DP2017-17

Do Agricultural M arketing Law s
Matter for Ru ral G rowth? Evidence
from the Indian State s*
Purnima PUROHIT
Katsushi S. IMAI
Kunal SEN

June 16, 2017
* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Do Agricultural Marketing Laws Matter for Rural Growth? Evidence
from the Indian StatesPurnima Purohit* and Katsushi S. Imai€, and Kunal Sen†
* the Adam Smith Business School, University of Glasgow, UK€ Department of Economics, The University of Manchester & RIEB, Kobe University, Japan†The Global Development Institute, University of Manchester, UK
This Version: 16th June 2017
Abstract
This article examines effects of the regulatory framework of post-harvest agricultural
markets on agricultural growth across Indian states over the period 1970-2008. We
propose a new measure that captures various legislative dimensions of a key ‘Act’ - theAgricultural Produce Markets Commission (APMC) Act & Rules - governing the
agricultural markets, evolved from the dismal colonial history of India, and use thismeasure to estimate growth models using panel methods. We have applied Fixed-Effects,
Feasible Generalized Least Squares, and Fixed-Effects Instrumental Variable models tothe panel data to address the endogeneity associated with the regulatory framework. Our
results show that the Act significantly promotes not only agricultural growth but the use
and the adoption of agricultural technology. Evidence presented suggests that a policy toremove market regulation rather than advancing effective ones would failconsequentially to draw investments and improve agriculture growth.

Keywords – law, regulation, agricultural markets, colonial institution, technology, economic
growth, panel data, Indian states
JEL codes: C23, D02, K23, Q13, Q18
Corresponding author: Purnima Purohit, Economics, College of Social Science, Adam
Smith Business School, University of Glasgow, Level 2, Room 239E; East Quadrangle
Glasgow G12 8QQ, UK; T: +44 (0)141 330 2026;
Emails: purnima.purohit@glasgow.ac.uk; Katsushi.Imai@manchester.ac.uk;
Kunal.Sen@manchester.ac.uk

Acknowledgements: The authors thank MS Jairath, National Institute of AgriculturalMarketing (NIAM), Government of India, Jaipur, India, S Mahendra Dev, Indira Gandhi
Institute of Development Research (IGIDR), Mumbai, India, and several of the governments
officers at the Ministry of Agriculture, Central Government of India, New Delhi for the
discussions and inputs on various state-level data, and also participants at the economic
development seminar in Manchester University for their comments. Errors are ours.

1

Do Agricultural Marketing Laws Matter for Rural Growth? Evidence
from the Indian States

Agricultural growth matters for the majority of the poor in developing countries. While muchof the literature on the determinants of agricultural growth has examined the issues such as
climatic factors, geography, irrigation, infrastructure, access to technology, extension services
and land reforms in fostering agricultural growth in developing countries [examples include
Feder, Gershon, Just and Zilberman (1985); Binswanger, Hans, Khandker and Rosenzweig(1993); Foster and Rosenweig (1995); Johnson, Hazell, and Gulati (2003); Palmer-Jones andSen (2003); Benerjee and Iyer (2005); Fan, Gulati and Thorat (2008); Besley and Burgess(2000); Evenson and Gollin (2003); Dercon, Gilligan, Hoddinott, and Woldehanna (2009);
Duflo, Kremer and Robinson (2011); Krishnan and Patnam, 2013; Emerick, de Janvry,
Sadoulet and Dar (2016)], relatively little is known about the effect of regulation and its
reforms pertaining to agricultural markets on agricultural growth. In this paper, we examine
the effect of post-harvest agricultural marketing regulation and its reforms on agricultural
growth in Indian states over the period 1970-2008 (See a series of papers by Simeon
Djankov, Edward Glaser, Rafael La Porta, Andrei Shleifer in 1990s and later – for instance,
De Long and Shleifer, 1993; Rodrik, 2003; Shleifer, 2005; Porta, Lopez‐de‐Silanes, andShleifer, 2006; Djankov, McLiesh and Ramalho, 2006; Acemoglu and Robinson, 2008 and soon – that examine the effects of regulation on growth in areas as diverse as labour markets
and barriers to entry in product markets. However, no studies, to our knowledge, haveexamined the role of regulation of agricultural markets on agricultural growth).
Inappropriate or ineffective regulations in agricultural markets can adversely affecteconomic activities of farmers. Firstly, in unregulated or inefficiently regulated markets,
farmers can be subject to exploitation of market agents who act as monopolists (by raising
prices of farm inputs) and/or monopsonists (by lowering prices of farm produce). Secondly,2

in poorly regulated markets, farmers - particularly those with little financial and social
resources or political leverage - face high, often prohibitive, costs in accessing information on
produce prices and demand conditions that can reduce incentives to invest in technology andto market their produce commercially. This, in turn, could lead to thin agricultural markets,
inadequate coordination, high transaction costs and high unit costs for infrastructuraldevelopment (North, 1990; Cullinan, 1999; Dorward, Fan, Kydd, Lofgren, Morrison,
Poulton, Rao, Smith, Tchale, Thorat, Uray and Wobst, 2004; Dorward, Kydd, Morrison andPoulton, 2005).
In this article, we will examine the growth effects of the Agricultural Produce Markets
Commission (hereafter, APMC) Act & Rules in India – a unique legislative institution ofcolonial lineage.1 As a regulatory institution, the APMC Act is the first exclusive statute on
regulation on the marketing of agricultural produce, dating back to the year 1886, whenelements of regulation were first introduced in the cotton market under the British rule inIndia. The APMC Act and Rules are set to establish transaction norms, correct market
failures, promote market competition and offer marketing infrastructure services in marketing
yards. It is expected to incentivize farmers with secured marketing system and encouragethem to adopt improved farm technology to increase crop production and augment foodsecurity of the country. Most agricultural markets in Indian states have functioned under theframework of the Act over the last 60 years. A key feature of the APMC Act is that
individual Indian states have changed important provisions in the Act over time, as
agriculture is under the purview of Indian states under the Indian Constitution. The crossstate variation in the legislative provisions of the Act as well as its time-series variation, thatis, how various regulations that are part of the Act have been enforced over time, provides us
1This is consistent with the theme of the World Development Report 2017: Governance andthe Law (World Bank, 2017).
3

with an ideal environment to test the effects of market regulations on agricultural growth,using India as a case-study.
To quantify the effects of the APMC Act, we have utilized a multidimensional index thatmeasures different components of the APMC Act & Rules for 14 states of India during theperiod 1970-2008. The strength of our measure is that it is comprehensive and varies bothover time and across states of India. We apply this index to evaluate the role of regulation of
the agricultural markets in explaining the variation in agriculture yield growth across Indianstates and over long time-span, controlling for other determinants of agricultural growth inIndian states. We use Fixed-Effects (FE) as well as Feasible Generalized Least Squares(FGLS), while we also employ the Fixed-Effects Instrumental Variable (FE-IV) model toaccount for endogeneity concerns that arise related to reverse causality from agricultural
growth to the regulatory framework. India offers a particularly interesting case, as the Indian
government has always placed agricultural growth at the centre of its growth strategy. Overthe last decade, the role of regulation of agricultural markets in economic development has
become contestable in India (Pal, Bahl, and Mruthyunjaya, 1993; Expert Committee Reporton Agricultural Marketing Reforms, 2001; Gujral, Joshi, and Anuradha 2011; Minten,
Vandeplas, and Swinnen, 2012). In addition, various Indian states have drastically changed oreliminated market regulations (such as the state of Bihar) with a view to encouraging private
sector investment in the agricultural marketing sector. Concurrently, wide differences inagricultural growth exist across the states of India (Pal et al., 1993; Mukherjee and Kurodo,2003). Using the index of nearly forty years of the regulatory framework of post-harvest
agricultural markets, we find that improvement in regulation by at least 0.1 point on a scale
increases agricultural growth by 0.24% points. As regards the effect on modern farmtechnology adoption, we also witness an increase by 53.7 % points in the area under highvariety yield (HVY) rice, 73.2% points in the area under wheat HVY, 0.34% points of4

fertilizer use and an increase by 10% points of land under irrigation. We conclude thatmalfunctioning agricultural markets need institutional reforms and not institutional vacuum.
The results are robust to changes in model specification and estimation methods.
The remainder of the article is organized as follows. The next section begins by providing
a historical account of the APMC Act to understand why regulation of agricultural markets
matters for agricultural performance. The section then describes the approach to read, codeand quantify the APMC Act & Rules for select fourteen Indian states. In Section 3, we will
present the empirical specification and discuss the econometric methodology, to be followed
by a description of the dataset and variables used for our empirical analyses. The main results
are explained in Section 4 and the FE-IV in Section 5. In Section 6 we report the results ofrobustness checks based on alternative specifications. The final section concludes.
Regulation of Agricultural Markets in India
Overview of colonial history of the APMC Act
In this section, we provide a brief discussion on the historical origins of the APMC Act tomotivate why the impact of agricultural market regulation on economic outcomes is anempirical matter. Until the outbreak of the Second World War, the system of marketing ofagricultural and allied produce that developed in India resulted in devastating socio-economic
conditions from time to time. The Berar Cotton and Grain Market Law of 1897 was the firstlaw instituted by the British rulers to enhance commercialization of cotton production andcontrol grain movement in order to make the colonial empire flourish. At that time, the sole
motivation behind elements of regulations was to ensure stable supplies of pure cotton as raw
material to the textiles mills in Manchester (England) at below world prices (Rajagopal,
1993). By setting farm payments substantially below world prices, the agricultural marketingboards under the British administration effectively levied a tax on farmers, which
discouraged farm production and dampened farmer income. Meanwhile, the surpluses the5government accrued were rarely used to improve marketing facilities or to stabilize price. The
marketing boards became not only the means for collecting patronage resources but also themedium for distributing them. Jobs within the agricultural marketing system became rewards
for party loyalty. As a result, the marketing boards soon developed a reputation as aninefficient institution that exploited farmers and discouraged agricultural production (Knight,
1954; Lele, 1971; Bhattacharya 1992).
Each market centre initiated and developed its own practices and code of business. A hostof functionaries and intermediaries deploying their services within market got involved in thesystem, and exploited the ignorance and weak bargaining position of cultivator-sellers, who
had no say in the disposal of their produce (Bhatia, 1990).
After the two centuries of the old colonial administrative design of agricultural market andstagnating agricultural development in India, the new improved model Act ‘Agricultural
Produce Markets (Commission) Act’ (APMC Act) was introduced in 1938 by the Ministry ofFood and Agriculture of the Indian government. The subsequent state-level agricultural
market law, whenever passed by the states either immediately or after an interval, wasvirtually based on the general principles embodied in the original British law (Bhatia, 1990).
Despite the efforts by the central government of India, the progress made with the regulated
markets in terms of growth and their geographical distribution remained slow and highly
uneven. The heavy concentration of the agricultural markets in the cotton-growing statesremained prominent. The markets did not embrace other agricultural produce, and werelargely confined to cotton marketing. Until the late 1960s, certain states of India, such as
Uttar Pradesh, West Bengal, and Assam had few regulated markets (Rajagopal, 1993). See
Appendix A for salient features of the colonial APMC Act, state-wise APMC legislation andRules enforcement (Table A1) and the number of the regulated markets in the states since theyear 1930 (Table A2).

