Assessing the Impact of Crop Diversification on Farm...

World Development Vol. 72, pp. 70–92, 20150305-750X/� 2015 Elsevier Ltd. All rights reserved.

www.elsevier.com/locate/worlddevhttp://dx.doi.org/10.1016/j.worlddev.2015.02.015

Assessing the Impact of Crop Diversification on Farm Poverty in India

PRATAP S. BIRTHAL a, DEVESH ROY b and DIGVIJAY S. NEGI c,*

a Agriculture Economics and Policy Research (NCAP), New Delhi, Indiab International Food Policy Research Institute (IFPRI) NASC Complex, New Delhi, India

c Indian Statistical Institute, New Delhi

Summary. — Crop diversification into high-value crops (HVCs) can be a strategy to improve livelihood outcomes for farmers. Usingdata from a nationally representative survey, we establish that households diversifying toward HVCs are less likely to be poor, the big-gest impact being for smallholders. Furthermore, using continuous treatment matching, we establish the relationship between degree ofdiversification (share of area dedicated to HVC) and poverty. Growers of HVCs need to allocate at least 50% area to HVCs to escapepoverty. Effect of diversification on poverty is in general positive but it withers after a threshold probably because of constraints i.e.,capital on smaller farms and labor on larger ones.� 2015 Elsevier Ltd. All rights reserved.

Key words — high-value crops, diversification, binary treatment, continuous treatment, instrumental variable

* We sincerely acknowledge the motivation for this research provided by

several scholars directly or indirectly such as Dr P K Joshi, Dr Praduman

Kumar, and several researchers in the National Agricultural Research

Systems in India. Final revision accepted: February 24, 2015.

1. INTRODUCTION

Despite its falling share in national income, agriculture inIndia continues to attract considerable attention because ofits strategic importance to food security and poverty reduc-tion. Though the sector currently contributes only 14% tothe national income, it engages about half of the country’sworkforce. Moreover, average farm size in India has been fall-ing over time and was just below 1.2 ha in 2010–11 (GoI,2013a). Over two-thirds of the households cultivate farmsmeasuring less than or equal to 1 ha.

Studies show that agricultural growth has a larger effect onpoverty reduction than the growth in other sectors (de Janvry& Sadoulet, 2010; Ravallion & Datt, 1996; Warr, 2003). How-ever, with continuous fragmentation of landholdings, onequestion that arises is whether such small holdings can allowfarm households to move out of poverty. Moreover, the abilityof agriculture to contribute to poverty reduction is now chal-lenged due to deceleration in productivity growth (Dev & Rao,2010) and declining labor absorption capacity (GoI, 2013b).

While agriculture continues to have excessive employmentpressure, past trends indicate limited opportunities for a rapidtransfer of labor to non-farm sectors (Chadha, 2008). With atardy shift of labor toward non-farm sectors, within agricul-ture, crop diversification out of staples toward high-valuecrops (HVCs) is one of the alternatives that can augmentincomes, generate employment, and reduce poverty (Ali &Abedullah, 2002; Barghouti, Kane, Sorby, & Ali, 2004;Birthal, Joshi, Roy, & Thorat, 2013; Joshi, Gulati, Birthal,& Tewari, 2004; Weinberger & Lumpkin, 2007). HVCs suchas vegetables, fruits, condiments and spices, flowers, aromaticand medicinal plants, and plantation crops like tea and coffeegenerate higher net returns per unit of land compared to sta-ples or other widely grown crops. These are important forthe poor when land is scarce and labor is abundant—endow-ments that are typical of the smallholder farmers. Small farm-ers may prefer HVCs since economies of scale are usually lessimportant in these relative to staple crops.

In this paper, we assess the impact of crop diversification onfarm poverty. This issue has been evaluated qualitatively inseveral studies (Barghouti et al., 2004; Birthal, Joshi, Negi,& Agarwal, 2014; IFPRI, 2005; Joshi et al., 2004; Minot &

70

Roy, 2007) and has been actively discussed in policy as well(Birthal et al., 2014; Gulati, Minot, Delgado, & Bora, 2007;Rao, Birthal, & Joshi, 2006; Roy & Thorat, 2007; Torero &Gulati, 2004; Weinberger & Lumpkin, 2007). However, tothe best of our knowledge, robust empirical link betweendiversification and poverty has not been established. We, thus,examine the association of crop diversification with poverty.

The poverty status of a farmer is measured in terms of his/her position above or below the poverty line as well as in termsof per capita household consumption expenditure. Note thatthe main pathway for impacts of HVCs on rural poverty isthrough the links with high-income urban markets where thedemand for HVCs has been rising continuously. 1

We use data from a nationally representative survey con-ducted by the National Sample Survey Organization (NSSO)to assess the state of farming in India (GoI, 2005). In termsof crop choices, we find that smaller farmers allocate largershares of land to HVCs, and are also comparatively efficientin production. Finally, our estimates show that the likelihoodof a farmer being poor is 3–7% less if he grows HVCs. By farmsize, the biggest impact of HVCs on poverty is assessed forsmallholder farmers with landholdings less than or equal to2 ha.

Going a step further, we estimate the relationship betweendegree of diversification (measured as share of cropped areaallocated to HVCs) and poverty. The precise research questionthat we address here is: what is the relationship between theintensity of diversification and the likelihood of being poor?Obviously, answer to this question would vary by landsize—a stratification that we maintain in our analysis. Thedose–response functions (DRF) establish the ranges in whichcrop diversification is effective in influencing poverty.

Based on the estimated DRFs for different land size classes,the probability of a household being poor is generally lower,higher the degree of diversification. Other salient features ofDRFs are: (i) these are estimated comparatively imprecisely

http://dx.doi.org/10.1016/j.worlddev.2015.02.015

http://crossmark.crossref.org/dialog/?doi=10.1016/j.worlddev.2015.02.015&domain=pdf

ASSESSING THE IMPACT OF CROP DIVERSIFICATION ON FARM POVERTY IN INDIA 71

for large farmers (>4 ha) because of their small number in thesample; and (ii) for small and medium farmers, the DRFsshow that beyond a certain point (approximately 60% landallocation to HVCs), the dose responses become flat i.e.,beyond this level the marginal effect on likelihood of farmerbeing non-poor through diversification is negligible.

This paper broadly relates to the studies that empiricallyestablish the pro-poor nature of HVCs. Based on small sam-ples and without addressing the issues of biases in their esti-mates, Achterbosch et al. (2007) and McCulloch and Ota(2002) find that HVC growers are relatively better off. In con-trast, in this paper we use a nationally representative sample ofover 50,000 farmers and try to address potential sources ofbias in assessment of the effects of HVCs on poverty.

From their studies in Africa, Jayne et al. (2010) and Bigstenand Tengstam (2011) clearly bring out the role of diversifica-tion in sustaining agriculture and agriculture-based liveli-hoods. Jayne et al. (2003) show that local demand forvegetables is comparatively elastic, and those having a shorterproduction cycle generate a regular stream of income. Hence,adoption of HVCs and their intensification could influencefarm poverty. 2 Other related literature also demonstrates thatwith access to some resources, smallholders are able to diver-sify toward HVCs. Burney, Naylor, and Postel (2013) showhow investments in distributed smallholder irrigation tech-nologies could be used to diversify farming systems andimprove farm incomes.

Note that, we have used the concept of diversification ascultivation of HVCs. In reality there are other types of diver-sification. Farmers can diversify their activities as well as theircrops. Fafchamps (1992) analyses the choice problem betweenfood and cash crops focusing on food security concerns. Therecan also be more minutely defined diversification that caninfluence the outcomes for farmers. For example Di Falcoand Chavas (2009) delve into the issue of wheat genetic diver-sity and show that greater variety richness i.e., diversificationon multiple plots improves farmer’s welfare. 3

The choices made by the farmers ultimately depend on sub-jective assessment of the risk-return trade-offs. The capacity tobear risk is lower for small farmers and thus riskiness of HVCsis likely to be an important consideration for them. The pov-erty impact of diversification toward HVCs is to a large extentrelated to the participation of small farmers in HVCs and isalso related with the labor intensity of the activities. Importantin this context is the stylized fact that poor farmers have com-paratively high labor endowments.

The paper is organized as follows. Section 2 briefly describesthe data used to analyze the association of agricultural diver-sification with farm poverty. Section 3 provides descriptivestatistics of farmers’ participation in HVCs and their relativeprofitability. Welfare or poverty outcomes of HVCs usinginstrumental variable technique are discussed in Section 4.The method of generalized propensity scores matching(GPSM) for examining the response of poverty to intensityof participation in HVCs is discussed in Section 5. Based onthe estimates, we discuss the importance of HVCs in povertyalleviation in Section 6. Conclusions and implications are dis-cussed in the final section.

2. DATA

The data for this paper comes from a nationally representa-tive survey of farm households conducted by the National Sam-ple Survey Organization (NSSO) of the Government of India in2003 (GoI, 2005). 4 The survey was conducted in 6638 villages

spread over almost all the districts in the country (566), withan average eight farm households per village and 12 villagesper district. It contains information on social, economic, institu-tional, and organizational aspects of farming generated from asample of 51,770 households. The dataset provides informationon the crops grown, and the costs and returns associated witheach crop. This enables us to study the pattern and the extentof diversification toward HVCs across land sizes and to analyzetheir profitability relative to other crops.

Alongside, the survey provides information on severalhousehold characteristics such as ownership of assets, socialand demographic variables, and income sources. Data onsome institutional dimensions of farming such as access tocredit, insurance, and extension; farmer’s awareness aboutgovernment-set minimum support prices of agricultural com-modities; farmer’s association with networks such as self-helpgroups and producer and marketing cooperatives are includedas well.

Importantly, the survey provides information on expendi-ture on food and non-food items, which we use to constructwelfare measures i.e., status of a household being above orbelow the poverty line and monthly per capita expenditure(MPCE), the two measures used in this paper. 5

3. DESCRIPTIVE STATISTICS

(a) Participation in HVCs

Overall, about 22% of the farm households in the countrygrow at least one HVC from the group comprising vegetables,fruits, spices, condiments, flowers, plantations, and medicinalplants (Table 1a). The proportion of HVC growers is similaracross land classes, excluding the large farmers. Table 1b pre-sents area allocated to HVCs and other crops by land size. Theproportion of area allocated to HVCs declines with land size.The share of cropped area allocated to HVCs is 8.6% and 5.9%on marginal and small farms respectively, compared to 5.4%on medium farms and 4.4% on large farms. Proportions ofboth the households growing HVCs and area allocation arehigher for vegetables compared to other HVCs and this pat-tern is distinctive on smaller farms (Table 1b).

Birthal, Joshi, Chauhan, and Singh (2008) show that mar-ginal and small farmers allocate a larger area to vegetablesthan to other HVCs. Vegetables generate quick and regularreturns and are comparatively labor-intensive. These charac-teristics match closely with the endowments and cash flowrequirements of the smallholders. On the other hand, mostfruit crops and some spices (example, areca-nut and car-damom) require more start-up capital, and have longer gesta-tion periods acting as disincentives for poor smallholderfarmers to grow such crops.

(b) Profitability of HVCs

Table 2 presents the estimated net returns per hectare fromcultivation of HVCs vis-a-vis other crops. 6 Although the costof cultivation of HVCs is higher relative to staple crops, thegross returns from these are substantially higher that maketheir cultivation more profitable (see Section I in the Appen-dix). On average, all categories of farmers, including marginalones, realize significantly higher returns from HVCs comparedto cereals. Within HVCs, profits are higher for fruits. More-over, for HVCs, there is a distinct inverse relationship betweenfarm size and productivity i.e., more pronounced than mainstaple crops.

Table 1a. Participation in high-value agriculture by landholding class

Marginal (<1 ha) Small (1–2 ha) Medium (2–4 ha) Large (>4 ha) All

% Households growing HVCs 22.22 23.61 21.47 19.23 22.22(1.16) (1.32) (1.25) (1.47) (1.01)[8285] [2864] [1497] [771] [13417]

% Households growing fruits 3.56 3.85 4.69 4.89 3.83(0.47) (0.38) (0.48) (0.67) (0.37)[1460] [551] [370] [209] [2590]

% Households growing vegetables 16.84 17.74 13.41 9.76 16.18(1.02) (1.25) (1.05) (0.91) (0.93)[6263] [2252] [1033] [421] [9969]

% Households growing spices 4.03 4.44 6.05 7.08 4.53(0.45) (0.43) (0.64) 0.92) (0.39)[2016] [718] [408] [256] [3398]

% Area allocated to HVCs by growers 38.70 24.79 25.25 22.62 33.50(1.67) (1.41) (1.52) (1.92) (1.40)

Figures in round parentheses are standard errors, figures in square parentheses are frequencies.

