2001-2011 (Government of India, 2017).<sup>7</sup> India’s reputation as a low internal migration country is also being reconsidered by economic historians (Cohen, 1995; Tumbe, 2018; Rajan and Sumeetha, 2019). Migration in the past two centuries consisted mostly of young men migrating via caste networks to areas of economic opportunity, returning every few years, and eventually settling back into their home villages. This type of migration was common place: Tumbe (2018) estimates that 150 districts experienced mass migration during the past 150 years (defined as more than 20% of households affected). Migration has increased in the last few decades and is more permanent in nature. The high-skilled are increasingly dominant in internal migration flows (Docquier and Rapoport, 2012). Yet, this recent increase in migration builds on the migration networks established in the past century and a half (Tumbe, 2018).

The contribution of this study is to link migration to changes in land tenancy. This link first became clear to us during a field visit in rural Utter Pradesh, North India, in 2008, when we spoke with landlords about their contract choice. We then confirmed these patterns with historical accounts from other parts on India. Reddy (1996), for instance, collected land contracts and interviewed farmers in Andhra Pradesh, South India, spanning the period 1850 to 1980. He repeatedly notes the unique role that absentee landlords play in the rural economy. His analysis suggests that concerns about moral hazard cause absentee landlord to prefer contracts involving simple, once-per-

> 6The increase is larger when accounting for within-district migrants, who constitute an estimated 60% of all internal migrants in 2001. The Census does not cover temporary migration, which is also on the increase due to factors such as changes in weather and National Rural Employment Guarantee Act (Rosenzweig and Udry, 2014; Kaur, 2019; Morten, 2019).

> 7See Hnatkovska and Lahiri (2015) for comparable statistics using National Sample Survey data.

8
year cash payments.<sup>8</sup> Other contemporaneous village level studies document similar features in the economic arrangements between landlords and tenants (Srinivas, 1976; Bliss and Stern, 1982; Walker and Ryan, 1990).

# **3 Data**

We digitize, compile and connect existing data to construct a district-level dataset. We draw measures of land tenancy from the National Sample Survey (NSS) Land and Livestock Survey, a repeated cross section survey of about 35,000 agricultural landholdings across India. We build on custom-made tables from the Population Census of India for measures of migration. We then add information on banking, roads, distances between districts, weather and agroclimate zones from other data sources. The details of these steps are included in the online appendix. Here, we discuss the main sources, the NSS and the Census, and highlight data challenges.

## **Tenancy: NSS Land and Livestock Survey**

To obtain district-level data on land contracts we compiled data from the National Sample Survey Land and Livestock Survey. The survey covers around 30,000 to 35,000 households from all of India (with exceptions in some rounds).<sup>9</sup> We make use of the rural sample of round 70 (conducted in 2013) and 59 (conducted in 2003). We utilize information from schedule 18.1 which lists all 8On page 49, Reddy notes that, ‘The agreements entered into by absentee landlords were more detailed than those of residents.’ On page 52, he notes that, ‘The rent in the case of absentee lessors was generally paid once per year,’ and on page 76 that, ‘Absent landlords preferred cash over grains except in times of sharp rising prices.’ This is followed up, on page 69, with, ‘The shorter the distance, the greater was the preference for receipts in kind.’ Detailed contracts, specifying the crops and often inputs, were standard; and clauses such as: ‘You should cultivate this particular plot of land as well and carefully as the neighbouring owner cultivators of similar field,’ commonplace.

> 9The survey uses a stratified multi-stage design in which a number of census villages are randomly drawn from each district (usually four in two independent samples). Within each village, eight agricultural households are selected, stratified by landholding class. Each sample household is visited twice, once at the end of the kharif season (rainy season) and once at the end of the rabi season (dry season).

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cultivated, owned and otherwise possessed field (also called plots) of the households. It is these plots which are our unit of analysis from which we generate (weighted) district-level statistics. The dependent variables are defined as a percentage of the rented in acreage or rented in plots at a district level. For each plot we have information on the location (within the village or not), soil type (soil texture), land use, acreage, irrigation facilities/flood frequency, as well as the ownership and rental agreement details.<sup>10</sup> Our analysis builds on plot-level data from the tenant side of the market, as all tenants, even landless tenants, are considered in the sampling process.

# **Migration: Census of India**

We use bilateral district-to-district migration data obtained from the 2001 and 2011 Population Censuses of India. The Census is the only data source with information on both the source and origin district of migrants for all the districts of India.

The Census, which is conducted every decade, asks every individual if the place in which they are enumerated during the census is different from their “last usual place of residence”. As in Kone et al. (2018), we consider a person a migrant if they reply in the affirmative.<sup>11</sup> Additional follow up questions allow us to capture bilateral migration flows. These include the location (district) of the last place of residence, reason for migration (marriage, education, employment etc.), and the duration of stay in the current residence since migration. The census also includes information on the sex, education and age of each individual (but notably not land ownership).

Since the census does not provide unit-level data, under a special administrative agreement we requested them to provide us with aggregated data on the volume of migration between every pair of districts. For a given pair 10We omit plots which are categorized as non-agricultural, and might contain lakes, forest, or other types of lands, in the analysis. We also drop plots which are located outside of the district, when this information is available.

> 11The previous residence does not necessarily imply the residence of the previous census, but can be a residence of 3 years ago, 7 years ago, more than 10 years ago, or even during childhood or shortly after birth - all will be considered migrants.

10
of source district (the district of immediate last residence) and destination district (the district of current residence and Census enumeration), this data set contains the number of individuals who have moved from the former to the latter, dis-aggregated by duration of stay in the destination district, sex, age, education and the reason for migration (but not by urban/rural parts of source/destination). We obtained these custom tables for the 2001 and 2011 Census. The dis-aggregation by the duration of stay in the destination district distinguishes between individuals who migrated at most five years ago and those who migrated six years ago or earlier. We supplemented these data with census data on district population, also dis-aggregated by sex, age and education.

To construct the dependent variable, we obtain the number of out-migrants for a given age/sex category for a given source district by summing up all migrants within this category who moved in the past five years to all destination districts. We distinguish between within-district and out-of-district migrants.<sup>12</sup>

# **Data challenges and limitations**

**Harmonizing districts** Our dataset is a district-level panel. Districts units represent the third level of administrative units in India, and are commonly used for data collection and dissemination purposes. Some district boundaries and names altered over the period of our study. Defining 2001 as the base year, we first checked for changes in names and renamed districts post 2001 using the 2001 name. In the case of district splits, we mapped the post-2001 district back to its parental district in 2001. This process automatically resulted in a reduction of sample size as there were fewer districts in 2001 than in later years (Kumar and Somanathan, 2015).

> 12It is possible that a person moved twice or more in the past ten years prior to the census. In this case, only his last residence would be mentioned, and we would rely on this information to construct the measures.

11
**Using household survey data** In most districts, the NSS collected data from four villages. We use data from these four villages to generate districtlevel aggregates. This process can result in sampling error. For example, the sample could contain no households who rent in land, despite such households being present in the district. This would result in missing data for this district/year: 11% of districts do not have any tenant records for either year, and 23% of districts have no tenant records for one out of two years. The latter would imply that rental markets come and go, something which seems unlikely.<sup>13</sup>

**Rural to urban migration** Our district-to-district migration data do not map exactly to the rural/urban distinction implied by the theory. We do not believe this to be a significant limitation. A significant share of migration is not of the traditional rural to urban variant. Aggregate Census data of 2001 indicate that 55% of migration is between rural areas, 15% is between urban areas, 21% is rural to urban migration, and 6% is urban to rural migration. Part of the explanation for this is that areas which have seen a recent surge in population are slow to be re-classified as urban areas. This results in errors in any statistics which rely on this distinction. Ellis and Roberts (2016) note that 30% of the increase in India’s urban population between 2001 and 2011 was due to a of census areas.

# **4 Descriptive statistics**

## **Land tenancy**

Table 1 presents statistics on the land rental market for the years 1992, 2002 and 2012. The top panel presents statistics converted to acreage terms while

> 13We do not have any other reliable data which could serve as a comparison point. We had coded up an alternative data source, the Agricultural Census. This Census is conducted every five years by the Agricultural Census Division of the Ministry of Agriculture since 1970 and collects information on agricultural operational holdings in India. However, data quality on land tenancy appeared to be poor, with often no land tenancy recorded in land-record states (where formal registration records are in place).

12
the bottom panel presents them in contract terms, that is, the number of fields (plots) under a given contract. Both panels are in percentages of rentedin land. The use of fixed rent has increased from 45% to 58% as a percentage of the total acreage rented in, over the course of twenty years, while sharecropping arrangements have decreased from 45% to 31%. The pattern is similar, albeit less striking, in contract terms. Fixed rent arrangements have increased from 35% to 49% while sharecropping arrangements have decreased from 49% to 38%.<sup>14</sup> We further split up fixed rent by fixed monetary rent (rent paid in money), and fixed crop output (rent paid in produce), a distinction we explore in the analysis.

We focus our analysis on the acreage terms data. Using acreage terms instead of contract terms gives us a more accurate representation as to the amount of land, and by extension, the effect on input decision-making, and regional yields.

Due to the presence of other contract forms, the total percentages in Table 1 (that is, fixed rent plus sharecropping) do not add up to 100%. In Appendix Figure A1 we display the percentage of contracts under various contract types, including: ‘for fixed money’, ‘for fixed produce’, ‘for service contract’, ‘for share of produce together with other terms’, ‘under usufructuary mortgage’, ‘from relatives under no specified terms’, and ‘under other terms’. Fixed and sharecropping represent the majority of the rental contracts. Again, one notes the increase in the various fixed terms (as opposed to sharing terms).

