Highways, Shocks and Labor Market Outcomes

Anita Bhide and Yiming He∗

September 2016

Abstract

Recently developing countries have seen huge increases in spending on transporta-tion networks. Given these large investments it’s important to understand the welfareconsequences. The paper will look at if access to these roads has improved agriculturalhousehold’s abilities to better cope with local productivity shocks. It looks particularlyat the Golden Quadrilateral Highway system in India. Proximity to this highway allowshouseholds easier access to labor markets that are uncorrelated with local market con-ditions. We build a theoretical model with precise empirical predictions; household’sequilibrium employment in agricultural and non-agriculture should respond more toshocks when located closer to roads. The changes in wages on the other hand will besmaller. These empirical predictions are tested using the REDS dataset: a nationallyrepresentative household survey of rural India. Using a difference-in-difference strategy,we look at how responses to rainfall shocks varies as distance to the GQ increases.

∗We thank Melanie Morten, Pascaline Dupas, Arun Chandrasekhar, Marcel Fafchamps and participantsof the Development Tea for for useful comments and feedback

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As has been noted in low-income developing countries like India, while markets for agri-

cultural inputs and outputs are well developed, that of credit and insurance markets lag

behind. An interesting question is then how poor farmers mitigate the impact of adverse

shocks. A large literature has looked at different risk insurance strategies rural households in

developing countries employ; village level risk insurance, sub-caste level insurance and saving

mechanisms. Townsend (1994)’s seminal work looked at village level risk insurance, where

idiosyncratic risk may be insured using transfers within a village. The evidence however did

not support this picture. Others have looked at several broader groupings of individuals:

Munshi and Rosenzweig (2016) look at sub-castes (jati’s) while Rosenzweig and Stark (1989)

analyze networks formed through marriages.

In this paper we look at the use of off-farm labor markets which may afford households

an opportunity to self-insure/diversify individually. The literature so far has ascertained the

role of off-farm labor supply; Kochar (1999), Rose (2001), Cameron and Worswick (2003)

and Fernandez et al. (2014) all provide evidence in favor of the use of off-farm labor markets

as means of mitigating the impact of negative income shocks.1

In spite of this there still remain more questions. To begin with how do labor markets

even play a significant role in providing diversification to farmers? Agricultural labor mar-

kets are highly correlated with rural households’ own productivity shocks (particularly in

the case of rainfall shocks). On the other hand rural non-agricultural labor markets through

the tradable sector, such as manufacturing, or urban labor markets are likely unaffected by

rainfall shocks,d d therefore would provide suitable opportunity to diversify risk for house-

holds. Further what role does access to transportation networks play? Accessing these labor

markets is highly related to the location of households. Those closer to urban centers and

with access to transportation networks (roads, bus routes and railway lines) are likely to have

the opportunity to turn to non-agricultural labor markets to diversify and smooth consump-

tion. Asher and Novosad (2016) highlight how those closer to roads are more likely to be

engaged in non-agricultural work. Is it then the case that those with better access to labor

markets and urban centers are also better able to diversify income shocks by easily accessing

labor markets that are not correlated with the agricultural shocks? As road networks have

expanded and will continue to do so, this paper will shed light on the impact on the rural

1Kochar (1999) looks at shocks to crops, Rose (2001) at rainfall shocks, Cameron and Worswick (2003)at idiosyncratic shocks such as sudden deaths and Fernandez et al. (2014) at violent conflict shocks.

2

sector.

We start with providing a theoretical framework with testable empirical predictions for

equilibrium wages and employments. The predictions differ based on each location’s road

access. We build a two-sector economy general equilibrium model with one aggregate utility-

maximizing household. The model builds heavily on Santangelo (2016). In our model the two

sectors, agriculture and manufacturing,2 hire workers in the same labor market. But different

from Santangelo (2016) working in the manufacturing sector incurs additional commuting

cost β, which is decreasing with better road access. We solve the equilibrium wages and

quantities and derive several testable predictions. First, for agricultural labor markets, a

positive rainfall shock increases both labor and wage at equilibrium. The positive labor

response is amplified and the positive wage response is mitigated when the commuting cost

is lower. Second for manufacturing labor markets, a positive rainfall shock decreases labor

and increases wage at equilibrium. The negative labor response is amplified and the positive

wage response is mitigated when the commuting cost is lower.3

Our paper looks at how equilibrium labor market employments and wages are differen-

tially affected by productivity shocks, based on their access to road networks in the setting

of India. The productivity shocks we look at are monsoon rainfall shocks; more specifically

standardized deviations from the historical mean. The access to road networks is based on

distance from the Golden Quadrilateral (GQ). The GQ is a system of roads linking four

major cities of Mumbai, Chennai, Kolkata and Delhi in India. Figure 3 in the appendix

displays the route of GQ. The GQ project is a major upgrading project of 5846 km of ex-

isting roads to four- and six-lane roads.4 It is part of the National Highways Development

Project (NHDP) initiated by the Indian government in 2001, with the goal of improving the

overall performance of its national highway system. GQ was targeted to be completed by

year 2004. In reality, by 2004 80 % of the work was finished, and 95 % of the work was

finished by the end of 2006 (Ghani et al., 2016). A similar highway project planned during

the same period, the North-South and East-West Corridor, experienced significant delays

and only less than 10 % of the roads were completed by 2006. Therefore in our empirical

2In this paper the words manufacturing and non-agriculture are interchangeable. We are aware of thefact that the non-tradable wage jobs are important sources of income for rural households, and we plan onincorporating this labor market into our model in the future.

3Here we model the manufacturing wage to be the wage that factories need to pay workers. At equilibriumthe effective wages are the same between agriculture and manufacturing sectors.

4The total cost of the project incurred at the end of year 2013 was 5.6 billion dollars

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setting (we use household-level and district-level data in years 1998 and 2005), we only use

GQ to construct area-specific road access variable. We consider 1998 as the pre-GQ period

and 2005 as the post-GQ period.

As section 2 will show, our estimation strategy compares labor outcomes in both agri-

cultural and non-agricultural markets to shocks in areas closer to the GQ before and after

the upgrading (1998 to 2005), to those areas far away. The identification strategy exploits

the fact that (1) Monsoon shocks are exogenous and (2) Absent the Golden Quadrilateral

project, labor responses in areas near the GQ would have followed the same linear trajectory

as those far from the GQ. Our ultimate aim is to analyze whether this leads to welfare gains

for those closer to transportation networks as they are better able to mitigate the impact of

productivity shocks by relying on non-agricultural labor markets, taking into account differ-

ential changes in wages. We use several datasets. Our data on household labor allocations

is obtained from the Rural Economic Development Survey (REDS), which provide details

on household members’ primary and secondary labor market activities, particularly in agri-

cultural and non-agricultural wage work. It also provides data on household’s demographic

variables, member characteristics and village level data. We obtain data on the Golden

Quadrilateral by georeferencing the raster map file obtained from the National Highway

Authority of India in ArcGIS. Rainfall data is obtained from the Terrestrial Air Tempera-

ture and Precipitation dataset prepared by the Center for Climate Research, University of

Delaware. Finally wage data is also obtained from both the ICRISAT Village Dynamics in

South Asia Macro-Meso Database and the Annual Survey of Industries of India.

The theoretical predictions hold for primary agricultural activity: a 1 unit increase in

standardized deviation in monsoon rainfall results in a 0.090 higher likelihood of having at

least 1 member primarily in agricultural activity in the year. It will also be associated with

an average increase in the number of household members involved in agricultural wage work.