Table 1b. Area shares of different crops by farm size categories (%)

Crops/crop groups Marginal Small Medium Large All(<1 ha) (1–2 ha) (2–4 ha) (>4 ha)

Share in landholdings 63.09 19.26 11.46 6.19 100(1.17) (0.5) (0.47) (0.44) –

Rice 41.6 33.19 25.54 15.86 36.55(1.88) (1.78) (1.68) (1.52) (1.72)

Wheat 18.18 15.03 13.62 12.38 16.69(1.24) (1.21) (0.91) (1.13) (1.06)

Maize 6.35 6.05 5.13 3.6 5.99(0.86) (0.79) (0.65) (0.51) (0.72)

Total cereals 74.63 67.99 60.17 51.2 70.24(1.15) (1.15) (1.51) (1.88) (1.1)

Fruits 1.33 1.13 1.36 1.21 1.29(0.16) (0.15) (0.2) (0.26) (0.13)

Vegetables 4.97 3.01 2.02 1.31 4.03(0.38) (0.29) (0.21) (0.16) (0.29)

Spices 1.07 0.91 1.27 1.25 1.07(0.16) (0.13) (0.23) (0.28) (0.14)

High-value crops 8.64 5.86 5.44 4.38 7.47(0.63) (0.43) (0.45) (0.53) (0.49)

Figures in parentheses are standard errors.Total cereals include rice, wheat, maize and coarse cereals like pearl millet, sorghum and barley. High-value crops include vegetables, fruits, condimentsand spices, flowers, aromatic and medicinal plants, and plantation crops like tea and coffee.

72 WORLD DEVELOPMENT

(c) HVCs, consumption expenditure, and poverty

The impact of high-value crops on poverty can occurthrough four mechanisms (Minot & Roy, 2007). In the firstmechanism, HVCs may affect poverty through backward link-ages to agricultural input sectors. HVCs typically require morepurchased inputs such as seed, fertilizer, and pesticides thantraditional crops. Thus, if HVCs increase the demand forlocally produced inputs, it may generate employment andincome in these sectors. Second, the growth in HVCs can influ-ence poverty by affecting farm incomes. Third, the expansionof HVCs may influence the demand for labor, either by grow-ers (including both small- and large-scale farmers) or by tra-ders, processors, and others in the marketing chain. Andfinally, the growth of HVCs may influence poverty by chang-ing the prices of food commodities faced by consumers, i.e.,the consumer price linkage.

HVCs usually require purchased seed, fertilizer, and pesti-cides and may require stakes, plastic sheets, and bags for green

houses and packaging. Expansion of HVCs can potentiallyhave relatively strong backward linkage effects on the inputsectors, benefiting those who produce and sell these inputs.From this channel, the poverty impact of the backward link-ages of HVCs depends on the intensity of use of different fac-tors (skilled and unskilled labor, capital, land) in the inputsectors and their degree of mobility and the ownership bythe poor. Growth in productive activities intensive in unskilledlabor is likely to benefit the poor (Minot & Roy, 2007).

To understand the impact of HVCs on income and povertyamong farmers, it is necessary to understand why many farm-ers do not adopt HVCs (Minot & Roy, 2007). There are con-straints such as lack of information which can increaseperceived risks. HVCs, even with all the available information,are subject to residual production risk due to weather, disease,and pests, and marketing risk due to the crop’s perishabilityand the small marketable surplus. Larger farmers are oftenbetter able to bear these risks. Moreover, some HVCs mightrequire significant investment. Fruit production involves

Table 2. Net returns from high-value crops vis-a-vis other crops by farm size (rupees/hectare)*

Crops/crop groups Marginal (61.0 ha) Small (1.0–2.0 ha) Medium (2.0–4.0 ha) Large (>4.0 ha) All

Paddy 9493 8328 9789 13,179 9436(539) (348) (1126) (3825) (441)

Wheat 10,241 9541 9858 9598 10,016(448) (458) (469) (637) (363)

Maize 6945 5496 5075 5353 6273(758) (566) (584) (601) (527)

Total cereals 9044 7099 7518 6164 8301(403) (256) (599) (456) (304)

Fruits 37,347 51,859 36,726 30,433 39,523(13,289) (19,187) (9283) (13,585) (9566)

Vegetables 22,423 19,226 20,641 19,114 21,459(2402) (1748) (4657) (3100) (1852)

Spices 45,191 41,403 23,818 17,666 38,520(10,930) (9574) (4645) (2483) (6632)

High-value crops 25,618 22,329 21,411 21,518 24,263(2834) (2292) (2486) (4014) (2091)

All crops 10,784 7598 7766 6576 9564(446) (287) (441) (415) (339)

Figures in parentheses are standard errors.Total cereals include rice, wheat, maize, and coarse cereals like pearl millet, sorghum, and barley. High-value crops include vegetables, fruits, condimentsand spices, flowers, aromatic and medicinal plants, and plantation crops like tea and coffee (also see Section I in the appendix).* One US$ = 47.62 in the survey year i.e., 2002–03.


planting trees and a gestation period of 3–5 years to bearfruits. Poor farmers often do not have the savings or accessto credit needed to make these investments and purchase theinputs.

Also, in HVC, location of the farmers matters. Farmers maynot be able to adopt a HVC if the agro-climatic conditions arenot favorable and more importantly, if the farmer and thebuyer are located far from each other. In this case, the effectivefarm-gate prices can be too low to justify production. In caseof highly perishable HVCs, production locations nearer tomarkets and a good marketing infrastructure are particularlyimportant (see Rao et al., 2006; Torero & Gulati, 2004).

The constraints to successfully adopting HVCs dependsignificantly on the type of end markets. The barriers to pro-ducing for low-income (often rural) consumers are compara-tively low because of the absence of complex quality andfood safety standards. On the other hand, higher-incomeurban consumers generally demand stricter quality and foodsafety standards (Minot & Roy, 2007). Even more demandingare the quality and safety standards in the export markets ofindustrialized countries. The poverty impact of HVCs dependssignificantly on the participation and competitiveness of thesmall farmer in the value chains. The competitiveness of smallfarmers relative to large farmers is not fixed and can changeover time, usually as a result of changes in physical, human,or social capital.

The general assumption is that HVCs, most of which areperishable, are more prone to risk vis-a-vis staple crops. Basedon the data, farmers however rarely switch completely fromstaples to HVCs; rather they combine the two in their produc-tion portfolio. Hence, it is possible that some farmersdiversifying into HVCs face lower risks, particularly if thereturns from staples and the high-value commodities are notcorrelated over time. Minten, Randrianarison, and Swinnen(2006) find that small farmers in Madagascar are able toreduce the length of the “hungry season” by producing andselling vegetables to an exporter during the off-season.

In summary, though we focus on farmers’ incomes but thegrowth in HVCs can affect poverty through different channels,for example through its effect on the labor market, both in

production as well as in marketing. HVCs also generateemployment in various post-harvest activities including pro-cessing, packaging, and marketing (Dolan & Sorby, 2003).Finally, their impact on poverty can occur also through thefood price mechanism. The effect on food prices mainly resultsfrom the substitution effect in production. As land and otherresources are shifted toward non-staple-food production, thesupply of HVCs expands, while that of staple foods contracts.Dorward (2012) states that this may result in an increase instaple food prices, with negative consequences for the urbanpoor and other poor net-buyers of food. This effect dependson the proportion of staple food area that is displaced and alsoprice elasticities of demand and supply. The effect also dependson the tradability of food products and finally on the net buyeror seller status of the households (see Minot & Roy, 2007).

Income as a measure for poor’s welfare is generally prone tomeasurement errors. Poor households derive their incomefrom multiple sources with no fixed frequency or receivableamounts creating potential for measurement errors. Hence,in deriving poverty measures, we follow the well-establishedpractice of outcome based on the consumption expenditure.

The poverty status of farm households (based on the officialpoverty line) is presented in Table 3. Poverty is higher amongfarmers with smaller land sizes—25.7% among marginal farm-ers and 19.4% among small farmers, compared to 9.8% amonglarge farmers. Table 3 also highlights differences in the head-count poverty and MPCE between growers and non-growersof HVCs. On average, MPCE for growers is about 13%higher, and the incidence of poverty is about six percentagepoints lower. Moreover, differences in these welfare measuresbetween growers and non-growers are statistically significant.

Mapping the distribution of MPCE of growers and non-growers (Figure 1a) and stratified by quartiles of area alloca-tion to HVCs (Figure 1b), a priori indicate a link between cropdiversification and economic wellbeing. To probe this further,we estimate a simple nonparametric regression, mðxÞ ¼ EðyjxÞ,by computing an estimate of the location of y within a specificband of x using LOWESS estimator (Cleveland, 1979).

In Figure 2, y represents the MPCE and x the proportionof area allocated to HVCs. The curve representing the

Table 3. Monthly per capita consumption expenditure (rupees)* and poverty status of farm households

Farm class Mean MPCE Head-count ratio

Growers of HVCs Non growers of HVCs Difference All farmers Growers of HVCs Non growers of HVCs Difference All farmers

Marginal 528.4 462.7 65.8*** 478.7 21.5 27.1 �5.6** 25.7(61.0 ha) (13.9) (6.9) (13.6) (7.0) (2.2) (1.4) (2.3) (1.3)Small 559.1 509.5 49.6*** 522.2 16.9 20.3 �3.4* 19.4(1–2 ha) (13.5) (12.8) (17.2) (10.6) (1.7) (1.3) (2.0) (1.1)Medium 617.0 540.5 76.4*** 558.4 10.9 17.5 �6.7*** 16.0(2–4 ha) (22.6) (12.3) (24.4) (11.5) (1.5) (1.7) (2.2) (1.4)Large 718.4 625.4 93.0*** 645.3 7.1 10.5 �3.4* 9.8(>4 ha) (28.5) (16.5) (30.8) (15.2) (1.6) (1.3) (2.0) (1.1)All 558.5 486.7 71.8*** 504.0 19.6 25.5 �5.9*** 24.1

(12.2) (7.0) (11.8) (7.0) (1.3) (1.0) (1.2) (1.0)

Figures in parentheses are standard errors.***, ** and * denote significance at the 1%, 5% and 10% level, respectively.* One US$ = 47.62 in the survey year i.e., 2002–03.

Figure 1. The distribution of monthly per capita expenditure for growers and non-growers of high-value crops.


relationship between HVCs and MPCE is flattest for farmerswith land sizes less than or equal to 1.0 ha. It is possible thatfor further diversification, the land/capital constraint turns outto be binding for these farmers. There is also a kink in theestimated relationship in the case of large farmers at almostsimilar level of intensity of HVCs, possibly owing to theirhigher labor requirement, which may constrain further diver-sification and its benefits.

Figure 2. Nonparametric regression showing the relationship between

intensity of high-value agriculture and monthly per capita expenditure.

4. DIVERSIFICATION AND POVERTY IN THE SETUPOF BINARY TREATMENT

(a) Instrumental variable estimation

To begin with, we estimate the OLS regression:

yi ¼ aþ ddi þ cX i þ ei ð1Þwhere, yi denotes the outcome of the ith farmer (state of beingabove or below the poverty line, or MPCE); di is a dummyvariable that takes a value of 1 if the farmer grows HVC,and 0 otherwise; and X i is a vector of farmer characteristics.If X i includes all the variables that influence the choice of


HVCs including district or village fixed effects, and these areuncorrelated with the error term ei, then the OLS estimates

of (1) will be consistent, and d can be treated as the true effectof HVCs on household welfare.

However, the OLS estimates can suffer from bias due tohousehold-specific unobserved heterogeneity. Unobservedability or characteristics like reputation, skill, or motivation

that cannot be controlled for may lead to a bias, as d wouldcapture the effect of these unobserved factors and not thatof HVCs per se. If, for example, farmers growing HVCs, have

poorer prospects without it owing to lower ability, d will bebiased downward. In this case, di will be correlated with ei.

In order to mitigate the potential bias, standard instrumen-tal variable technique can be used. The ideal instrument herewould be such that it is correlated with a farmers’ decisionto grow HVCs and not be correlated with the outcome i.e.,poverty status or MPCE. Here, we appeal to the role of localnetworks in information transmission and learning in searchfor appropriate instruments. The idea is that if a higher pro-portion of farmers in the neighborhood (appropriatelydefined) cultivate HVCs, it would translate into a greaterpossibility for a farmer to adopt these crops. To suit the con-text of rural India, we are careful in defining the neighborhoodwherein we include both geographical (based on location) aswell as social proximity (based on caste) in constructing thepeer group.

