Ubaid Ali
email: ali.ubaid@univ-pau.fr

Mazhar Mughal
email: mazhar.mughal@esc-pau.fr

Lionel De Boisdeffre
email: lionel.de.boisdeffre@wanadoo.fr

Migrant Remittances, Agriculture Investment and Cropping Patterns

Keywords: Migration, Remittances, Agriculture Investment, Cropping Patterns, Instrumental Variable, PSM. JEL Classification : D10, C21, F24

This study investigates how the receipt and amount of domestic or international transfers influences household decisions regarding farm investment and the selection of capital and labor-intensive crops. We develop a conceptual framework to postulate that even though recipient households may have the possibility to employ the additional income to raise their agricultural investment, the investment falls in the short run if labor constraints arising from the migrant member's absence are binding and capital accumulation is suboptimal.

Employing a set of endogenous treatment estimations, we empirically test this hypothesis on data on 5,636 rural households from Pakistan. Our findings show a substantial difference between recipient and nonrecipient households in terms of their economic behavior. Recipient households make 99.64% less agricultural investment and obtain 82% less production compared to non-recipient households. The estimates are found to be robust when tested with alternate empirical techniques Heckman Selection and matching. The impact is stronger in case of households which receive domestic transfers, with 99.87% less farm investment and 77% less production than non-recipient households. Remittances result in a decrease in production of both capitaland labor-intensive crops, reflecting a decline in overall farm activity. Similar farm investment and cropping patterns are observed relative to the amount of remittances received. The results are robust to different model specifications and estimation procedures.

1

Introduction

A common feature associated with the developing countries is the high incidence of outmigration from rural areas. Low yields, high risks due to climatic shocks, pest attacks, and tough working conditions push workers from farm households to seek employment in urban centers or to go abroad. There are an estimated 763 million internal migrants [START_REF] Bell | Cross-national comparisons of internal migration: an update of global patterns and trends[END_REF], and approximately 272 million international migrants constituting about 3.5% of the sending countries' population (United Nations Department of Economic and Social Affairs, 2019). Money transfers by international migrants to their home countries constitute a significant source of foreign exchange for a number of developing countries. Out of the total international remittances of $719.4 billion in 2019, $539.5 billion were remitted to low and middle income countries (World Bank, 2021).

A rich literature in economics explores the relation between migration and agriculture. The focus of the initial theoretical models moved from neoclassical approach of individual decision to migrate due to income difference [START_REF] Todaro | A model of labor migration and urban unemployment in less developed countries[END_REF] to the New Economics of Labor Migration (NELM) approach [START_REF] Stark | The new economics of labor migration[END_REF] based on household level decisions which are meant to reduce income risks arising out of uncertainty. These models and the corresponding empirical analysis have come up with conflicting and at times contradictory evidence of the impact of migration and remittances on farm households. Several studies based on the NELM framework support the hypothesis that migration's positive income effect on migrant household finances dominates the negative labor-constraint effect.

In the presence of imperfect labor market, if migration helps alleviate credit and insurance constraints, then the additional income from remittances can compensate for the loss of farm output due to lower family labor availability, as the household can invest in labor-saving technology or go for riskier or more profitable crops [START_REF] Atamanov | Heterogeneous effects of international migration and remittances on crop income: Evidence from the Kyrgyz Republic[END_REF][START_REF] Kinnan | Access to migration for rural households[END_REF][START_REF] Rozelle | Migration, remittances, and agricultural productivity in China[END_REF][START_REF] Taylor | Migration and incomes in source communities: A new economics of migration perspective from China[END_REF]. Migrant households can use remittances to increase their investment in agriculture by adapting high-yield varieties, using more herbicides, and acquiring livestock [START_REF] Abebaw | Does rural outmigration affect investment in agriculture? Evidence from Ethiopia[END_REF][START_REF] Böhme | Does migration raise agricultural investment? An empirical analysis for rural Mexico[END_REF][START_REF] Mendola | Migration and technological change in rural households: Complements or substitutes[END_REF][START_REF] Quinn | Estimating the impact of migration and remittances on agricultural technology[END_REF][START_REF] Tshikala | The impact of migration, remittances and public transfers on technology adoption: The case of cereal producers in rural Kenya[END_REF]. Likewise, households can afford to take greater risks and grow high risk, high profitability cash crops [START_REF] Kinnan | Access to migration for rural households[END_REF].

Other studies suggest that the labor loss effect outweighs the income effect. Migration, according to this strand of literature, leads to either lower farm output or to the farm household switching to less labor-intensive crops or even to abandon farming altogether [START_REF] De Brauw | Seasonal migration and agricultural production in Vietnam[END_REF][START_REF] Qian | The impact of migration on agricultural restructuring: Evidence from Jiangxi Province in China[END_REF]. International migration cannot overcome risk and credit constraints, thereby putting downward pressure on investment on own-cultivated and rented out land, labor inputs and agriculture income [START_REF] Hossain | Migration and household decision on occupational choice and investment: Evidence from Bangladesh[END_REF][START_REF] Miluka | The Vanishing Farms? The Impact of International Migration on Albanian Family Farming[END_REF]. Households choose strategies to overcome labor loss by shifting from high labor-intensive crops to less labor requiring crops [START_REF] De Brauw | Seasonal migration and agricultural production in Vietnam[END_REF]. Households with international migrant members allocate more land to low risky and less labor intensive pasture and less land to moderately labor and capital intensive staple crops [START_REF] Kinnan | Access to migration for rural households[END_REF][START_REF] Li | Migration, remittances, and agricultural productivity in small farming systems in Northwest China[END_REF][START_REF] Mccarthy | Assessing the impact of massive out-migration on Albanian agriculture[END_REF] (temporary/short-term versus permanent/long-term) can influence the investment, production and choice of crops of the recipient households.

We conduct our analysis on data for 5,636 rural households taken from the 2018-19 round of the Pakistan Social and Living standards Measurement (PSLM) survey (Pakistan Bureau of Statistics: Government of Pakistan, 2018-19). Majority of Pakistan's population (63.3%) resides in the rural areas. Agriculture absorbs about 38.5% of the country's labor force and contributes 18.5% to the country's GDP. Farm activities do not generate sufficient revenues and millions of surplus workers move to the urban centers or go abroad for work. Pakistan is among the top seven migrant-sending countries with about 3.26 million international emigrants in 2019 (IOM, 2020). Remittances from these non-resident Pakistanis constitute the biggest source of foreign exchange for the country. In the financial year 2020-21, the country received $29.4 billion in the form of migrant remittances (State Bank of Pakistan, 2021). Internal migration is estimated to be even more important, with 15% of the households reported to receive remittances from members living elsewhere in the country according to the Labor Force Survey Government of Pakistan (2018).

Existing literature examines the impact of migration and remittances to Pakistan on income inequality [START_REF] Mughal | Remittances, inequality and poverty in Pakistan: Macro and microeconomic evidence. CATT -UPPA -Université de Pau et des Pays de l'Adour[END_REF], asset accumulation [START_REF] Adams | Remittances, inequality and asset accumulation: the case of rural Pakistan: International Food Policy Research Institute[END_REF][START_REF] Adams | Remittances, investment, and rural asset accumulation in Pakistan[END_REF][START_REF] Ahmed | Great expectations? Remittances and asset accumulation in Pakistan[END_REF][START_REF] Oda | Dynamics of internal and international migration in rural Pakistan: Evidence of development and underdevelopment[END_REF], labor market [START_REF] Mughal | Labour effects of foreign and domestic remittances-evidence from Pakistan[END_REF]), education attainment and child labor [START_REF] Mansuri | Migration, school attainment, and child labor: evidence from rural Pakistan[END_REF], and household welfare (V. [START_REF] Ahmed | Remittances and household welfare: A case study of Pakistan[END_REF]. However, no study yet has addressed migrant remittance effects on agriculture. In this study, we compare the investment and cropping patterns of recipient and non-recipient households to test our theoretical assertions by using a set of instrumental-variable and matching estimations. We show that agriculture investment and farm production of recipient rural households in Pakistan is lower compared to non-recipient households. Migration and subsequent transfers result in a decrease in production of not only capital-intensive crops but also of labor-intensive crops, reflecting a decline in overall farm activity. The impact is stronger and significantly negative for households which receive domestic remittances.

The study is structured as follows: We begin by presenting our stylized model in Section 2. Section 3 overviews relevant literature on migration, remittances, farm investment, and production. Section 4 describes the data, the empirical methodology and the identification strategy followed by key findings in Section 5. Section 6 discusses some robustness checks. The final section concludes and underscores possible policy implications.

Stylized model

Consider an economy with one sector, agriculture, in which household's common behavior is to maximize total income per head, 𝑌. This income, 𝑌, depends on the household's capital per head, 𝐾. It also depends on the proportion of members who stay at the farm (i.e., do not migrate), 𝐿 ∈ [0,1]. Migrants send remittances to their family proportional to (1 -𝐿). Total remittances (per head) are thus of the form 𝑟(1 -𝐿), where 𝑟 > 0 is fixed. Households invest a fixed share per head, 𝑠, of their total income, to increase the capital stock.

The basic equations of the simplest model can thus be given as:

{ 𝑌 = 𝐴𝐾 ∝ 𝐿 𝛽 + 𝑟(1 -𝐿) 𝐼 = 𝑠𝑌
where (𝐾, 𝐿) ↦ 𝐴𝐾 ∝ 𝐿 𝛽 , the production function (per head), is assumed to have constant returns to scale (∝ +𝛽 = 1) and a fixed global productivity of factors, 𝐴 > 0.

Agriculture is a risky activity. Likewise, migration is a risky [START_REF] Bryan | Underinvestment in a profitable technology: The case of seasonal migration in Bangladesh[END_REF] as well as costly process. Some households are risk averse and keep the risks to the minimum by engaging only in agriculture and avoid additional risk associated with the migration process. These households thus have no interest in sending family members away (𝐿 = 1), and all available resource are devoted to local farm to make it efficient. While on the other hand, given the low productivity and wages in the local agriculture sector compared to perceived migrant wages, the utility of typical risktaking households is not maximized only by agriculture. Consequently, they select the migration level, 𝐿, to diversify and maximize the income per head, 𝑌. Initially, resources of such households are allocated to two activities, namely agriculture and migration.

Household with no migrant (𝑳 = 𝟏)

When no migration takes place, then 𝜕𝑌 𝜕𝐿 (𝐾, 1) ≥ 0, the initial capital stock (per head) satisfies:

𝐾 ≥ 𝐾°∶= ( 𝑟 𝐴𝛽 ) 1 ∝ .
Where 𝐾° is the optimum level of household's capital per head. Household labor concentrates on the local production of the farm. Thus, the household's production and investment are, respectively, 𝑌 = 𝐴𝐾 ∝ which is not lower than 𝑌°∶ = 𝑟 𝛽 -and 𝐼 = 𝑠𝑌 = 𝐴𝑠𝐾 ∝ .

A household may also be unable to send a member away due to the family situation, e.g., if it has only one breadwinner, the above condition (𝐾 ≥ 𝐾°) may fail.

Household with migrants (𝑳 < 𝟏)

Household which decides to send a member away need to finance migration in addition to spending on farm activities with existing resources. Thus However, the dynamics of investment and depreciation could be simply represented by an additional equation:

𝐾 𝑡+1 = (1 -𝑑)𝐾 𝑡 + s𝑌 𝑡 ,
where d stands for the annual rate of depreciation of capital and t indexes the year of reference. Provided the stock of capital increased over time (when investment dominates depreciation), this stock will tend to the threshold value K° where emigration stops4 . It follows from above that migrant households, as compared to non-migrant ones, produce and invest less in the short run.

Different crop types and migrant categories

The above basic model can be modified to break down production into capital-intensive and labor-intensive crops. Based on capital intensity, crops can be divided into highly capital-intensive crops (mainly cash crops such as sugarcane and cotton) and low or moderately capital-intensive crops ( chiefly subsistence crops such as wheat). Laborintensive crops can be divided into highly labor-intensive crops such as vegetables, fruits, and rice, and low or moderately labor-intensive crops such as pulses, maize, and animal fodder. The value (per head) of long duration capital-intensive cash crops is denoted by 𝑌 1 (occupying a portion, 𝐿 1 , of the household's labor force), while the production (per head) of moderately capital-intensive wheat and subsistence crops is denoted by 𝑌 2 (occupying a proportion, 𝐿 2 , of the household's labor force). The production value (per head) of labor-intensive crops and less labor-intensive crops is denoted by 𝑌 3 (occupying the share, 𝐿 3 , of household labor).

Migrants are divided into domestic migrants, in proportion 𝐿 4 of the labor force and international migrants, in proportion 𝐿 5 . In contrast to international migrant members, domestic migrants, being near their home, can participate in the production of short-duration crops. Long duration crops require continuous monitoring and thus cannot be adequately monitored by either the domestic or international migrants.

The basic equations of the model can thus be given as:

{ 𝑌 1 = 𝐴 1 𝐾 ∝ 1 𝐿 1 𝛽 1 𝑌 2 = 𝐴 2 𝐾 ∝ 2 𝐿 2 𝛽 2 𝑌 3 = 𝑏𝐿 3 + 𝑐𝐿 4 𝑌 = 𝑌 1 + 𝑌 2 + 𝑌 3 + (𝑟 -𝑐)𝐿 4 + 𝑟𝐿 5 𝐼 = 𝑠𝑌
where all parameters are fixed and 𝐿 1 + 𝐿 2 + 𝐿 3 + 𝐿 4 + 𝐿 5 = 1. Domestic and international migrants are assumed to contribute equally to the household's wealth (otherwise there would only be one type of migrants). Hence, the remittances from the domestic migrants are subtracted from their contribution to the farm's production.