6

This colonial history of the APMC Act serves as an important reference for the purpose ofundertaking an enquiry of present day legal framework of APMC Act in the Indian states. We
believe that the history of the APMC Act & Regulations we have reviewed in the section has
faded and the present agricultural market institutions are steadily reworked through reforms
by the state as part of the progressive growth of India. However, the evidence is needed to
identify the causal relationship between the regulations and agriculture growth given thecomplex mechanism of institutional transformations (North, 1990; Acemoglu and Robinson2008). These APMC legal provisions that traditionally provided and enforced codes ofconduct to a considerable degree in agricultural markets across the states of India appear toinfluence the cost of exchange and production, alter preferences of actors and serve interest
group (Acharya, 2004).

Measurement of the APMC Act and the composite APMC index
We propose a new composite measure of regulations of the agricultural markets based on thestatutory law: the APMC Act & Rules to understand the role of regulations of the agricultural
market in promoting agricultural yield growth. Purohit (2016) constructed a composite
measure of the APMC Act & Rules by making classification of legislative measures on thesix dimensions for fourteen major Indian states from 1970 to 2008. This APMC index isbased on variables from de jure (in law) set of indicators to construct a quantitative measureof the APMC Act & Rules evolved over the period of 38 years (1970–2008).2 The choice ofde jure (rules based) indicators controls biases both “for” and “against” a particular state. It
limits the selection of spurious indicators of the APMC Act, driven by political or ideological2

Our approach is similar to that of Besley and Burgess (2004), who code state-level

amendments to the Industrial Disputes Act 1947 of the Indian government, to study theeffects of labour regulations on industrial performance.
7

biases or beliefs that experts’ assessments may have. It also allows us to distinguish objective
indicators easily from the outcome indicators of the APMC Act & Rules. Methodologically,
the APMC index is inspired by the measurement approach proposed by Kolenikov andAngles (2009) and Branisa, Klasen, Ziegler, Drechsler and Jütting (2014). This method
applies principal component analysis (PCA) for continuous variables and tetrachoric PCA forbinary variables (0/1) to multiple indices to extract the common information of the variables
that belong to a sub-index in the form of the first principal component, as a weighted sum of
the standardized corresponding variable. The composite APMC index is calculated as anaverage of a non-linear function of the six sub-indices (Purohit, 2016, p.40). This is a new
approach to aggregate the quantitative measure of the APMC Act in several dimensions overtime, penalising sub-optimal regulatory framework in each dimension. As a multidimensional
measure, the APMC index allows only for partial compensation between dimensions. The six
sub-indices of APMC Act & Rules related to administrative and regulatory framework of the
agricultural markets that are combined to form APMC composite index are (i) scope of
regulated markets; (ii) constitution of market and market structure; (iii) regulating sales andtrading in market; (iv) infrastructure for market functions; (v) pro-poor regulations; and (vi)channels of market expansion3.

3Each single sub-index measuring a specific dimension of the APMC Act constitutes a set of

legal variables read from the state Act is guided by the history, semi-structured interviews
with subject experts and government officials at Directorate of Agricultural Marketing andInspection, Ministry of Agriculture, Government of India, New Delhi and National Instituteof Agricultural Marketing, Government of India, Jaipur, undertaken during the field-visit toIndia between January-July 2011 (Purohit, 2016).
8

Coding and Measuring APMC Act & Rules: Classification of Variables
The New Model APMC Act & Rules of 2003 is used as the baseline model Act & Rules forthe classification of the core legislative measures (variables) in the construction of state-wise
APMC index4. The historical background of regulated markets has guided to identify thevariables that link to creation of institution of the present day agricultural markets in thestates. Each state’s APMC “Act” corresponds with state APMC “Rules.” The rules serve as a
blueprint to implement the clauses of the Act which simply outlines how regulated markets
are to be established and function for effectively implementing the provisions of the APMC
Act. This means that the Act is enforced only if the Rules exist.

4

The model APMC Act & Rules of 2003 was introduced by the union government of India

as a response to address and overcome criticism of the existing regulated marketing system invarious states of India. It is comprised of 14 chapters and 111 sections, covering clause fordeclaration and establishment of markets to regulate notified agricultural produce,
constitution of market committee and marketing board, conduct of business and power andduties of the market committee, regulation of trading, model specification for contract
farming, private market yard, penalty, budget and the like. In this article, we, thus, recognize
the model Act 2003 as the reference document to relate and compare clauses of the state’s
Act & Rules with the union government’s model Act & Rules. The model Act 2003 isregarded as the one to facilitate the state achieving economic outcomes with reference to how
far they support or structure economic activities of the agricultural markets by reducing
uncertainly with the predictable, stable structure and supporting pro-poor growth model. A
copy of the model Act and Rules 2003 can be found at:
http://agmarknet.nic.in/amrscheme/modelact.htm ;http://agmarknet.nic.in/amrscheme/FinalDraftRules2007.pdf
9The Agricultural Produce Markets Act & Rules in different states differ in vital contents
across the states, while they show the underlying path dependency of institutions which have
evolved gradually over the long period. This provides variation in the key variables acrossstates over time in constructing the index. With the New Model APMC Act of 2003 as thebaseline law, a legal clause in the state’s APMC Act & Rules is scored either one (+1) if alegal provision exists in the state’s APMC Act & Rules or zero (0) otherwise. Such aclassification of legislative measures allows us to codify the level of the APMC Act & Rulesfor each state. It distinguishes between different states with different levels of APMC Act andRules over the time. To demonstrate this coding procedure, we take the example of a positive
regulatory market clause from Rajasthan, the clause on “terms and procedure of buying andselling” Section 15–D (2 a–c) of the Act 1961 reads as follows:
Section 15–D(2-a) of the Act, 1961 reads: ‘The price of agricultural produce broughtin the principal market yard or sub-market yard or private sub-market yard shall be
paid on the same day to the seller in principal market yard or sub-market yard or as
the case may be, private market yard…’
Section 15–D(2-b) of the Act, 1961 reads: ‘In case purchaser does not make payment
as specified under clause (2–a), he shall be liable to make payment within five days
from the date of purchase with an additional amount at the rate of 1% per day of thetotal price of the agricultural produce payable to the seller’
Section 15–D(2-c) of the Act, 1961 reads: In case the purchaser does not makepayment as specified in clause (b) within the said period of five days, his licenceshall, without prejudice to his liability under any other law, be deemed to have beencancelled on the sixth day and he shall not be granted any licence or permitted tooperate in a market area as any other functionary under this Act for a period of one
year from the date of such cancellation.’10Here Rajasthan gets a code of ‘+1’ in the data set since 1963. The rules to enforce the Act
which were framed in 1963 satisfy three identified variables: (i) provision of payment togrower/seller on the same day; (ii) provision of interest payment over the delayed payment
and (iii) penalty for default payment. In comparison, except for Madhya Pradesh andKarnataka that included similar clauses in 1986 and 2007 respectively, the Acts of otherstates’ excluded clauses on interest over the delayed payment and penalty for default
payment, and in this case these states included the only provision on point (1) of payment togrower/seller on the same day. So these states get zero on two of the three legal aspects. The
APMC Act & Rules of the selected 14 states are compared and quantitatively coded to draw
variables, which capture differences in administrative design, ways of efficiency in trading,special protection to disadvantaged farmers, and market orientation of agricultural sector toincrease agricultural income and attain economic welfare. The measurement of the APMC
Act & Rules was supplemented also with information on post-harvest hard infrastructure
which was collected from the secondary records, published by the Directorate of theAgricultural Marketing, Ministry of Agriculture, Government of India. Table A3 in Appendix
provides a list of clauses (variables) constituting each of the following dimension (sub-index)
of the APMC Act & Rules of 14 states of India.
(i) The Scope of Regulated Markets dimension measures the geographical spread of theregulated markets (accessibility) and the sufficiency (against shortage) of the number ofmarkets in the state.
(ii) The Constitution of Market and Market Structure dimension measures the level offairness (democracy) in the administrative structure of the APMC governing committee
which equitably represents diverse interests – farmers, trader/broker and consumer – involvedin sale and purchase of agricultural produce in the yard.

11

(iii) The Regulating Sales and Trading in the Market dimension measures the level ofregulatory provisions such as single-point tax, open auction, payment and receipt provisionetc. that fosters fair commercial rules for the farming community.
(iv) The Infrastructure for Market Functions dimension measures the level of physicalinfrastructural facilities and services in the market yard to facilitate trading in perishable andnon-perishable agricultural produce with less wastage and loss.
(v) The Pro-Poor Regulations dimension measures pro-poor regulatory environment to stop
exploitation ensuring social justice to small and marginalized farmers.
(vi) The Channels of Market Expansion dimension measures legal space for modern
alternative marketing channels such as contract farming, direct marketing, private markets, emarkets etc. in the State. Guided by the model Act 2003, this sub-index captures legal steps
to encourage private sector investment in the establishment of an alternative agricultural
marketing system and also private-public partnership in joint management to increase
marketing efficiency through the removal of barriers and monopoly in the state functioning ofagricultural markets. Details on the status of recent reforms in the APMC Act are indicated in
Annex table A4.
Each of the sub-indices as well as the composite index are statistically normalized to range
between 0 and 1, with 1 corresponding to optimal regulations and 0 to inferior regulations of
the agricultural markets. Table 1 shows the state-wise summary statistics of the six subindices and composite APMC index, 1970-2008. Figure 1 shows trends of the aggregate
APMC measure for each state during 1970-2008. An important feature of this measure is that
it varies in both time-series and cross-sectional dimensions, which is, to our knowledge, themost comprehensive empirical characterization of post-harvest agricultural marketing law for
India.
[Figure 1 to be inserted around here]

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Empirical Framework, Methods and Data(a) An Empirical Framework

Our interest centres around the effect of our measure of APMC Act & Rules on agricultural
yield per hectare. Figure 2 illustrates the relationship between the regulatory measure of
APMC Act and agricultural performance across different states over time.
[Figure 2 to be inserted around here]

We estimate the reduced form panel regression as follows:

Yit  0  it 1  2 APMCitk  t i   it(1)

where for state i at time t (year), Yit denotes a measure of agricultural yields Ln(Yields )it ,the logarithm of the aggregate yield of principal foodgrains per hectare in kilograms. X it is avector of control variables.  t and  i are time fixed effects and unobservable state fixed
effects.  it is an error term, independent and identically distributed.
The key parameter of interest is  2 in Equation (1), which measures the effect of thelagged regulatory measure of AMPC Acts & Rules that would guide code of business of postharvest agricultural produce markets in state i at time t  k on agricultural yields taking into
account the possible endogeneity of APMCit . Following the recent empirical literature on
institutions, a measure of the APMC Act is lagged by 5 years (Besley and Burgess 2000,
2004).5 APMCit is lagged not only because any legislation (even the effective one) will take5

We have also carried out the panel unit-root tests (Levin, Lin and Chu, 2002; Im,

Pesaran, and Shin, 2003) to see if the variables are stationary. We found that the mostvariables, including Yield and lagged APMC index, are stationary with or without
13

time to be implemented and to have an impact. This is also due to the endogeneity concern,that is, any form of shock, such as other policy changes or natural weather shocks, toagricultural growth outcome may be correlated with incentives to reform APMC. By taking
lags - together with the IV approach to be discussed later -, we will partly address the concernthat the APMC measure is endogenous to economic conditions due to the common andunobservable hidden factors influencing the APMC measure and the agricultural outputs atthe same time. The interview with local experts during the field work has confirmed that it
would take about 5 years for the impact of any changes in regulations to be realized.6 As an
extension, however, we will instrument APMCitk by the political variables, namely theshare of electoral seats of leftist or right wing parties in the legislature.
(b) Econometric Method

In a long panel, like the one used in the article, with a relatively long time periods for a
relatively small number of states, non-identically distributed errors (heteroscedasticity ) or
identically dependently distributed errors (autocorrelations) are often an issue in the panel
data analysis. There could also be a correlation or dependency among the errors of the samecross-sectional unit, though the errors from different cross-sectional units are independent.
Therefore, ignoring a possible correlation of regression disturbances over time and between
states can potentially lead to biased coefficient estimates (Driscoll and Kraay, 1998; Baltagi,
2005; Hoechle, 2007).
trend. We do not reject the null hypothesis for some input control variables like fertilizer
and capital, and labour, but we use them as levels because they tend to be slow to changeover time and previous works (e.g. Mundlak, Butzer and Larson, 2012) use their levels(in log) in estimating yield in levels (in log).
6

The main results are robust to different lag structures.
14

The diagnostic test results show that the panel heteroscedasticity and cross-sectional
dependency are present with statistical significance at the 5% level in our dataset. There areseveral estimation methods to deal with the problem of panel heteroscedasticity. Following
the empirical literature on panel data analyses, such as, Besley and Burgess, 2000; Lio andLiu, 2006; Mundlak et al., 2012, we will use a Feasible Generalized Least Squares (FGLS)
estimation technique that allows for richer modelling of error process than those specified in
the short-panel case to ensure the validity of statistical results. It satisfies the classicalassumptions with modifications to allow stochastic regressors and non-normality of errors(Greene, 2003; Frees, 2004; Wooldridge, 2010). The estimation via FGLS method allows theerror term,  it , in the model to be correlated over i (individual state) and allows for aheteroskedasticity in error structure with each state having its own error variance. It also
allows to model error term  it as AR(1) process where the degree of autocorrelation is statespecific, i.e.  it  i it 1  it . As noted, both state and time fixed effects are controlled in themodel. In the literature, although FGLS method is used in random effects models (RE) toaccount for a particular type of correlations among the errors, Maddala (2001:578) explainsthat FGLS method is consistent in fixed effects (within group estimation) whether the key
assumption under the RE model that  i are not correlated with xit in Equation (1) is valid or
not, since all time-invariant effects are subtracted out and as t gets larger, the estimates of
FGLS and FE tend to converge.7,8 In the article, according to the Gauss-Markov theorem,

7

Maddala (2001:578) gives the statistical derivative proof showing that estimates from therandom effects model and fixed effects model are the same.
15

FGLS transformation estimation is more efficient than FE model estimation in the linear
regression model, leading to smaller standard errors, narrower confidence intervals and larger
t-statistics (Wooldridge, 2010). We, thus, focus here on the FGLS approach.
We use the panel dataset at sub-national units for our empirical analysis. The data cover
14 out of the total 29 states and the period from 1970 to 2008 (38 years). The selected 14
states covered in the article account for over 88 percent of total value of output from total
agricultural and allied activities for each year in India. These states also comprise the bulk of
the Indian population (around 94 percent). As noted earlier, we use the new composite
measure of Agricultural Produce Markets Commission (APMC) Act & Rules, a state-specificand longitudinal index, reflecting multidimensional aspects of regulations (Purohit, 2016).
[Table 1 to be inserted around here](c) Data Description
The state-wise time-series data on agricultural foodgrain yields – both the aggregate andcrop-wise data – are the key outcome variable, defined as the logarithm of aggregate (crop8

Note that the Hausman specification test statistics suggests that unobserved individual effectare correlated with regressors and fixed effects model is recommended over random effects
model. For robustness checks, we have estimated FE model. The results from the FE areunbiased and consistent estimator but it is inefficient under the class of linear unbiased
estimators due to presence of panel heteroscedasticity and cross-sectional dependency.
Regression results from both FE and FGLS approaches will be reported. All FGLS results arebroadly similar to FE model results, except that magnitude of APMC coefficient from FE
estimation is larger. We mainly focus on the results from FGLS approach as it is more robust
technique for our data structure.
16

wise) production in kilograms divided by cultivated land area in hectare in a state. Data on
aggregate agricultural yield is an index of foodgrains, which includes (i) Cereals – rice,wheat, jowar, bajra, maize, ragi, barley and small millets and (ii) Pulses – Gram, tur and otherpulses. The data coverage in the article is limited to only foodgrains because regulated
markets established under the APMC Act & Rules has prevalently dealt with the marketing offood crops. We also model the use of modern farm inputs measured as area under highvariety yield (HVY) of rice and wheat and the use of fertilizer per hectare as a function of thelevel of APMC index.

Controls Variables
The earlier studies explain the spatial heterogeneity in the agricultural productivity growth inIndia by using the variables on farm inputs (e.g. irrigation; fertilizer), R&D, and/or
infrastructure (Dantwala, 1986; Chand, 2005; Fan et al., 2008). In line with the earlier studies(Chand et al., 2009; Mundlak et al., 2012), we include in the baseline specification use offertilizer, measured as the log of use of fertilizer in kilograms per hectare of land, area underirrigation, a percentage of land irrigated of the total gross cropped area and capital, an index
of number of pump-sets and number of tractors per thousand hectares of land, computed byprinciple component analysis (PCA technique). The state-specific (log of) average annual
rainfall in millimetres per year is also included to take into account the differential effects ofannual rainfalls on the state-level agricultural productivity at state levels (Calì and Sen,
2011). We also include a variable on labour, measured as the log of the number ofagricultural workers (cultivators + agricultural labourers) per thousand hectares of gross
cropped area. It controls for the economically active population in state agriculture in the year
and proxies state’s resource endowment (Bhalla and Singh, 2001).99

The variable on education has been dropped due to its high correlation with farm inputs.
17Some studies found that the use of technological innovations, such as new crop varieties
specifically for rain-fed, dry-land and other ecological settings helped farmers in lowproductivity areas to pick up the productivity growth and this explains regional differences inagricultural productivity across states after the initial phase of green revolution of the 1960s(Sawant and Achutan 1995; Bhalla and Singh, 2001). To capture use of such technology
innovation, we account for cropping intensity which is measured as the log of gross cropped
area (GCA) subtracted by net cropped area (NCA) (based on Chand et al., 2009). Intuitively,
the measure of cropping intensity captures the number of times land is sown by using new
variety seeds in a year.
In an expanded specification, we add three more variables: landholdings (average farmsize) to control for the size of farm land to capture efficiency effects on productivity, as wellas road density (length of the roads in kilometres per thousand population) and annual per
capita state expenditure on agriculture sector to capture the level of infrastructure in a state.
Finally, in all specifications, the state fixed effect are included to control for unobserved,
time-invariant differences, such as specific resource endowments, cultural and geographic
characteristics that may have an impact on agricultural performance across Indian states. For
example, this may be colonial land tenure institutions that have been found to affect districtlevel agricultural growth in India in the post-Green Revolution period (Banerjee and Iyer,
2005). They could also be unfavourable agro-ecological conditions that can negatively impact
on agricultural growth (Palmer-Jones and Sen, 2003). The year fixed- effects are also
included to capture any aggregate shock, such as a monsoon failure that would affectproductivity output across all states in a given year (Besley and Burgess, 2004; Calì and Sen,18

2011).10 The data are weighted by the land area or the state population data so that different
states are comparable. Table A3 in the appendix shows data source and base for data
standardization.
Table 2 shows the summary statistics of the key outcome and control variables averaged
over 1970-2008 for the full sample. The aggregate APMC index measure has a mean of 0.27,
with a standard deviation 0.12 for the full sample. Examining the levels and changes in thestate-wise APMC measure, we can infer that the APMC measure of Maharashtra, Punjab,
Haryana, Karnataka and Rajasthan started on a good footing. The index improved fromaround 0.20 to 0.40 or higher. Madhya Pradesh, particularly, as well as Tamil Nadu andAndhra Pradesh provide an example of states that set off from a very low-level score of the
APMC measure and demonstrated a leap positive change in their APMC measure over time.
For instance, Madhya Pradesh improves the measure from as low as 0.050 to as high as 0.52
in the same period. Gujarat starts at medium level (0.199) and remains at medium level(0.274). West Bengal, Uttar Pradesh, Assam, Bihar, and Odisha also demonstrate somepositive change from very low levels in the APMC measure, but the APMC score in thesestates remained low over the period (Purohit, 2016). We also observe a large variation in themean statistics with relatively high standard deviations of each of the six sub-indices. The
summary of the data provides us with a strong motivation to examine the relationshipbetween the state APMC measure and agricultural outcome in a multivariate econometricsetting.
[Table 2 to be inserted around here]10

A statistical test for joint significance of time fixed-effects with the full model specification(with all controls) has been carried out to examine whether time fixed effects should be
included in the FE model. The result suggests the inclusion of time effects in the model.
19

Econometric Results
APMC Index and agricultural yieldAs the primary focus of this article is the impact of composite APMC index on agricultural
yield growth, a starting point of our empirical modelling strategy is to recognize that we wantto model a yield growth as a function of changes in available APMC Act & Rules (via APMC
index measure while controlling for possible endogeneity). We begin doing so by estimating
Equation (1) by using time lags ( APMCit5 ). Table 3 present the results of the log of
aggregate agricultural yields in kilogram per hectare based on the use of different estimation
techniques: FGLS panel model (column 1-3) and FE panel model (column 4-6). We will
mainly focus on the results based on FGLS in this section as they are considered more robust.
We also separately run fixed-effects instrumental variable (FE-IV) yield growth model(column 7-8, table 3) and discussion of the IV regression results is presented in section 6.
The Column 1 of Table 3 presents the coefficient estimates of covariates such as labour,
fertilizers, irrigation, machine capital (the index of pumpsets and tractors), and the cropping
intensity conventionally used in the existing literature. In this case the APMC measure is
excluded to focus only on the relationship between yield per hectare of agricultural
production and these factors.11 The coefficient of conventional variables, except machine
capital variable, is positive and statistically significant.
APMC measure is introduced in Column 2 to gauge its effect on agricultural yield after
controlling for the same determinants. In line with model predictions, the coefficient estimate
11