In general, the definition of peer group or network is openended and is subject to the discretion of the researcher.Broadly, the reference group for a person is defined by theindividuals whose mean outcome and characteristics influencethe individual’s own outcome and characteristics. The con-struction of the network based on geographical as well associal proximity is along the lines of Fontaine and Yamada(2011), who define reference groups in the Indian contextbased on education, age, geographical proximity, and caste.For each farmer, the instrument (i.e., the proportion of HVCsgrowing farmers in the network) is derived after excluding thatindividual farmer.

In employing an instrumental variables (IV) strategy, weconsider the following equation that determines a farmers’decision of cultivating HVCs.

di ¼ bþ hZi þ ui ð2ÞIn (2) vector Zi comprises variables that determine a farm-

ers’ decision to grow HVCs. To satisfy the exclusion criterionof an instrument, there must be one variable in Zi that is not inXi in (1). The exclusion variable (the instrument) is the adop-tion of HVCs by the local network (including location as wellas social identity) as defined above. An ideal instrumentshould influence outcome only through the treatment variable,i.e., it should be correlated with treatment and not with unob-servable characteristics. It can then effectively randomize sub-jects across treatment, and can achieve equal distribution ofboth the characteristics as well as pre-treatment outcomes.The instrumental variable method is thus aimed at addressingboth overt and unobserved biases in estimating the averagetreatment effect (ATE). The exclusion restriction is the condi-tional independence assumption i.e., the instrument isindependent of the potential outcome.

Note that the instrument can suffer from some measurementerror, as the survey randomly selects households in a village,and the actual proportion of households cultivating HVCswithin a social group in a village may or may not equal theproportion estimated from the sample i.e., Zi ¼ Z�i þ vi. This

may lead to attenuation bias in h, resulting in an estimate

i.e., a lower bound of h. The estimated treatment effect willbe unbiased, so long as vi is uncorrelated with di and ei.

Imbens and Angrist (1994) show that with a dummyendogenous variable, instrumental variable (IV) method esti-mates causal effects for those whose behavior would be chan-ged by the instrument if it were assigned in a randomized trial.That is, the effect is estimated for subjects who take the treat-ment if assigned to the treatment group, but do not take thetreatment otherwise. This parameter is known as local averagetreatment effect (LATE). If each subject in the population hasthe same response to a particular intervention or treatment,the distinction between LATE and other parameters doesnot matter. But with “heterogeneous treatment effects”, theparameter identified by IV method may differ from the averagetreatment effect of interest. As there could be farm-levelheterogeneity, the IV method measures the LATE and notnecessarily the average treatment effect. We also implementa matching estimator where the cost is the inability to controlfor unobserved characteristics of the farmers since the propen-sity scores for matching are estimated based solely onobserved characteristics (Smith & Todd, 2005).

Table 4(a and b) and Table 5 present the results of OLS andIV regressions respectively. Since we have low variation ininstrument within village we use district fixed effects whileestimating IV regressions and for comparison present OLSresults, also with district fixed effects. Both OLS and IV speci-fications include a comprehensive set of observed householdcharacteristics as control variables. Our main interest whileselecting control variables is to maximize the explained varia-tion in the outcome variables and observe treatment effectmovements on adding controls. We discuss the rationalebehind this strategy in detail in the next section where we pre-sent tests of robustness for stability of treatment effect andinfluence of unobservable characteristics. The main idea is tominimize unexplained variation and thus the influence ofunobservable factors. We do not include the variables captur-ing distance to the market and distance to the input suppliersin the final specification because of their high correlation withvillage fixed effects, and when included in the regressions theseturned out to be statistically insignificant.

Both OLS and IV regressions show significant effects ofHVCs on MPCE and on the probability of being poor. FromOLS estimates with full sample we find that adoption of HVCsreduces the probability of falling below the poverty line by2.8%. 7 The IV regressions show a higher reduction in theprobability of being poor (6.7%), more so among small-holders.

(b) Robustness tests

(i) Stability of estimatesTo test for the stability of estimates with addition of differ-

ent controls, we start with a parsimonious specification byincluding just the diversification measure as an explanatoryvariable. Then we add more controls progressively. Table 6presents the results of this exercise for both the outcome vari-ables i.e., MPCE and poverty.

Table 6 highlights the importance of fixed effects in explain-ing the variation in outcome variables. District fixed effects inspecification (2) and village fixed effects in specification (3)explain a significant variation in MPCE and the poverty sta-tus. Similarly, the set of household level characteristics (in ital-ics in Table 4) also explains a significant variation in both theoutcome variables. In terms of coefficient movements, addi-tion of controls and fixed effects reduce the magnitude of treat-ment effect.

Table 4a. Ordinary least squares estimates for the effect of high-value crop production on monthly per capita consumption expenditure

Outcome: Ln(monthly per capita consumption expenditure) Marginal(61.0 ha)

Small(1.0–2.0 ha)

Medium(2.0–4.0 ha)

Large(>4.0 ha)

All

Treatment: If growing HVC = 1, 0 otherwise 0.0361*** 0.0554*** 0.0672*** 0.0603*** 0.0536***

(0.0063) (0.0105) (0.0143) (0.0201) (0.0053)

Ln(operated land in hectares) 0.0409*** 0.1179*** 0.1191*** 0.1174*** 0.0675***

(0.0027) (0.0195) (0.0275) (0.0195) (0.0025)Ln(age of household head in years) 0.1373*** 0.1727*** 0.1336*** 0.1430*** 0.1549***

(0.0072) (0.0140) (0.0195) (0.0274) (0.0062)=1 if male headed household, 0 otherwise 0.0185** �0.0408** �0.0857*** �0.0657 �0.0040

(0.0076) (0.0168) (0.0277) (0.0402) (0.0067)Ln(family size in numbers) �0.4124*** �0.4310*** �0.3967*** �0.4352*** �0.4043***

(0.0049) (0.0088) (0.0121) (0.0150) (0.0052)=1 if household head educated up to primary school, 0 otherwise 0.0472*** 0.0719*** 0.0710*** 0.0813*** 0.0587***

(0.0048) (0.0089) (0.0127) (0.0179) (0.0040)=1 if household head educated up to middle school, 0 otherwise 0.1014*** 0.1563*** 0.1351*** 0.1255*** 0.1202***

(0.0065) (0.0116) (0.0159) (0.0227) (0.0053)=1 if household head educated up to higher secondary including

diploma, 0 otherwise

0.1602*** 0.1944*** 0.2031*** 0.2062*** 0.1877***

(0.0079) (0.0134) (0.0176) (0.0253) (0.0064)=1 if household head education is graduation and above, 0 otherwise 0.2481*** 0.2663*** 0.2963*** 0.2629*** 0.2715***

(0.0170) (0.0223) (0.0328) (0.0353) (0.0116)Social group:=1 if belongs to scheduled tribe, 0 otherwise �0.0710*** �0.1038*** �0.1258*** �0.1981*** �0.0913***

(0.0091) (0.0158) (0.0220) (0.0260) (0.0083)Social group:=1 if belongs to scheduled caste, 0 otherwise �0.0676*** �0.0678*** �0.0958*** �0.1215*** �0.0779***

(0.0071) (0.0136) (0.0191) (0.0317) (0.0061)Social group:=1 if belongs to other backward class, 0 otherwise �0.0256*** �0.0268*** �0.0057 �0.0462*** �0.0256***

(0.0064) (0.0101) (0.0139) (0.0176) (0.0052)Household type:=1 if self-employed in non-agriculture, 0 otherwise �0.0972*** �0.0683*** �0.0888** �0.0745 �0.0924***

(0.0106) (0.0227) (0.0346) (0.0551) (0.0094)Household type:=1 if agricultural labor, 0 otherwise �0.2160*** �0.2221*** �0.2131*** �0.1428** �0.2026***

(0.0105) (0.0249) (0.0396) (0.0687) (0.0095)Household type:=1 if other labor, 0 otherwise �0.1763*** �0.1998*** �0.1255*** �0.1644** �0.1644***

(0.0115) (0.0279) (0.0469) (0.0665) (0.0104)Household type:=1 if self-employed in agriculture, 0 otherwise �0.1614*** �0.1561*** �0.1630*** �0.0791* �0.1499***

(0.0099) (0.0188) (0.0282) (0.0458) (0.0089)=1 if aware about Minimum Support Price, 0 otherwise 0.0307*** 0.0314*** 0.0670*** 0.0662*** 0.0414***

(0.0074) (0.0121) (0.0190) (0.0226) (0.0063)=1 if aware about government procurement, 0 otherwise 0.0444*** 0.0286** 0.0047 0.0137 0.0402***

(0.0093) (0.0145) (0.0186) (0.0251) (0.0074)=1 if household has/had crop insurance at any time, 0 otherwise 0.0438*** 0.0257 0.0798*** 0.0589*** 0.0614***

(0.0147) (0.0206) (0.0227) (0.0228) (0.0100)=1 if member of registered farmer organization, 0 otherwise 0.0937*** 0.0836*** 0.0454 0.1173*** 0.0943***

(0.0203) (0.0281) (0.0306) (0.0388) (0.0162)=1 if member of self-help group, 0 otherwise �0.0140 �0.0041 �0.0044 �0.0288 �0.0099

(0.0099) (0.0163) (0.0219) (0.0325) (0.0087)=1 if access to information on modern agricultural technology, 0 otherwise 0.0541*** 0.0632*** 0.0463*** 0.0476*** 0.0549***

(0.0052) (0.0091) (0.0116) (0.0167) (0.0045)=1 if own tractor, 0 otherwise 0.1506*** 0.1311*** 0.1160*** 0.1520*** 0.1869***

(0.0261) (0.0231) (0.0208) (0.0197) (0.0115)Livestock assets: Number of cattle owned 0.0207*** 0.0134*** 0.0015 0.0103*** 0.0066*

(0.0014) (0.0020) (0.0010) (0.0020) (0.0037)Livestock assets: Number of poultry birds owned 0.0001* 0.0006 0.0001*** �0.0000 0.0001***

(0.0000) (0.0004) (0.0000) (0.0001) (0.0000)=1 if taken credit for farm, 0 otherwise �0.0007 0.0009 0.0104 0.0069 0.0074*

(0.0055) (0.0094) (0.0115) (0.0148) (0.0044)=1 if any area under irrigation, 0 otherwise 0.0413*** 0.0582*** 0.1187*** 0.1240*** 0.0609***

(0.0061) (0.0103) (0.0152) (0.0178) (0.0053)Constant 6.3721*** 6.3074*** 6.4236*** 6.3301*** 6.3327***

(0.0307) (0.0601) (0.0889) (0.1318) (0.0267)Observations 28,832 8544 5002 3174 45,552F 359.6 138.9 73.7 59.5 637.1Adjusted R2 0.572 0.573 0.568 0.596 0.574

District fixed effects included. Figures in parentheses are village-clustered standard errors. ***, ** and * denote significance at the 1%, 5%, and 10% level,respectively.