The model is solved as above by first order conditions. Though, in short run one would expect non-migrant households to focus more on capital-intensive production than migrant ones, while the latter would rely more on laborintensive crops for lack of sufficient capital accumulation. However, in the long run, once capital per head reaches a certain threshold, migrant household production of capital-intensive crops and investment become positive. If labor constraint is binding and agriculture require the presence of household member to monitor the production process, the investment and production will remain negative, even with greater availability of capital per head. This can even lead to a poverty trap if the migrant households failed to save enough through time to upgrade their production. Our data confirm that migrant households produce less and invest less than non-migrant households. To solve the short-term model, we let 𝐾 1 ∶= (

Household with no migrant

The basic condition for migration is when (r > b), if b ≥ r then, a rational farmer can always produce locally without bearing any cost. Even if the (r > b) condition is met, a risk averse household will prefer not to migrate and rather divert available resources towards agriculture and specialize in local crops (𝐾 ≥ 𝐾 * ). We assume that b < r and that a risky and costly migration takes place. It follows that 𝐿 1 + 𝐿 2 < 1, whereas the relations

𝜕𝑌 1 𝜕𝐿 1 (𝐾, 𝐿 1 ) = 𝑟 = 𝜕𝑌 2 𝜕𝐿 2
(𝐾, 𝐿 2 ) hold, from the equality of the marginal productivities of labor across all types of migrants. After derivation, the latter relations yield:

𝐿 1 = 𝐾 ( 𝐴 1 𝛽 1 𝑟 ) 1 ∝ 1 = 𝐾 𝐾 1 and 𝐿 2 = 𝐾 ( 𝐴 2 𝛽 2 𝑟 ) 1 ∝ 2 = 𝐾 𝐾 2 .
This implies:

K < 𝐾 * = 𝐾 1 𝐾 2 𝐾 1 +𝐾 2
from the migration condition, 𝐿 1 + 𝐿 2 < 1.

The above model is an idealistic simplification that shows that in the short run, labor out migration and lack of capital results in less production and investment amongst migrant households.

In the presence of the migration process, under the conditions b < r and 𝐾 < 𝐾 * , the model rules out laborintensive production (𝑌 3 ). If the migrant households still produce labor-intensive crops (for instance for selfconsumption), the reason may be that the cost of producing crops locally and their market sale price is lower than their retail price when purchased with remittances.

Household with migrants

As seen above, the relatively high income that the migrants earn (r > b) leads to emigration decisions by rational households. Migration is a costly process which results in a lack of capital accumulation (𝐾 * ≥ 𝐾) and investment (I = sK). Choosing to send migrants away helps the household in need to make enough savings through remittances, and to invest on the modernization of the farm. In the long run, once the accumulated capital has reached the above threshold 𝐾 * , migrants can pursue the farm's modernization through local production, generating local savings and investment. These outcomes of the model are not only sensible but also explain the paper's empirical results in the short run: Farm households which send a member away for work invest and produce less compared to non-migrant farm households.

Literature on Remittance, Agricultural investment, and Output

An immediate effect of outmigration on the farm household is the loss of labor due to the departure of the migrant member [START_REF] Rozelle | Migration, remittances, and agricultural productivity in China[END_REF]. The money that the migrant subsequently sends home raises family income and relaxes the financial constraints owing to credit market imperfections. This allows the household to invest in laborcompensating technologies, thereby raising farm output [START_REF] Mccarthy | Assessing the impact of massive out-migration on Albanian agriculture[END_REF][START_REF] Quinn | Estimating the impact of migration and remittances on agricultural technology[END_REF][START_REF] Wouterse | Migration and technical efficiency in cereal production: Evidence from Burkina Faso[END_REF][START_REF] Zahonogo | Migration and agricultural production in Burkina Faso[END_REF]. This 'income effect' of migration should be stronger in the case of international remittances, given greater risks and higher returns. The opportunity cost for leisure of the stay-behind household member also increases as a result of additional income through migrant remittances [START_REF] Atamanov | Heterogeneous effects of international migration and remittances on crop income: Evidence from the Kyrgyz Republic[END_REF]. This can lead to lower interest in agriculture and lower labor participation of the household in farm activities [START_REF] Atamanov | Heterogeneous effects of international migration and remittances on crop income: Evidence from the Kyrgyz Republic[END_REF][START_REF] Miluka | The Vanishing Farms? The Impact of International Migration on Albanian Family Farming[END_REF][START_REF] Mughal | Labour effects of foreign and domestic remittances-evidence from Pakistan[END_REF].

Empirical evidence on how agriculture investment fares among remittance recipient households, and whether the income or the labor loss effect dominates is mixed: [START_REF] Abebaw | Does rural outmigration affect investment in agriculture? Evidence from Ethiopia[END_REF], for instance, find that migration and remittances have a positive and significant impact on investment in livestock and herbicides use, and an insignificant effect on the adoption of improved seed varieties and fertilizers. [START_REF] Böhme | Does migration raise agricultural investment? An empirical analysis for rural Mexico[END_REF] and [START_REF] Chiodi | Migration, remittances and capital accumulation: Evidence from rural Mexico[END_REF] both study the effects of international migration from Mexico and find a positive impact on investment in productive assets. [START_REF] Li | The impacts of temporary labour migration on farming systems of the Loess Plateau, Gansu Province, China[END_REF] report a negative impact of temporary rural urban migration and remittances on agriculture investment in China. Similarly, [START_REF] Castelhano | The effects of migration and remittances on productive investment in rural Mexico[END_REF] in Mexico estimate the effects of migration and remittances from other parts of Mexico on investment in farm and livestock and find no evidence of an increase in investment. [START_REF] Mendola | Migration and technological change in rural households: Complements or substitutes[END_REF] find that in Bangladesh, domestic migration reduces agriculture investment while international remittances lead to greater adoption of high-yield varieties.

In the literature, the net impact of migration and remittances on agriculture production is ambiguous. The impact is negative if the labor loss effect dominates and positive if the remittances' income effect prevails [START_REF] Atamanov | Heterogeneous effects of international migration and remittances on crop income: Evidence from the Kyrgyz Republic[END_REF][START_REF] Taylor | Does migration make rural households more productive? Evidence from Mexico[END_REF]. High disposable income, thanks to migrant remittances, allows the household to switch to high risk -high profit crops such as cash crops [START_REF] Rozelle | Migration, remittances, and agricultural productivity in China[END_REF][START_REF] Taylor | Human capital: Migration and rural population change[END_REF]. However, if the labor constraints resulting from the absence of the migrant member are binding, the household may be compelled to adopt less labor-intensive crops such as maize or fodder even though those crops might be less profitable. For many migrant households, farming becomes a secondary or part-time activity [START_REF] Rigg | Poverty and livelihoods after full-time farming: A South-East Asian view[END_REF]. The cultivation of crops is abandoned in favor of subsistence farming or fodder for household grains and animal feed [START_REF] Davis | Migration, remittances and smallholder decision-making: Implications for land use and livelihood change in Central America[END_REF]. However, given the symbolic value of land in the agrarian society and the absence of well-functioning farmland market in the rural areas, farm land is usually not sold [START_REF] Bolganschi | Rural Out Migration and Land Use in Moldova[END_REF].

Some studies [START_REF] Gibson | The impacts of international migration on remaining household members: omnibus results from a migration lottery program[END_REF][START_REF] Qin | Labor out-migration and agricultural change in rural China: A systematic review and meta-analysis[END_REF]) report a net positive impact of migration and remittances on agriculture, while others (e.g. [START_REF] Jokinen | Migration-related land use dynamics in increasingly hybrid peri-urban space: insights from two agricultural communities in Bolivia[END_REF] provide evidence for a negative impact. [START_REF] Li | Migration, remittances, and agricultural productivity in small farming systems in Northwest China[END_REF] find that in China, migration and remittances result in a greater focus on capital-intensive and more profitable fruit orchards, and a negative impact on labor-intensive wheat production. [START_REF] Qian | The impact of migration on agricultural restructuring: Evidence from Jiangxi Province in China[END_REF] report that left-behind family members of Chinese migrants turn to less labor-intensive subsistence grain production instead of more capital-intensive livestock cultivation, or even abandon their farmland.

Yet other studies fail to find evidence for any significant effect of migration on farm production. [START_REF] Quisumbing | Moving forward, looking back: The impact of migration and remittances on assets, consumption, and credit constraints in the rural Philippines[END_REF], for example, find no impact of migration, whether internal or international, on agriculture production in the Philippines. Similarly, [START_REF] Gibson | The impacts of international migration on remaining household members: omnibus results from a migration lottery program[END_REF] find that emigration from Tonga to New Zealand did not change the agriculture structure of the left behind households. De Brauw (2010) test the NELM hypothesis in the context of Vietnam and find no net impact on agriculture productivity. However, the production switches from laborintensive rice to land-intensive maize and legumes.

The aforementioned lack of consensus on the net effect of overall migration also appears in studies on domestic and international migration. International migration and the resulting remittances is found to be linked to greater farm productivity in Mexico [START_REF] Taylor | Does migration make rural households more productive? Evidence from Mexico[END_REF]) and a lower incidence of subsistence farming in Nepal [START_REF] Maharjan | International migration, remittances and subsistence farming: Evidence from Nepal[END_REF]. By contrast, [START_REF] Miluka | The Vanishing Farms? The Impact of International Migration on Albanian Family Farming[END_REF] find no impact of international migration on crop income in Albania. Likewise, [START_REF] Huy | Economic effects of labor migration on agricultural production of farm households in the Mekong River Delta region of Vietnam[END_REF] and [START_REF] Rozelle | Migration, remittances, and agricultural productivity in China[END_REF] report a positive impact of domestic migration and remittances on agriculture production, whereas T. M. K. [START_REF] Nguyen | Leaving the Village but Not the Rice Field: Role of Female Migrants in Agricultural Production and Household Autonomy in Red River Delta, Vietnam[END_REF] and [START_REF] Shi | Heterogeneous effects of rural-urban migration on agricultural productivity[END_REF] find a negative impact in the case of rice production in Vietnam and China, respectively. In this sample, households are identified based on receipt of domestic or foreign remittances. The survey provides information on whether any of the household member received income in form of internal or external remittances in the past twelve months, as well as the amount received. To highlight the difference between recipient and nonrecipient households, we define remittance receipt both as a binary as well as a continuous variable based on the amount received. The binary variable is preferable on the ground that the recall data on remittance amount received over a long period (twelve months) is expected to be an approximation rather than the actual amount. This problem of inaccuracy becomes acute if the information about remittance is not collected from the same member of the household than the one who received the transfers.

Data and Methodology

Data

Table 1 provides the overall description and definition of variables included in the study, while Table 2 highlights the difference between recipient and non-recipient households.

Outcomes

Table 1 shows the outcomes used to identify the investment behavior and cropping pattern of the recipient and non-recipient farm households in the past 12 months. For regression analysis, the values of these variables are transformed using inverse hyperbolic sine function [START_REF] Bellemare | Elasticities and the inverse hyperbolic sine transformation[END_REF].

The agriculture investment indicator measures household investment in long run agricultural assets including land purchase and related costs, machinery, tube well, farm buildings, and livestock purchased. The indicator is calculated as the sum of values given in response to the following questions:

What was the value (in PKR5 ) of the agricultural land (including farm buildings and tube wells) purchased during the past year?

What was the cost (in PKR) of reclamations or improvements of owned land during the past year?

What was the value (in PKR) of new construction and improvements made on farm buildings, tube wells etc. during the past year?

What was the value (in PKR) of any agricultural equipment (Tube well, Tractor, Plough, Thresher, Harvester, Truck, etc.) purchased during the past year?

Livestock purchased during the past year.

On average, farm households invest PKR.7,281 (Table 1). Recipient households invest PKR 5,105 compared to PKR 7,768 that the non-recipient households invest (Table 2). In the same manner, domestic and foreign remittance recipient households invest less compared to non-recipient households. These differences are however not statistically significant in the latter case.

Farm output is measured in terms of total annual agriculture production as well as the production of four types of crops: capital intensive, subsistence, highly labor-intensive, and less/moderately labor-intensive.

Capital-intensive crops consist of cash crops such as cotton and sugarcane. Wheat is the principal subsistence crop. About 92% of cultivated farmland of the country is allocated to wheat during the winter season. Unlike cash crops, wheat is only moderately labor and capital intensive. Both wheat and cash crops need to be replanted every year, and much of the production process can be mechanized to substitute labor [START_REF] Nolte | Labour market effects of large-scale agricultural investment: conceptual considerations and estimated employment effects[END_REF]. Highly labor-intensive crops include vegetables and fruits, and rice. These crops require extensive manual handling. Low/moderately labor-intensive crops include maize, pulses, and fodder. These crops usually do not require extensive human presence during cultivation.

Figure 1 shows the distribution of major crops by land allocation in Pakistan. In 2017-18, about 45% of the total cultivated land in Pakistan was allocated to wheat and rice, the country's two main cereal crops (PBS, 2018). In comparison, 16% of the land was dedicated to sugarcane and cotton. Pulses and vegetables were cultivated on 4.84% and 3% of the total cropped land respectively.

Source: Pakistan Bureau of Statistics 2018.

Table 2 shows that farm output of recipient households is significantly lower compared to that of non-recipient households. This is the case with the recipients of both domestic and foreign remittances. The production value of cash and subsistence crops of recipient households is almost half as much as that obtained by non-recipient households (PKR 164,626 vs PKR 277,767 and PKR 78,236 vs PKR 158,806,respectively). The production of highly and less labor-intensive crops is likewise lower among recipient households compared to non-recipient households. The difference in farm output is more visible in case of domestic remittance recipients whose production of all four types of crops is around half that of non-recipient households. This pattern of low crop production is also observed among the recipients of foreign remittances, except for the cash, high, and less labor-intensive crops.