Table 1 presents data from the kharif (rainy) season. The trends observed are similar for the rabi (dry) season (see Appendix Table A1). Although crop types and irrigation levels tend to differ across seasons, contractual forms may be similar across them because multi-season and multi-year contracts are increasingly common. In Appendix Figure A2 we indicate the duration of the contract: 75% are of at least one agricultural year. As the kharif season is the main season, we focus our analysis on this season. We consider this and other aspects of formality in Table 1, that is, whether the field is

> 14As a comparison point, in 1982, 45% of land rented in acreage was under sharecropping and 17% was under a fixed rent arrangement.

13
rented from relatives, the duration of the contract and whether a written contract is present. Considering the 2002-2012 period, we note an increase in formalization, with longer, written contracts.

In Figure 1 we display maps of selected areas, East, North and West, and South, in India, with district boundaries following the 2001 Census. Panel A displays the maps representing the situation in 2002. Panel B maps represent the situation in 2012. The blue colour represents high levels of fixed rent, while the yellow represents low levels (with the red an in between stage). Note that most of the populous states in the south of India, such as Tamil Nadu, Andhra Pradesh, Telangana and Kerala, have witnessed a substantial increase in fixed rent arrangements.

# **Migration**

We do not have any direct measure of landlord migrants, as there is no information on land ownership in the Census. We proxy for landlord migrants using high-skilled migrants, a group defined by a cut-off of at least 10 years of education.<sup>15</sup> This assumption is supported, at least to some degree, by the data. Using the NSS Land and Livestock Survey round 70 (conducted in 2013), we note a significant, positive correlation between acreage of land owned and the education (in years of education) of the household head (a correlation coefficient of 10%). In Appendix Figure A3 we present the histogram of the education level of the head of the household, by type of landholding. On the left-hand side are the landless, defined as possessing less than 2 hectares, and on the right-hand side, the landed, defined as owning more than 2 hectares. While there is an overlap between the educational distributions of the landless and the landed, we note that very few of the landless have more than ten years of education. We notice a similar pattern using the national ARIS-REDS panel survey of rural households from 2005. Using the sample of all individuals who engaged in migration that year (that is, temporary migrants), we find that

> 15This cut-off follows the structure of education in India: At the end of lower secondary education, in year 10, an exam has to be taken and passed in order to progress, and further education often requires travelling further afield.

14
households of high-skilled migrants have twice as much owned land compared to households of low-skilled migrants (1.8 acres as opposed to 0.86 acre).

In Table 2 we present the average number of out-migrants across districts, by year and by category (with standard deviations in parentheses). We only include male migrants between the ages of 20 and 64 who migrated in the past five years. These represent working age men.<sup>16</sup> We distinguish between lowskilled (less than 10 years of education) and high-skilled (more than 10 years of education) in the top and bottom panels, respectively. We also distinguish between within- and out-of-district migrants.

The number of low-skilled out-of-district migrants has hardly changed between 2001 and 2011, but the number of low-skilled within-district migrants has increased by 18%. The number of high-skilled out-of-district migrants has increased by 33% while the number of high-skilled within-district migrants has increased by 60%. Within-district migration is becoming more important over time, especially among the high-skilled. The increase in migration, in absolute numbers, is mostly noted among the high-skilled. A significant component of this might be that the number of high-skilled individuals themselves are increasing, as more individuals, as a percentage of the population in their age group, are now high-skilled.

Our analysis uses the number of high-skilled out-of-district migrants expressed as a percentage of the total number of out-of-district migrants as the key explanatory variable. We control for the total populations in both skill categories, as we want to capture the differential opportunities for high and low skilled, and not simply their supply.<sup>17</sup>

> 16Women in India tend to migrate largely for marital reasons (Rosenzweig and Stark, 1989).

> 17Appendix Table A2 presents the absolute numbers of high-skilled and low-skilled males residing in the districts between the ages of 20 and 64 in both 2001 and 2011. We note that both numbers increased, reflecting population growth, but that, relatively speaking, the number of high-skilled individuals increased more.

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# **5 Theoretical framework**

Our theoretical base is a principal-agent model in which the principal, the landlord, devises a contract to serve his interests and presents a take it or leave it offer to the agent, the tenant, assumed to be a landless laborer. The latter decides whether to accept the contract, comparing the contract with his outside opportunities (reservation utility) and conditional on accepting, decides on the amount of effort to put in.<sup>18</sup> To integrate the role of the absentee landlord, we modify this base as a double incentive problem, as first introduced by Eswaran and Kotwal (1985), in which the landlord too puts labor effort into the production of crops and needs to decide on his degree of engagement. We build on the interpretation of Singh (1989 and 2004).<sup>19</sup> We consider this to be a realistic model in the Indian context. Reddy (1996) notes that the type of tenancy, the quantity and quality of produce or amount in cash, the details of transport and delivery methods are commonly determined by the landlord.<sup>20</sup> Other village level studies, such as Bliss and Stern (1982) and Walker and Ryan (1990), concur.

> 18Perhaps this choice of model needs some justification. Why, for instance, do we not start with a general equilibrium setting instead? As first noted by Newbery (1974), the share in a sharecropping contract cannot be viewed as a price in the standard Walrasian equilibrium sense, as, if it were a price, the tenant taking the share as given will demand more land, up to the point where the marginal product of land is zero. An equilibrium share would then be determined as equating this demand to the village supply. Unless land is abundant, this equilibrium will not exist.

> 19We do not consider screening effects as in Hallagan (1978) and Newbery and Stiglitz (1979): while it is the case that in-migration might increase the percentage of unknown tenants, hence tempting landlords to use a menu of agreements to screen the tenants; the dominance of one agreement over another in most villages seems to conflict with this screening explanation.

> 20Reddy (1996) distinguishes between three contract components, the main lease (kaul), the mutchalika and the rent receipts. It is only the mutchalika which is joint written by the tenant.

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# **Setup**

We start with one landlord and one tenant. We suppress the quantity of land (assuming a fixed plot size), and abstract away from other inputs, such as, animal power, fertilizers, seeds, machinery and irrigation, and by consequence, ignore other forms of input sharing. The agricultural production function takes the following form: where _θ_ is a random variable with mean 1, capturing the role of, among others, weather and pests in agricultural production. _M_ denotes managerial input and _E_ effective labor input. The production function is concave and increasing in all its arguments, and its value is 0 when _θ_ is less than or equal to zero or when both forms of labor are zero. The effective labor input, _E_ , is as: where _S_ denotes supervisory input, _L_ labor inputs and _t_ (between 0 and 1) represents the supervision technology, capturing the importance of supervision in making the labor inputs effective (with if t=0, E=L, i.e., no supervision is needed). Substituting in for _E_ in Expression (1), we obtain: We can now introduce moral hazard. We assume that while _L_ is observable, _M_ and _S_ are not. We differentiate between the landlord and the tenant. The landlord is assumed to be high-skilled, while the tenant is low-skilled. This translates into the landlord being better at management, _M_ , with the tenant having an advantage in supervision, _S_ . We follow the notation of Singh (1989 and 2004) and denote the advantage of the landlord as _γ_ (1 hour of the tenant’s time on management is equivalent to _γ <_ 1 hours of the landlord’s time on this task) and the advantage of the tenant as _δ_ (1 hour of the landlord’s time 17
on supervision is equivalent to _δ <_ 1 hours of the tenant’s time on this task). Both landlord and tenant are constrained in their total number of labor units available, normalized to 1 unit.

# **Opportunity costs and migration**

We integrate migration into this model by considering the opportunity costs faced by both landlords and tenants.

In the rural areas, the only jobs available are agricultural labor jobs, which are low-skilled jobs, valued at _w_ . In urban areas, there is a range of jobs, in construction, the personal service sector, but also in IT, banking, engineering etc. The high-skilled jobs are only accessible to high-skilled individuals (i.e. the landlords) and are valued at _m_ . The low-skilled jobs in the urban areas are accessible to all, including the tenants, and are valued at _s_ . We assume that _w ≤ s < m_ , reflecting these differential opportunities. Hence, the opportunity cost of the landlord is _m_ and the opportunity cost of the tenant is _s_ . As noted, we use _w_ for the rural (casual) wage rate.

# **Contract choices**

We allow for three distinct contractual options. The first option is for the landlord to cultivate the land on his own. The second option is for the landlord to provide the land to the tenant for a fixed rent. The third option is sharecropping.

## **Self cultivation**

Assuming the landlord is risk-neutral, he maximizes the expected profit from self-cultivation. The profits consist of the revenues of the crop minus the costs of the labor inputs and whatever is earned in the urban sector.

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where the landlord provides both _M_ and _S_ but hires in _L_ . The value of the landlord’s time is evaluated at _m_ , its highest value, the opportunity cost in urban areas. The price of output is normalized at 1. Denote the maximand of this problem as:

# **Fixed rent**

Under the fixed-rent contract, the landlord charges rental rate _R_ and his profit is: where the landlord spends all his time on the alternative occupation valued at _m_ . The rental rate, _R_ , is set as to meet the participation constraint of the tenant. The tenant’s expected (gross) profit from crop cultivation under such an arrangement is: Assuming risk-neutrality, the tenant maximizes expression (7) with respect to _M, S_ and _L_ . Denote this maximand as: The landlord chooses _R_<sup>_∗_</sup> as to set this expected profit net of the rent paid equal to the tenant’s best outside opportunities, _s_ , or: The resulting outcome for the landlord is then: 19
# **Sharecropping**

In a sharecropping contract, both parties contribute to the production process. The landlord contributes managerial input, while the tenant contributes supervisory input. Both decide on their respective labor contributions, given the contract terms (which are decided beforehand by the landlord as to maximize his profits). The landlord’s profit may be derived using backward induction. We start by defining a restricted profit function, the expected output net of the hired labor cost: The optimal choice of labor _L_ , is conditional on the parameters of the problem, _w_ and _t_ , and the labor inputs of tenant and landlord: where we added the parameter _w_ to emphasize that this is conditional on the wage rate.