Table 4b. Linear probability model for the effect of high-value crop production on probability of being poor

Outcome:=1 if below poverty line, 0 otherwise Marginal(61.0 ha)

Small(1.0–2.0 ha)


Large(>4.0 ha)

All

Treatment: If growing HVC = 1, 0 otherwise �0.0256*** �0.0218** �0.0179* �0.0023 �0.0282***

(0.0059) (0.0093) (0.0104) (0.0121) (0.0046)

Ln(operated land in hectares) �0.0283*** �0.0426** �0.0541*** �0.0092 �0.0335***

(0.0027) (0.0179) (0.0199) (0.0121) (0.0021)Ln(age of household head in years) �0.0610*** �0.0628*** �0.0438*** �0.0359* �0.0672***

(0.0076) (0.0130) (0.0148) (0.0192) (0.0060)=1 if male headed household, 0 otherwise �0.0253*** 0.0051 0.0182 0.0286 �0.0136**

(0.0069) (0.0137) (0.0200) (0.0207) (0.0057)Ln(family size in numbers) 0.2698*** 0.1956*** 0.1351*** 0.0961*** 0.2155***

(0.0053) (0.0084) (0.0088) (0.0101) (0.0047)=1 if household head educated up to primary school, 0 otherwise �0.0449*** �0.0417*** �0.0498*** �0.0433*** �0.0471***

(0.0056) (0.0090) (0.0115) (0.0134) (0.0043)=1 if household head educated up to middle school, 0 otherwise �0.0628*** �0.0677*** �0.0634*** �0.0484*** �0.0645***

(0.0069) (0.0108) (0.0122) (0.0158) (0.0051)=1 if household head educated up to higher secondary including

diploma, 0 otherwise

�0.0739*** �0.0656*** �0.0695*** �0.0560*** �0.0722***

(0.0075) (0.0109) (0.0121) (0.0149) (0.0053)=1 if household head education is graduation and above, 0 otherwise �0.0912*** �0.0748*** �0.0583*** �0.0572*** �0.0754***

(0.0120) (0.0158) (0.0156) (0.0168) (0.0077)Social group:=1 if belongs to scheduled tribe, 0 otherwise 0.0576*** 0.0685*** 0.0602*** 0.0929*** 0.0606***

(0.0096) (0.0159) (0.0183) (0.0209) (0.0076)Social group:=1 if belongs to scheduled caste, 0 otherwise 0.0375*** 0.0213* 0.0519*** 0.0169 0.0368***

(0.0075) (0.0128) (0.0188) (0.0257) (0.0061)Social group:=1 if belongs to other backward class, 0 otherwise 0.0070 �0.0036 �0.0119 0.0140 �0.0008

(0.0063) (0.0090) (0.0101) (0.0115) (0.0047)Household type:=1 if self-employed in non-agriculture, 0 otherwise 0.0200** 0.0013 0.0065 0.0149 0.0219***

(0.0084) (0.0148) (0.0186) (0.0316) (0.0067)Household type:=1 if agricultural labor, 0 otherwise 0.1265*** 0.1489*** 0.0918** �0.0342 0.1252***

(0.0091) (0.0228) (0.0361) (0.0494) (0.0077)Household type:=1 if other labor, 0 otherwise 0.0594*** 0.1052*** -0.0148 0.0315 0.0586***

(0.0099) (0.0289) (0.0361) (0.0601) (0.0087)Household type:=1 if self-employed in agriculture, 0 otherwise 0.0675*** 0.0501*** 0.0436*** 0.0066 0.0514***

(0.0073) (0.0111) (0.0131) (0.0232) (0.0058)=1 if aware about Minimum Support Price, 0 otherwise �0.0265*** �0.0446*** �0.0419*** �0.0201 �0.0337***

(0.0071) (0.0103) (0.0117) (0.0139) (0.0053)=1 if aware about government procurement, 0 otherwise �0.0084 0.0071 0.0084 0.0045 �0.0010

(0.0081) (0.0112) (0.0116) (0.0138) (0.0058)=1 if household has/had crop insurance at any time, 0 otherwise �0.0274** �0.0058 �0.0160 �0.0359*** �0.0182**

(0.0111) (0.0182) (0.0155) (0.0134) (0.0075)=1 if member of registered farmer organization, 0 otherwise 0.0059 �0.0519*** 0.0166 �0.0091 �0.0010

(0.0144) (0.0177) (0.0189) (0.0135) (0.0097)=1 if member of self-help group, 0 otherwise �0.0094 0.0099 �0.0227 0.0023 �0.0081

(0.0090) (0.0135) (0.0166) (0.0220) (0.0070)=1 if access to information on modern agricultural technology, 0 otherwise �0.0380*** �0.0297*** �0.0062 0.0021 �0.0314***

(0.0054) (0.0082) (0.0091) (0.0113) (0.0042)=1 if own tractor, 0 otherwise �0.0551** �0.0576*** �0.0407*** �0.0218** �0.0526***

(0.0237) (0.0160) (0.0111) (0.0097) (0.0079)Livestock assets: Number of cattle owned �0.0189*** �0.0068*** 0.0001 �0.0052*** �0.0041

(0.0014) (0.0019) (0.0007) (0.0011) (0.0028)Livestock assets: Number of poultry birds owned �0.0001*** �0.0001* 0.0000 �0.0001 �0.0000

(0.0000) (0.0001) (0.0000) (0.0001) (0.0000)=1 if taken credit for farm, 0 otherwise �0.0097* �0.0174** �0.0189** �0.0007 �0.0109***

(0.0056) (0.0083) (0.0095) (0.0103) (0.0040)=1 if any area under irrigation, 0 otherwise �0.0411*** �0.0391*** �0.0629*** �0.0485*** �0.0443***

(0.0061) (0.0095) (0.0126) (0.0133) (0.0049)Constant 0.0334 0.1045* 0.1316** 0.1093 0.1078***

(0.0328) (0.0550) (0.0660) (0.0871) (0.0256)Observations 28,832 8544 5002 3174 45,552F 126.6 29.9 13.3 6.0 152.7Adjusted R2 0.299 0.247 0.222 0.141 0.276

District fixed effects included. Figures in parentheses are village clustered standard errors. ***, ** and * denote significance at the 1%, 5% and 10% level,respectively.


Table 5. Instrumental variable regression for the effect of high-value crop production on monthly per capita consumption expenditure and probability of beingpoor

First stage: Marginal(61.0 ha)

Small(1.0–2.0 ha)


Large(>4.0 ha)

AllDependent variable: If growing HVC = 1, 0 otherwise

Instrument: Proportion growing HVC within a social group in a village 0.4452*** 0.5092*** 0.4687*** 0.4817*** 0.4609***

Village clustered standard error (0.0126) (0.0201) (0.0288) (0.0446) (0.0109)District clustered standard error [0.0190] [0.0263] [0.0314] [0.0511] [0.0168]Observations 28,832 8544 5002 3174 45,552Adjusted R2 0.4450 0.4706 0.4250 0.3745 0.4323F- Stat for test of weak instrument (H0:Instrument = 0) 1255.6 642.2 263.9 116.9 1801.9

Second stage:Outcome: Ln(monthly per capita consumption expenditure)

Treatment: If growing HVC = 1, 0 otherwise 0.0734*** 0.0805*** 0.1500*** 0.0693 0.0772***

Village clustered standard error (0.0208) (0.0287) (0.0456) (0.0672) (0.0179)District clustered standard error [(0.0227] [0.0298] [0.0490] [0.0810] [0.0196]Observations 28,832 8544 5002 3174 45,552Adjusted R2 0.5707 0.5724 0.5638 0.5961 0.5737

Second stage:Outcome:=1 if below poverty line, 0 otherwise

Treatment: If growing HVC = 1, 0 otherwise �0.0778*** �0.0651*** �0.0018 �0.0586 �0.0691***

Village clustered standard error (0.0189) (0.0251) (0.0348) (0.0438) (0.0154)District clustered standard error [0.0211] [0.0270] [0.0369] [0.0499] [0.0167]Observations 28,832 8544 5002 3174 45,552Adjusted R2 0.2968 0.2445 0.2220 0.1350 0.2741

District fixed effects included. Figures in parentheses are village clustered standard errors. Includes all controls as in Table 4 in italics. The stars forsignificance correspond to village clustered standard errors. ***, ** and * denote significance at the 1%, 5% and 10% level, respectively.

Table 6. Results of tests for the stability of coefficients

Outcome: Ln(monthly per capita consumption expenditure) (1) (2) (3) (4) (5) (6)

Treatment: If growing HVC = 1, 0 otherwise 0.1653*** 0.0989*** 0.0998*** 0.1177*** 0.0536*** 0.0490***

(0.0077) (0.0063) (0.0070) (0.0063) (0.0053) (0.0056)

Other controls No No No Yes Yes YesDistrict fixed effects No Yes No No Yes NoVillage fixed effects No No Yes No No YesF 466.1 248.0 204.6 561.8 637.1 529.9Adjusted R2 0.024 0.323 0.430 0.381 0.574 0.656Outcome:=1 if below poverty line, 0 otherwiseTreatment: If growing HVC = 1, 0 otherwise �0.0691*** �0.0541*** �0.0456*** �0.0513*** �0.0282*** �0.0180***

(0.0048) (0.0048) (0.0056) (0.0046) (0.0046) (0.0054)

Other controls No No No Yes Yes YesDistrict fixed effects No Yes No No Yes NoVillage fixed effects No No Yes No No YesF 207.7 129.0 66.9 159.8 152.7 118.0Adjusted R2 0.007 0.161 0.255 0.157 0.276 0.359

Figures in parentheses are village clustered standard errors. Total observations in the regressions are 45,552. Other controls are variables in italics inTable 4. ***, ** and * denote significance at the 1%, 5% and 10% level, respectively.


Oster (2014) points out that focussing only on the coefficientmovements to gauge potential biases due to omitted variablesis not sufficient. Addition of an uninformative control willleave the coefficient largely unchanged but will add little tothe explained variation i.e., R2. The key factor is the changein R2, which diagnoses the poor quality of the proxy for unob-served omitted variables. This observation generalizes to allcases in which the observed controls share covariance proper-ties with the unobserved controls. Omitted variable bias is pro-portional to the coefficient movements, but only if suchmovements are scaled by the movements in R2.

(ii) Importance of unobserved factorsIn estimating (1) by OLS there is a possibility that our esti-

mate of d is confounded due to omitted household-specificunobserved characteristics contained in the error term ei. Wecheck for the extent of bias due to inability to account forthese unobserved factors. For the purpose, we follow themethod in Altonji, Elder, and Taber (2005) that exploits thecovariance of observables with the treatment variable to pro-vide evidence of bias due to unobservable factors. With theassumption that all unobservable characteristics share thesame covariance properties as the observables, the bias can


be identified (Altonji, Conely, Elder, & Taber, 2011).; Altonjiet al., 2005; Murphy & Topel, 1990 Following Altonji et al.(2005) we assume:

Assumption 1: Index of observables and unobservables areuncorrelated (subscripts are dropped for brevity).

CovðcX ; eÞ ¼ 0 ð3ÞIn practice, this can be mechanically generated since by def-

inition of c, cX and e are orthogonal. Given this assumption,the bias due to omitted unobservables in OLS estimate of dis expressed as:

Bias ¼ Covðd; eÞVarðcX ÞVarðdÞVarðcX Þ � ½Covðd; cX Þ�2

ð4Þ

Since Covðd; eÞ is unknown we make the following assump-tion to estimate the bias.

Assumption 2: Correlation between treatment and observ-ables is proportional to the correlation between treatmentand unobservables.

Covðd; eÞVarðeÞ ¼ k

Covðd; cX ÞVarðcX Þ ð5Þ

where k is the constant of proportionality between the covari-ance of treatment with observables and the covariance oftreatment with unobservables. With this, the bias can beexpressed as:

Bias ¼ kCovðd; cX ÞVarðeÞVarðdÞVarðcX Þ � ½Covðd; cX Þ�2

ð6Þ

This measure of bias is estimable from the data. Note thatsince e also contains measurement error of the outcomevariable, we estimate the upper bound on the bias. Now, weestimate (1) and calculate X c and variance of the residualV e. Further, we estimate T ¼ aþ C½X c� þ t and calculate thevariance of the residual V t. Now the bias can be estimated as:

dBias ¼ kCV e

V tð7Þ

If k = 1 (as assumed in Altonji et al., 2005) then estimatedbias equals true bias, otherwise this estimate is a close approx-imation of the true bias. As an additional robustness test wefollow Oster (2014) (as discussed above) who gives a methodof robustness (based on the stability of treatment effect) bylinking coefficient movements, R2 movements and omittedvariable bias. With assumptions 1 and 2 Oster (2014) showsthat true treatment effect can be recovered from: (i) the coeffi-cients on d with and without controls for observed variables;(ii) the R2 for controlled and uncontrolled regressions; and(iii) a value for k. The coefficients and R2 from theseregressions are recoverable; a value for k must be assumed.Robustness can, then, be described by an identified set ofthe treatment effect, Ds ¼ f~d; d0ðRmax; k ¼ 1Þg, where ~d is theestimated treatment effect from Eqn. (1) and d0 is the treatmenteffect and Rmax is the R2 from Eqn. (1) if we could fully controlfor omitted unobservable. This set can then be subject torobustness question like: Does it contain zero?

Oster (2014) argues that k 2 [0; 1] is a plausible bound onthe degree of selection. Note that, ~R < Rmax < 1, where ~R isthe R2 for Eqn. (1). This is an improvement over Altonjiet al. (2005) as it relaxes the assumption of k = 1. Since wecompare coefficients and R2 across equations the measurementerror in the error term in (1) is unlikely to influence the infer-ence. In this exercise, of the three parameters Rmax, d and k the

values of any two must be assumed to estimate the value ofthird. We use household characteristics (italicized in Table 4)and fixed effects as observables. From Table 6 we find theseparameters explaining considerable variation in both the out-come variables.