Covariates

The three variables of interest pertaining to remittances, namely, domestic remittance incidence, foreign remittance incidence, and overall remittance incidence are all binary variables which take the value of "1" if the household reports having received money from the away member during the past twelve months. We also consider the impact of the amount of domestic, foreign, and overall remittances in our analysis.

Several control variables are used to capture the impact of other factors on the outcome variables. These include household head characteristics (gender, age, marital status, and years of education), household specific economic and demographic variables (dependency ratio, borrowings, ownership of non-farm business and income), and agriculture-related controls (working on own-farm and total farm input cost). All income related variables are transformed using inverse hyperbolic sine function.

A total of 1,032 farm households (18.28% of the sample) reported to have received transfers during the past twelve months (Table 1). Out of these, 648 households received domestic remittances while 430 received international remittances6 . Domestic recipient households, on average, received 173,989 Pakistani Rupees (PKR), about 53% less than the average amount of PKR 296,708 international transfers received by the recipient households (Table 2).

Table 2 also shows the comparison between recipient and non-recipient households by a number of household characteristics. Heads of households with non-recipient status are younger, more often married and better educated compared with heads of recipient households. 79% of the heads of recipient households are male compared to 98% of the non-recipient ones. Pakistani households usually send male adult members or household heads to work in cities or other countries, leaving women to take up their responsibilities. The difference between the marital status and education level of the heads of recipient and non-recipient households is statistically significant in case of domestic but not for international remittances. Recipient households, on average, earn lower non-remittance income and have higher debt levels compared to their non-migrant counterparts.

The difference in agriculture-related indicators between the recipient and non-recipient households is interesting: While a significantly greater proportion of domestic and international remittance recipient household's own farmland, their farm output remains substantially lower compared to non-recipient households. The input cost of remittance recipient households also lower. However, there is evidence for an increase in non-farm activities in turn, particularly in the case of international remittance recipients, suggesting that higher remittance income allows some farm households to leave agriculture. 15% recipients of international remittances are involved in non-farm businesses compared to12% non-recipient ones. A reason for this general pattern of neglect of agriculture by both domestic and international remittance recipient households appears to be due to the low quality of their farmland. This fact can be substantiated by the data reported by the Pakistan Bureau of Statistics (2020): about 84% of non-recipient households' own canal-irrigated land, whose water availability is generally better than rain-fed or tube well-watered land. In comparison, only 64% of recipient households possess canal-irrigated land. This reflects the high-risk profile of recipient households' farming activity. Migration therefore occurs as part of the household's risk-reduction or income-diversification strategy. Households decrease or even abandon their farming activities in the presence of money transfers from the away member.

Empirical Model and Identification Strategy

Our baseline model is given as follows :

𝑌 𝑗𝑖 = 𝜑 𝑖 + 𝜆𝐷 𝑅 𝑖 + 𝛽 1 𝑋 𝑖 + 𝜀 𝑗𝑖 (1)
Where, 𝑌 𝑗𝑖 is the farm outcome 𝑗 of the household 𝑖, 𝐷 𝑅 𝑖 is remittance incidence, and 𝑋 𝑖 is the vector of control variables.

The models including domestic and international remittances are given as follows:

𝑌 𝑗𝑖 = 𝛷 𝑖 + 𝛼 1 𝐷 𝑑 𝑖 + 𝛼 2 𝑋 𝑖 + 𝜖 𝑗𝑖 (2) 𝑌 𝑗𝑖 = 𝛽 𝑖 + 𝜇 1 𝐷 𝑓 𝑖 + 𝜇 2 𝑋 𝑖 + 𝛿 𝑗𝑖 (3)
Where, 𝐷 𝑑 𝑖 is the dummy for domestic remittance incidence and 𝐷 𝑓 𝑖 is the dummy for foreign remittance incidence.

The farm households' migration and agriculture decisions are subject to their appetite for risk, which, in turn, depend on the households' observable and unobservable demographic and socioeconomic characteristics. Remittances and agriculture production are therefore endogenous processes as the two may be simultaneously determined [START_REF] Rozelle | Migration, remittances, and agricultural productivity in China[END_REF]. Family wealth or farmland, for instance, can serve both as a source of financing for the migration process and thereby the remittances, and as a means to buy farm inputs and machinery needed to enhance farm output.

Similarly, an adverse weather or health shock that affects agricultural production, thereby producing a deleterious impact on household income, will also result in an increase in remittance. Reverse causality is also a possibility:

higher farm production can be leveraged to fund riskier, longer-distance migration, particularly international migration, resulting in higher remittances; small-farm households, in contrast, are unable to avail these opportunities.

A number of studies [START_REF] Gray | Consequences of out-migration for land use in rural Ecuador[END_REF][START_REF] Maharjan | International migration, remittances and subsistence farming: Evidence from Nepal[END_REF][START_REF] Qian | The impact of migration on agricultural restructuring: Evidence from Jiangxi Province in China[END_REF] have employed an instrumental-variable approach to handle such sources of endogeneity. In this study, we employ this strategy by using two instruments. Following [START_REF] Kapri | Impact of remittances on household health care expenditure: Evidence from the Nepal Living Standards Survey[END_REF] and [START_REF] Hanson | Emigration and educational attainment in Mexico[END_REF], we use an indicator for network effects to instrument the receipt or otherwise of migrant transfers. Existing migrant networks facilitate the migration of new migrants originating from the area or the country. The success of early migrants from a particular area provides potential migrants the motivation and know-how of the migration process. In addition to disseminating information, earlier migrants can help new arrivals from the origin community get settled in the host economy. This leads to the development of migrant communities from similar geographical or cultural background. A case in point is the Pakistani migrant community in Norway, which started developing from the migration of a few workers in early 1970s, particularly from the district of Kharian [START_REF] Carling | How does conflict in migrants' country of origin affect remittancesending? Financial priorities and transnational obligations among Somalis and Pakistanis in Norway[END_REF]. Today, most of the approximately 40,000

Pakistani migrants living in Norway come from this district7 . We assume this network or peer effect is either weak or negligible for farm production and investment decisions. We based our assumption on the fact that, although network effect plays an important role in agricultural technology adoption and crops selection, the pace of technology adoption is generally slow in case of developing countries [START_REF] Krishnan | Neighbors and extension agents in Ethiopia: Who matters more for technology adoption[END_REF]. The adoption of agricultural technology is often low among poor small holder farmers, particularly those involved in production for own consumption (see for example, [START_REF] Bandiera | Social networks and technology adoption in northern Mozambique[END_REF]. We assume that the network effect should be negligible in the context of Pakistan where 94% farmers are small land holders with less than 12.5 acres of land, 76% of which produce only for home consumption (Pakistan Bureau of Statistics: Government of Pakistan, 2018-19).

We define the first instrument as the proportion of recipient households in the district in 2014-15. The second instrument is defined as the proportion of households in the neighborhood, i.e., the primary sampling unit (PSU) who have received money from a member living away from home during the past twelve months. For each household, the proportion is calculated by excluding the household surveyed. This second instrument is subject to criticism for not being fully exogenous due to the cross sectional nature of the sample data [START_REF] Angrist | The perils of peer effects[END_REF]. However, the first instrument tackles this issue by using district-level proportion of recipient households taken from the previous nationwide household survey carried out in 2014-15. This instrument captures the migration trend of the district in the recent past but does not relate to the current socio-economic status of the recipient households. Furthermore, given the random cross section nature of the two surveys, none of the households surveyed in 2014-15 are necessarily included in the 2018-19 sample. For the sake of convenience, the results of only the first instrument are discussed. The results of estimates with the second instrument are provided in the robustness section.

We take the remittance incidence (𝐷 𝑅 𝑖 , 𝐷 𝑑 𝑖 , 𝑎𝑛𝑑 𝐷 𝑓 𝑖 ) as the treatment criterion. Household with remittance recipient status equal to 1, received the treatment, 0 otherwise. The treatment effect of receiving remittances on the outcome (𝑌 𝑗𝑖 ) is defined as:

𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐸𝑓𝑓𝑒𝑐𝑡 = 𝑌 1𝑗𝑖 -𝑌 0𝑗𝑖 (4)
Where the 𝑌 1𝑗𝑖 is the estimated outcome when the household 𝑖 is receiving remittances and 𝑌 0𝑗𝑖 for the nonrecipient households. As observing both outcomes for a household at same time is not possible, we obtain Average

Treatment Effects (ATEs) to estimate the treatment effects.

We utilize the Probit-2SLS model to estimate the average treatment effects. The Probit-2SLS model uses a three-step procedure to consistently estimate 𝜆, 𝛼 1 , and 𝜇 1 :

Estimate the binary response models of 𝐷 on the instruments and 𝑋 𝑖 using Probit Estimate the fitted probabilities 𝐷 ̂, and

Using 𝐷 ̂ as an instrument for 𝐷 in an IV regression framework, estimate 𝜆, 𝛼 1 , and 𝜇 1 .

Our definitive specification is given as follows:

𝑌 𝑗𝑖 = 𝜑 𝑖 + 𝜆𝐷 ̂𝑅𝑖 + 𝛽 1 𝑋 𝑖 + 𝜀 𝑗𝑖 (5) 𝑌 𝑗𝑖 = 𝛷 𝑖 + 𝛼 1 𝐷 ̂𝑑𝑖 + 𝛼 2 𝑋 𝑖 + 𝜖 𝑗𝑖 (6) 𝑌 𝑗𝑖 = 𝛽 𝑖 + 𝜇 1 𝐷 ̂𝑓𝑖 + 𝜇 2 𝑋 𝑖 + 𝛿 𝑗𝑖 (7)
First, we use the set of controls 𝑋 𝑖 along with the instruments to alternately estimate the probability of receiving overall remittances (𝐷 ̂𝑅𝑖 ), and the probability of receiving domestic (𝐷 ̂𝑑𝑖 ) and foreign remittances (𝐷 ̂𝑓𝑖 ).

These fitted probabilities are used to instrument the original endogenous variables (𝐷 𝑅 𝑖 , 𝐷 𝑑 𝑖 and 𝐷 𝑓 𝑖 ) and estimate Equation (5-7) in a linear 2SLS framework.

This approach is more robust for endogenous dummy indicators compared to the commonly used 2SLS and Probit-OLS approaches [START_REF] Cerulli | ivtreatreg: A command for fitting binary treatment models with heterogeneous response to treatment and unobservable selection[END_REF]. Unlike the standard 2SLS approach, where the bias can affect the second stage of the regression, this approach does not require the first stage to be correctly specified. The asymptotic 2SLS standard errors and test statistics nonetheless remain valid (see e.g. Wooldridge, 2010, p.939, procedure 21.1). In the estimations where the variable of interest is the amount of remittances, we use direct 2SLS approach.

Results of first-stage estimations (given in Appendix Table A 1) show the coefficients of the instrument regressed on the incidence of remittance with household, household head and agriculture controls for each outcome. The coefficients of the instrument are positive and statistically significant. Post-estimation tests of exogeneity and weak instrument (First stage F-value) for level and incidence of remittances are provided at the bottom of the results tables.

The test statistics show that the variables for the receipt of aggregate, domestic and international remittances are all endogenous. The p-value of exogeneity test statistics for most of the estimations is close to zero, rejecting the assumption of exogeneity of migrant remittances, while F-Stat values are invariably greater than 10, suggesting that the instruments used are valid.

We carry out a number of alternate model estimations to establish the robustness of our Probit 2SLS estimates.

Firstly, we employ the selection model approach [START_REF] Heckman | Simple statistical models for discrete panel data developed and applied to test the hypothesis of true state dependence against the hypothesis of spurious state dependence[END_REF] to estimate average treatment effects (ATEs) by including the instrumental variable to avoid the assumption of joint normality of errors. Secondly, we obtain ATEs using three matching routines: Propensity Score Matching (PSM) using nearest neighbor matching, inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW). The results of these estimates are highly similar to our baseline estimates and are provided in the robustness section for comparison.

Results

Do the differences in agriculture investment and cropping patterns between the recipient and non-recipient households hitherto observed hold after controlling for other observable characteristics and accounting for the endogeneity of remittances? This part of the study attempts to answer this question by first exploring the impact of remittance incidence on agriculture investment. Results given in Table 3 show that the association between the two is negative. The effect remains negative and significant using OLS (column 1) as well as instrumental estimations (column 2) with controls.

According to the IV estimates, farm investment of recipient households is 99.64%8 lower compared to that of non-recipient households. A similar negative impact is observed for domestic (Table 3 columns 3 and4) and foreign remittance recipients (columns 5 and 6). The impact is strong for both the domestic (99.87%) and foreign remittance recipients (97.68%). These findings are also substantiated from the estimates for remittances amount (Table 4), where a 1% increase in the amount of remittances received is found to reduce agriculture investment of the recipient household by 0.51% (column 2). This reduction in investment is more obvious for domestic remittances (0.88%) compared to international remittances (0.41%). These results reflect the domination of migration's labour loss effect over the income effect. Remittances, that result from the household member's migration, do improve the recipient household's income but do not appear to fully compensate for the loss of labour that the absence of the migrant member causes.