The sharing rule takes the following shape: The share contract assigns _α_ of the net profits to the tenant, and the remainder (1 - _α_ ) to the landlord. _β_ is a fixed sum which goes to the tenant. Given a set contract, both tenant and landlord choose their labor allocation, non-cooperatively (taking into account the optimal hire of labor _L_ as in expression 12).

For the landlord: and for the tenant: 20
Note that the transfer of _β_ does not enter either maximization exercise. Recall also that we assumed full specialization: the tenant contributes supervision while the landlord contributes managerial input. The best response functions of the landlord and tenant are, respectively: Denote the resulting Nash equilibrium as ( _M_<sup>_∗_</sup> ( _α_ ) _, S_<sup>_∗_</sup> ( _α_ )). The landlord, anticipating these responses, now chooses the share parameter _α_ to solve the following equation:<sup>21</sup> Once the parameter _α_ is set, the landlord determines the second parameter of the share contract _β_ as to set the tenant’s profits equal to his outside option, or: Denote the maximand of this exercise (for the landlord) as:

# **Solution**

The landlord compares the profits between these three arrangements, that is, the profits from expressions (5), (10) and (20) and chooses the contract that yields the highest expected profit.

Instead of deriving an analytical solution, we present the result of a series of simulations, where we vary the opportunity cost of time for landlords _m_ . 21We omit _β_ and any reference to the various wages to keep the notation simple 21
Keeping _s_ constant, we map the landlord’s profits as a function of the ratio _m_ / _s_ (which, according to the assumptions above, is always a number above one) for the three scenarios, and observe when the landlord’s preferred contract shifts.

The parameters and specifications of this simulation are included in the online Appendix. We opt for a Cobb-Douglas specification, and following Eswaran and Kotwal (1985), ignore the uncertainty element and set _θ_ equal to its expected value of 1.

In Appendix Figure A4 we map the landlord’s profit as a function of the wage ratio _m/s_ in the case of self-cultivation (labelled ‘fixed wage’ in the figure as the landlord employs wage workers in that scenario). In this case, the landlord works on his own farm, but as the opportunity cost increases, replaces his own labor with wage labor. However, due to the nature of the production process, some own labor will always be required. Even when _m/s_ = 20, the optimal values are _M_ = 0 _._ 16; _S_ = 0 _._ 06 and _L_ = 6 _._ 4.

In Appendix Figure A5 we map the landlord’s profit as a function of the wage ratio _m/s_ in the case of fixed rent. In this scenario, all of the landlord’s time is spent on the alternative occupation valued at _m_ . The rental rate, _R_ , is set as to meet the participation constraint of the tenant. As _m_ increases, the profit obtained from farming does not increase (as the tenant simply remains against their participation constraint), but what the landlord can earn in urban areas increase linearly. This time, for _m/s_ = 20, the optimal values are _M_ = 0 _._ 71; _S_ = 0 _._ 28 and _L_ = 21 _._ 79. Note that the sum of _M_ and _S_ is 1, as expected, as the landlord pushes the tenant against their participation constraint and demands full-time work of the tenant.

In Appendix Figure A6 we map the landlord’s profit as a function of the wage ratio _m/s_ in the case of sharecropping. In this scenario, both the landlord and tenant specialize fully. We again compute the resulting labour choice for _m/s_ = 20, and obtain the values of _M_ = 0 _._ 1; _S_ = 1 and _L_ = 10 _._ 02. Note that _S_ is now 1, as expected, as the landlord works outside the farm while the tenant hits their participation constraint. As _m_ increase, profits increase but not by much, as the landlord must balance obtaining this outside wage with 22
continued farm work.

We bring the three functions together in Figure 2, capping the range of the wage ratio at 20. We are now in a position to trace the preferred contract. The sharecropping yields a higher profit when the wage ratio is low, reflecting the fact that twice as much labour is available for farming. However, as _m_ increases, the landlord starts taking up opportunities in urban areas, reducing their input into farming. At the ratio _m/s_ = 16, their preferred contract shifts from sharecropping to fixed rent recognizing that it has become too expense for them to contribute to farming directly.

# **6 Empirical strategy**

The theoretical model highlights the role of opportunity cost of labor for tenant and landlord as a driving force of contract choice. While the model represents this as wages, there are other factors which might impact the landlord’s choice. For example, absentee landlords cannot easily confirm yields and the cost of migration might be more easily born by wealthier landlords. Hence, changes in the wage ratios are only part of the driving force towards fixed rent. In the empirical analysis, we will use the migration flows themselves and not the wages. This also allows us to take advantage of our data on bilateral migration between districts.

We start with the following specification, where subscript _i_ denotes the district and subscript _t_ denotes the time period: The dependent variable _Y_ is a measure of land tenure arrangements in district _i_ and year _t_ , including: acreage under sharecropping, acreage under fixed rent (paid by money) and acreage under fixed rent (paid by produce), duration of the contract (above 2 years of duration) and degree of formality (whether the contract is written, and whether the land is leased from relatives), all as a percentage of the acreage leased in.

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The migration variable _M_ in equation (21) is the number of high-skilled male individuals, 20-64 years of age, who have moved residence in the past five years, expressed as a percentage of the total number of male migrants (of 20-64 years of age who have moved residence in the past five years). As discussed earlier, we define high-skilled as having completed 10 years of education. To set the stage for the instrumental variable specification, which builds on betweendistrict migration flows due to exogeneity concerns, we include only individuals who have left the district and omit within-district migrants.

The coefficient of interest is _β_ 1, which measures the relationship between migration ( _M_ ) and land tenure arrangements ( _Y_ ). Our hypothesis is that _β_ 1 is positive for fixed rent prevalence and measures of contract formalization, and negative for sharecropping and renting from relatives.

To take into account changes in the underlying population, the control vector _Xit_ includes the total population (in 1000s), the sex ratio (number of females for 100 males), the population density (in 1000s per square km) and the share of high-skilled males (as a percentage of the male individuals of 20-64 years of age).

The remainder of the time-variant control variables aim to capture the alternative mechanisms. We include the number of bank branches within each district (Reserve Bank of India), road length in each district (in km, ICRISAT), and a measure of rain and temperature events (University of Delaware). These measures are detailed in the Appendix. The inclusion of year fixed effects ( _δt_ ) allow us to further control for time variant country-wide factors, such as, trade rules and regulations relating to agricultural output prices.

To capture district-specific factors, we follow Derenoncourt (2022) and add a vector of time-invariant district-level controls which account for differences in agro-ecological zones: soils (NSS, using categories of soil texture), length of the growing season (in days, ICRISAT), and measures of water stress (ICRISAT). These measures are detailed in the Appendix.

We omit district-fixed effects as for a significant number of districts we have only one data point (see Figure 1), and the panel covers only two time periods (unlike, for example, Liu, Shamdasani and Taraz (2023)). In addition, the 24
majority of the variation is between district, not within districts (unlike, for example, Breza and Kinnan (2021)). This method has also been applied in the Indian context by Mukherjee (2020) who uses REDS district-level data to look at the link between credit markets and technology adoption, and includes a host of demographic, economic and soil controls, and Viswanathan and Kumar (2015) who connects weather with migration using district-level data, and includes controls for the agro-ecological zones.

The descriptive statistics of all variables are presented in Appendix Table A3.

# **IV specification**

While the number of control variables is extensive, we might have missed some remaining time-variant connections between land and labor markets. Unobserved shocks to local agro-industries, for example, labor strikes, accidents, and road construction, can both affect emigration (through wages and lack of jobs which might push migrants out) and land tenure (through changes in input markets and available agricultural technologies).

To make further headway in the identification strategy, we distinguish between migrant push factors versus migrant pull factors. Push factors relate to the conditions in the source district and “push” migrants out. Pull factors relate to conditions in the destination district and “pull” migrants in. Most concerns regarding identification of the effects of out-migration relate to push factors. Hence, we instrument for emigration with measures of pull factors that influence emigration but are plausibly exogenous to local land and labor market conditions.

This requires a measure of a pull force which varies across districts and time. We arrive at this measure using a shift-share approach which exploits differences in migrant networks across districts as measured in our bilateral migration data. This is an adaptation of an approach used in studies of the impact of international immigration on local labor markets (Card, 2001; Peri, Shih and Sparber, 2015). Our instrument for migration is composed of two 25
parts: (1) The shift component: a measure of the pull of potential destination districts, and (2) A share component: a measure of the strength of the connection between the source and the potential destination districts. We detail each in turn below and note that both shift and share component are skill-specific, as in Peri, Shih and Sparber (2015).

Our shift component - the proxy for the pull of each (destination) district - is the total immigration into that district in the past five years from all districts (barring the one source district under consideration, that is, a “leave-one-out” measure). It is based on the idea that when a location has many economic opportunities, it will attract migrants from a variety of locations.<sup>22</sup> Our share component - the measure of the migration connections between the source district and potential destinations - consists of the historical shares of various destination districts in the total out-migrants from the source district. Specifically, the connection between a destination district _j_ and source district _i_ in year _t_ is the share of district _j_ in the total number of individuals who migrated out of district _i_ five years or more before year _t_ .<sup>23</sup> This approach of using the historical migrant network as a measure of connection follows Card (2009). It is based on the insight that social networks have a strong influence on the location decisions of migrants, with migrants more likely to go to places to which more individuals from their network have previously migrated (Munshi, 2003; Munshi and Rosenzweig, 2016; Munshi, 2020). Within the Indian context, the role of networks in migrants’ destinations has also been highlighted by Gore (1970) who notes that ‘the nearest town is not always the best option’, referring to the importance of the familiarity of fellow villagers in a new location.<sup>24</sup>

> 22This approach of using total migration flows as the shift component follows studies such as Card (2001), Card (2009) and Goldsmith-Pinkham, Sorkin and Swift (2020) in spirit. The details differ because these studies are interested in arriving at exogenous measures of _immigration_ rather than _emigration_ . In this sense, our approach might be more similar to Anelli et al. (2023) and Madhok et al. (2024) who use destination country/district economic shocks as the shift for out-migration.