More importantly being located close to the GQ post upgrade leads to an even greater re-

sponse of agricultural work. This provides suggestive evidence in favor of a lower commuting

cost and thus better diversification. Unfortunately secondary agricultural activity responds

in the opposite direction. Non-agricultural results are supported by the secondary activity

employment. Unfortunately the differential effect for primary work attenuates the impact of

productivity shocks, contradicting our theoretical predictions.

We ideally see our paper having three primary contributions to the literature. First we

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analyze how improvements in transportation affects the responses to productivity shocks.

Second we would eventually like to link this to improvements in welfare; this will involve

taking into account the effect on wages in the relevant labor markets, and the impact on

consumption. Third our analysis will also improve upon current work, by using more detailed

data, with a broader scope. Kochar (1999) and Rose (2001)’s analyses occur primarily

between the 1970s to the 1980s. Further Kochar (1999) suffers from weak instruments5 and

Ito and Kurosaki (2009) from endogeneity concerns.6

The paper is related to several strands of literature. It is related to papers looking directly

at diversification in rural labor markets. It builds on Jayachandran (2006) who finds that

access to railways changes the of labor supply elasticity and thus equilibrium wages after

productivity shocks. Our analysis however improves upon the identification strategy as well

as looks at labor responses in the non-agricultural sector. Moreover we aim to link these

effects to welfare of households , something missing in previous literature. It relates closely to

Santangelo (2016) which looks at the impact of agricultural volatility on the local economy.

Santangelo (2016)’s work focuses primarily on firms and does not look at how households

diversify their labor choices, taking into account the fluctuations in local economy. We hope

to do so by incorporating the growing transportation network in India (in our case the Golden

Quadrilateral).

Our usage of GQ connects to a broad literature studying the impact of transportation

infrastructure on economic activities. Some existing findings are access to roads promotes

trade (Donaldson, 2010), decreases the occurrence of famines (Burgess and Donaldson, 2010),

increases GDP in the long-run (Banerjee et al., 2012), and decreases migration cost (Morten

and Oliveira, 2016). (Redding and Turner (2015) give a comprehensive review of the existing

literature.) More recently Ghani et al. (2016) study the impact of GQ on manufacturing

industry in India and find that districts close to GQ experienced industry activity growth

and increase in wages. However the impact of roads on rural areas are relatively thin. Allen

and Atkin (2016) find that market integration makes households switch towards safer crops,

and Qin and Zhang (2016) find that rural roads in China lead to agricultural production

specialization, and higher agricultural income. A closely related work by Asher and Novosad

5The author instruments crop shocks with demographic variables and crop choice, but has first stageF-statistics ranging from 1 to 4

6Due to their inclusion of wages. Previous papers attempt to control for wages by including village-occupation fixed effects.

5

(2016) find that villages with road access have higher wage market labor participation rate.

However there lacks one work that looks at the extent to which access to roads mitigates

negative income shocks through off-farm employment by commuting at individual household

level, and our project intends to fill the gap.

1 Theoretical Framework

In order to guide our empirical analysis, we develop a simple static general equilibrium

model of labor markets in a two-sector economy. Our model is built heavily based on

that in Santangelo (2016). In our model two sectors, agriculture and manufacturing, hire

workers in the same labor market. But different from Santangelo (2016), working in the

manufacturing sector incurs an additional per unit wage cost β for workers. We model that

households with better access to roads (in our setting access to Golden Quadrilateral) have

a lower commuting cost to manufacturing jobs, therefore a lower β. We solve the model to

derive several theoretical predictions on how both equilibrium wage and labor responses to

productivity shocks depend on β.

1.1 Model setup

1.1.1 Production side

There are two sectors in the economy, agriculture, A, and manufacturing, M . Both sectors

use only labor as input. We assume an open economy, so that output prices, PA and PM ,

are determined exogenously. For the sake of simplicity, we also assume that they both equal

to 1. The production functions have the following forms:

YA = θALαA

YM = θMLαM

where θA and θM refer to the productivity terms, and LA and LA refer to the labor input.

We assume that the labor shares α < 1 in the production functions are the same across two

sectors in order to derive a closed form solution. Firms in two sectors compete in the same

labor market for hiring, by offering wages WA and WM respectively.

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1.1.2 Consumption side

There is one aggregate household in the economy, which supplies her/his 1 unit of labor

inelastically. The labor is immobile across space but mobile across sectors. However working

in the manufacturing sector incurs additional commuting cost, β, so that the effective wage

that workers receive when working in manufacturing is WM

β. Household’s utility function

only depends on her/his consumption on both agricultural and manufacturing goods and

follows the Cobb-Douglas form:

U(CA, CM) = CγAC

1−γM

Household obtains her/his income from several sources including agricultural profits πA, wage

from agricultural work WALA, and wage from manufacturing work WMLM . 7 So she/he has

the following budget constraint:

WALA +WMLM + πA = CA + CM

1.2 Equilibrium condition

We define the equilibrium condition of the model as follows:

• Profit Maximization. Firms in two sectors maximize their profits respectively:

maxLA

πA = θALαA −WALA

maxLM

πM = θMLαM −WMLM

The FOC gives the following two equations:

αθALα−1A = WA (1)

αθMLα−1M = WM (2)

• Utility maximization. Household maximizes her/his utility subject to the budget con-

7We assume that manufacturing profits go to the owners who do not live in the same area.

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straint:

maxCA,CM

CγAC

1−γM

subject to WALA +WMLM + πA = CA + CM

The Cobb-Douglas utility function gives the following optimal expenditure share:

CA = γ(WALA +WMLM + πA)

CM = (1 − γ)(WALA +WMLM + πA)

• Labor markets clear:

LA + LM = 1 (3)

• Effective wage equalized across two sectors:

WA =WM

β= w (4)

1.3 Equilibrium labor quantities and wage

Combining the FOC conditions from equation 1 and 2, and the effective wage equalization

condition from equation 4, we obtain the following labor allocation ratio:

LALM

= (θAθMβ

)1

α−1 (5)

We define the ratio k = ( θMθAβ

)1

α−1 , we obtain the following equilibrium labor quantities and

wages:

LA =k

k + 1(6)

LM =1

k + 1(7)

w = (αθMβ

)(1

k + 1)α−1 (8)

The intuition is that when k > 1, agriculture sector hires the majority of the labor force.

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1.4 Comparative statics

We derive how equilibrium wage and labor quantities respond with respect to a positive

rainfall shock, modeled as an increase in agricultural productivity θA.

First we derive some results that help to simplify the algebra.

∂k

∂θA= (

θMβ

)1

α−1 (1

1 − α)θ

( 11−α−1)

A = (1

1 − α)k

θA> 0

∂k

∂β= (

θMθA

)1

α−1 (1

1 − α)β( 1

1−α−1) = (1

1 − α)k

β> 0

The intuition is that after a good rainfall shock, a larger share of labor force works in

agriculture. And when the commuting cost increases, a larger share of labor force stays in

agriculture rather than commuting to work in manufacturing.

1.4.1 Labor quantity in manufacturing sector

• First we derive the impact of a positive rainfall shock on equilibrium labor quantity in

the manufacturing sector. Equation 6 gives us LM = (k + 1)−1. We calculate the first

derivative with respect to θA:

Impact of rainfall on manufacturing labor =∂LM∂θA

= (−1)(k + 1)−2 ∂k

∂θA

= (−1)(k + 1)−2(1

1 − α)k

θA

= [1

(α− 1)θA](k + 1)−2k < 0

Proposition 1. After a good rainfall shock, manufacturing labor quantity decreases at

the new equilibrium.