These estimates are in line with those in case of international migration [START_REF] Li | The impacts of temporary labour migration on farming systems of the Loess Plateau, Gansu Province, China[END_REF] and domestic migration [START_REF] Mendola | Migration and technological change in rural households: Complements or substitutes[END_REF] but differ with the findings from [START_REF] Abebaw | Does rural outmigration affect investment in agriculture? Evidence from Ethiopia[END_REF] who found a positive association in Africa. Next, we study how does remittances affect farm output. The hypothesis, as before, is that recipient households' agriculture production should be higher if remittances' income effect prevails and lower if the labour loss effect dominates. Results shown in Table 5 and Table 6 again indicate that the impact of labour loss due to household member's migration on the household's farm production dominates the improvement that additional income from remittances procures. The farm output of households receiving remittances 82% lower than that of non-recipient households (Table 5, column 2). Similarly, for a 1% rise in the amount of remittances, the production falls by 0.31% (Table 6, column 2). This negative impact is visible both among domestic and foreign remittance recipient households. However, the decrease in farm output is larger for domestic remittance recipient household (77%) (Table 5, column 4) than for foreign (45%) (Table 5, column 6). The change in cropping patterns seen among recipient households helps understand the strong labor loss effect thus far observed. If the remittances' income effect prevailed, we would observe greater crop production, particularly that of capital-intensive cash crops. However, we find the effect on production to be the opposite (Table 7). We find that the production of cash and subsistence crops is significantly lower among recipient households compared to nonrecipient households, regardless of whether the estimations are carried out using OLS or Probit-2SLS. The production of the two groups of crops is lower by 97% (column 2) and 91% (column 4), respectively.

The production of cash crops and wheat also exhibits a negative trend when tested against the amount of remittance the households receive. A 1% increase in the amount of remittance received results in 0.39% lower production of cash crops and 0.19% lower production of wheat (Table 8, column 2 and 4). The production of highly and low/medium labor-intensive crops among recipient farm households is lower by 98% and 85% (Table 7, column 6 and 8). The trend is similar for remittance amount, with a 1% increase in the amount of remittances associated with 0.36% and 0.06% lower production of highly and medium/labor intensive crops. While the decrease is visible in the production of both high and low labor-intensive crops, the fall is stronger in case of more labor-intensive crops. The production of highly labor-intensive crops among recipient households is lower by almost PKR.100,000 which is much greater compared to low labor-intensive crops whose production drops by PKR.29,230.

One possible cause of this reduction in farm production could be that the extra income the household gains from remittances is not sufficient to acquire sufficient new farm assets in order to account for the labor requirements of labor-intensive crops [START_REF] Piras | Remittance inflow and smallholder farming practices. The case of Moldova[END_REF].

Crop-wise results for domestic and foreign remittances given in Tables 9101112, shed more light on this dimension of labor constraints the recipient households face. As reported in Table 9, the recipients of comparatively smaller domestic remittances witness substantial reduction in the production of cash crops (column 2), subsistence crops (column 4), highly labor-intensive crops (column 6) and low/medium labor-intensive crops (column 8). The respective reduction in the production of the crops is 99%, 90%, 99% and 76% respectively. The production of various categories of crops among the recipients of foreign remittances is likewise lower (Table 10), even though the decrease is not as important as that seen among the recipients of domestic remittances. The production of cash crops (column 2), wheat (column 4) and low labor-intensive crops (column 8) of foreign remittances recipient households are lower than that of non-recipient households by 98%, 64% and 59%, respectively.

The production pattern of the recipients of foreign remittances, by contrast, differs in case of highly laborintensive crops, whose production does not significantly differ from that of the non-recipient households. This may owe to the fact that the larger amounts of transfers that the recipients of international remittances receive help them hire workers or acquire labor-saving technology. According to the survey, foreign remittance recipients spent an average amount of PKR 116,599 on farm inputs such as fertilizers, pesticides and seeds compared to PKR 62,254 spent by the recipients of domestic remittances. These expenditures, those lower than what non-recipient households spend, are nonetheless much higher than those of the recipients of domestic remittances and suggest that labor constraints are more binding for the latter.

Here, the significant drop in the production of wheat by the recipients of both kinds of remittances merits our attention. Wheat production of the recipients of domestic remittances decreases by 90% while that of the recipients of foreign remittances drops by 64% compared to their non-recipient counterparts. Wheat is the chief staple food of Pakistani households. Many small farmers retain most or all of the grains to cover their home needs. Additional income, be it in the form of domestic or foreign remittances, alleviates the need for subsistence farming and allows the households to obtain their food requirements from the market. This is reflected in the fact that the food consumption expenditure of recipient households, at PKR 100,837 , is substantially higher compared to non-recipient households (PKR 86,720) (Pakistan Bureau of Statistics, 2020). Outcome variables are regressed on domestic remittance incidence. Estimates of the production of cash crops using OLS and IV with controls are reported in column 1 and 2. Similarly, estimates of wheat production are given in column 3 and 4, estimates of labor intensive in column 5 and 6, and low labor-intensive crops in column 7 and 8, respectively. The proportion of domestic remittance recipient households at district level in 2014-15 is used to instrument domestic remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

Robustness Checks

We obtain our estimates with another instrument (i.e., proportion of recipient households at the PSU level excluding the household surveyed) and compare the results. The results reported in Table 15 and Table 16 are similar to those of our baseline estimates. Remittances are associated with 96% lower farm investment (compared to 99% with the first instrument) and 63% lower farm output (82% with the first instrument). Farm investment and output are lower by 95% and 44%

among domestic remittance receiving households and 88% and 53% lower among foreign remittance receiving households compared to their non-recipient counterparts, respectively. The trend is similar for the amount of remittances received and matches the results obtained using OLS and the baseline set of IV estimates. Next, we check for the robustness of our findings by obtaining estimates using different methodologies. Firstly, we account for sample selection through the Heckman selection method. The average treatment effect of endogenous treatment (recipient vs non-recipient) is estimated using ivtreatreg command by assigning the model type as "Heckit", which is a generalized Heckman type selection model [START_REF] Cerulli | ivtreatreg: A command for fitting binary treatment models with heterogeneous response to treatment and unobservable selection[END_REF]. The results of the outcome and selection equations are reported in Table 17. Remittance recipient status is modeled in the selection equation with all the controls in the selection equation. In the second step, the outcome variable is regressed including controls for household, household head and agriculture factors, and lambda calculated from the first step. The results show that average treatment effect of being a recipient household is negative and significant. Farm investment among recipient households decreases by 99.53% compared to 99.64% estimated using Probit 2SLS, while farm output decreases by 71% compared to 82% estimated using Probit 2SLS. These results are in line with our main findings and support the conclusion that the labor loss impact of migration is dominant than the remittance income.

Secondly, we estimate the average treatment effects by matching the propensity scores. This sort of method is preferred in situations where the endogeneity arises due to selection on observables. We use nearest neighbors matching (nnmatch), inverse probability weights (IPW), and augmented probability weights (AIPW). Nearest neighbor matching and augmented probability weights are double robust methods. Average treatment effects estimated from these methods are presented in Table 18. The average treatment effects estimated again corroborate our main regression results. The estimates substantiate our hypothesis that the positive impact of remittance income is weak compared to the negative impact due to the labor loss of migrant member.

Conclusion

A large proportion of rural population in the developing countries is directly or indirectly associated with farm activities. The growing body of literature on agriculture's association with out-migration lacks consensus on the beneficial or detrimental nature of migration's impact. In this study, we shed light on the issue by drawing a conceptual model and testing the hypotheses empirically by analyzing detailed farm household data. We hypothesized about the direction of migration's impact on farm investment and output to answer the question as to which of the two effects, the income effect and the labor-loss effect, prevails. If the remittances income effect dominates, recipient households increase their investment in agriculture, but reduce it if the labor constraints dominate. If the income effect dominates, recipient households produce more cash crops like cotton and sugarcane and decrease the production of subsistence crops such as wheat. However, if the labor-loss effect prevails, recipient households move away from highly laborintensive crops such as rice and fruits and vegetables, and switch to less labor-intensive crops such as maize, pulses or fodder. We tested these hypotheses on data for 5,636 farm households from the 2018-19 round of Pakistan Social and Living-standards Measurement Survey. We come up with the following key findings: a) There is a strong evidence showing that the labor-loss effect dominates the income effect. Recipient households make 99% less agricultural investment and obtain 82% less production compared to non-recipient households.

Recipient households may be unable to overcome their resource constraints and come out of poverty trap in spite of the additional income through remittances, and are therefore unable to improve their farm assets [START_REF] Piras | Remittance inflow and smallholder farming practices. The case of Moldova[END_REF]. Our findings are line with those of Atamanov and Van den Berg (2012) and [START_REF] Gibson | The impacts of international migration on remaining household members: omnibus results from a migration lottery program[END_REF] who reported deleterious effects of migration on investment in production-enhancing and labor-compensating agriculture technology, [START_REF] Mughal | Labour effects of foreign and domestic remittances-evidence from Pakistan[END_REF] who report a higher likelihood to move out of agriculture among recipient farm households, and [START_REF] Qian | The impact of migration on agricultural restructuring: Evidence from Jiangxi Province in China[END_REF] who find a greater incidence of abandoning farm lands.

b) The negative impact on farm investment and production is observed among the recipients of both the domestic and foreign remittances. However, the impact is particularly strong in case of the domestic compared to foreign remittances. This possibly owes to the fact that domestic remittances are on average much smaller than foreign remittances, and the losses to domestic migrant households due to labor constraints are proportionally higher.

c) The labor-loss effect also manifests itself in farm households' cropping patterns. The production of all crops (including highly labor-intensive crops) among recipient households is significantly lower compared to nonrecipient households.

d) The decrease in production is more visible in the recipients of domestic remittances. The fall is as much as 99% in case of capital-intensive crops. The negative trend also prevails among the foreign remittance recipient households except for the production of highly labor-intensive crops where the difference is insignificant.

e) The labor participation of stay-behind members of the recipient households does not decrease, suggesting that the loss of farm output is more likely due to the absence of the migrant member.

These findings point to a substantial role of the process of migration and the resulting financial transfers in determining the level of agriculture investment and production. About 18% of Pakistani households receive money from family members living away from home. Although these migrant remittances help improve household wellbeing through higher income, they lead to sub-optimal farm investment and low agricultural production. This could have a non-negligible impact on the food import dependency and food security of a country where undernutrition is widespread, and a large segment of the population is vulnerable to significant international food price shocks. Besides, the fact that reduction in farm output is concentrated in small-farm households which make up 94% of the country's farm households highlights the challenges of low agriculture productivity and financial constraints that these households face.

Appendices.

  1) < 0, that is 𝐾 < 𝐾°. It persists until 𝜕𝑌 𝜕𝐿 (𝐾, 𝐿) = 0, that is, 𝐿 = 𝐾 𝐾° , by first order conditions. In the short run, production and investment amongst the migrant households is lower compared to the non-migrant households. Production (per head) amounts to 𝑌 = 𝑟𝐿 𝛽 + 𝑟(1 -𝐿), which is lower than the 𝑌°= 𝑟 𝛽 threshold.

  Data for this study come from the 2018-19 round of the Household Integrated Economic Survey (HIES) carried out along with the Pakistan Social and Living-standard Measurement (PSLM) survey (Pakistan Bureau of Statistics: Government of Pakistan, 2018-19). The HIES/PSLM 2018-19 is a nation-wide representative survey, conducted based on a two-stage stratified random sampling design. 24,809 households residing in 1,802 Primary Sampling Units (PSUs) were interviewed to collect information about demographic composition, income, education, consumption patterns, migration and remittances, agriculture, and financial situation. The survey covers all the four provinces of Pakistan with representative urban/rural breakdown. The target of this study are the households involved in agriculture regardless of whether they own or hire farmland. The corresponding sample contains 5,636 households.

Figure 1 .

 1 Figure 1.Figure 1. Land allocation by crop (% of total crop land)

Table 1 . Variables' description Variables Definition Obs. Mean (St. Dev.) Min Max Outcomes

 1

	Agriculture Investment	(PKR) Agriculture investment during the past year	5636	(40229) 7281	0
	Total Agriculture	Total value of farm output during the past year	5632	381219	3000
	Output	(PKR)		(574125)
	Cash Crops	produced during the past year (PKR) Total value of cash crops (cotton and sugarcane)	1781	(472428) 266840	1200
	Wheat	past year (PKR) Total value of wheat crop produced during the	4815	(193221) 144014	1500
	Crops Highly Labor-Intensive	(PKR) rice and fruits) produced during the past year Total value of labor-intensive crops (vegetables,	2445	(261900) 186667	400
	Labor-Intensive Crops Less/Moderately	during the past year (PKR) Crops (maize, pulses and fodder) produced Total value of less/moderately labor-intensive	3570	(158900) 85065	1200
	Remittances
	Domestic remittance	Domestic remittances recipient HH (1 if	5636	0.115	0
	incidence	received, 0 otherwise)		(0.32)
	Foreign remittance	Foreign remittances recipient HH (1 if received,	5636	0.076	0
	incidence	0 otherwise)		(0.27)
	Remittance incidence	Overall remittances recipient HH (1 if received,	5636	0.183	0
		0 otherwise)		(0.39)
	Domestic remittance	Domestic remittances amount (PKR)	5636	20045	0
	amount			(70095)
	Foreign remittance	Foreign remittances amount (PKR)	5636	22812	0
	amount			(104547)
	Remittance amount	Overall remittances amount (PKR)	5636	42857	0
				(127077)
	Controls
	HHH Gender	Gender of HH head, 1=male, 0 if female.	5636 0.95 (0.21) 0
	HHH Age	Age of HH head (Years)	5636	48.57	16
				(14.02)
	HHH Marital Dummy	Marital status of HH head, 1=married, 0	5636 0.93 (0.26) 0
		otherwise.
	HHH Education	Education of HH head (Years)	5636	3.82 (4.5) 0
	Dependency Ratio	Number of dependents HH member/total HH	5636 0.43 (0.22) 0
		size (ratio)
	Non-Agri Business	Equals 1 if HH involve in non-agri. business, 0	5636 0.12 (0.33) 0
	Dummy	otherwise
	Land Own Dummy	Equals 1 if HH own agri. land, 0 otherwise	5636 0.73 (0.45) 0
	Credit Dummy	Equals 1 if HH due a loan, 0 otherwise	5636 0.24 (0.43) 0
	HH Income (Inc. Rem) Total HH income including remittances (PKR)	5636	398319	21300
				(430329)
	HH Income (Exc.	Total HH income excluding remittances (PKR)	5636	355462	500
	Rem)			(416058)
	Total Input Cost	Input cost (PKR)	5636	138490	0
				(262890)
	Authors' calculations using PSLM 2018-19.
	Note: All variables measured in Pakistani Rupees (PKR) are transformed using inverse hyperbolic sine function when used in regressions.