> 23In calculating this stock, we consider only male individuals of 20-64 years.

> 24For more recent examples, see among others, Munshi and Rosenzweig (2006); Tumbe (2018).

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Putting these two components together, the instrument for the level of emigration of individuals of skill group _s_ (where _s ∈{low, high}_ ) from source district _i_ at time _t_ is: where _Mj,_<sup>_s_</sup> _−i,t_<sup>arethetotalnumberofmigrantsofskilllevel</sup><sup>_s_whomoved</sup> to district _j_ between _t_ and _t −_ 5 years from all districts other than _i_ (this is the the shift component). The share component _Nj,i,t_<sup>_s_</sup> _−_ 5<sup>isthehistorical(5</sup> years ago and earlier) share of destination district _j_ in the total migrants of skill level _s_ who left district _i_ (capturing the exposure of district _i_ to a shift in _j_ ). The instrumental variable regression uses both high-skilled and low-skilled instruments.

Madhok et al. (2024) and Anelli et al. (2023) are two recent studies which also employ shift-share instruments for out-migration. Like our study, Madhok et al. (2024) is in the context of internal migration in India. However, unlike Madhok et al. (2024) where the share component is based on distance, we exploit bilateral migration data in constructing the share component.

# **Residual network IV specification**

We present a unique variation on the share component which takes into account the fact that migrants tend to move to nearby districts (Kone et al., 2018).

One concern in this case is that nearby districts can be affected by the same agronomic shocks driving both land and labour market. Given that migrant networks are biased towards nearby districts, this could make our instrumental variable strategy invalid. While controls for local weather conditions might mitigate some of these concerns, we present a more demanding first-stage specification in which we use the variation in the historical migrant network that remains after the distance between them has been taken into account. In other words, we exploit that part of the historical migrant network that is not explained by distance (specifically, travel time, as detailed in the Appendix).

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Denote by _Timej,i,t−_ 5 the travel time between districts _j_ and _i_ at time _t−_ 5. We estimate the following regression on the bilateral district migration data: We then use the residual from this regression, _Rj,i,t_<sup>_s_</sup> _−_ 5<sup>, as the share variable</sup> when constructing the migration instrument for skill level _s_ :

# **Instrument validity**

## **Instrument relevance**

Table A4 presents the first stage results. Columns (1) through (3) refer to the standard instrumental variable, while Columns (4) through (6) refer to the residual network approach. Columns (2) and (5) add the main set of control variables: population, weather (temperature and rainfall adverse events), number of bank branches and road length. Columns (3) and (6) add controls for the agro-climate zone, including soils, length of the growing season (in days) and reference ETo (a measure of water stress). We note that the instrument is strong across the board, with an F-statistic ranging from 13 to 39.

## **Instrument exogeneity**

Identification with shift-share instruments is based on an assumption of exogenous shifts (Borusyak, Hull and Jaravel (2022)) or exogenous shares (GoldsmithPinkham, Sorkin and Swift (2020)). Translated to our setting, the assumption of exogenous shifts means that total immigration into a district from all districts other than a given source district is uncorrelated with current land outcomes in that source district (conditional on controls). The exogenous shares assumption translates into assuming that patterns of out-migration in the past are unrelated with current land outcomes (again, conditional on controls).

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This latter assumption may be more challenging to meet in our setting. The concern is that districts which are close in the sense of historical migrant networks are also spatially close, and could therefore be close in other ways which matter to the land market. For example, they could have close product market linkages which results in a regional response to drought. Our empirical strategy is set up to minimize this concern.

First, as explained earlier, the residual network specification removes the spatial proximity from the share component, relying exclusively on the variation in historical networks. We find that the results are comparable across this specification, and the standard specification, indicating that in this context, concerns related to linked output markets are limited.

Second, we exploit the difference in the network of high-skilled and lowskilled emigrants. In effect, controlling for the low-skilled migrant networkweighted shock, we rely on the high-skilled migrant network-weighted shock as the instrument. Hence, a key identifying assumption of our instrumental variable specification is that these high-skilled and low-skilled networks do not entirely overlap, and that their difference is exogenous to the phenomena under study.

Another way of making the point that high-skilled and low-skilled networks do not entirely overlap, and that their ratio is quasi-random, is to show that districts with high ratio are not fundamentally different from districts with a low ratio. We regress the ratio of the two instruments on our most extensive set of control variables, those in Column (3) in Table A4, and derive the residual of this regression. In Table A5, we present a t-test for differences between districts which have a higher than median residual (of this regression) compared to a lower than median residual. We select a set of variables which are not used within our set of control variables, but are informative: market access and output prices of the main crops. We find that the two sets of districts are comparable across these indicators.

To further illustrate how this strategy helps address concerns regarding spatial correlation, we follow Khanna et al. (2022), and create a heat map of the predicted migration variable. This is a geographical map of the number of 29
high-skilled as a percentage of the total number of male individuals of 20-64 years of age who have moved residence in the past five years predicted by the two instruments - as in Columns (1) and (4) of Table A4 but excluding the year fixed effect. Figure A7 presents the results, using 2001 census district boundaries. The top panel refers to the 2001 year, while the bottom panel refers to the 2011 year. The maps on the first row of each panel refer to the standard instrument, while the maps on the second row of each panel refer to the residual network instrument.

We draw two conclusions. First, we note little difference between the rows of each panel, reinforcing the claim that the residual instrument method yields similar results as the standard instrument. Second, we note that for most regions, there is little geographical clustering, alleviating concerns regarding any remaining, unobservable, regionally correlated effects.

# **7 Results**

Table 3 presents the correlation between migration and land tenure arrangements, exploiting variation between districts in 2002 and 2012. The dependent variable is the percentage acreage rented in under specific terms. Column (1) refers to fixed rent, column (2) to the sub-category of fixed rent under monetary terms, column (3) to the sub-category of fixed rent under crop terms, column (4) to sharecropping, column (5) to renting from relatives, column (5) to two-year or longer contracts and column (6) to written contracts. All the dependent variables are included in percentage terms, that is, as a percentage of the rented-in acreage.

We find that as hypothesized, the out-migration of high-skilled males has a statistically significant positive correlation with the prevalence of fixed rent contracts under money terms, and a negative correlation with the prevalence of sharecropping. The raw association between high-skilled male out-migration and the prevalence of fixed rent contracts paid in produce is negative; as discussed further below, such contracts are in between sharecropping and fixed rent contracts in money terms and their expected correlation with migration 30
is ambiguous.

Counter to our hypothesis, there is a positive correlation between the outmigration of high-skilled males and renting from relatives.

These correlations are sensitive to the specification, and in particular, the control variables included. Table 4 adds our main set of control variables to the OLS following specification 21: the vector of population controls, bank branches, road length, and measures of adverse weather events. The correlations between the out-migration of high-skilled men and the prevalence of fixed rent in money terms or sharecropping are no longer statistically significant. Expanding the list of control variables to measures of the agro-climatic zone (soils, length of growing season and water stress levels through evapotranspiration) yields similar patterns (Table A6). The latter specification has a smaller sample due the missing data in the ICRISAT dataset. Table 4 is our preferred specification as it controls for weather effects.

# **Main results: Standard IV specification**

Table 5 presents the results using our standard instrumental variable specification. The set of dependent variables is the same as in Table 4. The results indicate that migration has been a driving force in the shift from sharecropping to fixed rent: the coefficient on high-skilled male out-migrants is positive and significant for Column (1) and Column (2), both for fixed rent and fixed rent in monetary terms. The negative relationship with sharecropping is also statistically significant at the 5% level. A one percentage point increase in high-skilled (male) out-migrants increases fixed rent arrangements with 2.7 percentage points and reduces sharecropping with 1.9 percentage points in sharecropping arrangements. This corresponds to an effect size of, respectively, 6% and 5%. The sign on relatives has changed direction and is now the expected negative sign, but we note no statistically significant relationship with either contract length or written contracts in Columns (6) and (7).

The results with a more extensive set of controls in Appendix Table A8 are very similar, while the results without the control variables (Appendix table 31
A7) appear muted. Notably, in both cases the effect on written contracts is statistically significant and sizable. Note again the drop in sample size in the former specification.

This suggests that migration plays a role in the formalization of the land rental market. Recall that the probability of a written and longer contract increased between 2002 and 2012 (see Table 1). Migration increases the probability of a written contract (in most specifications), but does not affect contract length. Migration also (in most specifications) decreases the probability of renting from relatives.

We also note a differential impact between the two types of fixed rent contracts in Table 5. Most fixed rent contracts are in money terms (Appendix Figure A1), and it is on these types of contracts that we see a consistent impact. The lack of effect on the second type of contract could be due to statistical reasons, or because it represents an in-between contract. On the one hand, payment in produce might entail a significant transaction cost. On the other hand, moral hazard on the part of the tenant can largely be avoided as the amount of produce is fixed.<sup>25</sup>

# **Residual network IV specification**

Table 6 presents the results using our alternative instrumental variable specification, where the instrument is stripped from the component which relates to distances between districts. The set of dependent variables is again the same as in Table 4.

The results remain similar in terms of sign, size and significance compared to the standard instrumental variable, with the only difference that in this more demanding specification the effects on contracts with relatives lacks precision. In the Appendix, we again present the results without controls (Appendix Table A9), and a more extensive set of controls (Appendix Table A10). Again, the effects in the former case are more muted, while the robust errors on the contract with relatives varies as per specification.

> 25 Although a fluctuating output price might complicate matters further.