The result implies a crowding-out effect from manufacturing production after a positive

rainfall shock.

• Then we show whether having a high commuting cost (represented by a large β) affects

the marginal response to rainfall shock by deriving the cross derivative with respect to

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β:

∂ Impact of rainfall on manufacturing labor

∂β= (

1

(α− 1)θA)[(k + 1)−2 ∂k

∂β− 2k(k + 1)−3 ∂k

∂β]

= (k + 1)−3 ∂k

∂β(

1

(α− 1)θA)(k + 1 − 2k)

= (k + 1)−3 ∂k

∂β(

1

(1 − α)θA)(k − 1) > 0 if k > 1

Proposition 2. If agriculture is the dominant sector in the economy, i.e. k > 1, the

magnitude of manufacturing labor response after a good rainfall shock is larger when β

is small.

Since the first derivative ∂LM∂θA

is negative, having a positive cross derivative means that

when β is big, ∂LM∂θA

is less negative. Figure 1 below displays the result.

∂LM∂θA

β0

Manufacturing labor response to rainfall

Figure 1: Impact of rainfall shock on manufacturing labor quantity

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1.4.2 Labor quantity in agricultural sector

• First we look at the impact of a positive rainfall shock on equilibrium labor quantity

in agricultural sector. Equation 7 gives that LA = 1 − LM = k1+k

.

Impact of rainfall on agricultural labor =∂(1 − LM)

∂θA= −∂LM

∂θA

= −[1

(α− 1)θA](k + 1)−2k > 0

Proposition 3. After a good rainfall shock, agricultural labor quantity increases at the

new equilibrium.

• Secondly we study how have a larger commuting cost β affects the marginal response

to rainfall shock. We derive that:

∂ Impact of rainfall on agricultural labor

∂β< 0 if k > 1

Proposition 4. If agriculture is the dominant sector in the economy, i.e. k > 1, the

magnitude of agricultural labor response after a good rainfall shock is larger when β is

small.

Figure 2 below displays the result. It shows that having a low commuting cost β

amplifies the increase in equilibrium agricultural labor to a positive rainfall shock.

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∂LA∂θA

β0

Agricultural labor response to rainfall

Figure 2: Impact of rainfall shock on agricultural labor quantity

1.4.3 Manufacturing wage

From equations 4 and 8 we have WM = βw = (αθM)( 1k+1

)α−1. 8

• We first show how manufacturing wage responds to a positive rainfall shock:

Impact of rainfall on manufacturing wages =∂WM

∂θA

= (αθM)(1 − α)(k + 1)−α∂k

∂θA

= (αθM)(1 − α)(k + 1)−α(1

1 − α)k

θA

= (αθMθA

)(k + 1)−αk > 0

Proposition 5. After a positive rainfall shock manufacturing wage WM increases.

The economic intuition is that firms need to offer higher wage to compete with a more

productive agricultural sector for labor after a good rainfall shock.

• Then we show how the commuting cost β affects the manufacturing wage response by

8As we point out early in our model the manufacturing wage refers to the wage the firms pay, not theeffective wage the workers receive.

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doing the partial derivative with respect to β:

∂ Impact of rainfall on manufacturing wages

∂β= (

αθMθA

)∂((k + 1)−αk)

∂β

= (αθMθA

)((1 − α)k + 1)∂k

∂β(k + 1)−α−1

= (αθMθA

)((1 − α)k + 1)(1

1 − α)k

β(k + 1)−α−1 > 0

Proposition 6. The manufacturing wage increase after a positive rainfall shock is

smaller when β is small.

Firms located in places with high commuting costs need to offer an even higher wage

after a positive rainfall shock to attract workers, which implies a higher labor cost

volatility. And a lower commuting cost mitigates the change in manufacturing wages.

1.4.4 Effective wage that workers receive, i.e. the agricultural wage

Equation 8 gives us w = (αθMβ

)Lα−1M = (αθM

β)( 1k+1

)α−1

• First we show wage response to a positive rainfall shock by taking the first derivative:

Impact of rainfall on wage =∂w

∂θA= (

αθMβ

)(1 − α)(k + 1)−α∂k

∂θA

= (αθMβ

)(1 − α)(k + 1)−α(1

1 − α)k

θA

= (αθMβθA

)(k + 1)−αk > 0

Proposition 7. After a positive rainfall shock effective wage w increases.

We define A = αθMβθA

and B = (k + 1)−αk. We first calculate their partial derivative:

∂A

∂β= −A

β∂B

∂β=∂k

∂β(k + 1)−α + k(−α)(k + 1)−α−1 ∂k

∂β

= ((1 − α)k + 1)∂k

∂β(k + 1)−α−1

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• Then we show how commuting cost β affects the marginal response to rainfall by taking

the derivative with respect to β:

∂ Impact of rainfall on wage

∂β=∂A

∂βB +

∂B

∂βA

= −Aβ

(k + 1)−αk + A((1 − α)k + 1)∂k

∂β(k + 1)−α−1

= −Aβ

(k + 1)−αk + A((1 − α)k + 1)(1

1 − α)k

β(k + 1)−α−1

= A(k + 1)−α−1 k

β[−(k + 1) + ((1 − α)k + 1)

1

1 − α]

= A(k + 1)−α−1 k

β(

1

1 − α− 1) > 0

Proposition 8. The agricultural wage increase after a positive rainfall shock is smaller

when β is small.

The result implies that a lower commuting cost mitigates the wage response after an

agricultural productivity shock. Since our model assumes an inelastic labor supply,

the result also implies that having a lower commuting cost mitigates income volatility.

2 Empirical Strategy

We now look at the continuous version of the estimation. Below we depict our main esti-

mating equation: Equation (9).

2.1 Main Empirical Specifications

yjit =β1Disti + β2Postt + β3Monsoonit

+β4Monsoonit ·Disti + β5Monsoonit · Postt + β6Disti · Postt

+β7Disti · Postt ·Monsoonit +Xjit + εjit

(9)

Where:

yjit: Labor market outcome variables for household j in village i, year t for both agricultural

and non-agricultural sectors

Disti: The distance of village i’s to the Golden Quadrilateral

Postt: Indicator taking the value 1 if the year t is 2005

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Monsoonit: Standardized deviation of the monsoon rainfall based on historic mean and vari-

ance.

Xit refers to the same controls as before.

Marginal changes in outcomes due to a 1 unit increase in the standardized deviation in

monsoon is given below in Equation 10:

∂yjit∂Monsoonit

= β3 + β4Disti + β5Postt + β7Disti · Postt (10)

Here the empirical predictions after a rainfall shocks now depend on distance as well.

These are summarized in Tables 1-4.

2.2 Prediction

Empirically we want to test the differential impacts of the rainfall shocks on labor market

outcomes between treatment group who obtain the road access and control group. here for

brevity we focus on the β7 coefficient which captures this differential impact.

Coefficient Interpretation ∂β∂dist > 0 ∂β

∂dist ≤ 0

β3 Impact of rainfall in 1998 at GQ ? ?

β4 Differential Impact of rainfall in 1998 per km ? ?

β3 + β4dist Average Effect of rainfall in 1998 + +

β5 Time trend at 0 distance from GQ ? ?