According to the literature on agronomy, agricultural technology, such as short duration

cropping seeds, draught/ flood resistant seeds, etc., is available in most of the food cropvarieties to suit the various climatic regions of India, and improve the actual yield. Chand(2005) reports that even in agriculturally advanced state like Punjab the actual yield of paddy
can be raised by 87% using the existing improved technology.
20

of APMC measure is positive and significant. A point increase in the index measure of
APMC regulations is associated with a 0.24 percentage point increase in agricultural yield inthe states. This result suggests that the legal framework of the APMC measure influences thematerial and economic environment in which farmers operate to increase agricultural
production. The estimated coefficient on other conventional variables also remains
statistically significant at the 1% level. The magnitude of coefficients for two importantvariables, however, changes. The coefficient of cropping intensity becomes smaller. Since
this variable is a proxy for the use of modern seed varieties allowing for use of land areamultiple times during a crop year, the change in the coefficient of cropping intensity suggeststhat investment in agricultural technology may not be purely exogenous to the role of APMC
measure, that is, the regulation to an extent orients farmers to use modern farm innovations
and technology. Moreover, the coefficient on the use of labour shows a very large statisticallysignificant change of 0.10 from 0.09 in its magnitude (p<0.01) (column 1 & 2, in Table 3). It
suggests that APMC measure has a complimentary effect on the economic structure of thesector in addition to its significant positive effect on aggregate high yield outcome.
Column 3 of the table extends the baseline model and adds other farm controls, such as roaddensity, average land holdings, and per capita state expenditure on agriculture to check therobustness of the results. The coefficient on APMC measure remains robust and significantly
different from zero. We find a positive and significant coefficient on the effect of roaddensity which is measured as road-length in km per thousand number of population on yieldproductivity (Column 3). The result is consistent with earlier studies showing the role ofpublic investment in infrastructure in stimulating yield productivity in the long run (Acharya,
2004; Fan et al., 2008). It indicates that rural road connectivity to the regulated agricultural
markets would accelerate the transition from low productivity subsistence agriculture to a
productive agro-industrial economy.

21

[Table 3 to be inserted around here]

Contrasting capital and labour in terms of their role in the agricultural sector, we find thatthe coefficient estimate for labour remains highly significant in contrast to the statisticallyinsignificant coefficient estimate for capital. This emphasizes the persisting role of labour inthe state agricultures. In view of India’s labour surplus economy where more than 50 percent
of India’s workforce is engaged in agriculture as the principal occupation, the results are notsurprising. Also, the rate of mechanization in agriculture, especially the use of tractors forploughing is low due to small land-holdings within the states. Around 65 percent holds
marginal holdings (area less than 1 hectare), 18 percent small holding (1-2 hectare), about 16
percent medium holdings (more than 2 to less than 10 hectare) and just less than 1 percent
large holdings (10 hectare and above) (Eleventh Five Year Plan Report, Planning
Commission, GoI, 2008). As regards the role of land size for yield, the coefficient estimate is
weakly significantly positive. Bhalla and Singh (2001), however, demonstrate using thedataset on Indian states that agricultural productivity was becoming land-size neutral overtime. The article finds that the agricultural growth can occur either through net sown area,
where land size would matter, or through the increase in intensity of cultivation. The
estimated coefficient on rainfall has mostly shown an erratic impact on the dependent
variable in the regressions. It may be explained by the type of data used to capture rainfall
effect in the model. The variable on rainfall is aggregated rainfall data for the entire state tocapture annual weather. It does not capture clearly the heterogeneous weather conditions of
the state and thus fails to capture any consistent and significant impact. We would expect thata normal and well spread-out rainfall would lead to better agricultural yields whereas bothexcessive and very low rainfall would adversely affect production decisions and productivity.

22

Finally, with respect to the coefficients on time dummies in the model, we find that theysignificantly and negatively affect the agricultural performance over the period. These results
are unsurprising because year dummies control for common agricultural related policy
shocks, where India’s national agricultural policy is criticized for its ‘lacked direction’(Chand, 2005:21). India’s agricultural policy scenario is specifically characterized as ad hoc,
myopic and mere reaction to the situation that lacks direction as compared to its economic
policy towards building competitiveness in the industry sector (ibid; Acharya, 2004). Thesestatistically significant and positive results, nonetheless, suggest the dependence ofagricultural yield and sources of its growth on the APMC regulatory measure.

Instrumental Variable Estimation
In this section, we estimate the fixed-effects instrumental variable (FE-IV) model to address
the endogeneity of the AMPC index with a focus on the cases corresponding to Columns 7-8of Table 3. By using time lag of five years on the APMC index, the endogeneity concern of
causation was minimized in the results of Table 3 as agricultural outcomes at the current
period in a state cannot affect prior events such long as five years back a legal structure of
regulated agricultural markets. However, a possible concern of biased estimation may arise ifsome long-term positive policy shock to agricultural sector continues to affect agricultural
APMC regulated markets and thus bias the estimated relationship between APMC measure
and crop yields. For instance, with impressive agricultural development in the state of Punjab
due to the green revolution, the state experienced flourishing of the regulated agricultural
markets (Sidhu, 1990; Maheshwari, 1997). Also, the presence of measurement error in aregressor (APMC measure) could also underestimate the results. The estimated measure of
the APMC Act & rules is a close representation of the Act. As the literature shows, it isincorrect to assume a completely deterministic and perfect measurement process of a latent23

variable, such the APMC Act & Rules that cannot be measured directly. Therefore, anexistence of possible random or systematic measurement error in the APMC measure may
provide an inaccurate estimation of the relationship (Bollen and Paxton, 1998). For thesereasons, it is important to identify exogenous sources of variation in the APMC measure inorder to establish its relationship with the economic performance of the agricultural sector. In
order to address this endogeneity concern, we will apply IV model to the current value of
AMPC index given that most of the control variables are in the first lag.
Our IV for the APMC measure is the share of seats of hard left or right wing parties in thelegislature based on the results of the political elections at the state level (Besley and Burgess,2000). For decades, food policy reforms have played an important role in states’ politics(Mooij, 1998; Saez and Sinha, 2010). For instance, various schemes of food procurement,
allotment and distribution in the states are used for different party politics (leftist or rightwing parties) mostly as vote garnering instrument (See Pal et al., 1993; Mooij, 1998). Bothstate’s policy of food procurement and its distributions have direct implications on the
structure of regulations of the marketing system, since the government procures the
foodgrains from the regulated markets. Thus, in Indian states “the regulation of markets is
commonly understood as being a proper activity for the state” (Harriss-White, 1995:586).
State regulations of the agricultural markets are essentially designed and shaped by politicalinterests. The association between the regulatory APMC measure and political parties became
vital evidence considering the case of Bihar where the state government took the decision torepeal the state’s APMC Act and disband of Bihar’s Agricultural Produce Marketing Board
in 2006. The political regime having its individual approach to food policy in the state is
identified as a good instrument for the APMC measure. According to Besley and Burgess(2000), this instrument would be less suitable if shocks to agriculture yields (e.g. bad
weather) influence the election process and contribute in political party winning the state24election. In view of this concern, three-year lag is used for the identified political group thatserve as an instrument (Hard Left or Congress party) for the regulatory measure. It is safe toconsider that any contemporaneous shocks to current agricultural yields are uncorrelated witha shock that puts a particular group in power three years ago (Besley and Burgess, 2000).
We considered data from records of the number of seats won by political national parties
at each of the state elections under four board groups, classified by Besley and Burgess(2000). The data have been updated by Calì and Sen (2011) to cover the most recent
elections. The four groups are constructed as a share of the total number of seats won by
parties in the state legislative assembly. The parties affiliated to each group are noted
alongside the name of the group. They read as follows: (1) Congress Party (Indian National
Congress + Indian Congress Socialist + Indian National Urs + Indian National Congress
Organization), (2) Hindu Parties (Bhartiya Janata Party + Bartiya Jana Sangh); (3) a hard left
grouping (Communist Party of India + Communist Part of India Marxist); (4) a soft left
grouping (Socialist Party + Praja Socialist Party). The variable is described as a share of totalseats in the legislature. We used a hard left party of India in the main analysis.12 Congress
and left party as joint instruments are also considered in our supplementary analysis.
The first stage estimates for the regulatory measure is presented in Panel A of Table 4.
The results suggest that political variables significantly influence the APMC measure of the
agricultural markets. Column 1 of the table shows a positive and significant coefficient on
party variables (lagged by three year time): Congress and Hard left. The results on politicalparties indicate a significant political influence on the regulatory and administrative
12

The election results over the time suggest that Congress parties appears to have dominantseats in the assemblies, while hard left parties have been in power in Kerala and West Bengal
and Janata Parties were mostly prevalent in Bihar, Haryana, Karanataka, Madhya Pradesh,Rajasthan and Uttar Pradesh.
25

framework of the agriculture markets. F test for excluded instrument is 12.13 (columns 1) or
15.90 (column 2), above the threshold value of 10, confirming that the regressions do not
suffer from weak identification problems. In this case all the control variables are lagged by
one year period.
[Table 4 to be inserted around here]

Columns 1 and 2 of Panel B in Table 4 display the second stage results for IV estimation on
the full extended model by FE-IV regression. All controls and time effects were included in
the model. IV estimation has been performed instrumenting three period-lagged politicalvariables. The results confirm that instrumented regulatory measure indeed has a large andsignificant impact on agricultural yields (1.52 percentage points higher yields, Column 1).
The standard errors are also larger, but the coefficient on APMC measure continues to bestatistically significant for agricultural yield productivity. The diagnostic statistical tests usedin the exercise are statistically robust, that is, standard requisite IV tests – Sargan-Hanson testfor overidentifying restrictions, endogeneity test, tests of under-identification and test for
weak identification – would justify our instrumentation strategy. FE-IV estimation reconfirms
the robustness of the results obtained in the standard fixed effects and FGLS model. IV
results of a large yield coefficient 1.53 as compared to 0.24 (FGLS) further strengthen our
main finding that the measure of the APMC Act & Rules significantly increases agricultural
performance. However, it is noted that the larger coefficient estimate may be due to
instrumentation of the APMC index without lags (that is at time t).