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# **Additional robustness checks**

The National Rural Employment Act (NREGA), a large-scale public employment scheme rolled out in rural India in 2005, has affected rural labor markets during our study period, including by increasing wages (Imbert and Papp, 2015; Berg et al., 2018). While NREGA does not necessarily interfere with our identification strategy, we check the robustness of our findings with respect to this scheme. In Appendix Table A11 we present a split-sample analysis of Table 5. The first two columns are the districts where NREGA started in 2006 (Phase 1). The third and fourth columns are the districts where NREGA started in 2007 (Phase 2). The fifth and sixth columns are the districts where NREGA started in 2008 (Phase 3). We note similar effects in the first and last set of districts, but appear to lack power for the middle set. Chari et al. (2019) notes that while the exact criteria are not known, the assignment of districts into phases was intended to prioritize the most ‘backward’ districts. This might have resulted in the stronger result in the first set, which might be more rural as well.

Another concern relates to demand-side channels. Structural transformation is associated with demand shifting away from staple food crops (Herrendorf, Rogerson and Akos<sup> ́</sup> Valentinyi, 2014). This change in demand may impact the land market and also correlate with migration flows. To check this possibility, we use the standard IV specification and the acreage of cash crops (as a percentage of the cultivated acreage) as the dependent variable in Table A12.<sup>26</sup> There is no statistically significant relationship between migration and cash crop prevalence, suggesting that these types of correlated effects are unlikely to be a threat to the main results.

# **Absentee landlords and farm yields**

Finally, we examine the relationship between high-skilled migration and crop yields. The direction of this relationship is theoretically ambiguous. The Mar-

> 26This is the percentage acreage of crops which are not rice, wheat, maize or sorghum using ICRISAT data.

33
shallian argument suggests that the shift towards fixed rent contracts should increase yields. Remittances (which we cannot observe in our data) may further increase yields by facilitating large-scale investments, such as in tractors and irrigation systems. However, high-skilled migration may reduce yields through a loss of labor (as in Rozelle, Taylor and De Brauw (1999)); specifically, a loss of the landlord’s labor inputs and know-how which may not be easily replaceable.

In Appendix Table A13 we present the result of the standard IV specification, with the yield of all major crops (in kg/ha in natural log) as a dependent variables. Due to the large number of connected variables, we apply a standard multiple hypothesis correction following Anderson (2008). Note that the sample size varies as not all crops are cultivated in all districts.

We note imprecisely estimated null effects for the majority of crops. Focusing on the main grains, the food crops, we note a positive impact on rice and maize (estimated at 5% and 4%. respectively), and a negative impact on wheat (estimated at 5%). The former is consistent with Marshallian theory, with the labor incentive (and possibly remittance) effect dominating. The latter implies the dominance of other channels related to the loss of crop-specific knowledge and labor.

# **8 Conclusion**

This paper contributes to a longstanding question in the study of economic development: the causes and consequences of the reallocation of labor from rural to urban areas. Employing an empirical strategy that exploits districtlevel bilateral migration network data, we find that in early 21st century India, out-migration of high-skilled males contributed to the shift from sharecropping to fixed rental arrangements. This is a to-date undocumented consequence of rural-urban migration in developing countries, with important implications for efficiency and welfare.

High-skilled out-migration, through an institutional response of changed rental contracts, appears to increase yields for some key crops, countering the 34
effects of a loss of labour and knowledge. High-skilled out-migration also appears to contribute to formalization of land markets, increasing the length of land rental contracts and reducing renting to relatives. The productivity implications of this formalization would be interesting to study in future research. Longer contracts may improve incentives for cultivators, while decrease in within-family tenancy may improve allocation of talent on farms.

The results also advance our understanding of the changing incidence of risk in the rural sector of low- and middle-income economies in the 1990s and 2000s, an era in which trade liberalization and accelerating climate change increased farm income volatility. Our analysis suggests that higher skilled agricultural land-owning households are better placed to mitigate this risk by shifting its incidence to poorer tenant households through fixed rent contracts and migrating out of rural areas. This adds to a more standard view that farming (land-owning) households mitigate their risk from trade and climate shocks through changes in crop portfolio and planting decisions (for example, Allen and Atkin (2022); Taraz (2017); Cui and Xie (2022)).

Our paper contributes to a more comprehensive understanding of land rental markets. A large share of farmland in developing countries is under a rental arrangement: Otsuka (2007) estimated rates in the early 1990s at 10% in Latin America, to 12% in Asia and 18% in Africa. Recent studies suggest there could be sizable productivity gains from removing restrictions on agricultural land rental markets (Otsuka, 2007; Christiaensen and Demery, 2018; Chen, Restuccia and Santaeul`alia-Llopis, 2022). The results in this paper imply that the effects on efficiency will be contingent on the nature of the land rental contracts which emerge after the liberalization of these markets.

35
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45
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Table 1: Types of contracts, in percentage, by year

|||In acreage term|s|
|---|---|---|---|
|year|1992|2002|2012|
|sharecropping|45|43|31|
|fxed terms|45|50|58|
|fxed amount of money|25|30|41|
|fxed amount of crop output|20|20|17|
|from relatives|6|4|8|
|duration more than two years|42|41|65|
|written contract|18|10|15|
|||In contract ter|ms|
|year|1992|2002|2012|
|sharecropping|49|51|38|
|fxed terms|35|41|49|
|fxed amount of money|15|22|30|
|fxed amount of crop output|19|20|20|
|from relatives|10|3|9|
|duration more than two years|50|40|66|
|written contract|18|10|11|

Note: Kharif (rainy) season. India NSS Land and Livestock Survey Rounds 48, 59 and 70. The top panel presents the percentages in acreage terms (as a percentage of the total acreage rented in). The bottom panel presents the percentages in contract terms (as a percentage of the total number of rented in fields (plots)). Statistics are generated using NSS population weights to generate nationally representative statistics.

48
Table 2: Migration flows, in absolute numbers, by year

||Low-skilled||
|---|---|---|
|year|2001|2011|
|male migrants within-district|3,728 (3,952)|4,419 (5,375)|
|male migrants out-of-district|4,834 (4,935)|5,022 (4,684)|
||High-skilled||
|year|2001|2011|
|male migrants within-district|4,712 (5,557)|7,586 (11,158)|
|male migrants out-of-district|7,712 (6,762)|10,289 (9,341)|

Note: In this table we present the trends in district-level migration flows, in absolute numbers, from 2001 to 2011. The numbers presented are the average (and standard deviation in brackets) number of out-migrants across all districts. We only include male migrants between the ages of 20 and 64 who migrated in the past five years, but distinguish between low-skilled (less than 10 years of education) and high-skilled (more than 10 years of education) at the top and bottom panel, respectively.

49
Table 3: Correlation between migration and land tenure arrangements (OLS)

||(1)|(2)|(3)|(4)|(5)|(6)<br>(7)|
|---|---|---|---|---|---|---|
||fxed rent|fxed (money)|fxed (crop)|sharecropping|relatives|contract length<br>written contract|
|migration|0.182*|0.431***|-0.249***|-0.540***|0.291***|0.290***<br>-0.0232|
||(0.103)|(0.0879)|(0.0779)|(0.102)|(0.0673)|(0.0978)<br>(0.0740)|
|2012 year|-3.247|-0.307|-2.940|-3.947|7.420***|20.78***<br>3.541**|
||(2.618)|(2.427)|(1.807)|(2.578)|(1.518)|(2.468)<br>(1.702)|
|Constant|33.96***|0.905|33.05***|76.86***|-13.25***|18.82***<br>12.70***|
||(6.622)|(5.469)|(5.071)|(6.603)|(4.111)|(6.238)<br>(4.796)|
|Observations|927|927|927|927|927|927<br>927|
|R-squared|0.004|0.022|0.019|0.036|0.056|0.090<br>0.005|
|Note: This table|uses an O|LS regressio|n to present|the correlation|between|the percentage of rented-in acreage|
|under various te<br>percentage of th<br>percentage of acr|rms and t<br>e total ma<br>eage of re|he number o<br>le out-migra<br>nted in land.|f high-skilled <br>nts between <br> Robust stan|male out-mig<br> the ages of 2<br>dard errors. **|rants bet<br>0-64 years<br>* p_<_0.01,|ween the ages of 20-64 years (as a<br>).<br>The dependent variable is as a<br> ** p_<_0.05, * p_<_0.1.|
Table 4: Correlation between migration and land tenure arrangements (OLS with main controls)

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|-0.165|0.0194|-0.184*|-0.210|0.263***|0.196|-0.0558|
||(0.157)|(0.143)|(0.110)|(0.160)|(0.0935)|(0.145)|(0.108)|
|bank branches (number)|0.0236|0.0354*|-0.0118|-0.0279|0.0118|-0.000616|0.0323**|
||(0.0221)|(0.0195)|(0.0103)|(0.0227)|(0.0107)|(0.0152)|(0.0146)|
|road length (km)|6.74e-06|-6.72e-05|7.39e-05|0.000191|-0.000207|0.00165***|-0.000420|
||(0.000457)|(0.000440)|(0.000283)|(0.000476)|(0.000269)|(0.000428)|(0.000322)|
|2012 year|-5.238*|-2.888|-2.350|-4.203|7.715***|22.50***|3.854**|
||(2.880)|(2.638)|(1.933)|(2.860)|(1.812)|(2.700)|(1.864)|
|observations|878|878|878|878|878|878|878|
|R-squared|0.027|0.075|0.035|0.055|0.079|0.129|0.034|
|mean dep. var.|45.34|27.83|17.51|34.13|4.92|36.90|11.25|

Note: This table uses an OLS regression to present the correlation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
Table 5: IV regression: The effect of migration on land tenure arrangements