Table 2 .Household profile -Recipient vs non-recipient

 2

	Authors' calculations using PSLM 2018-19.

Table 3 . Remittance incidence and Agricultural investment -OLS and IV estimates

 3

		(1)	(2)	(3)	(4)	(5)	(6)
		Overall remittance	Domestic remittance	Foreign remittance
	Remittance incidence	-0.91***	-5.52***	-0.76***	-6.54***	-0.73***	-3.68***
		(0.12)	(0.48)	(0.13)	(0.76)	(0.17)	(0.68)
	HHH Gender	0.21	-2.63***	0.48**	-1.78***	0.64***	0.06
		(0.22)	(0.39)	(0.22)	(0.43)	(0.21)	(0.27)
	HHH Age	-0.004	0.02***	-0.01	0.01***	-0.01**	-0.004
		(0.004)	(0.01)	(0.004)	(0.01)	(0.004)	(0.004)
	HHH Marital Status	0.05	0.49**	-0.003	0.30	0.002	0.11
		(0.19)	(0.22)	(0.19)	(0.23)	(0.19)	(0.2)
	HHH Education	-0.03**	-0.041***	-0.03**	-.029**	-0.03**	-0.03**
		(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
	Dependency Ratio	0.26	0.25	0.28	0.42*	0.19	0.12
		(0.21)	(0.24)	(0.21)	(0.25)	(0.21)	(0.22)
	Non-Agri. Business	-0.57***	-0.73***	-0.56***	-0.76***	-0.54***	-0.58***
		(0.14)	(0.16)	(0.14)	(0.16)	(0.14)	(0.14)
	Land Ownership	0.84***	1.11***	0.82***	1.06***	0.80***	0.82***
		(0.09)	(0.12)	(0.09)	(0.12)	(0.09)	(0.1)
	Credit Dummy	-0.59***	-0.34***	-0.61***	-0.32**	-0.64***	-0.61***
		(0.1)	(0.12)	(0.1)	(0.12)	(0.1)	(0.1)
	HH Income (Inc. Rem)	0.71***	1.07***	0.63***	0.55***	0.72***	0.95***
		(0.1)	(0.11)	(0.1)	(0.11)	(0.1)	(0.11)
	Const.	-8.52***	-11.44***	-7.62***	-5.38***	-8.92***	-11.48***
		(1.23)	(1.42)	(1.22)	(1.38)	(1.26)	(1.42)
	R-squared	0.039	0.00	0.03	0.00	0.03	0.00
	Observations	5636	5462	5636	5462	5636	5462
	Exogeneity: F-value		170.32***		111.23***		27.32***
	First-stage (F-value)		362.67		147.96		164.60
	Controls	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.

Note: Standard errors clustered at household level are shown in parentheses. Outcome variable is agricultural investment during the previous year. Variable of interest is the remittance incidence. Columns 1 and 2 show OLS and IV estimates with controls for overall remittances, column 3 and 4 for domestic and column 5 and 6 show estimates for foreign remittance . Proportion of recipient households at district level in 2014-15 is used as an instrument to estimate overall remittance (column 2). Similarly, the proportion of domestic and foreign remittance recipient households at district level in 2014-15 is used to instrument domestic (column 4) and foreign (column 6) remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, credit, and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.

Table 4 . Remittance amount and Agricultural investment -OLS and IV estimates

 4

		(1)	(2)	(3)	(4)	(5)	(6)
		Overall remittance	Domestic remittance	Foreign remittance
	Remittance amount	-0.03***	-0.51***	-0.03***	-0.88***	-0.02	-0.41***
		(0.01)	(0.04)	(0.01)	(0.08)	(0.01)	(0.05)
	HHH Gender	-0.003	-3.39***	0.09	-3.38***	0.15	-0.91***
		(0.22)	(0.42)	(0.22)	(0.56)	(0.21)	(0.31)
	HHH Age	-0.003	0.004***	-0.004	0.04***	-0.01	0.01
		(0.004)	(0.01)	(0.003)	(0.01)	(0.003)	(0.004)
	HHH Marital Status	0.08	0.74***	0.06	0.62**	0.07	0.32
		(0.19)	(0.24)	(0.19)	(0.28)	(0.19)	(0.21)
	HHH Education	-0.03**	0.02	-0.03**	0.02	-0.03**	-0.01
		(0.01)	(0.02)	(0.01)	(0.02)	(0.01)	(0.01)
	Dependency Ratio	0.25	0.01	0.27	0.37	0.21	-0.02
		(0.21)	(0.26)	(0.21)	(0.30)	(0.21)	(0.23)
	Non-Agri. Business	-0.55***	-0.32*	-0.57***	-0.56***	-0.57***	-0.44***
		(0.14)	(0.17)	(0.14)	(0.18)	(0.13)	(0.15)
	Land Ownership	0.86***	1.39***	0.85***	1.4***	0.85***	0.97***
		(0.09)	(0.13)	(0.09)	(0.15)	(0.09)	(0.1)
	Credit Dummy	-0.63***	-0.32**	-0.63***	-0.13	-0.64***	-0.6***
		(0.1)	(0.13)	(0.1)	(0.16)	(0.1)	(0.11)
	HH Income (Inc. Rem)	1.62***	-0.36***	0.64***	-0.66***	0.7***	0.52***
		(0.1)	(0.12)	(0.1)	(0.17)	(0.08)	(0.09)
	Const.	-7.19***	6.78***	-7.45***	10.62***	-8.26***	-5.46***
		(1.01)	(1.62)	(0.98)	(2.31)	(0.95)	(1.14)
	R-squared	0.04	0.00	0.04	0.00	0.04	0.00
	Observations	5636	5462	5636	5462	5636	5462
	Exogeneity: F-value		250.12***		220***		98.35***
	First-stage (F-value)		481.86		164		203
	Controls	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.

Note: Standard errors clustered at household level are shown in parentheses. Outcome variable is agricultural investment during the previous year. Variable of interest is the remittance amount. Columns 1 and 2 show OLS and IV estimates with controls for overall remittances, column 3 and 4 for domestic and column 5 and 6 show estimates for foreign remittance . Proportion of recipient households at district level in 2014-15 is used as an instrument to estimate overall remittance (column 2). Similarly, the proportion of domestic and foreign remittance recipient households at district level in 2014-15 is used to instrument domestic (column 4) and foreign (column 6) remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, credit, and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

Table 5 . Remittance incidence and farm output

 5

		(1)	(2)	(3)	(4)	(5)	(6)
		Overall remittance	Domestic remittance	Foreign remittance
	Remittance incidence	-0.28***	-1.69***	-0.24***	-1.44***	-0.14**	-0.58**
		(0.05)	(0.24)	(0.05)	(0.25)	(0.06)	(0.23)
	HHH Gender	0.22***	-0.54***	0.29***	-0.13	0.35***	0.28***
		(0.07)	(0.15)	(0.07)	(0.11)	(0.07)	(0.08)
	HHH Age	-0.001	0.01***	-0.001	0.003***	-0.002**	-0.001*
		(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
	HHH Marital Status	-0.06	0.06	-0.07*	-0.01	-0.07*	-0.06
		(0.04)	(0.05)	(0.04)	(0.05)	(0.04)	(0.04)
	HHH Education	-9***	-.01***	-0.004**	-0.01**	-0.004**	-0.01***
		(0.002)	(0.003)	(0.002)	(0.002)	(0.002)	(0.001)
	Dependency Ratio	0.001	-0.02	0.01	0.03	-0.001	-0.01
		(0.05)	(0.06)	(0.05)	(0.05)	(0.05)	(0.05)
	Non-Agri. Business	-0.32***	-0.43***	-0.31***	-0.38***	-0.3***	-0.32***
		(.03883)	(.05303)	(.03867)	(.04583)	(.03766)	(0.04)
	Land Ownership	-0.51***	-0.42***	-0.51***	-0.46***	-0.52***	-0.52***
		(0.02)	(0.03)	(0.02)	(0.03)	(0.02)	(0.02)
	Credit Dummy	-0.15***	-0.11***	-0.15***	-0.1***	-0.16***	-0.15***
		(0.03)	(0.03)	(0.03)	(0.03)	(0.03)	(0.03)
	HH Income (Inc. Rem)	0.26***	0.44***	0.23***	0.25***	0.24***	0.29***
		(0.03)	(0.05)	(0.03)	(0.03)	(0.03)	(0.04)
	Total Input Cost	0.57***	0.47***	0.58***	0.53***	0.58***	0.56***
		(0.05)	(0.05)	(0.05)	(0.05)	(0.04)	(0.05)
	Const.	3.18***	2.57***	3.43***	4.02***	3.21***	2.84***
		(0.29)	(0.31)	(0.31)	(0.39)	(0.29)	(0.32)
	R-squared	0.69	0.49	0.69	0.58	0.69	0.67
	Observations	5632	5458	5632	5458	5632	5458
	Exogeneity: F-value		46.54***		33.38***		4.22**
	First-stage (F-value)		210.41		103.46***		143
	Controls	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.

Note: Standard errors clustered at household level are shown in parentheses. Outcome variable is agricultural output during the previous year. Variable of interest is the remittance incidence. Columns 1 and 2 show OLS and IV estimates with controls for overall remittances, column 3 and 4 for domestic and column 5 and 6 show estimates for foreign remittances. The proportion of recipient households at district level in 2014-15 is used as an instrument to estimate overall remittance (column 2). Similarly, the proportion of domestic and foreign remittance recipient households at district level in 2014-15 is used to instrument domestic (column 4) and foreign (column 6) remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, credit, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

Table 6 . Remittance amount and farm output

 6

		(1)	(2)	(3)	(4)	(5)	(6)
		Overall remittance	Domestic remittance	Foreign remittance
	Remittance amount	-0.02***	-0.31***	-0.02***	-0.47***	-0.008*	-0.31***
		(0.003)	(0.02)	(0.004)	(0.04)	(0.004)	(0.03)
	HHH Gender	0.34***	-1.69***	0.42***	-1.43***	0.48***	-0.36**
		(0.07)	(0.19)	(0.07)	(0.26)	(0.06)	(0.16)
	HHH Age	-0.001	0.02***	-0.002**	0.02***	-0.003***	0.01***
		(0.001)	(0.002)	(0.001)	(0.003)	(0.001)	(0.001)
	HHH Marital Status	-0.08*	0.31***	-0.09**	0.19	-0.11**	0.09
		(0.05)	(0.1)	(0.05)	(0.12)	(0.05)	(0.08)
	HHH Education	-0.003	0.02***	-0.004	0.02***	-0.005	0.01
		(.00336)	(0.01)	(0.003)	(0.01)	(0.003)	(0.01)
	Dependency Ratio	-0.11*	-0.25**	-0.10*	-0.04	-0.11*	-0.29***
		(0.06)	(0.11)	(0.06)	(0.13)	(0.06)	(0.09)
	Non-Agri. Business	-0.66***	-0.52***	-0.67***	-0.65***	-0.67***	-0.57***
		(0.04)	(0.07)	(0.04)	(0.08)	(0.04)	(0.06)
	Land Ownership	-0.46***	-0.12**	-0.47***	-0.16**	-0.48***	-0.36***
		(0.03)	(0.05)	(0.03)	(0.06)	(0.03)	(0.04)
	Credit Dummy	-0.43***	-0.22***	-0.43***	-0.14*	-0.45***	-0.4***
		(0.03)	(0.06)	(0.03)	(0.07)	(0.03)	(0.05)
	HH Income (Exc. Rem)	0.58***	0.01	0.59***	-0.09	0.62***	0.49***
		(0.03)	(0.06)	(0.03)	(0.08)	(0.03)	(0.05)
	Const.	5.76***	14.04***	5.55***	15.12***	5.16***	7.28***
		(0.35)	(0.75)	(0.35)	(1.08)	(0.33)	(0.58)
	R-squared	0.28	0.00	0.28	0.00	0.27	0.00
	Observations	5632	5458	5632	5458	5632	5458
	Exogeneity: F-value		1143***		783***		593***
	First-stage (F-value)		422		164		202
	Controls	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.