26

Robustness checks and alternative model specifications
Results on Agricultural Investments in modern inputs
In this section, we will provide the results based on alternative model specifications. First, we
will estimate how much farm investment decisions in the states are influenced by theregulatory system of the agricultural markets. It would be surprising if APMC measure that
affected aggregate agricultural yield did not impact on key modern farm input investments.
Due to the small sample size, we will focus only on the relationship between APMC
regulation and investment in farm technology.
Table 5 shows the results on each outcome variable of Column 1 (use of fertilizer kg per
hectare, 1970-2008), Column 2 (proportion of land irrigated, 1970-2008), Column 3 (% area
under HVY rice, 1984-1996) and Column 4 (% area under HVY wheat, 1984-1996). We run
panel regression model separately to estimate the effect of the lagged APMC on each of thesedependent variables representing the use of the modern farm input investment. The effect of
APMC measure ( APMC it 5 ) is positive and significant to boost the use of fertilizer andirrigation. The results show that a one-percentage point improvement in APMC measureleads to 0.33% points higher use of fertilizer (Table 5, Column 1) and 10.3% points higher
proportion of land under irrigation (Table 5, Column 2). Improving APMC Act amplifies
both land area under HYV (high yield variety) wheat and HYV rice by 73.2% points and53.8% points. These results on modern farm investment indicate that post-harvest market
regulation (APMC measure) tends to increase investment in the modern farm inputs, which islikely to boost the yield improvement.
[Table 5 to be inserted around here]27

Results on APMC Sub-indices and Agricultural Yield
Second, for further robustness checks of our main results, another alternative model
specification is explored in Table 6. Here we perform the yield analysis using the six subcomponents of the APMC Act measure as independent variables, using FGLS panel method,that is, (1) Scope of Regulated Markets; (2) Constitution of Market and Market Structure; (3)Regulating Sales and Trading in Market; (3) Infrastructure for Market functions; (5) ProPoor Regulations; and (6) Channels of Market Expansion. It investigates which of thedimensions drive the impact of APMC measure on agricultural yield.
All components of the APMC measure, except one, are lagged by five-year time periods,
which is to keep in line with the earlier analysis of the composite index. The index on
Channels of Market Expansion is lagged by two-year time period because certain legal
variables that feature the measurement of ‘market expansion’ sub-index were enforced in thestates around the year 2006. It is largely based on new legal features of the model APMC Act
of 2003. Columns 1-6 of Table 6 show the results of six different measures of the APMC
index included separately in each specification with a full set of controls, state and time fixed
effects. Column 7 of the table displays the results on six regulatory components of the APMC
measure controlled together in a model, after including all other explanatory controls, state
fixed effects and year effects in the model.
[Table 6 to be inserted around here]

The coefficient estimates on market expansion (Column 3), pro-poor regulations (Column5) and market infrastructure (Column 6) are positive and significant. Table 6 shows that apoint improvement in market expansion measure leads to 0.26% points higher yield (Column3); a point improvement in pro-poor regulations measure leads to 0.058% points higher yield(Column 5) and a point improvement in Market infrastructure measure leads to 0.20% points

28

higher yield (Column 6). The results imply that each of the regulatory components of the
APMC market impacts the yield outcome independent of the presence of other APMC
components.
Column 7 of Table 6, where all six components are regressed together in a single
specification, not only reconfirms the significant and positive role of market expansion, propoor regulations and Market infrastructure on yield productivity of the states but now also
displays positive and significant coefficient on market scope. The coefficient on constitutionof market and market structure and regulating sales and trading in the market continue toappear insignificant. The coefficient on regulating sales and trading in the market also showsa negative correlation (Column 7).

Discussion of the regression results: APMC sub-indices
What sense do the results presented on sub-indices make? The results of Column 7 in Table 6
indicate that both hard (physical infrastructure) and soft (rules and administration
infrastructure) complements each other in order to efficiently structure the agricultural
markets to affect agriculture performance in the states. We discuss the interpretation of theestimates in terms of why do we observe these varying effects.
(i) Role of Regulated Markets Infrastructure
The results on regulated markets infrastructure are consistent with the existing literature.
Studies on India finds that physical infrastructure (such as roads, railways, transport facilities,
electrification, agricultural produce storage facilities, cold stores, grading, packing,processing and so on) is instrumental in increasing the integration of spatially separated
markets of the country. They significantly enhance the performance of marketing functions
and expand the size of the markets through increased horizontal and vertical integration ofagricultural produce markets, which improves the process of price discovery and transmitting

29

price signals from deficit to surplus areas (Acharya, 2004). So the results on theinfrastructure sub-index in our article reconfirm that marketing infrastructure assumes criticalimportance in agricultural development.

(ii) Role of Regulated Market Scope
Statistically insignificant results on the sub-index on market scope (when examined alone,
and together with other components) are consistent with the existing literature. The results
suggest that the benefits available to the farmers from regulated markets depend on the
trading facilities and amenities available within the market-yard rather than the number ofmarket places in the area. An earlier study by Rao, Rao and von Oppen (1984) finds that theeffect of a number of regulated markets establishment on productivity increases at adecreasing rate from a certain point of saturation with markets of 132 to 161 markets per
100,000 sq. Km. Further regulated markets have no productivity effects on the aggregate
level. Our results suggest a similar story. According to the secondary sources, the severe
infrastructure shortages were prevalent across the states of India. Both covered and open
auction platforms exist in only two-thirds of the regulated markets and only one-fourths ofthe markets have common drying yards. To facilitate trading in the market yard, godown andplatform facility in front of a shop is available in only 63 percent of agricultural markets, thecold storage units exist in only 9 percent of the markets and grading facilities exists in lessthan one-third of the markets (Acharya, 2004). Clearly, our results show that the number ofmarket-yards alone cannot fully explain the variation in agricultural performance and the roleof trading facilities in the market yard is more important in strengthening the scope of
regulated markets.

30

(iii) Role of Pro-poor Regulations
The results indicate that the pro-poor sub-component of the legal framework of the APMC
Act plays a significant role. The sub-index captures how certain market regulation ensures theprotection of farmers’ interest by monitoring the market conduct and establishes fair trading
practices to ensure protection to the weak farmers. The results suggest that some states are
more successful in using APMC Act as a development-cum- administrative measure thatsignificantly incentivizes small and marginal cultivators to augment agricultural yield.

(iv) Role of Market’s Administrative Structure and Trading Practices
The insignificant results on both Market Administration sub-index and Trading Practices
sub-index reflect current market realities in the states of India. The literature on Indian
experience of agricultural markets reveals the bureaucratization of the management ofregulated markets. The statistics suggest that more than 80 percent of the market committees
are superseded and state administrators manage the markets notified by the state governments(Acharya, 2004). Criticism goes that state-officials are neither under compulsion to provide
needed marketing services for efficient trading nor could any other private agency is easily
allowed to enter this venture (unless APMC reforms are suitably undertaken andimplemented). The complex and expensive legal provisions, such as licensing bureaucracy,
undermines the quality of reforms of the agricultural market. The insignificant empiricalresults of the article (Table 3) confirm how non-dynamic market governing administration
undermines competition and its impact on agricultural yield in the states of India.

(v) Role of Market Expansion Analysis
The coefficient estimate of the sub-index on market expansion is significant and positive.
From 2003 onwards, some of the Indian states initiated legislative reforms in the APMC Act

31

to improve efficiency and competitiveness of Indian agriculture. The main reform initiative in
the Act was to liberalize the regulated markets and allow for an alternative agricultural
marketing system in private sector, promoting direct marketing by farmers, legalizing
contract farming etc. in the states. Mistrust and challenge to the new set of regulatory reforms
are prevalent. Our empirical result on sub-index market expansion, therefore, is of criticalimportance to inform the public debate.
In light of the result of the sub-index on market expansion, the instance of the repeal of the
APMC Act in the state of Bihar in 2006, instead of correcting it with a view to facilitating
private investment in the sector, raises significant apprehension for pro-poor agricultural
growth in the state. The problem of weak agricultural performance in Bihar and most of otherstates in India is not the absence of private markets, but that of its correction for theefficiency of the system as a whole (Maheswari, 1997; Minten et al., 2012). According to
Acharya (2004), the private trade handles around 80% of the total marketed quantities of all
agricultural commodities taken together. The marketed surplus handled by cooperatives is
estimated as 10% and that by public agencies as 10%.
The combined results on components of the APMC Act suggest that markets areinteractive and regulatory components of the system need to operate in tandem. Each of thecomponents of the APMC Act reinforces and strengthens each other for a resultant composite
institutional engineering to be economically productive for the agricultural performance inIndian states.

Conclusion
This article examines the effect of agricultural marketing law –specifically the Agricultural
Produce Markets Commission Act (the APMC Act & Rules) – on agricultural outcomes for
14 Indian states over the period 1970-2008. Using a range of empirical models, we have32

found a positive relationship between the legal framework of the APMC Act and agricultural
yields. We have applied Fixed-Effects, Feasible Generalized Least Squares, and FixedEffects Instrumental Variable models to the panel data to address the endogeneity associatedwith the regulatory framework. The results show that regulation plays a decisive role inmotivating farm investment decisions and agricultural production, independent of otherfactors that have been found to be important in explaining differences in agricultural
performance across the states over time. Our findings suggest that a framework of regulation
appropriate for well-functioning markets of agricultural produce is important in enhancing
agricultural growth.
The nature of the agricultural marketing regulations assumes critical importance for‘balancing act’, such as maintaining a degree of rationality in price fixation, moderating
trading practices of private traders, market agents and farmers in a way to enhance economic
viability of both better-off surplus producing classes as well as of huge mass of subsistence
farmers in the states. Our results highlight the importance of well-regulated agricultural
markets to address market failures and promote the effective functioning of domestic
agricultural trade in order to impact on agricultural growth.