Table 7 shows results of the robustness checks with villagefixed effects in panel (a) and district fixed effects in panel (b).The estimated bias in col. (2) is the bias due to unobservablesin the upper bound of the identified set (col. 1). For both theoutcome variables, the bias estimated following Altonji et al.(2005) is the highest in the case of fruit growers and lowestin the case of vegetable growers. This implies that givenassumption 2 the unobservables exert a greater influence onthe choice of growing fruits than vegetables. This is expected,because of much higher start-up capital needs of fruit cultiva-tion and their longer gestation period compared to vegetables,hence factors like entrepreneurial skills could be compara-tively important.

Col. 3 and 4 of Table 7 contain results estimated followingOster (2014). We assume Rmax to be 0.80, which is very highfor a cross sectional sample that has considerable noise inthe outcome variables. In other words, we assume that themeasurement error in outcome variables accounts for 20% ofthe variation therein. For MPCE the results seem to be robustto influence of unobservables, especially in the case of veg-etable growers. These are also robust for growers of HVCsas an aggregate. The treatment effect set identified (col. 1and 3) for HVCs and vegetables does not contain a zero inpanel (a) as well as in panel (b) indicating that the treatmenteffect is positive. The last column provides estimates of theconstant of proportionality for degree of selection due to omit-ted unobservable factors. For example, in the case of the veg-etable growers in panel (a) of Table 7 the proportionalitycoefficient is 3.58, meaning that for treatment effect to be zerothe influence of omitted unobservables should be 3.58 timesthe influence of observables.

These findings allow us to infer that in general HVCs have apositive effect on the welfare of farm households and the effectis more pronounced for vegetable growers. A comparativelyrobust effect in the case of vegetables should not be surprising.As discussed above fruits have greater capital requirements(Joshi, Joshi, & Birthal, 2006; Weinberger & Lumpkin,2007).Vegetables also generate a regular stream of income.These properties match with the resource endowments andcash flow requirements of the smallholders. Note that, farmersalso use HVCs for home consumption, and thus save on theirfood expenditure. Hence, a part of estimated treatment effectis also due to their home consumption (see Tables 10 and 11in Section II of the Appendix).

5. ESTIMATING THE IMPACT OF DEGREES OFDIVERSIFICATION: A GENERALIZED PROPENSITY

SCORE MATCHING APPROACH

Research in program evaluation has made comprehensiveuse of matching methods when experimental data are absent.Several extensions have been made to the method of matchingto capture different types of treatments. These include multi-valued treatments (Imbens, 2000; Lechner, 2002) and continu-ous treatments (Hirano & Imbens, 2004; Imai & van Dyk,2004; Imbens, 2000). Table 13 in Section III of the Appendixpresents the results of the propensity score matching (PSM)with binary treatment.

To assess the relationship between degree of diversificationand poverty, we follow the continuous treatment approach

Table 7. Results of the robustness tests

OLS/fixed effects Altonji et al. (2005) method Oster (2014) method

(1) Estimatedtreatment effect d

(2) Bias dueto unobservables

(3) Estimatedtreatment effect d0

(assuming Rmax = 0.80& k = 1)

(4) Estimatedk = for d0 = 0

assumingRmax = 0.80

(a) Robustness tests with village fixed effects

Outcome: Ln(monthly per capita consumption expenditure)

Treatment: If growing HVC = 1, 0 otherwise 0.049(0.006) 0.023 0.032 2.84Treatment: If growing fruit = 1, 0 otherwise 0.059(0.010) 0.112 0.021 1.50Treatment: If growing vegetable = 1, 0 otherwise 0.030(0.006) �0.014 0.022 3.58Treatment: If growing spices = 1, 0 otherwise 0.045(0.010) 0.052 0.015 1.46

Outcome:=1 if below poverty line, 0 otherwise

Treatment: If growing HVC = 1, 0 otherwise �0.018(0.005) �0.049 0.022 0.45Treatment: If growing fruit = 1, 0 otherwise �0.006(0.008) �0.211 0.075 0.08Treatment: If growing spices = 1, 0 otherwise �0.001(0.008) �0.087 0.079 0.01Treatment: If growing vegetable = 1, 0 otherwise �0.017(0.006) 0.022 0.004 0.80(b) Robustness tests with district fixed effects

Outcome: Ln(monthly per capita consumption expenditure)

Treatment: If growing HVC = 1, 0 otherwise 0.054(0.005) 0.034 0.010 1.20Treatment: If growing fruit = 1, 0 otherwise 0.067(0.010) 0.147 �0.037 0.66Treatment: If growing vegetable = 1, 0 otherwise 0.032(0.006) �0.013 0.011 1.47Treatment: If growing spices = 1, 0 otherwise 0.061(0.010) 0.055 �0.018 0.79

Outcome:=1 if below poverty line, 0 otherwise

Treatment: If growing HVC = 1, 0 otherwise �0.028(0.005) �0.069 0.048 0.37Treatment: If growing fruit = 1, 0 otherwise �0.010(0.007) �0.257 0.174 0.05Treatment: If growing spices = 1, 0 otherwise �0.019(0.007) �0.087 0.134 0.13Treatment: If growing vegetable = 1, 0 otherwise �0.029(0.005) 0.015 �0.001 1.05

Figures in parentheses are village clustered standard errors. Total observations in the regressions are 45,552. Includes all controls as in Table 4 in italics.


based on generalized propensity score (GPS) developed byHirano and Imbens (2004) who estimate the entire dose-response function (DRF) of a continuous treatment. As in bin-ary treatment, adjusting for GPS removes the bias associatedwith differences in the covariates. This allows estimating themarginal treatment effect of a specific level of diversificationon poverty i.e., the outcome of a specific level of land allocatedto HVCs vis-a-vis another level (counterfactual units), condi-tional on their characteristics being similar.

The estimation of DRF associated with each value of thecontinuous dose (percentage area allocated to HVCs) givesthe associated poverty level. A continuous DRF thus relatesthe share of cropped area allocated to HVCs to the individualpost-treatment outcomes in terms of poverty status andMPCE. The methodology for GPSM and PSM are technicallysimilar. In the continuous treatment case, there are bounds onthe treatment d but any value within the bounds are admissiblei.e., treatment d 2 ½d0; d1� In contrast to the PSM, whichrequires joint independence of all the potential outcomes,Hirano and Imbens (2004) make weaker assumption forGPSM which requires conditional independence to hold foreach value of treatment. The generalized propensity scorefor continuous treatment is denoted rðd;X Þ i.e., the condi-tional density of treatment given the covariates.

Hirano and Imbens (2004) generalize the unconfoundednessassumption for binary treatment made by Rosenbaum andRubin (1983) to the continuous case. Similar to binary treat-ment, for identification it is assumed that selection byindividuals into different treatment levels (degree of cropdiversification) is made based on an observed set of covariatesand on unobserved components not correlated with the poten-tial outcomes (Flores, Flores-Lagunes, Gonzalez, &Neumann, 2011).

This is a straightforward extension of the “uncon-foundedness” or selection-on-observables assumption com-monly used in binary-treatment (e.g., Firpo, 2007; Hirano,Imbens, & Ridder, 2003; Imbens, 2004; Heckman, et al.,1998). Hirano and Imbens (2004) show that if assignment totreatment is weakly unconfounded, given the covariates X,then it is also weakly unconfounded given the GPS. With weakunconfoundness assumption Hirano and Imbens (2004) showthat GPSM can be used to eliminate any biases associated withdifferences in the covariates. In notation

bðd; rÞ ¼ EfY ðdÞjrðd;X Þ ¼ rg ¼ EðY jd ¼ dt;R ¼ rÞ and

lðdÞ ¼ E½bfd; rðd;X Þg� ð8ÞFinally, the GPSM has a balancing property similar to that

of the standard PSM. Within strata with the same value ofrðd;X Þ, the probability that d ¼ dt does not depend on thevalue of X. Overall, the implementation of the GPSM com-prises three steps. In the first step, the conditional distributionof the treatment, given the covariates is estimated through themaximum likelihood procedure assuming a normal dis-tribution. 8 The normal distribution assumption of the inter-vention is confirmed according to the residual analysis of themodel. We use the skewness and kurtosis test for normalitywhich is validated.

The second step is to test for the balancing property bydividing the treatment values into intervals based on the sam-ple distribution of the treatment variable. Finally, the proce-dure involves estimating the dose–response functions (DRFs)i.e., the conditional expectation lðdÞ of the outcome. The stan-dard errors are then obtained by bootstrapping given thetreatment and the GPSM.


(a) The Dose–Response Functions (DRF) for poverty impacts

Y ðdÞd2½d0;d1 � denotes the parametrically estimated DRF. Weare interested in average DRF denoted as lðdÞ. After estimat-ing the GPS in the first step, most studies adopt a parametricapproach in the second step employing a polynomial approx-imation for the conditional expectation of the outcome vari-able given the treatment and the GPS (Bia et al., 2011). Weestimate the DRF with a quadratic specification as below (sup-pressing the subscripts).

EðY jd; RÞ ¼ a0 þ a1:d þ a2:d2 þ a3:Rþ a4:R2 þ a5:d:R ð9Þ

where the R denotes the estimated GPS. Doing this for eachlevel of treatment, we get an estimate of the entire DRF as amean weighted by each estimated GPS. As the flexible para-metric forms make DRFs sensitive to the model specification,alternatively nonparametric approaches have been applied inthe second step such as in Bia, Flores, and Mattei (2011)and Flores et al. (2011). Bia et al. (2011) employ cubic splineestimator and penalized cubic spline estimator while Floreset al. (2011) introduce an inverse weighting kernel estimatorin the second stage that involves estimating the DRF and mar-ginal treatment effect functions. In this paper, we follow theroutine in second stage as in Bia et al. (2011). The nonpara-metric DRFs are not presented here but are available uponrequest.

(b) Results from generalized propensity score matching(GPSM)

Figure 3 shows the density of the share of total cropped areaallocated to HVCs in the sample. As the distribution of areaallocation is highly skewed, to satisfy the condition of normal-ity we make a zero-skewness Box–Cox transformation of thetreatment, and then use maximum likelihood procedure toget the GPS (Bia & Mattei, 2008). To check for balance, wefollow Hirano and Imbens (2004) procedure and divide thesample into three groups based on the distribution of areaunder HVCs—less than 10% (group 1), 10–20% (group 2)and above 20% (group 3). In the second step, each group isfurther divided into quintiles of the GPS evaluated at the med-ian.

Within each quintile, we calculate the difference-in-means ofcovariates with respect to individuals that have a GPS such

Figure 3. Density of the percentage area under high-value crops.

that they belong to that quintile, but have a treatment leveldifferent from the one being evaluated. This procedure testsif for each of these quintiles the covariate means of individualsbelonging to the particular treatment-level group are signifi-cantly different from those of individuals with a different treat-ment level, but similar GPS.

A weighted average of all the quintiles in each treatment-level group is used to calculate t-statistic of the differences-in-means between the particular treatment-level group andall other groups. The procedure needs to be repeated foreach treatment-level group and for each covariate. If adjust-ment for the GPS balances the covariates, we expect allthose differences-in-means to not be statistically differentfrom zero.

The variables that satisfy balancing property and parame-ter estimates for GPS specification that maximize the bal-ance in covariates are available upon request. We alsocheck for the common support of the GPS. Figure 4(a)plots the distribution of the GPS of the group 1 superim-posed over the same distribution of the rest of the sample.Figure 4(b) and (c) present same distribution for groups 2and 3 respectively. It is evident from these figures thatexcept at tails, there is sufficient overlap in the distributionsfor common support condition to be satisfied. The evidencefor balance in covariates and common support for otherfarm categories are available upon request.

Table 8 reports t-statistic for the difference-in-means ofthree groups generated following the procedure outlinedabove. For brevity, only the case of marginal farmers is pre-sented here. Without adjustment, t-statistic for most covari-ates is significant, indicating an unbalanced distribution ofthe covariates. The adjusted t-statistic for all covariates isinsignificant indicating an improvement in the balance afteradjustment for the GPS).

(c) Estimated DRFs

Figure 5(a) and (b) respectively show the GPS adjustedparametric DRFs and marginal treatment effect functions(MTEF) by farm size along with their bootstrapped 95% con-fidence intervals for MPCE. DRFs are convex for all cate-gories of farm households; and the confidence intervals,except for large farmers (with comparatively few observa-tions), are tight indicating a statistically significant responseto the treatment. Although slope and significance of theresponse function vary across farm categories, in the treatmenteffect function we observe a change in slope with the point ofinflection varying across farm size classes.