β3 + β5 + (β4 + β7)dist Average Effect of rainfall in 2005 + +

β7 Differential effect − 0/+

Table 1: Predictions on the signs of coefficients of Employment (Agriculture)

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Coefficient Interpretation ∂β∂dist > 0 ∂β

∂dist ≤ 0

β3 Impact of rainfall in 1998 at GQ ? ?

β4 Differential Impact of rainfall in 1998 per km ? ?

β3 + β4dist Average Effect of rainfall in 1998 + +

β5 Time trend at 0 distance from GQ ? ?

β3 + β5 + (β4 + β7)dist Average Effect of rainfall in 2005 + +

β7 Differential effect + 0/−

Table 2: Predictions on the signs of coefficients of Wage (Agriculture)

The hypothesis we want to test is that villages close to the GQ having a lower commuting

cost: ∂β∂dist

> 0. The alternative hypothesis would be ∂β∂dist

≥ 0. The model from section 1

predicts that at the average distance a postive rainfall deviation should raise the agriculutural

employment and wage: 3 and 7. We thus expect β3 +β4dist and β3 +β5 +(β4 +β7)dist to be

positive for both wage and labor equations. Our theoretical framework does not predict the

signs of coefficients β3 and β5. β3 is simply the impact at 0 distance from the GQ. This does

not exist in out data, and the ”intercept” of the marginal effect has no interpretation. β5 on

the other hand is simply the common time trend. Our key parameter of interest, β7, captures

the differential changes in impacts of rainfall on agricultural labor market outcomes between

treatment group and control group from 1998 to 2005. In the case of labor under proposition

4 we predict that β7 is negative under the condition that agriculture is the dominant sector

in the economy. Under the alternative we expect that β7 be positive (Or 0). For wages under

proposition 8 β7 is positive but negative or 0 under the alternative.

Similarly Tables 3 and 4 below summarize the predictions on non-agricultural market

outcomes based on our theoretical framework. In our theoretical framework the difference

between household close and far from the roads is depicted in the difference in commuting

cost (β).

16

Coefficient Interpretation ∂β∂dist > 0 ∂β

∂dist ≤ 0

β3 Impact of rainfall in 1998 at GQ ? ?

β4 Differential Impact of rainfall in 1998 per km ? ?

β3 + β4dist Average Effect of rainfall in 1998 − −

β5 Time trend at 0 distance from GQ ? ?

β3 + β5 + (β4 + β7)dist Average Effect of rainfall in 2005 − −

β7 Differential effect + 0/−

Table 3: Predictions on the signs of coefficients of Employment (Non-Agriculture)

Coefficient Interpretation ∂β∂dist > 0 ∂β

∂dist ≤ 0

β3 Impact of rainfall in 1998 at GQ ? ?

β4 Differential Impact of rainfall in 1998 per km ? ?

β3 + β4dist Average Effect of rainfall in 1998 + +

β5 Time trend at 0 distance from GQ ? ?

β3 + β5 + (β4 + β7)dist Average Effect of rainfall in 2005 + +

β7 Differential effect + 0/−

Table 4: Predictions on the signs of coefficients of Wage (Non-Agriculture)

The hypothesis we want to test is that villages close to the GQ having a lower commuting

cost ∂β∂dist

> 0. The alternative hypothesis would be ∂β∂dist

≤ 0. The model from section 1

predicts that labor in non-agricultural sector decreases, and wage in non-agricultural sector

increases at equilibrium for both control group and treatment group and for both year 1998

and year 2005, as predicted by propositions 1 and 5. We thus expect β3 + β4dist, and

β3 +β5 + (β4 +β7)dist to be positive for wage, and negative for labor. Our key parameter of

interest, β7, captures the differential changes in impacts of rainfall on non-agricultural labor

market. In the case of labor under proposition 2 we predict that β7 is positive under the

condition that agriculture is the dominant sector in the economy. Under the alternative we

expect that β7 be negative (Or 0). For wages under proposition 6 β7 is positive but negative

or 0 under the alternative.

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2.3 Ways to improve the identification strategy

There are several potential ways to improve our identification strategy. One concern is that

although some of the curvatures in the actual GQ network may be attributed to construction

cost concerns, some may be shaped with the intention to either connect growing cities or to

help failing cities. We will instrument the actual distance to GQ with distance to a striaght-

line network between the four nodal cities. Our identification assumption is that the fact of

being close to the straight line between four cities only affects the change in labor market

outcomes with respect to rainfall shock only through the GQ access. This assumption would

be violated if between 1998 and 2005 one unobserved economic shock happened that only

benefited cities located on the straight lines.

Secondly we will employ one highway project, the North-South and East-West (NS-EW)

corridor, which was planned during the same period but experienced significant delay during

construction by the end of 2005. One potential control group for the treated districts that

obtained access to GQ is the set of districts that are close to the NS-EW network, who were

planned to obtain access to highway but did gain access by 2005. Here our identification

assumption is that the the only factor that affects labor markets differentially with respect to

rainfall shocks between treatment group and control group from 1998 to 2005 is the actual

road access. One violation of this assumption can be that the government endogenously

chose to build GQ first over NS-EW because of the fact that districts close to GQ have better

economic potentials. Those districts would have developed a more diversified economy and

a more developed financial institutions between 1998 and 2005 without GQ, which would

help households to better cope with rainfall shocks.

3 Data

3.1 Household Data

We will be using several datasets to analyze this question. The first is the Rural Economic

Development Survey (REDS)9. This dataset provides details on the household’s labor market

activities; particularly which sectors they work in. The primary source of information on

labor markets is derived from questions on the primary and secondary activity statuses of

9This has been referred to as the ARIS-REDS as well

18

individuals in a household. Following the literature (Kochar (1999), Rose (2001)) we conduct

analysis at the household level. We thus use data on primary and secondary statuses to create

our main variables for analysis: (1) Indicators for whether the household has at least one

member in (a) Agricultural wage work (b) Non-agricultural wage work (c) On farm work and

(2) The total number of individuals in the three sectors. It is important to note that wage

work refers to casual, non-contract labor while farm labor refers to self-employment in farm

work or agricultural family work. In addition the REDS dataset also provides details on

household expenditures and consumption. The dataset is a panel covering the years: 1969-

1971, 1982, 1998-1999 and 2005-2006. We currently use the 1998-1999 round and 2005-2006

round.

3.2 Rainfall Data

To obtain monthly Indian village-level rainfall we use the Terrestrial Air Temperature and

Precipitation dataset compiled by the Center for Climate Research, University of Delaware,

which covers the period 1900-2014. The gridded rainfall is provided at a frequency of 0.5

by 0.5 degree grid of points across India. The rainfall data is matched to villages based on

closest distance criterion. This provides the main rainfall variables: realized monsoon season

precipitation (mm), deviation from the long-run mean level of precipitation, and long-run

standard deviation of rainfall. This leaves us with 242 villages which we can use. For district

level analysis on wages we employ the rainfall data from ICRISAT Village Dynamics in South

Asia Macro-Meso Database.

3.3 GQ Data

We download the GQ map from the National Highway Authority of India 10. We georeference

the raster map file and retrieve the GQ highway in ArcGIS. Second we geocode the locations

of REDS villages using Google API and calculate the distance from villages to the GQ

highway. Third we use the district boundary shapefile from the 2001 India Census 11 to

calculate the distance of district centroids to the GQ highway. We also calculate the closet

distance of REDS villages to any of the four nodal cities as well as the closest distance from

district centroids to the any of the four nodal cities.