33

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40

AssamBihar

GujaratHaryana

Karnataka

Madhya Pradesh

Maharashtra

OrissaPunjabRajasthan

Tamil Nadu

.2 .4 .6
0.2 .4 .601970 1980 1990 2000 20101970 1980 1990 2000 2010
West Bengal.2 .4 .6

Uttar Pradesh

0

APMC Index0.2 .4 .6

Andhra Pradesh

1970 1980 1990 2000 20101970 1980 1990 2000 2010

year
Graphs by statename

Figure 1: APMC composite index by state, 1970-2008 (Purohit, 2016)

41

.25
7.4

.3

.2

1990
Year

2000

2000

.5
.3
.2

1970

1990
Year

2000

.2

7.4

.1

.15

pcaapmcindex

7

2010

.05

6.4

6.6

6.8

.4
.3

1990
Year

1970

pcaapmcindexnew

1980

1990
Year

lnyieldkgha

2000

2010

pcaapmcindexnew

1990
Year

lnyieldkgha

2000

2010

.3
pcaapmcindex

7.6

.1

7.4
7.2

.45
.4

2000

2010

1970

pcaapmcindexnew

1980
lnyieldkgha

1990
Year

2000

2010

pcaapmcindexnew

.25

7.8

.2
pcaapmcindex

.2

7.4

.15
0

6.5

7

.05

.05

7.2

.1

lnyieldkgha

pcaapmcindex

7.6

.25

West Bengal

8
7.5

1990
Year

lnyieldkgha

Uttar Pradesh

7

1980

0

7

1970

pcaapmcindexnew

.15

1980

.1

1970

6

.35

.3

.35

lnyieldkgha

6.5

.45

7

pcaapmcindex

.5

.5

.55

.4

Tamil Nadu

.4lnyieldkgha

2010

7.2

.6
.5

lnyieldkgha

pcaapmcindex

6.5
6
5.5
5

1980

7.5

.55

8.4

2000
pcaapmcindexnew

Orissa4.5

1970

lnyieldkgha

8.2

pcaapmcindex

1980
lnyieldkgha

Rajasthan

8.4pcaapmcindex

7.2
7
6.8
6.6

2010

pcaapmcindexnew

7

.5
.4
.3
.2
.1
0

2010

pcaapmcindexnew

7.8

.15
.6

7.4

1990
Year

Maharashtra

7.6

lnyieldkgha

2010

.4

1980
lnyieldkgha

lnyieldkgha

pcaapmcindex

2000

2000
pcaapmcindexnew.35

lnyieldkgha

pcaapmcindex

7.5
7
6.5

1970

Punjablnyieldkgha

1990
Year

Karnataka7
lnyieldkgha

6.5

1990
Year

lnyieldkgha

1980

.45

8.5

.26
.24
.2

.22

lnyieldkgha

pcaapmcindex

2010

6

1980

.05

1970

lnyieldkgha

8

.28

7.5
lnyieldkgha

72000
pcaapmcindexnew

Madhya Pradesh

1970

0

2010

pcaapmcindexnew

Haryana

6.5

1990
Year

lnyieldkgha

.1

7
6.8

1980
lnyieldkgha

6

1980

6.6

0

1970

Gujarat1970

pcaapmcindex

7.2

.2
.1

7

lnyieldkgha

pcaapmcindex

7.2

.3
.25

2010

.2

2000
pcaapmcindexnew

7.8

1990
Year

lnyieldkgha

.3

1980

.25

1970

6.8

6.5

.15

.2

lnyieldkgha

pcaapmcindex

7.5
7lnyieldkgha

.35

8

Bihar

7.4

Assam
.4

Andhra Pradesh

1970

1980
lnyieldkgha

1990
Year

2000
pcaapmcindexnew

2010

1970

1980
lnyieldkgha

1990
Year

2000

2010

pcaapmcindexnew

Figure 2: State-wise trend plots of regulatory measure and agricultural yield, 1970-2008
Note: dash line: ln(yield) kilogram per hectare and solid line: APMC Act & Rules measure42

Table 1: State-wise Summary Statistics (Mean and Std.Dev) of the Sub-indices and
APMC Index, 1970-2008
1
State

2
APMC

AndhraPradesh
Assam

0.264
(0.055)0.153(0.069)0.169(0.058)

3
4
5
6
7
8
Structure Alternative
Sales
Pro- Infrastr Scope
Mkt&Trade poor
uctureChannels
0.359
0.109
0.684
0.151
0.267
0.886
(0.108)(0.243)(0.035) (0.127) (0.050) (0.111)
0.597
0.026
0.583
0
0.218
0.073
(0.244)(0.092)(0.226)(0)(0.082) (0.219)
0.236
0.096
0.357
0
0.084
0.856
(0.114)(0.073)(0.169)(0)(0.081) (0.179)

0.223(0.018)0.382(0.087)0.319(0.059)0.307(0.143)0.374(0.086)0.120(0.034)0.452(0.053)0.381(0.063)0.241(0.098)0.153(0.038)0.201(0.066)0.668(0.093)0.405(0.145)0.780(0.065)0.495(0.241)
0.763(0.165)0.192(0.034)0.418(0.052)0.723(0.142)0.481(0.388)0.172(0.150)0.283(0.194)0.039(0.135)0.209(0.137)0.034(0.132)0.031(0.070)0.071(0.225)0.022(0.081)
0.128(0.139)0.056(0.183)0.184(0.250)0.015(0.033)0.104(0.070)0.257(0.020)0.866(0.114)0.673(0.020)0.489(0.271)0.703(0.044)0.493(0.044)0.815(0.141)0.710(0.045)0.275(0.283)0.421(0.205)0.398(0.269)

0
(0)
0
(0)
0.051
(0.223)0.675(0.399)0.261(0.273)
0
(0)
0
(0)
0.769
(0.078)0.256(0.00)
0
(0)
0
(0)

0.219
(0.100)0.576(0.069)0.135(0.037)0.145(0.059)0.388(0.077)0.082(0.040)0.901(0.098)0.130(0.085)0.368(0.082)0.193(0.087)0.365(0.104)0.857(0.090)0.973(0.019)0.871(0.071)0.753(0.158)0.886(0.070)0.625(0.181)1.000(0.002)0.744(0.165)0.767(0.157)0.745(0.222)0.800(0.262)0.267(0.122)0.469(0.268)0.080(0.158)0.552(0.251)0.154(0.289)0.291(0.229)0.774(0.265)Bihar
GujaratHaryana
Karnataka
MadhyaPradeshMaharashtra
OrissaPunjabRajasthan
Tamil Nadu
Uttar
PradeshWest
BengalTotal

Note: Standard Deviation in parenthesis (Purohit 2016)43

Table 2: Summary Statistics of the Main Variables, 1970-2008
Variable
APMC composite

Market StructureMarket Expansion

Sales and Trade

Pro-poor Market

Market InfrastructureMarket Scope

Yield (kg/hec) (log)
Congress share of seats
Hard left share of seats
Irrigated % of gross
cropped area
Agricultural workers per‘000 GCA (log)Cropping Intensity (log)
Fertilizers (kg/hec) (log)
Capital Index (no. of
tractors and pumpsetsper ‘000 hec land)
Average land size (hec)(log)
Road Density (Km/1000
popu)
Agricultural Expenditure
per capita (INR) (log)Average Actual annual
Rain (mm, log)
Proportion of highyielding varieties (HYV)
of rice
Proportion of highyielding varieties (HYV)
of wheatLeveloverallbetweenwithinoverallbetweenwithinoverallbetweenwithinoverallbetweenwithinoverallbetweenwithinoverallbetweenwithinoverallbetweenwithinoveralloveralloveralloverallMean.2674108.1039933.069911
0.469399.2107443.1747789
0.080383.0612565.1470676
0.551612.1958812.1645701.1546828.2591848.1463679
0.290696.2240237.0769725
0.773865.2245226.1535634
7.140898.4126128.06870337.96172

Std. Dev.
.1222625.1200582.0102181
0.268075
0.210744
0.174779
0.158492
0.061257
0.147068
0.250554
0.195881
0.16457
0.250554
0.195881
0.16457
0.229373
0.224024
0.076973
0.265476
0.224523
0.153563
0.502102.2636241.1458207
23.53948

Min
0.006668.1200582.010218100.171688-0.12778
00.015381-0.1290100.256977-0.0310900-.520539
00.082013-0.02556
00.072973-0.02583
4.49981000.557951

Max.6097309.4525169.55382211
0.780047
0.988241
0.209397
1.009441
10.865987
0.889074
1.7698666
1.10340110.900523
0.639548
1
0.999744
1.416151
8.35585.93.67
97.8804

Observations
N = 546n = 14
T = 39N = 546n = 14
T = 39N = 546n = 14
T = 39N = 546n = 14
T = 39N = 546n = 14
T = 39N = 546n = 14
T = 39N = 546n = 14
T = 39
N=546
N=532
N=532
N=546

overall

6.823341

0.506881

4.316968.00963

N=546

overalloveralloverall

7.692027
3.71174
0.058098

0.64086
1.090916
0.077354

6.15909
0.04879011.30485.47943
1

N=546
N=546
N=546

overall

2.087445

1.156354

0.01

5.46

N=546

overall

0.340348

0.258253

0

1

N=546

overall

3.986192.685572

0.77

15.05

N=546

overall

6.801804

0.623922

3.94061

8.09704

N=546

overall

70.18567

20.1323

24.12

100

N=282

overall

82.07275

19.77883

28.56

100

N=269

Source: Authors’ calculations

44

Table 3: Agricultural Yield explained by the APMC Act & Rules and Other Controls, 1970-2008
Dep. Var: Ln(Yield) kg/hec

(1)
FGLS
(No APMC )APMC Index (t-5)

(2)
FGLS
(APMC)
0.244***(3)
FGLS(Extra controls)
0.205**[0.082][0.096](4)
FE(No APMC )(5)FE(APMC)
0.289*(6)
FE(Extra controls)
0.330*[0.168][0.169]Instrumented APMC Index (IV)
Proportion of irrigated area
Ln(Labour) (000’/GCA)
Ln(Fertilizer use (kg/hec)
Ln(Cropping intensity)
Capital (tractors & pumpsets) ‘000/hec
Ln(Rainfall)

0.003***[0.001]
0.099***[0.029]0.002***[0.000]0.165***[0.017]0.165[0.146]0.001[0.012]0.003***[0.001]0.101***[0.031]0.002***[0.000]0.146***[0.017]0.031[0.151]-0.001[0.011]

5.192***[0.290]Yes
Yes

5.351***[0.282]Yes
Yes

Ln(Landholding) (hec)Road density (km/’000 popu)
Ln( Agri Expenditure pc)
Constant

Time fixed effects
State fixed effects
R2
No. of excluded instruments
F (Weak identification test/ test for
excluded Instrument)
Underidentification test (p-value)
No. of States
14
14
Observations
546
476
Standard errors in brackets * p < 0.1, ** p < 0.05, *** p < 0.01

0.001**[0.001]0.084**[0.039]0.002***[0.000]0.155***[0.020]-0.072[0.150]0.004[0.012]0.031*[0.017]0.062*[0.032]0.028**[0.013]5.443***[0.386]
Yes
Yes

0.004***[0.001]0.139***[0.048]0.002***[0.000]0.153***[0.028]0.090[0.152]-0.007[0.023]0.004***[0.001]0.147***[0.049]0.002***[0.000]0.129***[0.027]-0.047[0.142]-0.007[0.022]

4.833***[0.476]
Yes
Yes
0.751

4.951***[0.483]
Yes
Yes
0.730

0.004***[0.001]0.148***[0.050]0.002***[0.000]0.129***[0.028]0.013[0.152]0.000[0.022]0.015[0.026]0.123*[0.074]-0.017[0.013]4.871***[0.518]
Yes
Yes
0.733