Note: Standard errors clustered at household level are shown in parentheses. Outcome variable is agricultural output during the previous year. Variable of interest is the remittance amount. Columns 1 and 2 show OLS and IV estimates with controls for overall remittances, column 3 and 4 for domestic and column 5 and 6 show estimates for foreign remittances. The proportion of recipient households at district level in 2014-15 is used as an instrument to estimate overall remittance (column 2). Similarly, the proportion of domestic and foreign remittance recipient households at district level in 2014-15 is used to instrument domestic (column 4) and foreign (column 6) remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, credit, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

Table 7 . Remittance incidence and cropping pattern -OLS and IV estimates

 7

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Cash crop	Wheat production	Labor intensive crop	Low labor-intensive
						production	crops production
	Remittance	-0.31***	-3.51***	-0.26***	-2.38***	-0.47***	-3.65***	-0.19***	-1.86***
	incidence
		(0.07)	(0.64)	(0.05)	(0.28)	(0.11)	(0.41)	(0.04)	(0.28)
	HHH Gender	0.53***	-0.97**	0.24***	-0.94***	0.2	-1.41***	0.29***	-0.6***
		(0.15)	(0.44)	(0.08)	(0.18)	(0.17)	(0.3)	(0.07)	(0.16)
	HHH Age	-0.01***	0.01*	-0.0003	0.01***	-0.001	0.01***	-0.003***	0.004**
		(0.002)	(0.003)	(0.001)	(0.002)	(0.002)	(0.003)	(0.001)	(0.002)
	HHH Marital Status	-0.17**	-0.03	-0.05	0.13**	0.01	0.26*	-0.2***	-0.04
		(0.07)	(0.13)	(0.05)	(0.07)	(0.11)	(0.15)	(0.05)	(0.07)
	HHH Education	-0.01	-0.01	-0.004*	-0.01**	-0.01	-0.02**	-0.01***	-0.02***
		(0.01)	(0.01)	(0.002)	(0.003)	(0.01)	(0.01)	(0.003)	(0.01)
	Dependency Ratio	0.07	-0.02	-0.04	-0.07	0.05	0.11	-0.1	-0.09
		(0.08)	(0.14)	(0.05)	(0.08)	(0.13)	(0.17)	(0.06)	(0.08)
	Non-Agri. Business	-0.19***	-0.26**	-0.24***	-0.38***	-0.68***	-0.91***	-0.32***	-0.47***
		(0.07)	(0.11)	(0.04)	(0.06)	(0.11)	(0.14)	(0.05)	(0.07)
	Land Ownership	-0.81***	-0.63***	-0.57***	-0.42***	-0.84***	-0.62***	-0.21***	-0.17***
		(0.04)	(0.07)	(0.03)	(0.03)	(0.06)	(0.08)	(0.04)	(0.05)
	Credit Dummy	-0.03	0.09	-0.25***	-0.19***	-0.4***	-0.27***	-0.26***	-0.25***
		(0.05)	(0.08)	(0.03)	(0.04)	(0.07)	(0.09)	(0.03)	(0.04)
	HH Income (Inc.	0.36***	0.62***	0.21***	0.46***	0.37***	0.75***	0.38***	0.61***
	Rem)
		(0.08)	(0.09)	(0.04)	(0.06)	(0.07)	(0.09)	(0.03)	(0.06)
	Total Input Cost	0.5***	0.31***	0.48***	0.34***	0.39***	0.2***	0.33***	0.21***
		(0.09)	(0.09)	(0.04)	(0.05)	(0.07)	(0.07)	(0.04)	(0.04)
	Const.	1.97***	2.03**	3.88***	3.12***	2.89***	1.22	3.02***	2.08***
		(0.6)	(0.89)	(0.33)	(0.42)	(0.64)	(0.89)	(0.35)	(0.42)
	R-squared	0.42	0.00	0.56	0.09	0.35	0.00	0.38	0.02
	Observations	1781	1770	4815	4662	2445	2383	3570	3490
	Exogeneity: F-value		80.51***		80.85***		115.28***		52.4***
	First-stage (F-value)		38.17		162		140.44		92.55
	Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.

Note: Standard errors clustered at household level are shown in parentheses. Outcome variables are regressed on overall remittance incidence. Estimates of the production of cash crops using OLS and IV with controls are reported in column 1 and 2. Similarly, estimates of wheat production are given in column 3 and 4, estimates of labor intensive in column 5 and 6, and low labor-intensive crops in column 7 and 8, respectively. The proportion of recipient households at the district level in 2014-15 is used to instrument overall remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

Table 8 . Remittance amount and cropping pattern -OLS and IV estimates

 8

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Cash crop	Wheat production	Labor intensive crop	Low labor-intensive
						production	crops production
	Remittance amount	-0.01	-0.39***	-0.01***	-0.19***	-0.02**	-0.36***	0.005*	-0.06***
		(0.01)	(0.06)	(0.003)	(0.02)	(0.01)	(0.04)	(0.003)	(0.02)
	HHH Gender	0.41***	-1.74***	0.16*	-1.15***	0.09	-2.15***	0.18**	-0.23*
		(0.13)	(0.59)	(0.08)	(0.16)	(0.17)	(0.36)	(0.07)	(0.13)
	HHH Age	-0.01***	0.02***	0.0001	0.02***	-0.001	0.02***	-0.003**	0.003
		(0.002)	(0.005)	(0.001)	(0.002)	(0.002)	(0.004)	(0.001)	(0.002)
	HHH Marital Status	-0.16**	0.09	-0.05	0.21***	0.04	0.45**	-0.18***	-0.08
		(0.07)	(0.20)	(0.05)	(0.07)	(0.11)	(0.19)	(0.05)	(0.06)
	HHH Education	-0.01	0.03***	-0.003	0.02***	-0.01	0.01	-0.01***	-0.003
		(0.004)	(0.01)	(0.002)	(0.004)	(0.006)	(0.011)	(0.003)	(0.004)
	Dependency Ratio	0.08	-0.24	-0.04	-0.14*	0.06	0.06	-0.11*	-0.14*
		(0.09)	(0.21)	(0.05)	(0.08)	(0.13)	(0.2)	(0.06)	(0.07)
	Non-Agri. Business	-0.19***	-0.01	-0.23***	-0.16***	-0.68***	-0.55***	-0.32***	-0.31***
		(0.07)	(0.15)	(0.04)	(0.06)	(0.11)	(0.16)	(0.05)	(0.05)
	Land Ownership	-0.8***	-0.46***	-0.56***	-0.33***	-0.84***	-0.34***	-0.21***	-0.17***
		(0.05)	(0.1)	(0.03)	(0.04)	(0.06)	(0.1)	(0.04)	(0.05)
	Credit Dummy	-0.04	0.07	-0.26***	-0.15***	-0.4***	-0.16	-0.28***	-0.26***
		(0.04)	(0.1)	(0.03)	(0.04)	(0.07)	(0.11)	(0.03)	(0.04)
	HH Income (Exc. Rem)	0.35***	-0.25*	0.17***	-0.16***	0.36***	-0.14	0.33***	0.22***
		(0.07)	(0.14)	(0.03)	(0.04)	(0.07)	(0.09)	(0.03)	(0.04)
	Total Input Cost	0.5***	0.48***	0.48***	0.43***	0.38***	0.31***	0.33***	0.31***
		(0.09)	(0.12)	(0.05)	(0.05)	(0.07)	(0.07)	(0.03)	(0.03)
	Const.	2.22***	11.32***	4.34***	9.89***	3.28***	11.54***	3.73***	5.66***
		(0.58)	(1.77)	(0.35)	(0.83)	(0.66)	(1.45)	(0.40)	(0.76)
	R-squared	0.43	0.00	0.56	0.004	0.35	0.00	0.38	0.30
	Observations	1781	1770	4815	4662	2445	2383	3570	3490
	Exogeneity: F-value		281.68***		160.6***		247.83***		14.73***
	First-stage (F-value)		62.28		334.73		189		215
	Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.

Note: Standard errors clustered at household level are shown in parentheses. Outcome variables are regressed on overall remittance amount. Estimates of the production of cash crops using OLS and IV with controls are reported in column 1 and 2. Similarly, estimates of wheat production are given in column 3 and 4, estimates of labor intensive in column 5 and 6, and low labor-intensive crops in column 7 and 8, respectively. The proportion of recipient households at the district level in 2014-15 is used to instrument overall remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

Table 9 . Domestic remittance incidence and cropping pattern -OLS and IV estimates

 9

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Cash crop	Wheat production	Labor intensive crop	Low labor-intensive crops
						production	production
	Domestic remittance	-0.38*** -4.91*** -0.21*** -2.22***	-0.60***	-5.02***	-0.12**	-1.40***
	incidence
		(0.08)	(1.31)	(0.05)	(0.34)	(0.13)	(0.73)	(0.05)	(0.33)
	HHH Gender	0.58***	-0.45	0.31***	-0.38**	0.25	-1.18***	0.36***	-0.07
		(0.14)	(0.46)	(0.08)	(0.16)	(0.16)	(0.34)	(0.07)	(0.14)
	HHH Age	-0.01***	0.01	-0.001	0.01***	-0.001	0.01***	-0.003***	0.001
		(0.002)	(0.004)	(0.001)	(0.001)	(0.002)	(0.003)	(0.001)	(0.002)
	HHH Marital Status	-0.17**	0.03	-0.07	0.05	-0.001	0.18	-0.22***	-0.16**
		(0.07)	(0.16)	(0.05)	(0.06)	(0.12)	(0.17)	(0.05)	(0.06)
	HHH Education	-0.01	-0.01	-0.004	-0.003	-0.006	-0.01	-0.01***	-0.01***
		(0.004)	(0.01)	(0.002)	(0.003)	(0.01)	(0.01)	(0.003)	(0.004)
	Dependency Ratio	0.09	0.17	-0.03	-0.03	0.06	0.24	-0.1	-0.06
		(0.09)	(0.16)	(0.06)	(0.07)	(0.13)	(0.18)	(0.06)	(0.07)
	Non-Agri. Business	-0.18***	-0.18	-0.23***	-0.32***	-0.67***	-0.84***	-0.31***	-0.39***
		(0.07)	(0.12)	(0.04)	(0.05)	(0.11)	(0.14)	(0.05)	(0.06)
	Land Ownership	-0.8***	-0.67*** -0.58***	-0.48***	-0.85***	-0.6***	-0.22***	-0.19***
		(0.05)	(0.08)	(0.03)	(0.04)	(0.06)	(0.09)	(0.05)	(0.05)
	Credit Dummy	-0.03	0.16	-0.25***	-0.18***	-0.39***	-0.17	-0.26***	-0.23***
		(0.05)	(0.1)	(0.03)	(0.04)	(0.07)	(0.11)	(0.03)	(0.04)
	HH Income (Inc. Rem)	0.35***	0.44***	0.18***	0.19***	0.33***	0.39***	0.35***	0.37***
		(0.08)	(0.08)	(0.03)	(0.04)	(0.07)	(0.07)	(0.03)	(0.03)
	Total Input Cost	0.51***	0.36***	0.49***	0.42***	0.39***	0.24***	0.34***	0.29***
		(0.09)	(0.07)	(0.04)	(0.05)	(0.07)	(0.07)	(0.03)	(0.04)
	Const.	2.06***	3.1***	4.1***	5.17***	3.37***	5.19***	3.19***	3.78***
		(0.6)	(0.96)	(0.35)	(0.48)	(0.64)	(0.96)	(0.36)	(0.41)
	R-squared	0.42	0.00	0.56	0.25	0.35	0.00	0.37	0.21
	Observations	1781	1770	4815	4662	2445	2383	3570	3490
	Exogeneity: F-value		43.2***		67.34***		123***		22.01***
	First-stage (F-value)		17.66		86.57		68.89		58
	Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.

Note: Standard errors clustered at household level are shown in parentheses.

Table 10 . Foreign remittance incidence and cropping pattern -OLS and IV estimates

 10 Standard errors clustered at household level are shown in parentheses. Outcome variables are regressed on foreign remittance incidence. Estimates of the production of cash crops using OLS and IV with controls are reported in column 1 and 2. Similarly, estimates of wheat production are given in column 3 and 4, estimates of labor intensive in column 5 and 6, and low labor-intensive crops in column 7 and 8, respectively. The proportion of foreign remittance recipient households at the district level in 2014-15 is used to instrument foreign remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Cash crop	Wheat production	Labor intensive crop	Low labor-intensive crops
						production	production
	Foreign remittance	-0.11	-3.85***	-0.16***	-1.02***	-0.05	0.27	-0.16***	-0.89***
	incidence
		(0.13)	(1.27)	(0.06)	(0.26)	(0.14)	(0.38)	(0.06)	(0.26)
	HHH Gender	0.64***	-0.29	0.35***	0.19**	0.43***	0.49***	0.37***	0.25***
		(0.17)	(0.49)	(0.08)	(0.09)	(0.16)	(0.16)	(0.07)	(0.09)
	HHH Age	-0.01***	-0.002	-0.001	-0.001	-0.002	-0.003	-0.004***	-0.003***
		(0.002)	(0.002)	(0.001)	(0.001)	(0.002)	(0.002)	(0.001)	(0.001)
	HHH Marital Status	-0.18**	-0.16	-0.07	-0.05	-0.02	-0.04	-0.21***	-0.18***
		(0.08)	(0.11)	(0.05)	(0.05)	(0.12)	(0.12)	(0.06)	(0.06)
	HHH Education	-0.01	-0.01	-0.004	-0.01*	-0.01	-0.01	-0.01***	-0.01***
		(0.01)	(0.01)	(0.002)	(0.002)	(0.006)	(0.006)	(0.003)	(0.004)
	Dependency Ratio	0.08	-0.1	-0.041	-0.07	0.04	0.05	-0.11*	-0.12*
		(0.09)	(0.15)	(0.06)	(0.06)	(0.13)	(0.13)	(0.06)	(0.06)
	Non-Agri. Business	-0.18***	-0.27***	-0.22***	-0.25***	-0.65***	-0.65***	-0.31***	-0.34***
		(0.07)	(0.1)	(0.04)	(0.04)	(0.11)	(0.11)	(0.05)	(0.05)
	Land Ownership	-0.82***	-0.74***	-0.58***	-0.57***	-0.88***	-0.88***	-0.22***	-0.21***
		(0.05)	(0.07)	(0.03)	(0.03)	(0.06)	(0.06)	(0.05)	(0.05)
	Credit Dummy	-0.05	-0.05	-0.26***	-0.26***	-0.43***	-0.43***	-0.25***	-0.27***
		(0.05)	(0.06)	(0.03)	(0.03)	(0.07)	(0.07)	(0.03)	(0.03)
	HH Income (Inc.	0.35***	0.55***	0.19***	0.28***	0.33***	0.31***	0.37***	0.45***
	Rem)
		(0.08)	(0.12)	(0.03)	(0.04)	(0.07)	(0.07)	(0.03)	(0.04)
	Total Input Cost	0.52***	0.43***	0.49***	0.47***	0.41***	0.41***	0.34***	0.32***
		(0.09)	(0.12)	(0.05)	(0.05)	(0.07)	(0.07)	(0.03)	(0.04)
	Const.	1.95***	1.29	3.86***	3.25***	3.11***	3.26***	3.01***	2.35***
		(0.61)	(0.81)	(0.34)	(0.36)	(0.63)	(0.71)	(0.36)	(0.39)
	R-squared	0.42	0.00	0.56	0.52	0.34	0.33	0.37	0.34
	Observations	1781	1770	4815	4662	2445	2383	3570	3490
	Exogeneity: F-value		18.79***		12.9***		0.79		10.34***
	First-stage (F-value)		13.93		126.45		73		106
	Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.
	Note:

Table 11 . Domestic remittance amount and cropping pattern -OLS and IV estimates

 11 Standard errors clustered at household level are shown in parentheses. Outcome variables are regressed on domestic remittance amount. Estimates of the production of cash crops using OLS and IV with controls are reported in column 1 and 2. Similarly, estimates of wheat production are given in column 3 and 4, estimates of labor intensive in column 5 and 6, and low labor-intensive crops in column 7 and 8, respectively. The proportion of domestic remittance recipient households at district level in 2014-15 is used to instrument domestic remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Cash crop	Wheat production	Labor intensive crop	Low labor-intensive
						production	crops production
	Domestic remittance	-0.02**	-0.67***	-0.01**	-0.32***	-0.03***	-0.69***	0.006	-0.06**
	amount
		(0.01)	(0.12)	(.004)	(0.04)	(0.01)	(0.09)	(0.004)	(0.03)
	HHH Gender	0.39***	-1.21*	0.19**	-1***	0.09	-2.37***	0.16**	-0.07
		(0.13)	(0.69)	(0.08)	(0.22)	(0.16)	(0.56)	(0.07)	(0.13)
	HHH Age	-0.004***	0.02***	-0.0003	0.02***	-0.001	0.03***	-0.003**	0.001
		(0.002)	(0.01)	(0.001)	(0.003)	(0.002)	(0.01)	(0.001)	(0.002)
	HHH Marital Status	-0.16**	0.22	-0.05	0.19**	0.03	0.4	-0.17***	-0.13**
		(0.07)	(0.25)	(0.05)	(0.09)	(0.11)	(0.26)	(0.05)	(0.06)
	HHH Education	-0.01	0.03*	-0.004	0.02***	-0.01	0.02	-0.01**	-0.01
		(0.005)	(0.01)	(0.002)	(0.01)	(0.01)	(0.01)	(0.003)	(0.004)
	Dependency Ratio	0.08	0.09	-0.03	-0.05	0.07	0.29	-0.11*	-0.11
		(0.09)	(0.25)	(0.06)	(0.1)	(0.13)	(0.26)	(0.06)	(0.07)
	Non-Agri. Business	-0.19***	0.04	-0.23***	-0.23***	-0.68***	-0.55***	-0.32***	-0.32***
		(0.07)	(0.19)	(0.04)	(0.06)	(0.11)	(0.19)	(0.05)	(0.05)
	Land Ownership	-0.79***	-0.51***	-0.56***	-0.34***	-0.83***	-0.22	-0.21***	-0.19***
		(0.05)	(0.12)	(0.03)	(0.05)	(0.06)	(0.14)	(0.04)	(0.05)
	Credit Dummy	-0.04	0.24	-0.26***	-0.09*	-0.4***	0.09	-0.28***	-0.25***
		(0.05)	(0.16)	(0.03)	(0.06)	(0.07)	(0.17)	(0.03)	(0.04)
	HH Income (Exc. Rem)	0.35***	-0.3*	0.18***	-0.27***	0.34***	-0.56***	0.33***	0.23***
		(0.07)	(0.18)	(0.03)	(0.06)	(0.07)	(0.17)	(0.03)	(0.05)
	Total Input Cost	0.5***	0.47***	0.48***	0.44***	0.38***	0.29***	0.33***	0.32***
		(0.09)	(0.09)	(0.05)	(0.05)	(0.07)	(0.09)	(0.04)	(0.04)
	Const.	2.34***	11.37***	4.27***	10.99***	3.52***	17.14***	3.76***	5.28***
		(0.6)	(2.14)	(0.35)	(1.09)	(0.67)	(2.52)	(0.42)	(0.91)
	R-squared	0.43	0.00	0.56	0.00	0.35	0.00	0.38	0.31
	Observations	1781	1770	4815	4662	2445	2383	3570	3490
	Exogeneity: F-value		275.88***		155***		258.81***		5.17**
	First-stage (F-value)		34.63		131.85		66.95		77.51
	Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.
	Note:

Table 12 . Foreign remittance amount and cropping pattern -OLS and IV estimates

 12 Standard errors clustered at household level are shown in parentheses. Outcome variables are regressed on foreign remittance amount. Estimates of the production of cash crops using OLS and IV with controls are reported in column 1 and 2. Similarly, estimates of wheat production are given in column 3 and 4, estimates of labor intensive in column 5 and 6, and low labor-intensive crops in column 7 and 8, respectively. The proportion of foreign remittance recipient households at the district level in 2014-15 is used to instrument foreign remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. All regressions include sample weights.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Cash crop	Wheat production	Labor intensive crop	Low labor-intensive crops
						production	production
	Foreign remittance	0.01	-0.38***	-0.004	-0.14***	0.007	-0.17***	0.004	-0.06***
	amount
		(0.01)	(0.1)	(0.004)	(0.02)	(0.01)	(0.03)	(0.004)	(0.01)
	HHH Gender	0.47***	-0.76	0.22***	-0.19	0.23	-0.26	0.15**	-0.03
		(0.13)	(0.61)	(0.08)	(0.12)	(0.16)	(0.22)	(0.07)	(0.08)
	HHH Age	-0.01***	0.003	-0.001	0.003***	-0.002	0.003	-0.002**	-0.0002
		(0.002)	(0.003)	(0.001)	(0.001)	(0.002)	(0.003)	(0.001)	(0.001)
	HHH Marital Status	-0.17**	-0.11	-0.05	0.02	0.02	0.12	-0.18***	-0.12**
		(0.07)	(0.14)	(0.05)	(0.06)	(0.11)	(0.12)	(0.05)	(0.06)
	HHH Education	-0.007	0.008	-0.003	0.002	-0.008	-0.006	-0.01**	-0.005
		(0.005)	(0.01)	(0.002)	(0.003)	(0.01)	(0.01)	(0.003)	(0.003)
	Dependency Ratio	0.09	-0.24	-0.04	-0.11*	0.07	-0.01	-0.11*	-0.14**
		(0.09)	(0.18)	(0.06)	(0.07)	(0.13)	(0.15)	(0.06)	(0.07)
	Non-Agri. Business	-0.19***	-0.17	-0.23***	-0.21***	-0.69***	-0.67***	-0.32***	-0.31***
		(0.07)	(0.11)	(0.04)	(0.05)	(0.11)	(0.12)	(0.05)	(0.05)
	Land Ownership	-0.81***	-0.67***	-0.57***	-0.51***	-0.87***	-0.79***	-0.21***	-0.19***
		(0.05)	(0.08)	(0.03)	(0.03)	(0.06)	(0.07)	(0.04)	(0.05)
	Credit Dummy	-0.04	-0.09	-0.27***	-0.26***	-0.43***	-0.44***	-0.28***	-0.28***
		(0.05)	(0.08)	(0.03)	(0.04)	(0.07)	(0.08)	(0.03)	(0.04)
	HH Income (Exc.	0.37***	0.16	0.19***	0.15***	0.38***	0.38***	0.32***	0.30***
	Rem)
		(0.07)	(0.11)	(0.04)	(0.04)	(0.07)	(0.08)	(0.03)	(0.03)
	Total Input Cost	0.5***	0.51***	0.49***	0.47***	0.39***	0.37***	0.33***	0.32***
		(0.09)	(0.13)	(0.05)	(0.05)	(0.07)	(0.07)	(0.04)	(0.04)
	Const.	2.02***	5.53***	4.11***	5.15***	2.84***	3.33***	3.87***	4.35***
		(0.57)	(1.42)	(0.33)	(0.46)	(0.65)	(0.77)	(0.38)	(0.45)
	Exogeneity: F-value		70.55***		76.16***		46.29***		19***
	First-stage (F-value)		21.84		187.39		90		156.61
	Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.
	Note: Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1

Table 13 . Absent household members and farm investment and output

 13 Standard errors clustered at household level are shown in parentheses. Outcome variables are regressed on number of absent household members. Results of agricultural investment estimated using IV without and with controls are reported in column 1 and 2. Similarly, estimates of total output are given in column 3 and 4, cash crop production in column 5 and 6, wheat production in column 7 and 8, labor intensive crops in column 9 and 10, and low labor-intensive crops in column 11 and 12, respectively. The proportion of remittance recipient households at the district level in 2014-15 is used to instrument remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

		1	2	3	4	5	6	7	8	9	10	11	12
		Agricultural	Total output	Cash crops	Wheat production	Labor intensive crops	Low labor-intensive
		investment										crops
	Absent member	-13.88***	-28.67***	-11.49*** -7.89*** -13.36*** -22.73	-13.63*** -9.15*** -13.63***	-15.69** -11.57**	0.01
		(2.59)	(8.48)	(2.04)	(2.57)	(4.91)	(17.44) (3.35)	(2.79)	(3.35)	(6.36)	(4.95)	(0.025)
	HHH Gender		0.72		0.41		1.79		0.44		-0.59		0.75**
			(1.3)		(0.36)		(1.35)		(0.47)		(1.73)		(0.3)
	HHH Age		0.01		0		0.01		0.01		0.01		0
			(0.02)		(0)		(0.02)		(0.01)		(0.02)		(0)
	HHH Marital		-0.35		-0.17		0.69		-0.22		-1.8		-0.17
	Status
			(1.02)		(0.29)		(1.25)		(0.37)		(1.44)		(0.22)
	HHH Education		0.03		0.01		0		0.02		0.02		-0.01
			(0.06)		(0.02)		(0.07)		(0.02)		(0.05)		(0.02)
	Dependency Ratio		0.88		0.17		-0.56		0		0.65		0.05
			(0.84)		(0.23)		(10.08)		(0.29)		(0.81)		(0.22)
	Non-Agri.		-1.04		-0.46**		-0.22		-0.41		-1.7**		-0.51**
	Business
			(0.71)		(0.2)		(0.85)		(0.26)		(0.73)		(0.21)
	Land Ownership		2.04***		-0.17		0.08		-0.13		-0.11		-0.07
			(0.53)		(0.15)		(0.83)		(0.18)		(0.41)		(0.16)
	Credit Dummy		-1.38***		-0.37***		0.29		-0.5***		-0.92**		-0.46***
			(0.5)		(0.14)		(0.72)		(0.17)		(0.46)		(0.17)
	HH Income (Inc.		3.68***		1.1***		2.16		1.2***		2.19**		.96**
	Rem)
			(1.05)		(0.32)		(1.53)		(0.37)		(0.91)		(0.39)
	Total Input Cost				0.53***		0.49**		0.46***		0.3**		0.32***
					(0.06)		(0.22)		(0.07)		(0.14)		(0.05)
	Const.	2.96***	-47.11***	14.38***	-7.32*	13.95***	-22.63	13.37***	-8.56**	13.72***	-17.45*	13.06***	-4.56
		(0.3)	(13.2)	(0.22)	(3.78)	(0.47)	(20.71) (0.22)	(4.35)	(0.37)	(9.76)	(0.65)	(4.95)
	Obs.	5462	5462	5458	5458	1770	1770	4662	4662	2383	2383	3570	3490
	Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Source: Authors' calculations using PSLM 2018-19.
	Note:

Table 14 . Remittance and households' farm labor supply

 14 Standard errors clustered at household level are shown in parentheses. Outcome variable is the average of household monthly number of days of farm labor during the previous year. Columns 1 and 2 show IV estimates without and with controls for overall remittances, column 3 and 4 for domestic, while column 5 and 6 for foreign remittances. The proportion of recipient households at district level in 2014-15 is used to instrument total remittances. Similarly, the proportion of domestic and foreign remittance recipient households at district level in 2014-15 is used to instrument domestic and foreign remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.