10http://www.nghai.org/gqmain_english.htm#11We obtain the shapefile from Stanford Geospatial Center

19

3.4 Wage Data

The eventual aim of this paper is to link improvements in transport infrastructure to welfare

gains due to increased employment opportunities. To do so we have to look at both wage

changes to ascertain income changes, and look at changes in consumption patterns. For

wages we turn to two sources of data. Daily agricultural wages are derived from the ICRISAT

Village Dynamics in South Asia Macro-Meso Database which is obtained from various official

government data sources. The database covers 308 districts from 1966 to 2009 using the 1966

district boundaries ensure consistency over time. Daily factory wages are obtained from the

Annual Survey of Industry (ASI). The ASI surveys manufacturing factories across the entire

India. It includes all factories employing 10 or more workers using power and all factories

employing 20 or more workers. It also surveys a random third of unregistered factories.

3.5 Descriptive Statistics

Summary statistics for the main variables used are reported in Tables 5 and 6, for both 1998

and 2005 as well as those villages close and far. It is clear that in primary activity a greater

proportion of family members work in agriculture in 1998; While a quarter of families have

at least one member whose primary activity is agricultural wage work, only a fifth have it

at least one member primarily in non-agricultural wage work. This difference does even out

in 2005.

An ideal test of the identifying assumption would be a placebo test where we would run

equation ??, but look at years prior to the GQ project. The REDS dataset does include

previous year’s data: 1969-1971 and 1981.12 One concern is that there is a 17 year gap

between 1981 and 1999, and it would be hard to ascertain the parallel trends assumption.

However we feel that the similarity in primary labor market activity in 1998 for those close

and far is suggestive that far away villages are a good control group. Looking at column

(3) of Table 5, for the primary activity status the differences between those close to the

Golden Quadrilateral and those far is negligible in 1998 (prior to the GQ project) 13. This

suggests that the labor market patterns of those who are close to the GQ and those far were

not significantly different. Secondary activity status does vary significantly in the baseline

12We are currently working on compiling this to run the placebo test13The total number who are primarily in agriculture is significantly higher for those further than those

closer

20

year. Though this does not invalidate our identification strategy, we feel more confident in

primary activity status.

There are some changes over time. Primary agricultural wage work sees a decline from

1998 to 2005; the proportion of families with at least one member declines from 0.25 to 0.15

for those close and 0.26 to 0.18 for those far. There is also an increase in Secondary Activity

Wage work in both agriculture and non-agriculture.

Table 6 on the other hand depicts the descriptive statistics for the key control variables.

Looking at column (3) it is clear that there are significant differences in households close

and far from the GQ, which motivates our use of controls in all regressions. A reassuring

fact is that the distance to the GQ does not change too much over the years. Highlighting

that the GQ was not started in 1999, what this implies is the composition of participants in

the years has not changed significantly.

3.6 Number of factories

This paper assumes that being closer to the GQ reduces commuting costs when traveling to

local factories for work. Villages closer to the GQ will see a rise in the number of factories

both in their village and nearby, thus reducing the commuting costs compared to villages

further from the GQ. Table 7 provides evidence in favor of this. Columns 1 and 2 indicate

that after the GQ was completed, villages closer to the GQ had more factories near the

village and in total than those further away. This also matches Ghani et al. (2016) which

finds that the GQ led to a movement of indian manufacturing from urban to rural districts.

4 Results

4.1 Employment Outcomes

Table 8 in Appendix C shows the preliminary regression results using having at least 1

member whose primary activity is agriculture, non-agriculture and farm labor as outcome

variables. It is clear that monsoon has a strong impact on casual labor allocations. While

a 1 unit increase in a standardized deviation in monsoon rainfall leads to a 0.090 increase

in the likelihood of having at least 1 family member in primary agricultural wage work it

21

leads to a 0.059 decrease in the likelihood for non-agricultural work. This is the prediction

obtained from the theoretical framework. A good rainfall shock increases agricultural pro-

ductivity and thus labor demand for agricultural work. Our theoretical framework predicts

that equilibrium employment increases. On the other hand a positive rainfall shock results

in reduced labor supply for non-agricultural work through substitution towards agricultural

work. As we assume non-agricultural demand does not change, we have that equilibrium

employment reduces in the non-agricultural labor market.

Unfortunately this is not universal result. For villages close to the GQ the overall response

in 1999 is in-fact negative. This is evident by looking at the coefficient on NearGQ·Monsoon

which when added to that on Monsoon leads to a negative effect. This does not necessarily

invalidate our empirical strategy, it does contradict our theoretical prediction that the impact

of monsoon on agricultural labor is positive. This does not however occur for non-agricultural

work. Here the overall effect on non-agricultural work is negative for all villages, and is not

significantly different.

Next we turn to the key parameter for this paper: the coefficient on the triple interaction

term (β7). This captures the differential impact on households in villages close to the GQ.

Being close to the GQ increases the equilibrium response to productivity shocks for agricul-

tural work. As can be seen in column (1) of Table 8 being close to a GQ is associated with

a significantly positive impact on the likelihood of having at least 1 member in agricultural

wage work. There is an implied increase of 0.15 in the likelihood. This is in accordance with

the theoretical framework presented in section 1 which predicts that β7 should be positive.

When looking at non-agricultural wage work, being close to the GQ paradoxically mitigates

the effect of a rainfall shock, as can be seen by the positive coefficient on the triple interac-

tion in the second column of Table 8 which implies a smaller reduction in wage work closer

to the GQ. This coefficient is both positive and statistically insignificant, contradicting our

theoretical prediction.

The results are similar when using the number of individuals in each labor market activity

per household as outcome variable, depicted in Table 9. The impact of a monsoon shock

leads to an increase in the number of agricultural wage work members but a decrease in

the number of non-agricultural work members. When looking at the triple-interaction term

access to GQ amplifies the impact of a positive rainfall shock, as the coefficient in column

(1) is positive, though not significant. However once again for non-agricultural wage work

22

villages close to the GQ decrease less as the coefficient before the triple interaction term in

column (2) is positive, though not significant, which contradict our theoretical predictions.

The coefficients on the controls are largely as expected: having more males and females

between the ages of 15 and 45 increases participation in casual work and on farm work.

Obtaining education leads to reduced casual and on-farm work. More land, and in particular

irrigated land leads to greater on farm work and less casual work.

Tables 10 and 11 depict the results for secondary labor market activities. Again we look

at wage work as this refers to casual work that can respond in the short run to shocks.

The results largely contradict those found in primary agricultural labor markets. Looking

at column (1) of both tables the result is that after a rainfall shock equilibrium agricultural

wage labor falls, contradictory to both our theoretical and primary activity analysis. The

effect is however amplified by access to roads, as the coefficients before the triple-interaction

terms are negative and statistically significant. Looking at non-agricultural work in column

(2) of Tables 10 and 11 show similar results to those in primary activity, which is as theory

predicts. The impact of a monsoon shock leads to a reduction in the likelihood of having at

least 1 member in agricultural work by 0.08 and on average 0.11 people in the household.

This is statistically identical in near and far villages as can be seen by the insignificance on

NearGQ ·Monsoon. Looking at the coefficient on the triple interaction term we can see

that as predicted access to roads amplifies the equilibrium response.