14
476

14
546

14
476

14
476

45

(7)
FE-IV
(Instrumented)

(8)
FE-IV
(Extra controls)

1.527**[0.656]0.006***[0.001]
0.142[0.090]0.030[0.031]0.002***[0.001]-0.316[0.221]-0.007[0.022]

1.635**[0.716]0.006***[0.001]
0.142[0.097]0.021[0.029]0.002***[0.001]-0.458*[0.265]0.004[0.023]0.112[0.083]0.024[0.032]-0.044***[0.013]

Yes
Yes
0.684
1
15.897

Yes
Yes
0.686
1
14.77

14.938(.0001)
14504

12.27(.0005)
14
504

Table 4: First Stage, Instrumental Variable Estimation of Agricultural Yield, 1970-2008(1)
Panel A: First Stage, IV
APMC Index (with 2
Dep. Var: APMC Index
FE-IV)
Congress (t-3) (Instrument)
0.0310***[0.009 ]Hard left (t-3) (Instrument)
0.327***[0.0688 ]Proportion of irrigated area (t-1)-0.0003[0.0003]
Ln(Labour) (000’/GCA) (t-1)-0.077**[0.034]
Ln(Fertilizer use (kg/hec) (t-1)-0.0016[0.008]Ln(Cropping intensity) (t-1)-0.0005***[0.0002]
Capital (tractors & pumpsets) ‘000/hec (t-1)
0.221***[0.079]Ln(Rainfall)
0.005[0.006]Time fixed effects
Yes
State fixed effects
Yes
R2
0.713
F
45.835
No. of States
14
Observations
504
Robust Standard errors in brackets * p < 0.1, ** p < 0.05, *** p < 0.01

(2)
APMC Index (with 1
FE-IV)

0.290***[0.067]-0.0003[0.0004]-0.078**[0.033]-0.002[0.008]-0.0005***[0.0002]
0.222***[0.077]0.005[0.006]
Yes
Yes
0.684
38.072
14
504

Table 4: Second Stage, Instrumental Variable Estimation of Agricultural Yield, 19702008
Panel B: Second Stage, IV
Dep. Var: Ln(Yield) kg/hec
APMC Index (IV) (Instrumented, current)
Proportion of irrigated area (t-1)
Ln(Labour) (000’/GCA) (t-1)
Ln(Fertilizer use (kg/hec) (t-1)
Ln(Cropping intensity) (t-1)
Capital (tractors & pumpsets) ‘000/hec (t-1)
Ln(Rainfall) (t-1)

(1)
2 IVs
1.072*[0.556]0.005***[0.001]0.108[0.078]0.029[0.029]0.002***[0.000]-0.228[0.200]-0.005[0.020](2)
1 IV
1.527**[0.656]0.006***[0.001]
0.142[0.090]0.030[0.031]0.002***[0.001]-0.316[0.221]-0.007[0.022]

Constant
Time fixed effects
Yes
Yes
State fixed effects
Yes
Yes
No. of excluded instruments
2
1
F (Weak identification test/ test for excluded
12.12915.897
Instrument)
Underidentification test (p-value)
20.695(0.0000)
14.938(0.0001)
Over identification test (p-value)
0.985(0.3210)
No. of States
14
14
Observations
504
504
Standard errors in brackets; * p < 0.1, ** p < 0.05, *** p < 0.01
Note: APMC instrumented by IV: l3congress l3hardleft in column 1 and, with l3hardleft in column 2 and farm
inputs by (internal instruments, lag by one period)

46

Table 5: Use of Modern Farm Technology explained by the APMC Act & Rules
Dependent variable

APMC Index (t-5)
Coastal region
dummy
Ln(Landholding)(hec)
Ln(Rainfall) mm
Literacy rate
Constant
Time fixed effects
State fixed effects
R2
No. of states
Observations(1)
Ln(Fertilizer use)(kg/hec)
1970-2008
FGLS
0.337***[0.101]
1.999***(2)
Proportion ofirrigated area
1970-2008
FGLS
10.310***[1.893]-28.829***(3)% Rice area under
HVY
1984-1996
LSDV¥
53.788**[20.784]-17.272(4)% Wheat area
under HVY
1984-1996
LSDV¥
73.252***[27.432]-20.562[0.107]-0.098***[1.763]1.437***[23.533]
2.745[27.350]-12.738[0.018]0.013[0.014]-0.008***[0.003]3.687***[0.255]
Yes
Yes[0.279]0.516**[0.253]0.253***[0.045]46.114***[3.546]
Yes
Yes

14
476

14
476

[7.719]-1.465[1.841]-1.380[1.025]165.030**[79.157]
Yes
Yes
0.878
14
182

[9.741]-2.007[2.161]-2.498**[1.060]282.747***[92.717]
Yes
Yes
0.747
13
169

Standard errors in brackets: * p < 0.1, ** p < 0.05, *** p < 0.01¥: Unbalanced panel does not support FGLS. FE results are also robust but it drops timeinvariant coastal dummy. So, results are produced using least square dummy variables(LSDV), which is a manual form of FE technique.

47

Table 6: Agricultural Yield explained by Sub-components of the APMC Act & Rules: Extended
Model, 1970-2008, FGLS
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Dep. Var:
Ln(Yield) kg/hec
Scope (t-5)
0.055
0.092**[0.035][0.040]
Structure (t-5)
0.018
0.013
[0.031][0.049]
Expansion (t-2)
0.257***
0.161**[0.068][0.082]
Sales & Trade (t-5)-0.016-0.038[0.028][0.042]Pro-poor (t-5)
0.058*
0.063*[0.035][0.037]
Infrastructure (t-5)
0.203*** 0.204***[0.055][0.059]Proportion of
0.003*** 0.003*** 0.004*** 0.002*** 0.003***0.001**
0.001
irrigated area
[0.001][0.001][0.001][0.001][0.001][0.001][0.001]
Ln(Labour)
0.065**
0.067**
0.102***
0.051
0.106***
0.079**
0.110***(000’/GCA)[0.032][0.033][0.032][0.034][0.036][0.034][0.042]
Ln(Fertilizer use
0.002*** 0.002*** 0.002*** 0.002*** 0.002*** 0.002*** 0.001***(kg/hec)[0.000][0.000][0.000][0.000][0.000][0.000][0.000]Ln(Cropping
0.138*** 0.142*** 0.154*** 0.154*** 0.141*** 0.147*** 0.154***
intensity)[0.019][0.019][0.019][0.020][0.019][0.020][0.020]Capital (tractors &
0.059
0.067
0.040
-0.0230.100-0.107-0.213pumpsets) ‘000/hec[0.206][0.200][0.200][0.144][0.201][0.156][0.176]
Ln(Rainfall)
0.012
0.011
0.011
0.012
0.011
0.006
0.002
[0.011][0.011][0.011][0.012][0.011][0.011][0.012]
Ln(Landholding)-0.016-0.014-0.0120.022
0.00020.032*
0.055***(hec)[0.015][0.015][0.015][0.017][0.017][0.017][0.019]Road density
0.084*
0.097**
0.141***
0.067**
0.101**
0.066**
0.058*(km/’000 popu)[0.043][0.044][0.043][0.031][0.043][0.033][0.032]
Ln( Agri-0.010-0.009-0.015*** 0.027**-0.0100.035**
0.037***
Expenditure pc)[0.007][0.006][0.005][0.013][0.006][0.014][0.014]
Constant
5.663*** 5.650*** 5.199*** 5.715*** 5.380*** 5.558*** 5.244***[0.307][0.318][0.323][0.351][0.321][0.342][0.395]Time fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
State fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No. of states
14
14
14
14
14
14
14
Observations
476
476
518
476
476
476
476
Standard errors in brackets; * p < 0.1, ** p < 0.05, *** p < 0.01

48

Online Appendix A
Refer section two on ‘Regulation of Agricultural Markets in India’ of the maintext
History in Brief: The APMC Act
The history of establishment of regulated markets in India dates back to 1886, whenelements of regulation were introduced in the Karanja Cotton Market under theHyderabad Residency’s Order. The motive behind this regulatory measure by the then
British rule was to ensure supply of pure cotton at reasonable prices to the textile millsin Manchester, England, and so the first regulated market was established in India.
Subsequently in the year 1897, a special legislation known as “The Berar Cotton andGrain Market Law” was enacted in Berar, then known as “Hyderabad Assigned
District” in 1897. Under the provisions of this Act, the British Resident acquired theauthority to declare any place in an assigned district a market for sale and purchase of
agricultural produce, and to form a committee to supervise these regulated markets. It
was the first exclusive statute on regulation of marketing of agricultural produce.
Subsequent Acts, whenever passed were generally modelled on the general principles
embodied in this law (Acharya and Agarwal, 2009).
The salient features of the colonial agricultural marketing law were as follows:13 All themarkets that existed on the date of enforcement of the law fall under the state’s law fold;(i) The British Resident could declare any additional markets or bazaars for the sale ofagricultural produce. (ii) A Commissioner was to appoint from among the list of eligible
persons, a committee ordinarily of five members: two representing the Municipal
Authority with the remaining three from amongst the cotton traders for enforcing thelaw. (iii) Unauthorized markets and bazaars were banned within five miles of a notified
market. (iv) Trade allowance or prevalent local market customs in the Resident wereabolished; (v) Market functionaries were required to take licenses. (vi) The Resident
was empowered to make rules for some specific matters such as levy and collection offees, licensing of brokers, weighmen and also for checking of weights and measures(services), (vii) The Act was applicable to both cotton and grain markets. (viii) Penalties
for breach of certain provisions of the law were laid down.
The serious drawback of this law was that it provided no representation for thegrowers/farmers on the market committee even though the grower would needlegislative protection (Bhatia, 1990). Though the Act provided for the regulation ofmarket for all agricultural produce, only markets for cotton were established. There was
no independent machinery for the settlement of disputes between the seller and thebuyer. Further, limitations emerged in the course of time, for instance, it was found that
regulated markets were turning into a source of municipal revenue as the Act provided
that after expenses has been paid out of revenue derived of the market fees, surplus (ifany) should be given to respective municipalities in which the market was located. It
was later recommended that revenue raised from the markets should be spent indeveloping facilities and services in the markets that would benefit producers etc. But
the progress under the Act was very discouraging because the process of obtaining
necessary resolution from the District local Boards, municipalities and other bodies wasquite lengthy (Gosh, 1999).
13