	(1)	(2)	(3)	(4)	(5)	(6)
	Overall remittance	Domestic remittance	Foreign remittance

Table 15 . Remittance incidence and farm investment and output: IV

 15 Standard errors clustered at household level are shown in parentheses. Outcome variables are agricultural investment and total output. Variable of interest is the remittance incidence. Columns 1 and 2 show IV estimates with controls for overall remittances, columns 3 and 4 for domestic remittances, and columns 5 and 6 show estimates for foreign remittances. The proportion of overall recipient households at the PSU level excluding the household in question is used to instrument remittances (columns 1 and 2). Similarly, the proportion of domestic and foreign remittance recipient households at the PSU level excluding the household surveyed is used to instrument domestic (column 3 and 4) and foreign (column 5 and 6) remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

				-2 estimates
		1	2	3	4	5	6
		Overall remittance	Domestic remittance	Foreign remittance
		Agricultural investment	Total output	Agricultural investment	Total output	Agricultural investment	Total output
	Remittance incidence	-3.16***	-0.98***	-2.99***	-0.57***	-2.1***	-0.74***
		(0.33)	(0.14)	(0.45)	(0.11)	(0.44)	(0.15)
	HHH Gender	-1.2***	-0.16	-0.41	0.18**	0.35	0.24***
		(0.3)	(0.1)	(0.3)	(0.07)	(0.24)	(0.07)
	HHH Age	0.01	0001**	0.001	0.001	-0.01	0.001
		(0.0001)	(0.0001)	(0.0001)	(0.0001)	(0.0001)	(0.001)
	HHH Marital Status	0.26	0.0001	0.11	-0.05	0.06	-0.05
		(0.2)	(0.04)	(0.2)	(0.04)	(0.19)	(0.04)
	HHH Education	-0.04***	-0.01***	-0.03**	0.0001**	-0.03**	0***
		(0.01)	(0.0001)	(0.01)	(0.0001)	(0.01)	(0.0001)
	Dependency Ratio	0.27	-0.01	0.34	0.02	0.16	-0.02
		(0.22)	(0.05)	(0.22)	(0.05)	(0.21)	(0.05)
	Non-Agri. Business	-0.64***	-0.37***	-0.64***	-0.33***	-0.55***	-0.31***
		(0.14)	(0.04)	(0.14)	(0.04)	(0.14)	(0.04)
	Land Ownership	0.99***	-0.46***	0.92***	-0.5***	0.82***	-0.52***
		(0.1)	(0.02)	(0.1)	(0.02)	(0.09)	(0.02)
	Credit Dummy	-0.46***	-0.13***	-0.48***	-0.14***	-0.63***	-0.16***
		(0.1)	(0.03)	(0.11)	(0.03)	(0.1)	(0.03)
	HH Income (Inc. Rem)	0.9***	0.35***	0.61***	0.23***	0.83***	0.3***
		(0.1)	(0.04)	(0.1)	(0.03)	(0.1)	(0.04)
	Total Input Cost		0.52***		0.57***		0.57***
			(0.05)		(0.05)		(0.05)
	Const.	-10.07***	2.86***	-6.83***	3.59***	-10.08***	2.75***
		(1.3)	(0.28)	(1.25)	(0.32)	(1.32)	(0.26)
	Obs.	5636	5632	5636	5632	5632	5632
	Exogeneity: F-value	65.66***	38.52***	30***	9.3***	12.59***	16.85***
	First-stage (F-value)	627	445	332	280	284	260
	Controls	Yes	Yes	Yes	Yes	Yes	Yes
	Source: Authors' calculations using PSLM 2018-19.
	Note:

Table 16 . Remittance amount and farm investment and output: IV-2 estimates

 16 Standard errors clustered at household level are shown in parentheses. Outcome variables are agricultural investment and total output. Variable of interest is the remittance amount. Columns 1 and 2 show IV estimates with controls for overall remittances, columns 3 and 4 for domestic remittances, and columns 5 and 6 show estimates for foreign remittances. The proportion of overall recipient households at the PSU level excluding the household in question is used to instrument remittances (columns 1 and 2). Similarly, the proportion of domestic and foreign remittance recipient households at the PSU level excluding the household surveyed is used to instrument domestic (column 3 and 4) and foreign (column 5 and 6) remittances. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

		1	2	3	4	5	6
		Overall remittance	Domestic remittance	Foreign remittance
		Agricultural investment	Total output	Agricultural investment	Total output	Agricultural investment	Total output
	Remittance amount	-0.29***	-0.09***	-0.36***	-0.1***	-0.2***	-0.07***
		(0.03)	(0.01)	(0.05)	(0.01)	(0.03)	(0.01)
	HHH Gender	-1.83***	-0.41***	-1.29***	-0.21**	-0.36	0.0001
		(0.33)	(0.1)	(0.33)	(0.09)	(0.25)	(0.08)
	HHH Age	0.02***	0.01***	0.01***	0.0001***	0.0001	0.0001
		(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
	HHH Marital Status	0.42**	0.06	0.27	0.01	0.19	-0.01
		(0.21)	(0.04)	(0.21)	(0.04)	(0.19)	(0.04)
	HHH Education	0.001	0.001	-0.01	0.001	-0.02*	0.001
		(0.01)	(0.001)	(0.01)	(0.001)	(0.01)	(0.001)
	Dependency Ratio	0.14	-0.04	0.33	0.02	0.1	-0.04
		(0.23)	(0.06)	(0.23)	(0.05)	(0.22)	(0.05)
	Non-Agri. Business	-0.42***	-0.29***	-0.57***	-0.33***	-0.49***	-0.3***
		(0.15)	(0.04)	(0.14)	(0.04)	(0.14)	(0.04)
	Land Ownership	1.17***	-0.41***	1.08***	-0.44***	0.92***	-0.48***
		(0.11)	(0.03)	(0.11)	(0.03)	(0.1)	(0.02)
	Credit Dummy	-0.44***	-0.12***	-0.4***	-0.11***	-0.63***	-0.16***
		(0.11)	(0.03)	(0.11)	(0.03)	(0.1)	(0.03)
	HH Income (Exc. Rem)	0.1	0.09***	0.13	0.11***	0.61***	0.22***
		(0.1)	(0.03)	(0.11)	(0.03)	(0.08)	(0.03)
	Total Input Cost		0.54***		0.55***		0.56***
			(0.05)		(0.05)		(0.05)
	Obs.	5636	5632	5636	5632	5636	5632
	Exogeneity: F-value	103***	66***	72***	63***	40***	38***
	First-stage (F-value)	557	523	290	278	314	310
	Controls	Yes	Yes	Yes	Yes	Yes	Yes
	Source: Authors' calculations using PSLM 2018-19.
	Note:

Table 18 . Remittances and farm investment and output: Matching estimates

 18 Robust standard errors are shown in parentheses. Column 1-6 show agricultural outcome variables. Subsequent columns report the Average Treatment Effect (ATE) estimated using Nearest Neighbor matching, Inverse Probability Weighting (IPW) and Augmented Inverse Probability Weighting (AIPW) models. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. IPW and AIPW models are estimated using sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

			1			2			3
	Dependent	Agriculture Investment		Total output		Cash crops production
	Model type	NNMATCH	IPW	AIPW	NNMATCH	IPW	AIPW	NNMATCH	IPW	AIPW
	ATE	-0.94***	-0.88***	-0.57***	-0.83***	0.83***	-0.54***	-0.79***	-0.69***	-0.37***
		(0.23)	(0.11)	(0.14)	(0.06)	(0.88)	(0.05)	(0.1)	(0.09)	(0.1)
	Obs.	5636	5636	5636	5632	5632	5632	1781	1781	1781
	Controls		Yes			Yes			Yes
	Authors' calculations using PSLM 2018-19.
	Note:

Table 18 .

 18 Continue. Robust standard errors are shown in parentheses. Column 1-6 show agricultural outcome variables. Subsequent columns report the Average Treatment Effect (ATE) estimated using Nearest Neighbor matching, Inverse Probability Weighting (IPW) and Augmented Inverse Probability Weighting (AIPW) models. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. IPW and AIPW models are estimated using sample weights. Significant coefficients are marked with stars as *** p<0.01, ** p<0.05, * p<0.1.

			4			5			6
		Wheat production		Labor intensive crop production	Low labor-intensive crops production
	Model type	NNMATCH	IPW	AIPW	NNMATCH	IPW	AIPW	NNMATCH	IPW	AIPW
	ATE	-0.72***	-0.72***	-0.52***	-1.31***	-1.03***	-0.83***	-0.38***	-0.42	-0.12***
		(0.06)	(0.05)	(0.05)	(0.15)	(0.12)	(0.1)	(0.06)	(0.05)	(0.05)
	Obs.	4815	4815	4815	2445	2445	2445	3570	3570	3570
	Controls		Yes			Yes			Yes
	Authors' calculations using PSLM 2018-19.
	Note:

  Standard errors clustered at household level are shown in parentheses. First-stage regression results of endogenous overall remittance incidence estimated using probit model and overall remittance amount using linear model with household controls are alternately reported in columns 1 and 2. The instrument is the proportion of recipient households at district level in 2014-15. Similarly, domestic and foreign remittance incidences and amount are presented in column 3-6. Domestic and foreign remittances are estimated using the proportion of domestic and foreign recipient households at district level in 2014-15. Controls include household head controls (gender, age, education, marital status), household income, dependency ratio, non-agricultural business, Credit dummy, input cost and a dummy for land ownership. All regressions include sample weights. Significant coefficients are marked with stars as*** p<.01, ** p<.05, * p<.1.

	Table A 1. First stage regressions
		1	2	3	4	5	6
		Overall remittance	Domestic remittance	Foreign remittance
		Incidence	Amount	Incidence	Amount	Incidence	Amount
	Prop.Rec.dist15	2.71***	10.31***
		(0.19)	(0.52)
	Prop.Dom.Rec.dist15			3.26***	8.02***
				(0.26)	(0.66)
	Prop.For.Rec.dist15					4.34***	12.24***
						(0.3)	(0.86)
	HHH Gender	-1.54***	-5.58***	-1.14***	-3.53***	-0.7***	-1.75***
		(0.12)	(0.36)	(0.11)	(0.41)	(0.13)	(0.39)
	HHH Age	0.02***	0.07***	0.02***	0.05***	0.01**	0.02***
		(0.0001)	(0.01)	(0.0001)	(0.0001)	(0.0001)	(0.0001)
	HHH Marital Status	0.37***	1.2***	0.25**	0.61**	0.22	0.5**
		(0.12)	(0.29)	(0.12)	(0.24)	(0.14)	(0.21)
	HHH Education	-0.02***	0.07***	-0.01	0.04***	-0.01	0.03***
		(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
	Dependency Ratio	-0.18	-0.62**	0.03	0.11	-0.5***	-0.75***
		(0.11)	(0.28)	(0.12)	(0.24)	(0.14)	(0.21)
	Non-Agri. Business	-0.3***	0.15	-0.31***	-0.17	-0.13	0.23
		(0.08)	(0.2)	(0.09)	(0.15)	(0.1)	(0.15)
	Land Ownership	0.23***	0.33**	0.16**	0.17	0.18**	0.14
		(0.06)	(0.13)	(0.07)	(0.12)	(0.09)	(0.09)
	Credit Dummy	0.001	0.03	0.17***	0.39***	-0.3***	-0.39***
		(0.06)	(0.15)	(0.06)	(0.13)	(0.08)	(0.11)
	HH Income (Inc. Rem)	0.37***	-20.05***	-0.03	-10.48***	0.56***	-0.59***
		(0.05)	(0.11)	(0.05)	(0.11)	(0.06)	(0.1)
	Total Input Cost	-0.14***	0.07	-0.11***	0.0001	-0.07***	0.13**
		(0.02)	(0.06)	(0.02)	(0.05)	(0.02)	(0.06)
	Const.	-4.65***	27.21***	-0.36	20.02***	-8.45***	6.72***
		(0.55)	(1.24)	(0.58)	(1.27)	(0.66)	(1.23)
	Observations	5462	5462	5462	5462	5462	5462
	Pseudo R 2	0.26	0.32	0.19	0.21	0.25	0.15
	Controls	Yes	Yes	Yes	Yes	Yes	Yes
	Authors' calculations using PSLM 2018-19.
	Note:

This implication of the model, of a standard Solow type, cannot be confirmed on available cross-sectional data, and is left for subsequent research.

All the monetary values are reported in Pakistani Rupees (PKR). According to the State Bank of Pakistan (https://www.sbp.org.pk/ecodata) the average exchange rate of Pakistani rupees per US dollar for the survey period (August 2018-June 2019) is 137.16.

46 households received both internal and foreign remittances. Therefore, the total does not sum up to 1032. They are excluded while analyzing separate impact of domestic and foreign remittance.

https://www.norway.no/en/pakistan/

The percentage change in inverse hyperbolic sin transformed outcome variable due to change in dummy variable can be defined as exp(𝛽 ̂) -1 and can be interpreted in the same way as log transformed variables (see for detail,[START_REF] Bellemare | Elasticities and the inverse hyperbolic sine transformation[END_REF][START_REF] Woolley | A rant on inverse hyperbolic sine transformations[END_REF]. We take anti-log of the coefficients using [ 𝑒 𝛽 -1]100.

The negative impact of the incidence and amount of remittances observed thus far suggests the prevalence of labor loss effect. This effect can be more clearly seen by focusing on the household's migration history. However, our dataset, while providing information on the incidence, type, amount and origin of migrant remittances, does not give any information on the migrant. This is a serious limitation of the dataset. We try to overcome this difficulty in two ways:

1) Following [START_REF] Bertoli | Migration and co-residence choices: Evidence from Mexico[END_REF], we take the household member absent at the time of the survey as a proxy for migrant member and look the impact on outcome. Although it is not perfect substitute for migration, it could nonetheless provide some suggestive evidence. The intuition for this variable is as follows: Controlling for remittances, absence of one or more household members would significantly and negatively affect farm production and investment if labor constraints are binding. Estimations reported in Table 13 are in line with this argument. The impact of the number of absent members is negative for aggregate farm output and investment, and the production of all types of crops. However, the impact is much stronger for highly laborintensive crops compared to less labor-intensive ones.

2) An indirect way of inferring the presence of labor loss effect could be to observe the impact of remittances on the household's labor supply. If higher household income thanks to migrant remittances leads the household away from farming activities, it should show in falling labor participation in agriculture among the stay-behind household members as well as lower farm output. The results of the impact of remittance incidence and amount on household farm labor supply (shown in Table 14) do not support this argument. Remittances' impact on the average number of days of farm activity per household is found to be either positive or not significantly different from zero, implying that the loss of farm activity is probably arising due to the absence of the migrant member.