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|2.607***|2.199***|0.409|-1.787**|-1.041*|0.209|0.459|
||(0.832)|(0.652)|(0.502)|(0.865)|(0.577)|(0.728)|(0.425)|
|bank branches (number)|-0.0252|-0.00298|-0.0222*|-0.000115|0.0347**|-0.000842|0.0233|
||(0.0242)|(0.0204)|(0.0131)|(0.0255)|(0.0174)|(0.0198)|(0.0151)|
|road length (km)|-0.000722|-0.000640|-8.20e-05|0.000606|0.000136|0.00164***|-0.000556|
||(0.000631)|(0.000556)|(0.000316)|(0.000553)|(0.000360)|(0.000473)|(0.000348)|
|2012 year|-5.709*|-3.258|-2.451|-3.936|7.936***|22.50***|3.767**|
||(3.400)|(3.003)|(1.956)|(3.028)|(2.013)|(2.685)|(1.884)|
|observations|878|878|878|878|878|878|878|
|mean dep. var.|45.34|27.83|17.51|34.13|4.92|36.90|11.25|

Note: This table uses the shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
Table 6: IV regression: The effect of migration on land tenure arrangements, using the residual approach

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|2.682***|2.182***|0.500|-1.882**|-0.916|0.150|0.462|
||(0.855)|(0.663)|(0.517)|(0.886)|(0.587)|(0.745)|(0.427)|
|bank branches (number)|-0.0265|-0.00269|-0.0239*|0.00156|0.0325*|0.000205|0.0232|
||(0.0245)|(0.0207)|(0.0131)|(0.0257)|(0.0174)|(0.0200)|(0.0153)|
|road length (km)|-0.000742|-0.000636|-0.000106|0.000631|0.000103|0.00166***|-0.000557|
||(0.000641)|(0.000558)|(0.000322)|(0.000560)|(0.000351)|(0.000476)|(0.000348)|
|2012 year|-5.722*|-3.255|-2.466|-3.920|7.915***|22.51***|3.766**|
||(3.428)|(2.998)|(1.972)|(3.048)|(1.977)|(2.685)|(1.882)|
|observations|878|878|878|878|878|878|878|
|mean dep. var.|45.34|27.83|17.51|34.13|4.92|36.90|11.25|

Note: This table uses the residual shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
# **For online publication: Appendix**

## **Data**

This section provides details on other data sources employed.

### **Credit: Reserve Bank of India**

We obtained data on bank branches from the Reserve Bank of India.<sup>27</sup> We obtained the number of commercial (which include both private and public sector banks) by district for the years 2002 and 2012 for both rural and urban areas.<sup>28</sup> . This measure provides a proxy for banking activity in the district.

### **Weather: University of Delaware**

We also aim to include control variables for adverse weather conditions in the years preceding the year under consideration. We utilized two databases of the University of Delaware’s Global Climate Database: Earth Precipitation: 19002017 Monthly Time Series in Grid, and Earth’s Air Temperature: 1900-2017 Time Series in monthly grid.<sup>29</sup> These databases contain monthly precipitation and air temperature for the 1900-2017 period, based on weather station data, but interpolated to a 0.5-degree by 0.5-degree latitude/longitude grid (with the grid nodes centered on the 0.25 degree).

Using the 2001 district census boundaries, we average the precipitation/air temperature data of all data points within each district boundary to create district-level averages for all the years between 1970 and 2012.

Using these district-level time series, we classify years (in each district) as to whether or not adverse weather conditions prevail, and then aggregate this information (at the district level) over five years.

We define an rain adverse event as a year in which the annual rainfall is below the 20th percentile or over the 80th percentile of the long term (30

> 27 see: https://www.rbi.org.in/ under the annual publications tab.

> 28The data from 2012 used the 2008 district boundaries; we mapped up the data points using our district harmonization method

> 29See: http://climate.geog.udel.edu.

54
year) series. An adverse temperature event is similarly defined. The long series covers the available years 1971-2001 for the 2002 year and the years 1981-2011 for the 2012 year.

Next, we define a measure of weather adversity. Let _ai,τ_ takes the value of 1 if an adverse event happened at time _τ_ in district location _i_ . _Ni,t_ counts the total number of events within district boundary _i_ during the five years under consideration (this is usually 5, unless there are missing data). Then, the percentage of adverse events (for either temperature or rainfall) is defined as:

# **Agro-climatic zones, roads and yields: ICRISAT**

We obtained information on the agro-climatic zone from the ICRISAT mesolevel database.<sup>30</sup> We use a standardized measure of crop water requirements, the yearly evapotranspiration (measured in mm), and the length of the growing period (defined as the period when normal precipitation exceeds 0.5 ETo).<sup>31</sup> These time-invariant data are presented using the 2011 district census boundaries. We imputed missing values in these ICRISAT data using information on the nearest neighbour using 2011 district census boundaries.<sup>32</sup> In addition, we obtained the 2001 and 2011 ICRISAT time-variant data on the total road length and the number of markets in the district (in km). For both years, we also obtained measures of crop (output) price (Rs/100 kg), crop

> 30See: http://data.icrisat.org/dld/

> 31ICRISAT records what it terms the normal monthly and annual potential evapotranspiration. Most documents refer to this concept as the ”reference crop evapotransporation” denoted ETo. This refers to a hypothetical crop and relies on climatic inputs only, not taking into account the specific crop or soil conditions. See Allen et al. 1998. We use the monthly values and compute an annual value from this.

> 32There are 421 districts in the ICRISAT file, compared to a total of 640 in India 2011 NSS. So about 35% are missing. However, our final dataset has about 575 districts (because we don’t use all the states), so according to this 27% districts are missing in ICRISAT.To deal with the missing, we assigned the value of the nearest non-missing neighbor out of 4 closest neighbors.

55
area (in ‘000 hectares) and crop productions (in ’000 tons) and crop yields (in kg/ha), for the main crops in the country. This includes maize and rice. These allow us to create a measure of crop yields and shares.

# **Soils: NSS Land and Livestock Survey**

Recall that the NSS land and livestock survey collected information on the soil texture. These data are only available in Round 59 (which covers year 2002), but as texture tends to be stable over time, this suffices for our purpose of generating a district level control variable on soil conditions. Soils, at the field level, are classified into the following texture categories: sand - 1, loam - 2, silt - 3, light clay - 4, heavy clay - 5, others - 9. We use a district-level (weighted) vector which indicates the percentage of soils into each soil category.

# **Distance: Digitization of road maps and computation of the minimum travel time between districts**

We obtained scanned paper maps of India from 1996 and 2011 via the work of Allen and Atkin (2022). These maps included different types of roads which are distinguished by color. For instance, a highway has a different color than a local road. Different road types imply different travel times: for example, it would typically take less time to travel 1 km on a highway than on a local road.

Using GIS software, we superimposed the road maps onto district shape files of 2001 and 2011 (geo-coding, in GIS parlance). We then converted the road map into color-coded pixel data (raster data, in GIS parlance). Using information on the location of each district, and the roads connecting them, we computed a measure of the minimum travel time between districts.<sup>33</sup> This calculation attributed a “cost of distance” to each road type: the highways were attributed a cost of one, medium roads a cost of two, and small roads a cost of three per cell. That is, higher costs reflect slower average speeds. To compute the travel time along a given path, the distances/cells 33 We are grateful to Michael Norton (World Bank) for generating these calculations.

56
traveled along these various types of roads on that path were weighted with the associated costs.<sup>34</sup> Next, the fastest of all possible paths between two districts (the “accumulated least cost distance”) was computed using Dijkstra’s algorithm (Dijkstra (2022)). For this calculation, the centroid of the subdistrict with the highest population density within a district was chosen as the starting/ending point of that district.<sup>35</sup> Note that the resulting unit of this minimum travel time is not in hour, or minutes, but a km based measure.

We validated this method using Bing Maps travel time, comparing our 2011 estimate of the minimum travel time with the current travel time according to Bing Maps. Our method accounts for 95.7% of the variation in travel times of Bing maps using a random sample of 100 district pairs. Note that this method is similar to what Allen and Atkin (2022) use to link to location of highways to trade costs. It is likely to be an improvement over the more commonly used as-the-crow-flies distance (for example, in Madhok et al. (2024)) as it accounts for the variation in road connectivity within India.

We use the matrix of minimum travel time (in our weighted km unit) between districts in the construction of the instrumental variable.

> 34While the use of raster data allows us to do these tasks in a time-efficient manner; the method can result in district combinations which are not connected, that is, which have gaps in the roads. When computing the travel time we allowed for “jumps” assuming that our digitization/ process is imperfect.

> 35Using the centroid of the district was less satisfactory as it would often be separated from the road network by some distance.

57
# **Simulations**

This section provides the technical details of the simulation exercise.

We opt for a standard Cobb-Douglas production function with decreasing returns to scale: Across the three scenarios, we fix the following parameters: _t_ , recall the supervision technology, is set at 0.5. The remaining production parameter, _a_ is set at 0.4 and _b_ is set at 0.5. Then, _γ_ and _δ_ , the time management equivalent ratios, are both set at 0.8. These ratios are critical in determining how the three solutions match up; for further analysis of their role, refer to Eswaran and Kotwal (1985).<sup>36</sup> In the first scenario the landlord’s payoff can be written as: We vary _m_ from 0.05 to 1 (and set _s_ at 0.05 and _w_ at 0.01) - which implies that _m/s_ ranges from 1 to 20 - and maximise the landlord’s payoff for each value of _m_ by choosing the values of _M_ , _S_ and _L_ subject to constraints (recall, the sum of _M_ and _S_ is capped at one). Recall also that _γ_ is set at 0.8.

The result of this exercise is presented in Appendix Figure A4. We compute that at _m/s_ = 20 we have _M_ = 0 _._ 16; _S_ = 0 _._ 06 and _L_ = 6 _._ 4, hence the landlord in this scenario works both on the land as well as make use of outside options.