The DRF and MTEF for poverty status are shown in Fig-ure 6(a) and (b) respectively. These suggest that escaping pov-erty requires greater area allocation to HVCs than formaximization of MPCE; the additional area is probablyneeded to reduce depth and severity of poverty. In general,for those currently poor, likelihood of escaping povertyrequires growers to allocate at least 50% of their area toHVCs.

Expectedly, a higher threshold for moving out of poverty isrequired for marginal farmers who comprise two-third of thetotal growers of HVCs and they need to allocate greater areato these crops to augment their income to the level that takesthem above the poverty line. The DRF for large farmers isestimated imprecisely with large confidence intervals becauseof fewer observations on them. Figure 6b also shows thatfor small and medium farmers after reaching a thresholdintensity of about 60%, the effect on the probability of beingabove the poverty line remains largely unchanged.

Figure 4. Common support condition.

Table 8. Balance in covariates in case of marginal farmers, given the generalized propensity scores: t-statistics for equality of means

Covariates Unadjusted Adjusted

]0, 10] ]10, 20] ]20, 100[ ]0, 10] ]10, 20] ]20, 100[

Ln operated area (hectare) �11.861*** �5.609*** 14.820*** �2.084 �1.901 1.768Area under irrigation (yes = 1, no = 0) �6.333*** �1.238 6.504*** 2.279 0.590 �2.447Involved in non-farm business (yes = 1, no = 0) 0.900 1.221 �1.726 �1.441 0.397 0.694Owns livestock (yes = 1, no = 0) �4.441*** �2.459 5.787*** 1.540 �0.518 �0.998Ln family size (number) �8.087*** �2.349 8.907*** 0.161 �0.495 0.900Ln family size squared �9.457*** �1.947 9.795*** �0.165 0.042 1.003Ln age of the household-head (years) 0.693 �0.666 1.931 �0.995 �1.153 2.026Male household-head (yes = 1, no = 0) 1.618 �0.374 �1.130 0.081 �0.980 1.277Head education:=1 if primary school, 0 otherwise (ow) 6.576*** 1.010 �6.540*** 0.708 �0.399 �0.526Head education:=1 if middle school, 0 ow �0.156 0.880 �0.539 �0.023 0.849 �1.027Head education:=1 if secondary school, 0 ow �2.647*** 0.617 1.844 �2.109 0.587 1.603Head education:=1 if graduate and above, 0 ow �0.686 1.207 �0.326 �1.197 1.031 0.136Social group:=1 if scheduled tribe, 0 ow 5.302*** �1.159 �3.75*** 1.414 �1.114 �0.578Social group:=1 if scheduled caste, 0 ow �0.539 0.688 �0.056 1.150 0.781 �1.047Social group:=1 if other backward castes, 0 ow �4.909*** 0.108 4.216*** �1.077 0.168 1.386Household type:=1 if Self-employed in non-agriculture, 0 ow 1.950 0.051 �1.748 �1.205 �0.854 0.968Household type:=1 if Agricultural laborer, 0 ow 5.679*** 2.493 �6.902*** 1.014 0.482 �0.389Household type:=1 if Other laborer, 0 ow 3.811*** 0.151 �3.454*** 1.445 �0.852 �0.428Household type:=1 if Self-employed in agriculture, 0 ow �8.044*** �1.907 8.524*** �0.168 0.909 �0.669Access to information on technology (yes = 1, no = 0) 3.152*** 2.776*** �4.899*** �0.862 1.888 �1.272Member of farmer organization (yes = 1, no = 0) 2.252 1.089 �2.811*** �0.146 �0.081 0.427Member of self-help group (yes = 1, no = 0) 3.98*** 1.910 �4.959*** �1.163 0.024 1.378Taken credit for farm (yes = 1, no = 0) 6.906*** 1.606 �7.291*** 2.132 0.179 �1.901Agro climatic zones:=1 if Humid zone, 0 ow 6.784*** �0.349 �5.669*** �0.492 �1.271 0.621Agro climatic zones:=1 if Semi-arid temperate zone, 0 ow �11.083*** �0.219 9.862*** �0.503 1.166 1.282Agro climatic zones:=1 if Semi-arid tropic zone, 0 ow 2.092 1.043 �2.635*** 0.616 0.853 �1.935

***Significant at 1% level.


Figure 5. Parametric dose response (a) and treatment effect (b) functions for MPCE (in logarithms).


6. DISCUSSION

Fueled by technological change in cereals, Indian agricul-ture has experienced an annual growth of around 3% in thepast three decades. This helped improve farm incomes andreduce rural poverty (de Janvry & Sadoulet, 2010; Ravallion& Datt, 1996; Warr, 2003). de Janvry and Sadoulet (2010)

estimated elasticity of rural poverty reduction with respect tocereal yield growth equal to �1.2 in India.

However, sources of growth in Indian agriculture havechanged considerably over time (Birthal et al., 2014). Landfrontiers are increasingly getting exhausted indicating limitedscope for enhancing agricultural growth through area expan-sion. 9 The technological changes, primarily in rice and wheat

Figure 6. Parametric dose–response (a) and treatment (b) effect functions for status below the poverty line.


that were the mainstay of agricultural growth in the 1980s,have atrophied afterward. Now, the growth is increasinglybeing driven by HVCs.

We estimate lower incidence of poverty among the house-holds who grow HVCs irrespective of land size. To validateour findings, we estimate incidence of poverty among house-holds growing “only cereal crops”. As a test of robustness,we apply PSM by making treatment group to be exclusive

cereal growers and the control being farmers growing cerealsin combination with other crops. Results presented inTable 14 in Section III in the Appendix show incidence ofpoverty to be 6% higher among households allocating theentire area to cereal crops. The effects are estimated to besimilar across all farm size categories i.e., exclusive cerealgrowers are more likely to be poor in their respective landsize group. The capability of small farmers to diversify


toward HVCs is but questioned on several counts. First, thelandholdings of the poor farmers may be too small to allowthem to divert land from staples at the cost of their cereal-based food security (Vyas, 1996). However, HVCs and staplefood crops might not necessarily compete with each other.Birthal et al. (2014) find that diversification toward HVCsin India occurred largely by replacing low-value coarse grainsand not rice and wheat.

Second, because of their poor economic status, smallholderslack access to credit, agricultural inputs such as high-yieldingseeds, technology, and information. Third, local rural marketsfor these commodities are thin, and the marketed surplus ofthe individual producers is usually too small to be remunera-tively traded in the distant urban markets due to higher trans-portation and marketing costs.

One of the reasons for larger effect of diversification intoHVCs on poverty reduction among smallholders could bethe inverse size-productivity relationship for HVCs and thehigher proportion of area allocation to labor-intensive cropssuch as vegetables that match with their labor endowments.Many field operations in HVCs cannot be accomplished bymachines, and require human and animal labor. Most HVCsneed to be monitored continuously for plant health, weeding,pruning, irrigation, harvesting and packaging based onindividual plants or pieces of fruits and vegetables. Smallfarmers’ labor and supervision advantages can compensatefor higher marketing and transaction costs and limited accessto credit and information.

India’s agricultural policy has focused on enhancing cerealproduction to ensure food security. Evidence from severalstudies however indicates a continuous shift in consumptionpatterns away from cereals during the past two decades (seeJoshi & Kumar, 2012). The potential of HVCs to reduce pov-erty is good news; and rising incomes, urbanization, andchanging food consumption preferences are opening up newmarket opportunities for HVCs. By 2030, India’s demandfor fruits and vegetables is expected to be threefold of the cur-rent demand (Joshi & Kumar, 2012). Moreover, growth indemand for high-value products is being accompanied by atransformation of the agri-food marketing system toward ver-tical coordination from an exclusive reliance on spot markets.Evidence suggests that vertical coordination in HVCs in Indiahas a benign effect on the smallholders in terms of improvedaccess to technology, inputs, services and output markets,and reduction in marketing and transaction costs (Birthalet al., 2005).

Then, is the area under HVCs in line with the actual area allo-cated by different categories of farmers in order to come out ofpoverty? Growers of HVCs on average allocate close to one-fourth of their area to these crops. Results show that marginalfarmers would need to increase their area under cultivation ofHVCs from 39% to about 50% to be able to escape poverty.For small and medium farmers though actual area allocationto HVCs hovers around the optimal area, yet many of theseremain poor. Note that there is an inverse relationship betweenfarm size and productivity of HVCs, and these categories offarmers could focus on enhancing farm productivity rather thanbringing additional area under HVCs.

Finally, a note on our empirical strategy. One of the threatsto identification remains from the role of unobserved factorsaffecting the outcome i.e., being in a state of “below the pov-erty line”. We cannot rule out the bias because of unobserv-able factors. We can say at least that it is likely to be alower order concern in case of continuous treatment. Forunobserved factors to bias the results in a significant way, vari-ables that determine choice of a specific level of diversificationwould need to be systematically correlated with likelihood ofthe farm household falling below the poverty line. With regardto this concern, some assurance we get from implementationof an instrumental variable strategy that yields similar results.The results from instrumental variable estimation support thehypothesis that adoption of HVCs lead to reduction in theprobability of a farm household being poor. We also checkfor the extent of possible bias because of unobserved factorsusing recent developments in econometric methods. In caseof continuous measure of welfare i.e., consumption expendi-ture we can at least show that bias is not likely to be large.

7. CONCLUSIONS AND POLICY IMPLICATIONS

In this paper, we assess the effect of crop diversificationtoward HVCs on poverty among Indian farmers. We look atboth the choice to diversify as well as the intensity of diversi-fication. These issues are quite relevant in the context of frag-menting landholdings and negligible shift of labor fromagriculture to other sectors.

We employ recent developments in impact evaluation litera-ture involving continuous treatment matching estimation toassess the link between poverty and degrees of crop diversifica-tion. Expectedly different landholding sizes require dissimilardoses of high-value cropping to pull out of poverty. We estab-lish thresholds and upper limits wherever they exist (in farmsize categories) in choosing the levels to which diversificationneeds to be pushed.

The estimation of average dose response that captures therelationship between extent of diversification and likelihoodof being poor is obviously of significance for policy as it givesthe average outcome for all possible values of the treatment. Itis clearly of interest to the policymakers to know the level ofdiversification that will maximize the average net benefits tothe farmers or maximize the probability (averaged acrossfarmers) of their moving out of poverty.

The policy implications of the results are reinforcements ofthe view that diversification is pro-poor. The analysis is notadequate to assign a diversification level under different exter-nal conditions such as technology, credit, infrastructure, andsupply chains. The threshold level of diversification could belower if these supporting conditions were improved for achiev-ing higher level of productivity of HVCs.

It remains a topic of future research if diversification strat-egy should focus on directly providing for it in terms of seedsor other inputs or should instead focus on creating conditions(such as cold chains) that would let farmers choose optimallevels of diversification by themselves. This is an importantpolicy question that this paper has not addressed.

NOTES

1. We thank the anonymous referee for suggesting this aspect ofdiversification and its relationship with rural poverty.

2. The potential of HVCs to mitigate farm poverty has become prominentover time owing to significant demand shifts. The rising per capita income,a larger share of youth in population and changing lifestyles due to


urbanization are causing a shift in the food basket in favor of high-valuecommodities (Gulati et al., 2007; Joshi & Kumar, 2012; Minot & Roy,2007). Also, international trade in high-value food commodities has beengrowing fast, creating opportunities from exports (Minot & Roy, 2007).

3. We thank an anonymous referee for suggesting this aspect ofdiversification.

4. NSSO has used a multi-stage stratified random sampling design toconduct this survey, the details of which are available in GoI (2005).

5. We use state-specific poverty lines for 2004–05 (deflated to 2002–03using Consumer Price Index for Agricultural Laborers) to estimate thehead-count poverty.

6. Net returns per hectare (rupees) = (gross returns � total expenses)/-gross cropped area. Gross returns = value of output + value of by

products. Total expenses = expenses on seeds + pesticides + fertil-izers + irrigation + repair and maintenance on machinery and farmequipment + interest + lease rent for land + labor (regular and ca-sual) + other expenses.

7. The treatment effect in the percentage terms is calculated as:ðed � 1Þ � 100, where d is the estimated coefficient.

8. Other flexible parametric specifications of the generalized propensityscore i.e., the conditional distribution of the treatment given the covariateshave been tried in the literature viz. Inverse Gaussian distribution,Gamma distribution and beta distribution.

9. India’s net cropped area has been stagnating around 140 millionhectares.

REFERENCES

Abadie, A., & Imbens, G. W. (2006). Large sample properties of matchingestimators for average treatment effects. Econometrica, 74(1), 235–267.