Looking at non-agricultural labor, the results in secondary labor activity are more in

line with our theoretical predictions. Monsoon shocks cause significant declines in Non-

Agricultural participation. Furthermore looking at column (2) in Table 10 β7 the coefficient

of interest is negative and significantly. The results hold in Table 11.

4.2 Wage Responses

The next section looks at responses of wages. Table 12 depicts the impact on daily agricul-

tural wages. Column (3) suggest that a 1 unit positive monsoon shock leads to an increase

in daily wages of 2.39 rupees. The positive effect applies to districts close to the GQ in 1999.

This can be seen in the fact that coefficient on GQDummy ·Rainfall is positive and the sum

β3 + β4 is positive as well. However as can be seen in 2005Dummy ·Rainfall the coefficient

is negative and the sum β3 + β5 is also negative. This implies that for districts far from the

23

GQ, agricultural wages do not rise. The coefficient on the triple interaction suggests that

wage response is smaller as districts come closer to the Golden Quadrilateral in 2005, though

not statistically significantly. This is in keeping with our theoretical framework.

Table 13 conducts the similar exercise for non-agricultural wages. These are daily factory

wages for all workers in factories, excluding those in managerial positions. Looking at column

(3) depicts that a good rainfall shock paradoxically leads to a decrease in wages. This does

not vary across districts depending on if they are close or far, though this (measured by

coefficient on GQDummy · Rainfall) is also quite noisily measured. Finally looking at

the triple interaction term we obtain a small increase. While the result is in contrary to

the theoretical predictions, the large standard errors render the data too noisy to draw a

conclusion. It is noteworthy that the overall effect for β3 + β4 + β5 + β7 is positive, though

the remaining predictions do not hold.

5 Conclusion

Can labor markets even play a significant role in providing diversification to farmers? This

was the primary question we wish to answer in this paper. We began by building a static

general equilibrium framework on rural labor markets. The framework incorporates both

non-agricultural and agricultural labor markets, and provides predictions on equilibrium

outcomes. The improved ability to diversify is captured by a lower commuting cost to non-

agricultural jobs. If farmer with better access to roads (and thus urban labor markets),

we should see differential responses to the same productivity shocks in the agricultural and

non-agricultural labor markets, implying different ability to cope with shocks. The theory

predicted that this better ability to diversify should be seen in greater equilibrium responses

to shocks in terms of employment. At the same time, wages too would adjust differentially,

and to account for this we look at wage responses separately.

Our main findings remain ambiguous. Productivity shocks do seem to change wage

work as expected at times: primary work for agriculture and secondary for non-agriculture.

Unfortunately the results are contradicted by secondary activity for agriculture and primary

activity for non-agriculture. Furthermore the results for non-agricultural work are often

insignificant again contradicting the predictions. The results on wages are noisily estimated,

therefore fail to provide solid evidence in support of the theory.

24

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26

A Figures

Figure 3: Golden Quadrilateral map

27

Figure 4: Treatment districts and control districts

28

B Descriptive Statistics

29

Tab

le5:

Com

par

ison

30K

min

side

and

outs

ide

asdis

tance

toG

Q

1998

2005

(1)

(2)

(3)

(4)

(5)

(6)

≤30km

>30km

Diff

≤30km

>30km

Diff

mea

nm

ean

bt-

stat

mea

nm

ean

bt-

stat

Pri

mar

yA

ctiv

ity

inA

gric

ult

ure

:A

tle

ast

1m

emb

er0.2

50.2

60.0

0(0

.33)

0.1

50.1

80.0

3∗∗

(2.7

3)

Pri

mar

yA

ctiv

ity

inA

gric

ult

ure

:T

otal

Nu

mb

er0.4

70.5

60.0

8∗∗

(2.7

9)

0.2

30.2

90.0

6∗∗

(2.6

6)

Pri

mar

yA

ctiv

ity

inN

on-A

gric

ult

ure

:A

tle

ast

1m

emb

er0.1

80.1

7-0

.01

(-0.7

0)

0.1

50.1

60.0

1(0

.89)

Pri

mar

yA

ctiv

ity

inN

on-A

gric

ult

ure

:T

otal

Nu

mb

er0.3

00.3

0-0

.01

(-0.2

9)

0.2

00.2

10.0

1(0

.65)

Sec

ond

ary

Act

ivit

yin

Agr

icu

ltu

re:

At

leas

t1

mem

ber

0.0

40.0

50.0

1(1

.14)

0.1

20.1

70.0

5∗∗

(4.3

8)

Sec

ond

ary

Act

ivit

yin

Agr

icu

ltu

re:

Tot

alN

um

ber

0.0

50.0

70.0

2∗

(1.9

4)

0.1

70.2

50.0

8∗∗

(4.1

5)

Sec

ond

ary

Act

ivit

yin

Non

-Agr

icu

ltu

re:

At

least

1m

emb

er0.0

30.0

50.0

1∗∗

(2.1

7)

0.0

80.1

00.0

2∗∗

(2.0

0)

Sec

ond

ary

Act

ivit

yin

Non

-Agr

icu

ltu

re:

Tota

lN

um

ber

0.0

40.0

60.0

2∗∗

(2.7

9)

0.1

00.1

30.0

3∗∗

(2.4

1)

Ob

serv

atio

ns

1296

4507

5803

1095

4364

5459

∗p<

0.10,∗∗p<

0.05,∗∗∗p<

0.01

30

Tab

le6:

Com

par

ison

30K

min

side

and

outs

ide

asdis

tance

toG

Q

1998

2005

(1)

(2)

(3)

(4)

(5)

(6)

≤30km

>30km

Diff

≤30km

>30km

Diff

mea

nm

ean

bt

mea

nm

ean

bt

Mon

soon

-0.0

00.2

70.2

7∗∗

(24.4

0)

0.1

60.1

2-0

.05∗∗

(-2.4

9)

Dis

tG

Q12.4

0165.4

1153.0

1∗∗

(81.9

0)

12.8

0181.0

7168.2

8∗∗

(84.6

0)

Fem

ales

15-4

51.4

71.4

80.0

0(0

.14)

1.2

61.2

6-0

.00

(-0.0

7)

Fem

ales

15-4

5:P

riE

du

c0.2

70.2

6-0

.00

(-0.1

3)

0.2

50.2

0-0

.05∗∗

(-2.8

4)

Fem

ales

15-4

5:M

idd

leE

du

c0.2

10.1

7-0

.04∗

∗(-

2.7

5)

0.2

80.3

20.0

3∗

(1.8

1)

Fem

ales

15-4

5:H

igh

erE

du

c0.3

20.2

8-0

.05∗∗

(-2.1

5)

0.7

30.7

40.0

1(0

.39)

Mal

es15

-45

1.6

51.6

0-0

.05

(-1.4

2)

1.2

61.2

5-0

.01

(-0.2

5)

Ave

rage

Age

ofM

ales

15-4

533.0

032.9

6-0

.05

(-0.1

6)

28.9

529.2

80.3

3(1

.15)

Ave

rage

Age

ofM

ales

15-4

5(S

q.)