In the second scenario, the landlord spends all her time on the alternative occupation valued at _m_ . The rental rate, _R_ , is set as to meet the participation constraint of the tenant. We keep _t_ , the supervision technology, set at 0.5. The remaining production parameters _a_ and _b_ are set at 0.4 and 0.5, respectively, as before. _δ_ , the time management equivalent ratio is set at 0.8. We follow

> 36They show that if _γ_ and _δ_ are low (meaning both landlord and tenant are ill suited at doing each-other’s tasks of management and supervision, respectively), sharecropping is preferred. However, when _γ_ is high, meaning the tenant is quite skilled at management tasks, a fixed rent contract dominates, and if _δ_ is high, meaning the landlord is capable to engage in supervision, owner cultivation is preferred.

58
a sequence of steps to compute the landlord’s payoff. We again vary the parametric values of _m_ ranging from 0.05 to 1 (while fixing _w_ at 0.01 and _s_ at 0.05). We then compute the optimal values of _M_ , _S_ and _L_ subject to constraints (with the sum of _M_ and _S_ capped at one), and use these to compute the landlord’s payoff.

The result of this exercise is presented in Appendix Figure A5. We note that for _m/s_ = 20, we obtain optimal values of _M_ = 0 _._ 71; _S_ = 0 _._ 28 and _L_ = 21 _._ 79. Note that the sum of _M_ and _S_ is 1, as expected, as the landlord pushes the tenant against their participation constraint and hence will demand full-time work of the tenant.

In the third scenario, we assume full specialization. We first derive the conditional profit function by finding the optimal _L_ as a function of _M_ and _S_ . Again, we fix _t_ at 0.5 and the production parameters _a_ and _b_ at 0.4 and 0.5, respectively. We fix _w_ at 0.01 and _s_ at 0.05. The optimal _L_ as a function of _S_ and _M_ is: When taking this conditional profit function to the next step, a standard Nash game, it should be noted that the responses of both parties depend on the exact value of _α_ . So we fix _α_ as a number between 0 and 1, and then plug the conditional profit function into the Nash Equilibrium optimisation exercise using the NIRA Nash Algorithm (Nikaido-Isoda Relaxation Algorithm) and compute the Nash _S_ and _M_ responses for this fixed value of _α_ . We plug the choices of _M_ and _S_ back into the optimal response for _L_ and use the updated unconditional profit to obtain a measure for the payoff of the landlord and tenant.

We repeat this exercises for selected values of _α_ within the 0-1 range, and compute the landlord’s payoff. The result of this scenario is presented in Appendix Figure A6. We again compute the resulting labor choice for _m/s_ = 20, and obtain the values of _M_ = 0 _._ 1; _S_ = 1 and _L_ = 10 _._ 02. Note that _S_ is now 1, as expected, as the landlord pushes the tenant against his participation constraint but himself works outside the farm.

59
# **For online publication: Appendix Figures and Tables**

60
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Table A1: Types of contracts, in percentage, by year

|||In acreage term|s|
|---|---|---|---|
|year|1992|2002|2012|
|sharecropping|51|42|31|
|fxed terms|36|51|57|
|fxed amount of money|17|31|42|
|fxed amount of crop out|19|19|15|
|from relatives|9|3|6|
|duration more than two years|48|43|59|
|written contract|14|9|12|
|||In contract ter|ms|
|year|1992|2002|2012|
|sharecropping|53|49|38|
|fxed terms|32|42|49|
|fxed amount of money|13|23|32|
|fxed amount of crop output|19|20|17|
|from relatives|11|3|7|
|duration more than two years|48|38|62|
|written contract|17|9|10|

Note: Rabi (dry) season. India NSS Land and Livestock Survey Rounds 48, 59 and 70. The top panel presents the percentages in acreage terms (as a percentage of the total acreage rented in). The bottom panel presents the percentages in contract terms (as a percentage of the total number of rented in fields (plots)). Statistics are generated using NSS population weights to generate nationally representative statistics.

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Table A2: Numbers of high and low-skilled individuals, by year

|year|2001|2011|
|---|---|---|
|low-skilled individuals|628,889 (503,887)|772,797 (646,578)|
|high-skilled individuals|139,840 (138,648)|203,442 (211,104)|

Note: In this table we present the trends in district-level high and low-skilled individuals from 2001 to 2011 in absolute numbers. The standard deviations are included in parenthesis next to the averages. We only include male individuals between the ages of 20 and 64 who migrated in the past five years. Low-skilled are defined as less than 10 years of education and high-skilled as more than 10 years of education.

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Table A3: Descriptive statistics, by year

|||2002||2012|||
|---|---|---|---|---|---|---|
||mean|sd|n|mean|sd|n|
|area under fxed rent (%)<br>|45.34|38.66|452|42.88|39.72|475|
|(monetary rent) (%)<br>|27.83|35.64|452|29.39|37.43|475|
|(crop rents) (%)|17.51|26.88|452|13.49|26.72|475|
|area under sharecropping (%)|43.13|38.71|452|36.85|39.57|475|
|area with contract to relatives (%)|4.92|16.46|452|13.60|28.66|475|
|area with contract longer than 2 years (%)|36.90|35.36|452|58.93|38.66|475|
|area with written contract (%)|11.25|22.60|452|14.69|27.99|475|
|migration ratio (%)|62.69|13.59|560|67.21|12.25|575|
|percentage of adverse rain events (%)|31.64|20.69|560|42.40|20.53|575|
|percentage of temperature adverse events (%)|46.39|21.23|560|47.37|16.57|575|
|bank branches (number)|107.83|89.82|560|162.69|163.29|575|
|road length (km)<br>|2934.80|3096.62|489|2324.33|3289.62|575|
|total population (in 1000s)<br>|1747.68|1304.18|560|2028.08|1574.15|575|
|population density (in 1000s per square km)|0.46|0.51|543|0.53|0.49|562|
|sex ratio (in %)|93.74|6.03|560|94.74|5.98|575|
|high-skilled males (as % of total)|17.29|5.05|559|19.85|5.57|573|
|sandy soils (%)|11.70|19.43|560|11.73|19.43|560|
|loam soils (%)|26.77|27.13|560|26.74|27.12|560|
|silt soils (%)|7.55|11.73|560|7.55|11.73|560|
|clay soil (%)|39.71|27.42|560|39.81|27.54|560|
|heavy clay soil (%)|10.39|16.03|560|10.28|15.89|560|
|other soil (%)|3.89|11.50|560|3.89|11.50|560|
|growing period (days)|191.58|48.87|436|191.58|48.87|436|
|annual ET0 (mm)|1480.62|201.89|526|1477.57|210.18|537|

Note: This table presents the mean and standard deviation of the variables associated with the analysis tables, by year. The area variables are as a percentage of acreage of rented in land.
Table A4: First Stage Regressions: The relationship between the instruments and migration

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Migration|Migration|Migration|Migration|Migration|Migration|
|IV<br>~~h~~igh|136.4***<br>(16.69)|56.80***<br>(13.53)|48.33***<br>(13.13)||||
|IV<br>~~l~~ow|-151.9***<br>(17.20)|-85.19***<br>(14.31)|-72.45***<br>(14.55)||||
|IVR<br>~~h~~igh||||0.132***<br>(0.0164)|0.0581***<br>(0.0137)|0.0473***<br>(0.0130)|
|IVR<br>~~l~~ow||||-0.149***<br>(0.0173)|-0.0854***<br>(0.0146)|-0.0741***<br>(0.0147)|
|perc. of adverse rain events||0.0208|0.0157||0.0212|0.0154|
|||(0.0137)|(0.0149)||(0.0137)|(0.0148)|
|perc. of adverse temp events||-0.0169|-0.0283||-0.0193|-0.0284|
|||(0.0168)|(0.0178)||(0.0167)|(0.0178)|
|bank branches||0.0132***<br>(0.00384)|0.00720**<br>(0.00359)||0.0132***<br>(0.00385)|0.00696*<br>(0.00364)|
|road length (km)||0.000278**<br>(0.000119)|0.000389***<br>(0.000127)||0.000251**<br>(0.000119)|0.000402***<br>(0.000126)|
|population||-0.00230***|-0.00167***||-0.00234***|-0.00165***|
|||(0.000442)|(0.000417)||(0.000444)|(0.000419)|
|population density||-1.061|-1.462*||-0.951|-1.296|
|||(0.878)|(0.868)||(0.878)|(0.868)|
|sex ratio||0.263***|0.151**||0.276***|0.169**|
|||(0.0557)|(0.0677)||(0.0562)|(0.0683)|
|ratio high-skilled males||1.601***|1.676***||1.596***|1.676***|
|||(0.0687)|(0.0738)||(0.0687)|(0.0736)|
|2012 year|3.034***|-0.0101|0.576|3.391***|0.177|0.692|
||(0.759)|(0.613)|(0.620)|(0.755)|(0.609)|(0.614)|
|Constant|63.25***|13.27**|26.14***|62.76***|11.71**|24.23***|
||(0.685)|(5.572)|(7.329)|(0.636)|(5.632)|(7.402)|
|agro-ecological zone|No|No|Yes|No|No|Yes|
|observations|1,135|1,048|843|1,135|1,048|843|
|R-squared|0.094|0.554|0.576|0.089|0.553|0.576|
|IV F-stat|39.41|19.18|12.78|37.90|17.92|13.16|

Note: This table presents the first-stage regressions. The outcome variable is the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The first three columns present the standard shift-share instrument, the second three columns present the residual shift-share instrument. The agro-ecological controls include soils, length of the growing season (in days) and reference ETo (a measure of water stress). Robust standard errors included. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1
Table A5: The relationship between the instrument residual and economic conditions

|re|sidual below median|residual above median|p-value|
|---|---|---|---|
|number of markets|8.24 (0.98)|7.30 (0.65)|0.43|
|wheat price (Rs/100 kg)|676 (7.60)|660 (8.05)|0.15|
|paddy price (Rs/100 kg)|517 (0.40)|515 (8.20)|0.87|

Note: This table presents the results of a t-test with unequal variances between two groups of districts in 2002. The districts are grouped based on their value of the residual of a regression which maps the ratio of the high-skilled over the low-skilled instrument on the control variables. Column (1) presents the mean and standard error of the residuals of the districts which have a residual below the median. Column (2) presents the mean and standard error of the residuals of the districts which have a residual above the median. Column (3) presents the p-value of the t-test comparing these means.