Achterbosch, T., Allbritton, A., Quang, D. V. , Eaton, D., de Jager, A.,Meijerink, G. W., Njue, E., Ssonko, R., Stallen, M., Wertheim-Heck,S., van Wijk, S. (2007). Poverty alleviation in the horticulture sector:Insights from Uganda and Vietnam. Paper presented at the 106thSeminar. European Association of Agricultural Economists, Montpel-lier, France October 25–27.

Ali, M., & Abedullah, M. (2002). Economic and nutritional benefits fromenhanced vegetable production and consumption in developing coun-tries. Journal of Crop Production, 6(1–2), 145–176.

Altonji, J. G., Conely, T., Elder, T. E., & Taber, C. R. (2011). Methods forusing selection on observed variables to address selection on unobservedvariables. Mimeo: Yale University.

Altonji, J. G., Elder, T. E., & Taber, C. R. (2005). Selection on observedand unobserved variables: Assessing the effectiveness of catholicschools. Journal of Political Economy, 113(1), 151–184.

Barghouti, S., Kane, S., Sorby, K., & Ali, M. (2004). Agriculturaldiversification for the poor, ARD discussion paper 1. Washington DC:The World Bank.

Bia, Michela., Flores, Carlos. A., Mattei, Alessandra. (2011). Nonpara-metric estimators of dose-response functions. CEPS/INSTEAD Work-ing Paper Series, no. 40, CEPS/INSTEAD.

Bia, M., & Mattei, A. (2008). A Stata package for the estimation of thedose-response function through adjustment for the generalizedpropensity score. Stata Journal, 8(3), 354–373.

Bigsten, A., & Tengstam, S. (2011). Smallholder diversification andincome growth in Zambia. Journal of African Economies, 20(5),781–822.

Birthal, P. S., Joshi, P. K., Gulati, A. (2005). Vertical coordination in high-value food commodities: implications for smallholders. MTID Discus-sion Paper 85. International Food Policy Research Institute, Wash-ington DC.

Birthal, P.S., Joshi, P.K., Negi, D.S., Agarwal, S. (2014). Changing sourcesof growth in Indian agriculture: Implications for regional priorities foraccelerating agricultural growth. Discussion paper 01325. InternationalFood Policy Research Institute, Washington DC, USA.

Birthal, P. S., Joshi, P. K., Chauhan, S., & Singh, H. (2008). Canhorticulture revitalize agricultural growth?. Indian Journal of Agricul-tural Economics, 63(3), 310–321.

Birthal, P. S., Joshi, P. K., Roy, D., & Thorat, A. (2013). Diversificationof Indian agriculture toward high-value crops: The role of small-holders. Canadian Journal of Agricultural Economics, 61, 61–91.

Burney, J. A., Naylor, R. L., & Postel, S. L. (2013). The case fordistributed irrigation as a development priority in sub-Saharan Africa.Proceedings of the National Academy of Sciences. http://dx.doi.org/10.1073/pnas.12035971.

Chadha, G. K. 2008. Employment and poverty in rural india: Which way togo now? ILO Asia-Pacific Working Paper Series. International LaborOrganization.

Cleveland, W. S. (1979). Robust locally weighted regression and smooth-ing scatterplots. Journal of the American Statistical Association,74(368), 829–836.

de Janvry, A., & Sadoulet, E. (2010). Agricultural growth and povertyreduction: additional evidence. The World Bank Research Observer,25(1), 1–20.

Dev, S. M., & Rao, N. C. (2010). Agricultural price policy, farmprofitability and food security. Economic and Political Weekly, 55(26–27), 174–182.

Di Falco, S., & Chavas, J. P. (2009). On crop biodiversity, risk exposure,and food security in the highlands of Ethiopia. American Journal ofAgricultural Economics, 91(3), 599–611.

Dolan, C., Sorby, K. (2003). Gender and employment in high valueagriculture industries. Agriculture and Rural Development WorkingPaper no 7. World Bank, Washington, DC.

Dorward, A. R. (2012). The short and medium term impacts of rises instaple food prices. Food Security, 4, 633–645.

Fafchamps, M. (1992). Cash crop production, food price volatility, andrural market integration in the third world. American Journal ofAgricultural Economics, 74(1), 90–99.

Firpo, S. (2007). Efficient semi-parametric estimation of quantile treat-ment effects. Econometrica, 75(1), 259–276.

Flores, C. A., Flores-Lagunes, A., Gonzalez, A., & Neumann, T. C.(2011). Estimating the effects of length of exposure to a trainingprogram: The case of job corps. The Review of Economics andStatistics, 94(1), 153–171.

Fontaine, X., Yamada, K. (2011). Envy and hope: Relevant others’consumption and subjective well-being in urban India. GCOE DiscussionPaper 212, Osaka University.

GoI (Government of India), (2005). Situation assessment survey offarmers: Some aspects of Farming. Report no. 496(59/33/3). NationalSample Survey Organization, Ministry of Statistics and ProgrammeImplementation, New Delhi.

GoI (Government of India), (2013a). Agricultural census 2010-11.Department of Agriculture and Cooperation, Ministry of Agriculture,New Delhi.

GoI (Government of India), (2013b). Employment and skill development(chapter 22) in twelfth five year plan (2012–2017): Social sectors,volume 3. Planning Commission, New Delhi.

Gulati, A., Minot, N., Delgado, C., & Bora, S. (2007). Growth in high-value agriculture in Asia and the emergence of vertical links withfarmers. In J. F. M. Swinnen (Ed.), Global supply chains, standards andthe poor. Wallingford: CABI Publishing.

Heckman, J., Ichimura, H. H., & Todd, P. (1998). Matching as aneconometric evaluation estimator. Review of Economic Studies, 65,261–294.

Hirano, K., & Imbens, G. W. (2004). The propensity score withcontinuous treatments. In A. Gelman, & X. L. Meng (Eds.), Appliedbayesian modeling and causal inference from incomplete-data perspec-tives. West Sussex: Wiley InterScience.

http://refhub.elsevier.com/S0305-750X(15)00048-0/h0005



































http://dx.doi.org/10.1073/pnas.12035971

http://dx.doi.org/10.1073/pnas.12035971















































Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation ofaverage treatment effects using the estimated propensity score. Econo-metrica, 71(4), 1161–1189.

IFPRI (2005). The future of small farms. Proceedings of a researchworkshop. Wye, UK. June 26–29.

Imai, K., & van Dyk, D. A. (2004). Causal inference with generaltreatment regimes: Generalizing the propensity score. Journal of theAmerican Statistical Association, 99(467), 854–866.

Imbens, G. W. (2000). Efficient estimation of average treatment effectsusing the estimated propensity score. Econometric Society WorldCongress 2000 Paper no. 1166, Econometric Society.

Imbens, G. W. (2004). Nonparametric estimation of average treatmenteffects under exogeneity: A review. Review of Economics and Statistics,86(1), 4–29.

Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation oflocal average treatment effects. Econometrica, 62(2), 467–475.

Jayne, T. S., Mason, N., Myers, R., Ferris, J., Mather, D., Beaver, M.,Lenski, N., Chapoto, A., Boughton, D. (2010). Patterns and trends infood staples markets in Eastern and Southern Africa: Toward theidentification of priority investments and strategies for developingmarkets and promoting smallholder productivity growth. MSU Interna-tional Development Working Paper. Number 104.

Jayne, T. S., Yamano, T., Webber, M., Tschirley, D., Benfia, R., Neven,D., et al. (2003). Smallholder income and land distribution in Africa:Implications for poverty reduction strategies. Food Policy, 28, 253–275.

Joshi, P. K., Kumar, P. (2012). Food demand and supply projections forIndia. Unpublished manuscript. International Food Policy ResearchInstitute, New Delhi Office.

Joshi, P. K., Gulati, A., Birthal, P. S., & Tewari, L. (2004). Agriculturaldiversification in South Asia: Patterns, determinants and policyimplications. Economic and Political Weekly, 39(24), 2457–2468.

Joshi, P. K., Joshi, L., & Birthal, P. S. (2006). Diversification and itsimpact on smallholders: Evidence from a study on vegetables.Agricultural Economics Research Review, 19(2), 219–236.

Kassie, M., Shiferaw, B., & Geoffrey, M. (2011). Agricultural technology,crop income, and poverty alleviation in Uganda. World Development,39(10), 1784–1795.

Lechner, M. (2002). Program heterogeneity and propensity score match-ing: An application to the evaluation of active labor market policies.The Review of Economics and Statistics, 84(2), 205–220.

McCulloch, N., Ota, M. (2002). Export horticulture and poverty in Kenya.IDS Working Paper no. 174. Institute of Development Studies, Sussex.

Minot, N., Roy, D. (2007). Impact of high-value agriculture and modernmarketing channels on poverty: An analytical framework. MTID,International Food Policy Research Institute, Washington, DC

Minten, B., Randrianarison, L., Swinnen, J.F.M. (2006). Global retailchains and poor farmers: Evidence from Madagascar, LICOS Discus-sion Papers 164/2006, Leuven, Belgium (http://www.econ.kuleuven.be/LICOS/DP/dp.htm).

Murphy, K., & Topel, R. (1990). Efficiency wages reconsidered: theoryand evidence. In Y. Weiss, & G. Fishelson (Eds.), Advances in thetheory and measurement of unemployment (pp. 204–240). New York: St.Martin’s Press.

Oster, E. (2014). Unobservable selection and coefficient stability: Theoryand evidence. <http://faculty.chicagobooth.edu/emily.oster/papers/se-lection.pdf>.

Rao, P. P., Birthal, P. S., & Joshi, P. K. (2006). Diversification towardshigh value agriculture: Role of urbanization and infrastructure.Economic and Political Weekly, 41(26), 2747–2753.

Ravallion, M., & Datt, G. (1996). How important to India’s poor is thesectoral composition of growth in India. World Bank EconomicReview, 10, 1–25.

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of thepropensity score in observational studies for causal effects. Biometrika,70, 41–55.

Roy, D., & Thorat, A. (2007). Success in high value horticultural exportmarkets for the small farmers: The case of Mahagrapes in India. WorldDevelopment, 36(10), 1874–1890.

Smith, A. J., & Todd, P. E. (2005). Does matching overcome LaLonde’scritique of non-experimental estimators?. Journal of Econometrics,125(1), 305–353.

Torero, M., & Gulati, A. (2004). Connecting smallholders to markets: Roleof infrastructure and institutions. Washington, DC: Policy brief.International Food Policy Research Institute.

Vyas, V. S. (1996). Diversification in agriculture: Concept, rationale andapproaches. Indian Journal of Agricultural Economics, 51(4), 636–643.

Warr, P. (2003). Poverty and economic growth in India. In K. Kalirajan,& U. Shankar (Eds.), Economic reform and the liberalization of theIndian economy. Cheltenham: Edward Elgar.

Weinberger, K., & Lumpkin, T. A. (2007). Diversification into horticul-ture and poverty reduction: A research agenda. World Development,35(8), 1464–1480

.

APPENDIX A

I Cost of cultivation and profitability of HVCs vis-a-vis cereals

A common argument is that cultivation of HVCs is moreprofitable as compared to staple crops such as rice, wheat,and maize but their cost of cultivation is relatively high. Con-sider a farm household growing both cereals and high-valuecrops. Let Di be the difference between the net returns fromvegetable cultivation and cereals, i.e., Di ¼

Qvegi �

QCerealsi .

Note that, Di will be free of the household-specific unobservedheterogeneity.

In the Figure A1 we plot the kernel density of net returns perhectare for cereals and vegetables/HVCs for the sample ofhouseholds cultivating both.

Similarly Figure A2 plots the kernel density of cost per hec-tare for cereals and vegetables/HVCs for households cultivat-ing both crops.

Table 9 shows the costs and returns per hectare for farmhouseholds and the difference when growing vegetables andcereals simultaneously. There were 8199 farmers growing bothvegetables and cereals (panel a) and 10093 farm householdsgrowing both HVCs and cereals (panel b). Since the returnsand costs of the two crop groups are for the same households,the difference in the net returns of the two crop groups (vegeta-bles vs. cereals or HVCs vs. cereals) will be free of any house-hold-specific unobservables. The figures in bold can be takenapproximately as difference in difference estimates of theimpact of vegetable (or HVC) cultivation on profits.