1125.3

91121.7

7-3

.62

(-0.1

8)

896.2

0911.8

815.6

8(0

.92)

Mal

es15

-45:

Pri

Ed

uc

0.2

60.3

10.0

5∗∗

(2.7

1)

0.2

10.2

0-0

.01

(-0.5

3)

Mal

es15

-45:

Mid

dle

Ed

uc

0.2

70.2

70.0

0(0

.15)

0.3

90.4

10.0

2(0

.99)

Mal

es15

-45:

Hig

her

Ed

uc

0.7

50.6

1-0

.14∗∗

(-4.2

7)

0.6

60.6

4-0

.02

(-0.7

6)

Are

aIr

riga

ted

331.7

0268.0

8-6

3.6

1∗∗

(-3.4

2)

2.5

12.7

40.2

3(1

.36)

Are

aL

and

453.6

5536.3

382.6

9∗∗

(3.6

1)

3.6

94.8

61.1

8∗∗

(5.6

5)

Dis

tan

ceto

Pos

tO

ffice

19.7

316.8

8-2

.85∗∗

(-2.1

8)

1.1

41.6

70.5

3∗∗

(9.4

8)

Dis

tan

ceto

Tow

n131.0

4114.9

2-1

6.1

2∗∗

(-3.1

9)

10.6

014.1

43.5

4∗∗

(13.3

9)

Ob

serv

atio

ns

1296

4507

5803

1095

4364

5459

∗p<

0.10,∗∗p<

0.05,∗∗∗p<

0.01

31

Table 7: Number of Factories

Factories inside Factories nearby Factories total

Dep var: type of work b/se b/se b/se

Post 0.34 7.14*** 7.49***

(0.33) (2.67) (2.71)

Distance 0.00068** -0.00040 0.00027

(0.00033) (0.00029) (0.00060)

Distance x Post -0.0018 -0.018** -0.020**

(0.0013) (0.0085) (0.0087)

Vill. Clust Yes Yes Yes

N 11262 11262 11262

r2 0.005 0.032 0.033

32

33

C Results

Table 8: At least 1 member in Primary Activity

Primary Ag Primary Non-Ag Primary Farm

Dep var: b/se b/se b/se

Monsoon 0.090*** -0.059*** 0.023

(0.031) (0.012) (0.046)

Post -0.052*** 0.040* 0.22***

(0.019) (0.022) (0.046)

NearGQ 0.011 0.0089 -0.045

(0.021) (0.011) (0.051)

NearGQ x Monsoon -0.18*** 0.036 0.21***

(0.055) (0.044) (0.017)

Post x Monsoon -0.044 -0.047* 0.045

(0.039) (0.027) (0.052)

NearGQ x Post -0.032 -0.061** 0.011

(0.030) (0.026) (0.058)

Post x NearGQ x Monsoon 0.15** 0.0034 -0.19***

(0.064) (0.054) (0.048)

Females 15-45 0.027*** 0.024** 0.089***

(0.0082) (0.010) (0.014)

Females 15-45: Pri Educ -0.034*** -0.045*** -0.036*

(0.0093) (0.0089) (0.018)

Females 15-45: Middle Educ -0.048*** -0.053*** -0.072***

(0.013) (0.014) (0.023)

Females 15-45: Higher Educ -0.032*** -0.023** -0.077***

(0.0097) (0.011) (0.019)

Males 15-45 0.042* 0.062*** 0.045

(0.024) (0.014) (0.034)

Average Age of Males 15-45 -0.0062 0.0000038 0.018***

(0.0054) (0.0041) (0.0060)

Average Age of Males 15-45 (Sq.) 0.000096 -0.0000083 -0.00035***

(0.000093) (0.000067) (0.000095)

Males 15-45: Pri Educ -0.023 -0.0075 0.0064

(0.024) (0.014) (0.027)

Males 15-45: Middle Educ -0.046** -0.022 -0.0079

(0.023) (0.014) (0.030)

Males 15-45: Higher Educ -0.038* -0.036*** -0.019

(0.021) (0.013) (0.027)

Distance to Post Office 0.00031* -0.00069*** -0.00019

(0.00017) (0.00013) (0.00061)

Distance to Town 0.000087 0.000010 -0.000019

(0.00018) (0.00012) (0.00015)

Area Irrigated -0.00014*** -0.000062*** 0.00019***

(0.000034) (0.000019) (0.000050)

Area Land -0.000042*** -0.000014*** 0.000066***

(0.000014) (0.0000032) (0.000018)

Vill. Clust Yes Yes Yes

N 4630 4630 4630

r2 0.054 0.053 0.074

34

Table 9: Total Number of members in Primary Activity

Primary Ag Primary Non-Ag Primary Farm

Dep var: b/se b/se b/se

Monsoon 0.21*** -0.061*** 0.040**

(0.044) (0.023) (0.017)

Post -0.10*** 0.056** 0.66***

(0.037) (0.027) (0.067)

NearGQ -0.065** 0.0099 0.00069

(0.029) (0.021) (0.065)

NearGQ x Monsoon -0.27*** 0.0059 0.037

(0.100) (0.056) (0.12)

Post x Monsoon -0.13** -0.082** 0.24***

(0.063) (0.040) (0.079)

NearGQ x Post 0.032 -0.089** -0.17*

(0.044) (0.036) (0.096)

Post x NearGQ x Monsoon 0.19 0.062 -0.049

(0.12) (0.069) (0.18)

Females 15-45 0.080*** 0.056 0.31***

(0.017) (0.034) (0.017)

Females 15-45: Pri Educ -0.099*** -0.071*** -0.020

(0.025) (0.023) (0.042)

Females 15-45: Middle Educ -0.12*** -0.090** -0.15***

(0.026) (0.038) (0.036)

Females 15-45: Higher Educ -0.085*** -0.044 -0.17***

(0.025) (0.033) (0.038)

Males 15-45 0.12*** 0.16*** 0.41***

(0.044) (0.029) (0.077)

Average Age of Males 15-45 -0.016* -0.0021 0.055***

(0.0094) (0.0054) (0.017)

Average Age of Males 15-45 (Sq.) 0.00025 0.000019 -0.0010***

(0.00017) (0.000087) (0.00030)

Males 15-45: Pri Educ -0.039 -0.044** 0.017

(0.041) (0.021) (0.053)

Males 15-45: Middle Educ -0.11*** -0.081*** -0.096

(0.039) (0.024) (0.059)

Males 15-45: Higher Educ -0.084** -0.089*** -0.053

(0.036) (0.022) (0.047)

Distance to Post Office 0.00074*** -0.0012*** 0.0010

(0.00022) (0.00018) (0.00099)

Distance to Town 0.00019 -0.000024 0.00027**

(0.00033) (0.00015) (0.00011)

Area Irrigated -0.00027*** -0.00010*** 0.00028**

(0.000066) (0.000025) (0.00012)

Area Land -0.000078*** -0.000024*** 0.00011***

(0.000025) (0.0000035) (0.000029)

Vill. Clust Yes Yes Yes

N 4630 4630 4630

r2 0.074 0.068 0.236

35

Table 10: At least 1 member in Secondary Activity

Secondary Ag Secondary Non-Ag Secondary Farm

Dep var: b/se b/se b/se

Monsoon -0.083*** -0.080*** 0.051**

(0.015) (0.025) (0.026)

Post 0.095*** 0.038* -0.15***

(0.021) (0.022) (0.038)

NearGQ -0.048** -0.035*** -0.010

(0.024) (0.0046) (0.036)

NearGQ x Monsoon 0.054* 0.15** 0.071

(0.030) (0.060) (0.053)

Post x Monsoon 0.15*** 0.0011 -0.0080

(0.043) (0.031) (0.049)

NearGQ x Post -0.014 0.014 0.012

(0.032) (0.026) (0.058)

Post x NearGQ x Monsoon -0.10* -0.14** -0.11

(0.053) (0.068) (0.090)