72
Table A6: Correlation between migration and land tenure arrangements: Acreage, Extended Controls

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|-0.279|-0.0768|-0.202|0.0685|0.118|0.194|-0.0675|
||(0.176)|(0.157)|(0.127)|(0.182)|(0.0945)|(0.166)|(0.125)|
|bank branches (number)|0.0239|0.0209|0.00304|-0.0254|0.00423|0.0129|0.0201|
||(0.0247)|(0.0205)|(0.0124)|(0.0233)|(0.0122)|(0.0153)|(0.0123)|
|road length (km)|-0.000366|-0.000518|0.000152|0.000279|-0.000118|0.00156***|-0.000629*|
||(0.000498)|(0.000484)|(0.000299)|(0.000514)|(0.000291)|(0.000480)|(0.000371)|
|2012 year|-4.445|-1.020|-3.425|-5.512*|7.714***|21.96***|5.725***|
||(3.150)|(2.858)|(2.113)|(3.174)|(1.925)|(3.034)|(2.093)|
|Observations|722|722|722|722|722|722|722|
|R-squared|0.090|0.181|0.059|0.095|0.053|0.141|0.057|

Note: This table uses an OLS regression to present the correlation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. The agro-ecological controls include soils, length of the growing season (in days) and reference ETo (a measure of water stress). The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
Table A7: IV regression: The effect of migration on land tenure arrangements: Acreage, No Controls

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|0.769*|0.778**|-0.00873|-0.735*|-0.0306|0.501|0.476*|
||(0.411)|(0.347)|(0.284)|(0.433)|(0.238)|(0.402)|(0.270)|
|2012 year|-5.788*|-1.807|-3.980*|-3.106|8.813***|19.86***|1.377|
||(3.187)|(2.863)|(2.238)|(3.155)|(1.860)|(2.979)|(2.089)|
|Constant|-2.660|-20.72|18.05|88.99***|6.824|5.606|-18.49|
||(25.68)|(21.60)|(17.64)|(27.08)|(14.90)|(25.12)|(16.84)|
|Observations|927|927|927|927|927|927|927|

Note: This table uses the shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
Table A8: IV regression: The effect of migration on land tenure arrangements: Acreage, Extended Controls

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|3.371***|2.917***|0.454|-1.759*|-1.700**|-0.229|0.912*|
||(1.087)|(0.865)|(0.585)|(1.000)|(0.742)|(0.885)|(0.538)|
|bank branches (number)|-0.0188|-0.0142|-0.00465|-0.00403|0.0255|0.0179|0.00865|
||(0.0246)|(0.0211)|(0.0130)|(0.0233)|(0.0183)|(0.0185)|(0.0121)|
|road length (km)|-0.00170**|-0.00162**|-8.84e-05|0.000949|0.000549|0.00171***|-0.000988**|
||(0.000813)|(0.000707)|(0.000359)|(0.000649)|(0.000470)|(0.000598)|(0.000434)|
|2012 year|-6.661*|-2.837|-3.824*|-4.403|8.818***|22.22***|5.130**|
||(3.995)|(3.521)|(2.144)|(3.429)|(2.292)|(3.043)|(2.201)|
|Observations|722|722|722|722|722|722|722|

Note: This table uses the shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. The agro-ecological controls include soils, length of the growing season (in days) and reference ETo (a measure of water stress). The dependent variable is as percentage of acreage of rented in land. The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
Table A9: IV regression: The effect of migration on land tenure arrangements, using the residual approach: Acreage, No controls

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|0.959**|0.982***|-0.0228|-0.823*|-0.0471|0.457|0.445*|
||(0.417)|(0.350)|(0.287)|(0.438)|(0.316)|(0.404)|(0.266)|
|2012 year|-6.611**|-2.691|-3.919*|-2.721|8.885***|20.05***|1.511|
||(3.228)|(2.901)|(2.253)|(3.169)|(2.110)|(2.980)|(2.051)|
|Constant|-14.52|-33.45|18.93|94.52***|7.859|8.357|-16.55|
||(26.01)|(21.77)|(17.81)|(27.39)|(19.71)|(25.25)|(16.62)|
|Observations|927|927|927|927|927|927|927|

Note: This table uses the residual shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
Table A10: IV regression: The effect of migration on land tenure arrangements, using the residual approach: Acreage, Extended Controls

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|3.144***|2.646***|0.498|-1.769*|-1.483**|-0.231|0.788|
||(1.036)|(0.802)|(0.574)|(0.974)|(0.715)|(0.863)|(0.505)|
|bank branches (number)|-0.0162|-0.0110|-0.00516|-0.00393|0.0230|0.0179|0.0101|
||(0.0242)|(0.0207)|(0.0129)|(0.0231)|(0.0176)|(0.0183)|(0.0120)|
|road length (km)|-0.00162**|-0.00152**|-0.000104|0.000953|0.000469|0.00171***|-0.000942**|
|2012 year|-6.523*|-2.673|-3.850*|-4.397|8.686***|22.22***|5.205**|
||(3.905)|(3.418)|(2.157)|(3.428)|(2.206)|(3.039)|(2.172)|
|Observations|722|722|722|722|722|722|722|

Note: This table uses the residual shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. The agroecological controls include soils, length of the growing season (in days) and reference ETo (a measure of water stress). The dependent variable is as percentage of acreage of rented in land. The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
Table A11: IV regression: The effect of migration on land tenure arrangements by NREGA start date

||(1)<br>fxed rent|(2)<br>sharecropping|(3)<br>fxed rent|(4)<br>sharecropping|(5)<br>fxed rent|(6)<br>sharecropping|
|---|---|---|---|---|---|---|
|migration|1.469*|-1.138|1.508|0.450|5.466|-4.604|
||(0.801)|(1.091)|(1.201)|(1.109)|(3.526)|(3.162)|
|banks|-0.0514|0.0880|0.0821*|-0.114***|-0.0327|0.0154|
||(0.0484)|(0.0556)|(0.0445)|(0.0351)|(0.0441)|(0.0422)|
|road length (km)|-0.00106|0.000105|0.00151|-0.000294|-0.000671|0.00123|
||(0.000786)|(0.000764)|(0.00136)|(0.00108)|(0.00137)|(0.00113)|
|year 2012|0.0527|-7.608|-10.39|-5.021|-4.005|-4.432|
||(5.612)|(5.877)|(6.636)|(5.950)|(7.808)|(6.834)|
|observations|296|296|202|202|375|375|

Note: This table uses the shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). Columns (1) and (2) are districts with NREGA start = 1, Columns (3) and (4) are districts with NREGA start = 2, Columns (5) and (6) are districts with NREGA start = 3. The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
Table A12: IV regression: The effect of migration on cash crops prevalence

||cash crops (%)|
|---|---|
|migration|-0.257|
||(0.438)|
|observations|980|
|mean dep. var.|40|

Note: This table uses the shift-share instrumental variable regression to present the relation between acreage of cash crops as a percentage of the total cultivated acreage (where cash crop is defined as not being wheat, rice, maize and sorghum), and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

79
Table A13: IV regression: The effect of migration on yields

||(1)<br>Wheat|(2)<br>Rice|(3)<br>Sorghum|(4)<br>P millet|(5)<br>Maize|(6)<br>F millet|(7)<br>Barley|(8)<br>Chickpea|(9)<br>Pigeonpea|
|---|---|---|---|---|---|---|---|---|---|
|migration|-0.0461***|0.0454***|0.00150|0.00361|0.0392***|-0.0148|0.00201|-0.00680|-0.0132|
||(0.0113)|(0.00993)|(0.00903)|(0.0154)|(0.0109)|(0.0114)|(0.0115)|(0.00704)|(0.00992)|
|FDR q-values|[0.001]|[0.001]|[0.510]|[0.510]|[0.001]|[0.267]|[0.510]|[0.402]|[0.267]|
|observations|838|919|412|365|533|195|286|509|510|
|mean dep var.|7.45|7.26|6.41|6.43|7.20|6.68|7.38|6.53|6.35|
||(10)<br>Groundnut|(11)<br>Sesame|(12)<br>R/M|(13)<br>Castor|(14)<br>Linseed|(15)<br>Sun|(16)<br>Soya|(17)<br>Sugar|(18)<br>Cotton|
|migration|0.0186|0.0371**|-0.0767***|-0.0426|0.0101|0.0183*|-0.00656|-0.0165|-0.0200|
||(0.0168)|(0.0167)|(0.0229)|(0.0318)|(0.00915)|(0.00993)|(0.0123)|(0.0106)|(0.0140)|
|FDR q-values|[0.332]|[0.079]|[0.004]|[0.267]|[0.332]|[0.167]|[0.462]|[0.262]|[0.267]|
|observations|467|511|462|231|259|259|227|490|286|
|mean dep var.|6.68|5.48|6.34|6.10|5.97|6.67|6.55|8.34|5.20|

Note: This table uses the shift-share instrumental variable regression to present the relation between the yields of all major crops (in natural log in kg/ha) and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). P millet refers to pearl millet. F millet refers to finger millet and R/M refers to rapeseed and mustard seed. The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. Robust standard errors are reported in simple parenthesis. FDR-adjusted standard errors following Benjamini, Krieger and Yekutieli (2006) and using the code of Anderson (2008) are reported in block parenthesis. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.