II Bias due to home consumption

From the data-set we can identify households those con-sume home produced fruits and vegetables but it does notallow us to separate the expenditure on home consumptionand purchases from outside. Therefore, we attribute all con-sumption expenditure to home produced fruits and vegetables.By this approach, there are chances of overestimating the con-sumption expenditure on home produced fruits and vegetablesand underestimating the impact of HVCs on MPCE (withouthome consumption of fruits and vegetables). But we will get anupper bound on the benefit to households from consumingown-produced fruits and vegetables.

With high value crop grower as treatment, the outcome isMPCE without consumption of own-produced fruits and veg-etables. With fruit (vegetable) grower as treatment, the out-come is MPCE without consumption of fruits (vegetables).

III Propensity score matching (PSM) with binary treatment

To account for the heterogeneous treatment effects inestimating the relationship between HVCs and outcome vari-ables, we implement propensity score matching to get the aver-age treatment effects on the treated (ATT). In PSM we includean extensive set of covariates. The set of variables that satisfythe technical requirements of common support and balancing




































http://www.econ.kuleuven.be/LICOS/DP/dp.htm

http://www.econ.kuleuven.be/LICOS/DP/dp.htm






http://faculty.chicagobooth.edu/emily.oster/papers/selection.pdf

http://faculty.chicagobooth.edu/emily.oster/papers/selection.pdf




































Figure A1. Kernel density of net returns per hectare of HVCs vs. cereals.

Figure A2. Kernel density of cost of cultivation per hectare of HVCs vs. cereals.


property are provided in Figure A3 and Table 12. The vari-ables not satisfying the technical requirements are marked“No”. Nearest neighbour matching estimators based onAbadie and Imbens (2008) are applied to estimate the impactof diversification on household welfare. Our PSM estimationis similar to Kassie, Shiferaw, and Geoffrey (2011) who exam-ined impacts of adoption of improved groundnut varieties oncrop income and poverty in Uganda.

Table 13 presents the ATT for 1, 3, and 5 nearest neigh-bour matches. Probability of being poor is lower by 4.4 per-centage points for those growing HVCs. The biggest

probability of being above the poverty line of over 6% isestimated for medium sized farmers growing HVCs. PSMestimates show no significant effect on the poverty statusof large farmers. Table 13 also presents results for theMPCE. The estimated effects of diversification are highlysignificant at 1% level with average MPCE being higherby 12% for the households growing HVCs. Comparing theestimated effects from matching with those from IV esti-mates, the effects of HVCs on household MPCE are positiveand significant and the difference in estimates across meth-ods is small. For example, for the full sample the assessed

Table 9. Cost of cultivation and profits (for the sample of households cultivating both)

Gross returns (Rupees/hectare) Cost of cultivation (Rupees/hectare) Difference (Rupees/hectare)

Panel (a): N = 8199

Vegetables 41751 19888 21863***

(1387) (1277) (1697)Cereals (rice, wheat & maize) 18818 6629 12189**

(671) (163) (607)Difference 22933*** 13259*** 9674***

(1151) (1262) (1517)

Panel (b): N = 10093

HVCs 43976 19070 24906***

(1535) (1051) (1628)Cereals (rice, wheat & maize) 18274 6839 11435***

(572) (141) (522)Difference 25702*** 12231*** 13471***

(1485) (1038) (1588)

Figures in parentheses are village clustered standard errors.High-value crops include vegetables, fruits, condiments and spices, flowers, aromatic and medicinal plants, and plantation crops like tea and coffee.Although these are selected samples, almost all farm households cultivate a mix of crops with negligible number of farm household cultivating onlyvegetables/HVCs.Out of 9969(13342) households growing vegetables (HVCs), 8199 farm households in panel (a) grow both vegetables and cereals within which 58% aremarginal farmers and 25% are small farmers.Out of 10093 farm households in panel (b) grow both HVCs and cereals within which 56% are marginal farmers and 25% are small farmers.Results are similar if we compare HVCs with total cereals which include rice, wheat, maize and coarse cereals like pearl millet, sorghum and barley.***, ** and * denote significance at the 1%, 5% and 10% level, respectively.

Table 11. Treatment effect before and after accounting for home consumption

MPCE with home consumption MPCE without home consumption1 2

Treatment: If growing HVC = 1, 0 otherwise 0.0490*** 0.0318***

(0.006) (0.0057)Treatment: If growing fruit = 1, 0 otherwise 0.0590*** 0.0541***

(0.010) (0.0105)Treatment: If growing vegetable = 1, 0 otherwise 0.0300*** 0.0108*

(0.006) (0.0061)

Figures in parentheses are village clustered standard errors. All specifications include full set of controls and village fixed effects.***, ** and * denote significance at the 1%, 5% and 10% level, respectively.

Table 10. Monthly per capita consumption expenditure after accounting for home consumption of HVC

Farm class MPCE with homeconsumption of

fruits and vegetables (rupees)

MPCE without homeconsumption of fruits and

vegetables (rupees)

MPCE without homeconsumption offruits (rupees)

MPCE without homeconsumption of

vegetables (rupees)

Marginal 470 459 469 461(61.0 ha) (6.8) (6.6) (6.7) (6.8)Small 523 511 522 512(1–2 ha) (10.6) (10.6) (10.6) (10.6)Medium 560 547 558 549(2–4 ha) (11.5) (11.3) (11.4) (11.3)Large 644 631 642 632(>4 ha) (15.3) (15) (15.3) (15.1)All 504 492 502 494

(6.8) (6.7) (6.7) (6.8)

Standard errors in parenthesis.


Table 12. List of covariates used in regressions and matching

Covariates Matching: high-value crops Matching: cereals

Marginal Small Medium Large Marginal Small Medium Large

Logarithm of operated land by household in hectares No Yes Yes Yes Yes Yes Yes YesLogarithm of operated land by household in hectares: squared No Yes Yes Yes No Yes Yes YesLogarithm of number of family members Yes Yes Yes Yes Yes Yes Yes NoLogarithm of number of family members: squared Yes Yes Yes Yes Yes Yes No No=1 if male headed household, 0 otherwise Yes Yes Yes Yes No Yes Yes YesLogarithm of age of the household head in years Yes Yes Yes Yes Yes Yes Yes YesLogarithm of age of the household head in years: squared Yes Yes Yes Yes Yes Yes Yes Yes

Educational attainment of the head of household

=1 if household head is illiterate, 0 otherwise Yes No No No No Yes Yes No=1 if household head has schooling upto primary level, 0 otherwise No Yes Yes Yes Yes Yes Yes Yes=1 if household head has schooling upto middle school, 0 otherwise Yes Yes Yes Yes Yes Yes Yes Yes=1 if household head has schooling up to secondary school includingdiploma, 0 otherwise

Yes Yes Yes Yes Yes Yes Yes Yes

=1 if household head is a graduate or above, 0 otherwise Yes Yes Yes Yes Yes No Yes Yes

Social group

=1 if household belong to a scheduled tribe, 0 otherwise No Yes Yes Yes No Yes Yes Yes=1 if household belong to a scheduled caste, 0 otherwise No Yes Yes Yes Yes Yes Yes Yes=1 if household belong to a backward caste, 0 otherwise No Yes No Yes Yes Yes Yes Yes=1 if household belong to upper caste, 0 otherwise No No Yes No Yes No No No

Household type: household type is based on source of the household’s net income during the 365 days preceding the date of survey

=1 if self-employed in non-agriculture, 0 otherwise No Yes Yes Yes Yes Yes Yes Yes=1 if agricultural labor, 0 otherwise Yes Yes Yes Yes No No No Yes=1 if other labor, 0 otherwise=1 if self-employed in agriculture, 0 otherwise Yes Yes Yes Yes Yes Yes Yes Yes=1 if not in above four categories, 0 otherwise Yes Yes No No Yes Yes Yes No

Institutional

=1 if has area under irrigation, 0 otherwise No No No Yes Yes Yes No Yes=1 if owns a tractor, 0 otherwise No No No No Yes Yes Yes Yes=1 if taken credit for farm 0 otherwise No Yes Yes Yes Yes Yes Yes Yes=1 if has access to information on modern agricultural technology Yes Yes Yes Yes Yes Yes Yes Yes=1 if is aware about minimum support prices, 0 otherwise Yes Yes Yes No Yes Yes Yes Yes=1 if is a member of registered farmer organizations, 0 otherwise Yes No Yes Yes Yes No No Yes=1 if is a member of self-help group, 0 otherwise Yes Yes Yes Yes Yes Yes Yes Yes

(continued on next page)

01

23

45

Perce

nt

0 .2 .4 .6Generalised propensity scores

Growers Non growers

High value crops

02

46

Perce

nt

0 .2 .4 .6Generalised propensity scores

Growers Non growers

Total cereals

Figure A3. Common support condition.


Table 12—continued

Covariates Matching: high-value crops Matching: cereals

Marginal Small Medium Large Marginal Small Medium Large

Agro-climatic zones: district based classification

=1 if the household is in a district belonging to humid zone, 0 otherwise No Yes Yes Yes Yes Yes No No=1 if the household is in a district belonging to semi-arid temperate, 0otherwise

No No No Yes Yes Yes No Yes

=1 if the household is in a district belonging to semi-arid tropic, 0otherwise

No Yes Yes Yes No No No Yes

=1 if the household is in a district belonging to arid, 0 otherwise No Yes Yes No Yes Yes No YesRoad density in the district: km/1000square kilometre area No No Yes No No No No Yes

Interactions: with Logarithm of operated land by household in hectares

=1 if any under irrigation, 0 otherwise No No No Yes No No No No=1 if has access to information on modern agricultural technology No Yes Yes Yes Yes Yes Yes Yes=1 if agricultural labor, 0 otherwise No Yes Yes No No No Yes No=1 if self-employed in agriculture, 0 otherwise No Yes Yes No No Yes Yes No=1 if the household is in a district belonging to humid zone, 0 otherwise No No No Yes No Yes No No=1 if the household is in a district belonging to semi-arid temperate, 0otherwise

No No No No No Yes No Yes

=1 if household head is a graduate or above, 0 otherwise No Yes No No No No No No

High-value crops include vegetables, fruits, condiments and spices, flowers, aromatic and medicinal plants, and plantation crops like tea and coffee.Cereals includes rice, wheat and maize.

Table 13. Estimated effects of high-value agriculture on consumption expenditure and poverty

Method Marginal (61.0 ha) Small (1.0–2.0 ha) Medium (2.0–4.0 ha) Large (>4.0 ha) All

(A) % point difference in probability of falling below poverty line by diversifying into HVCs

IV �7.49 �6.30 �0.18 �5.69 �6.68LPM �2.53 �2.16 �1.77 �0.23 �2.78

Nearest neighbor matching

m(1) �5.01 �3.47 �6.44 �1.38 �4.36m(3) �4.36 �2.70 �6.44 �1.47 �4.46m(5) �4.36 �2.89 �6.46 �1.46 �4.40

(B) % difference in monthly per capita consumption expenditure with growers as treated

IV 7.62 8.38 16.18 7.18 8.03OLS 3.68 5.70 6.95 6.22 5.51

Nearest neighbor matching

m(1) 12.8 9.5 13.7 11.7 12.3m(3) 12.5 9.8 12.2 9.4 12.5m(5) 12.8 9.4 12.2 10.4 12.6

Table 14. Estimated effects of exclusive cereal cultivation on consumption expenditure and poverty

Matches Marginal (61.0 ha) Small (1.0–2.0 ha) Medium (2.0–4.0 ha) Large (>4.0 ha) All

% Difference in monthly per capita consumption expenditure with growers as treated

1 �10.25*** �10.36*** �7.93*** �13.75*** �10.34***

3 �10.31*** �9.40*** �9.59*** �14.61*** �10.45***

5 �10.45*** �10.22*** �9.90*** �13.81*** �10.43***

% Difference in head count of households below poverty line with growers as treated

1 5.10*** 4.45*** 3.87 4.31 5.91***

3 5.30*** 4.21*** 5.77*** 4.39 6.00***

5 5.45*** 4.68*** 5.53*** 3.93 6.05***

*** Significant at 1% level. Cereals include rice, wheat and maize.



relationships between HVCs and MPCE from PSM aresimilar to those from IV estimates. For head count poverty,the estimates are larger from IV regressions compared toOLS and PSM estimates.

APPENDIX B. SUPPLEMENTARY DATA

Supplementary data associated with this article can befound, in the online version, at http://dx.doi.org/10.1016/j.worlddev.2015.02.015.

ScienceDirectAvailable online at www.sciencedirect.com



Date post:	26-May-2018
Category:	Documents
Upload:	voanh
View:	215 times
Download:	0 times

Assessing the Impact of Crop Diversification on Farm...

Documents