Females 15-45 0.028*** 0.025* -0.0012

(0.0064) (0.014) (0.013)

Females 15-45: Pri Educ -0.030** -0.035** -0.037

(0.012) (0.018) (0.024)

Females 15-45: Middle Educ -0.056*** -0.047** -0.022

(0.0099) (0.020) (0.025)

Females 15-45: Higher Educ -0.012 -0.018 0.032*

(0.0089) (0.016) (0.019)

Males 15-45 0.024*** 0.00054 0.017

(0.0075) (0.014) (0.020)

Average Age of Males 15-45 -0.0034 -0.0012 -0.0038

(0.0049) (0.0045) (0.0082)

Average Age of Males 15-45 (Sq.) 0.000053 0.000021 0.000068

(0.000080) (0.000072) (0.00014)

Males 15-45: Pri Educ -0.013 0.0066 -0.061**

(0.011) (0.014) (0.024)

Males 15-45: Middle Educ -0.030*** -0.0015 0.0067

(0.0095) (0.016) (0.021)

Males 15-45: Higher Educ -0.026** -0.0049 0.0026

(0.011) (0.013) (0.020)

Distance to Post Office 0.00018 0.000037 0.00048*

(0.00016) (0.000079) (0.00024)

Distance to Town 0.00011** -0.000055* -0.000025

(0.000053) (0.000032) (0.00017)

Area Irrigated -0.000021 -0.000043*** 0.000014**

(0.000021) (0.0000065) (0.0000063)

Area Land -0.000021*** -0.000011*** -0.000024

(0.0000044) (0.0000041) (0.000015)

Vill. Clust Yes Yes Yes

N 4630 4630 4630

r2 0.050 0.034 0.028

36

Table 11: Total Number of members in Secondary Activity

Secondary Ag Secondary Non-Ag Secondary Farm

Dep var: b/se b/se b/se

Monsoon -0.17*** -0.11*** -0.0013

(0.040) (0.042) (0.066)

Post 0.14*** 0.043 -0.11

(0.026) (0.033) (0.078)

NearGQ -0.11** -0.048*** -0.022

(0.046) (0.0061) (0.13)

NearGQ x Monsoon 0.12* 0.19** 0.46***

(0.062) (0.079) (0.095)

Post x Monsoon 0.30*** 0.022 0.12

(0.083) (0.048) (0.12)

NearGQ x Post -0.0085 0.016 -0.077

(0.056) (0.034) (0.17)

Post x NearGQ x Monsoon -0.22** -0.17* -0.53***

(0.098) (0.090) (0.19)

Females 15-45 0.067* 0.036** 0.22***

(0.037) (0.016) (0.044)

Females 15-45: Pri Educ -0.063 -0.049** -0.17***

(0.042) (0.023) (0.056)

Females 15-45: Middle Educ -0.11*** -0.062*** -0.19***

(0.036) (0.024) (0.049)

Females 15-45: Higher Educ -0.012 -0.020 -0.071

(0.038) (0.020) (0.051)

Males 15-45 0.093** 0.028 0.17***

(0.040) (0.029) (0.030)

Average Age of Males 15-45 0.0044 -0.0015 -0.020

(0.0075) (0.0067) (0.023)

Average Age of Males 15-45 (Sq.) -0.000074 0.000029 0.00036

(0.00012) (0.00011) (0.00041)

Males 15-45: Pri Educ -0.033 -0.0053 -0.12**

(0.036) (0.027) (0.046)

Males 15-45: Middle Educ -0.075** -0.0057 -0.020

(0.037) (0.030) (0.038)

Males 15-45: Higher Educ -0.063 -0.023 0.054

(0.042) (0.026) (0.036)

Distance to Post Office -0.000092 -0.000087 0.0016

(0.00059) (0.00019) (0.0011)

Distance to Town 0.00028** -0.000015 -0.00017

(0.00012) (0.000033) (0.00015)

Area Irrigated -0.000037 -0.000070*** 0.00020***

(0.000031) (0.0000096) (0.000052)

Area Land -0.000037*** -0.000011* -0.000093**

(0.000012) (0.0000061) (0.000044)

Vill. Clust Yes Yes Yes

N 4630 4630 4630

r2 0.055 0.030 0.057

37

Table 12: Agricultural male wages with summer rainfall shock and GQ

(1) (2) (3)

Dep var: daily agricultural male wages b/se b/se b/se

Summer rainfall shock 1.52 4.30*** 2.39

(1.02) (1.44) (1.47)

Dummy = 1 if Distance to GQ < 30km -6.69* -3.83 -5.60

(3.47) (4.28) (5.20)

Dummy = i if 2005 14.2*** 24.7*** 18.1***

(1.47) (2.27) (2.11)

GQ Dummy ∗ Rainfall 0.92 1.81 7.59

(1.95) (4.42) (4.99)

2005 Dummy ∗ Rainfall -0.74 0.49 -3.81

(1.86) (3.19) (3.31)

2005 Dummy ∗ GQ Dummy 7.49* -1.73 -4.00

(4.04) (5.11) (5.89)

2005 Dummy ∗ GQ Dummy ∗ Rainfall -3.79 -2.94 -4.58

(3.76) (5.56) (7.40)

Road length (in 000 km) -1.24*** -1.57***

(0.17) (0.25)

Fertilizer consumption (in 000 tons) -0.048 -0.097**

(0.043) (0.042)

Total area using HYV seeds -0.012 -0.016

(0.011) (0.011)

Net irrigated area (in 000 hectares) 0.091*** 0.098***

(0.015) (0.015)

Net cropped Area (in 000 hectares) -0.033*** -0.0095

(0.0061) (0.0071)

Population (in 000) 0.000045

(0.0028)

Rural population (in 000) -0.013**

(0.0051)

Total rural literates (in 000) 0.036***

(0.0080)

Total rural cultivators (in 000) -0.032*

(0.017)

N 561 384 383

r2 0.076 0.304 0.464

Standard errors in parentheses

Standard errors clustered at district level

∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

38

Table 13: Factory worker wages on rainfall shock and GQ

(1) (2) (3)

Dep var: daily factory wages among workers b/se b/se b/se

Summer rainfall shock -1.29 -0.75 -4.96***

(1.65) (1.77) (1.84)

Dummy = 1 if DistancetoGQ < 30 19.9 20.3 23.3

(14.7) (15.4) (16.3)

Dummy = i if 2005 22.7*** 23.0*** 26.4***

(2.26) (2.61) (2.51)

GQ Dummy ∗ Rainfall -1.18 -2.36 -3.90

(9.54) (9.73) (11.2)

2005 Dummy ∗ Rainfall 4.39* 2.92 9.13**

(2.56) (2.90) (3.58)

2005 Dummy ∗ GQ Dummy -11.6 -11.4 -16.4

(14.0) (14.0) (14.7)

2005 Dummy ∗ GQ Dummy ∗ Rainfall 8.12 9.03 8.45

(10.1) (10.3) (11.4)

Population (in 000) -0.0023 -0.0013

(0.0026) (0.0047)

Urban population (in 000) 0.0051** 0.0040

(0.0026) (0.0024)

Total literates (in 000) -0.00022 0.0031

(0.0045) (0.0063)

Road length (in 000 km) -1.41***

(0.26)

N 778 768 612

r2 0.068 0.077 0.144

Standard errors in parentheses

Standard errors clustered at district level

∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

39