The Berar Cotton and Grain Markets Law, 1897, vide Appendix VI to “Report of the

India Cotton Committee, published in 1919, p. 236-38 (Gosh, 1999)49

Subsequent Historical Account of evolution of the APMC Act in the States of India
The Indian Cotton Committee (General Cotton Committee) was appointed by theGovernment of India in 1917 to look into the problems of marketing of cotton. This
Committee had observed that in most of the cases the cotton growers were selling
cotton to a village trader-cum-money lender, under whose financial obligation theywere, at a price much below the ruling market rate and other agriculturalists wereseriously handicapped in securing adequate price for their produce because of long
chain of middleman in the marketing process. The Committee therefore, recommended
that markets for cotton on Berar system should be established in other provinces having
compact cotton tracts. This could be done by introduction of suitable provisions in the
Municipal Acts or under a special regulation as in the case of Berar.
The Government of Bombay presidency was the first to implement thisrecommendation by enacting the Bombay Cotton Markets Act in 1927. This Act was an
improvement over the Berar Cotton and Grain Markets Law of 1897 as it provided for
representation to the growers on the market committee and also contained a provision
for spending the surplus funds of the marketing committee, which should be transferredto the respective local bodies in whose jurisdiction the market was established in 1929
and the first regulated market was established under this Act at Dhulia during the year
1930-31.
The Royal Commission on Agriculture, in its report submitted in 1928, recommended
the regulation of market practices and the establishment of regulated markets in India on
the Berar pattern as modified by the Bombay Cotton Markets Act 1927, with special
emphasis on the application of the scheme of regulation to all agricultural commodities
instead of cotton alone. The Commission advized to include provisions forestablishment of machinery in the form of Board of Arbitration for the settlement ofdisputes; prevention of brokers from acting for both buyers and sellers in the markets;adequate storage facilities in the market yards; standardization of weights and measures
under a single all pervading Provincial legislation. The Commission also recommended
that the Provincial Governments should take initiative in the establishment of regulated
markets and grant loans to market committees for meeting initial expenditure on landand buildings. Its recommendations were subsequently endorsed by the Central Banking
Enquiry Committee, 1931. This recommendation had an effect on the states as borne out
from the fact that a number of states have enacted regulated markets Acts thereafter.
In the year 1930, the Hyderabad Agricultural Markets Act, largely modelled on theBombay Agricultural Markets Act, 1927 was passed. The Central provinces (nowMadhya Pradesh) came next with the ‘Central Provinces Cotton market Act’, 1932. In
1935, another law called Central Provinces Agricultural Produce Markets Act’ was onlines of ‘Central Provinces Cotton market Act’ 1932. According to this Act, markets
could be regulated for the sale and purchase of all kinds of agricultural produce otherthan cotton as the latter was already covered by the Cotton Markets Act of 1932. Market
regulation was introduced in Madras (now Tamil Nadu) under the Madras Commercial
Crops Markets Act, 1933 and the first regulated market was established in the State in1936 at Tirupur in Coimbatore District.
In 1935, Government of India established the office of the Agricultural Marketing
Adviser (Directorate of Marketing and Inspection) under the Ministry of Food andAgriculture to look into the problems of the marketing of agricultural produce. The
Directorate recommended to the State Governments that markets be regulated tosafeguard the interest of the producers and to remove prevalent malpractices in themarkets. In 1938, the Directorate of Marketing and Inspection prepared a model Bill, on50

the lines of which several states drafted their own Bills. Since then, State Governments
have enacted legislation for the regulations of markets in their states.
They are: the Hyderabad Agriculture Market Act, 1930; The Madras Commercial Crops
Market Act, 1935. In 1939, the Government of Bombay enacted the Bombay
Agricultural Produce Markets Act and made it applicable to all the agriculturalcommodities including cotton. As a result, the Cotton Market Act on 1927 was repealed
and all the market committees set up under this Act were declared deemed to be themarket committees under the new Act. In Mysore State (now Karnataka), the ‘Mysore
Agricultural Produce Markets’ Act was passed in 1939. However, the first regulated
market at Tiptur could be established only about a decade later i.e. in November, 1948.
The outbreak of the Second World War in September 1939 dislocated the normal
economic activities in the country. Controls on food grains and other essential
commodities were imposed and their free movement was restricted. The levy system fordirect procurement of food grain from producers was resorted to and price control andstatutory/informal rationing was introduced. As a result, very limited progress could beachieved in the field of regulation during the war period. Market regulation was
introduced in the erstwhile Patiala State in January, 1948 under the Patiala Agricultural
Produce Markets Act, 1947. The Government of Madhya Bharat passed the Madhya
Bharat Agricultural Produce Markets Act in 1952. This was modeled mostly on lines ofBombay Act. All regulated wholesale markets which were governed by the previous
laws of the respective merged states were declared as regulated under the new Act. In
the mean time, Andhra Pradesh adopted Madras Act, Gujarat and Maharashtra States
inherited the Bombay Act and Delhi and Tripura passed legislation on the lines ofBombay Model Act. Figure A1 shows present major cotton and grain growing states.
The Agricultural Produce Market Acts, in force, in different states are given in the Table
A2. After independence, despite efforts by the central government, the progress madewith the regulated markets in terms of growth and their geographical distribution
remained slow and highly uneven. Heavy concentration of the agricultural markets inthe cotton growing states remained prominent. This largely explains why in 1964, 80
percent of the total 1000 regulated markets, then in existence, were located in the fivewestern states, although, they accounted for only 30 percent of India’s population. The
markets did not embrace other agricultural produce, and were largely confined to cottonmarketing. Until late 1960s, certain states of India such as Uttar Pradesh, West Bengal,
and Assam hardly had any regulated market (Rajagopal, 1993). See table A3 for market
distribution in the states of India.
The colonial policy was to serve the interests of colonialism rather than planning thelong-range development of the Indian agriculture or welfare of the rural population ofIndia (see Sir Henry Knight, 1954; Lele, 1971; Bhattacharya, 1992). Bhattacharya(1992) explains that British officials were confronted with the contradictory demands ofcolonialism and the need to resolve them. On the one hand there was need to enhancerevenue and augment the financial resources of the British state, and on the other handthe desire to maintain the purchasing power of the peasantry in order to expand themarket for British manufactures. Regulation of markets for agricultural product wasstressed by several Committees and Commissions from time to time. The importantones are the Banking Enquiry Committee, 1931; The Congress Agrarian Reforms
Committee, 1947; The Rural Marketing Committee of the National Congress, 1948; The
Planning Commission, 1958; The All India Rural Credit Committee, 1954, theAgricultural Production Team on Ford Foundation and the Task Force on AgriculturalMarketing Reforms, 2001.
Source: Acharya and Agarwal, 2009:268-270; Rajagopal, 1993:31-34
51

Figure A 1: Major Cotton, Rice and Wheat Producing States of India

Source: http://www.mapsofindia.com/ as on January 201252

Table A 1: Agricultural Produce Market Act & Rules in force in different States of
India

S.no
1.

State
AndhraPradeshTitle of the APMC Act
The Andhra Pradesh Agricultural Produce andLivestock Markets Act, 1966 (AP Act 16 of
1966)
2.
Assam
The Assam Agricultural Produce Markets Act,
1972 (Assam Act 23 of 1974)
3.
Bihar
The Bihar Agricultural Produce Markets Act,
1960 (Bihar Act 16 of 1960)
4.
Gujarat
The Gujarat Agricultural Produce Markets
Act, 1963 (Gujarat Act 20 of 1964)
5.
Haryana
The Haryana Agricultural Produce Markets
Act, 1961 (Haryana Act 23 of 1961)
6. Karnataka The Karnataka Agricultural Produce
Marketing (Regulation) Act, 1966 (Karnataka
Act 27 of 1966)
7.
MadhyaThe Madhya Pradesh Krishi Upaj Mandi
PradeshAdhiniyam, 1972 (Madhya Pradesh Act 24 of
1973)
8. Maharashtra The Maharashtra Agricultural Produce
Marketing (Regulation) Act, 1963
(Maharashtra Act 20 of 1964)
9.
OrissaThe Orissa Agricultural Produce Markets Act,
1956 (Orissa Act 3 of 1957)
10.
PunjabThe Punjab Agricultural Produce Markets
Act, 1961 (Punjab Act 23 of 1961)
11. Rajasthan The Rajasthan Agricultural Produce Markets
Act, 1961 (Rajasthan Act 38 of 1961)
12. Tamil Nadu The Tamil Nadu Agricultural Produce
Markets Act, 1959 (Tamil Nadu Act 23 of
1959)
13.
Uttar
The Uttar Pradesh Krishi Utpadan Mandi
PradeshAdhiniyam, 1964 (Uttar Pradesh Act 25 of
1964)
14.
West
The West Bengal Agricultural Produce
BengalMarketing (Regulation) Act, 1972 ( West
Bengal Act 35 of 1972)
Source: Various State Laws

53

Rules
1969

1975
1975
1965
1962
1968

1973

1967

1958
1962
1963
1962

1965

1982

Table A 2: Number of Regulated Agricultural Markets pre and post Independent
Indian States, 1931- 2010
S.no
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.

State
AndhraPradeshAssam
Bihar*
GujaratHaryana
Karnataka
MadhyaPradeshMaharashtra
OrissaPunjabRajasthan
Tamil Nadu
Uttar
PradeshWest
Bengal1931401019415035

19516086196170123

197170
525

1985

2008

2010

568

891

901

5
-

23
3

72
86

144
236
150
168
246

14
438
297
177
318
317

32
798
324
255
372
514

224
526
414
284
498
501

226
0
414
284
501
513

52
11
-

121
92
11
-

280
15
132
37
-

315
54
243
152
95
132

512
67
481
297
218
617

759
129
665
380
272
630

880
314
437
428
292
587

880
314
488
430
292
605

-

-

-

1

1

2

684

687

Note: The number for Gujarat is included in Maharashtra up to 1960; The number for
Haryana is included in Punjab up to 1960; *The Bihar APMC Act was repealed witheffect 1.9.2006.
Source: Directorate of Agriculture Marketing and Inspection, Ministry of Agriculture,
Government of India

54

Online Appendix A
Table A 3: Data Sources and Standardization of Variables
Variables
Variable Description
Data Source
APMC Act
Read and quantitatively coded clauses of theState APMC Act & Rules published by local state law
state Agricultural Produce Markets Commission agency; obtained from respective State’s Government
Act (APMC Act) of each 14 states that captures
Agricultural Marketing Board (SAMB), State Ministry ofthe differences in clause on accessibility of theAgriculture.
markets (Market Scope); administrative design(Market Structure); Efficiency in trading(Market Sales n Trade); protection for farmers(Pro-poor Market); liberal market orientation(Market Expansion); and availability of theinfrastructure (Market Infrastructure). We
combined the codes ranging between 0 and 1 togenerate regulatory measure of the post-harvest
agricultural markets.
Market Scope
It uses the data on (i) average area covered by aData on state land area was obtained from the Economic(Scope of theregulated markets in square km, calculated by:
Organisation and Public Policy Programme (EOPP, LSE) –Regulated Market) state area in square kms/total regulated markets
Indian states’ database at the London School of Economics.
in the state; and (ii) population served by each
The EOPP compiled the data of Indian states from theregulated markets per thousand people,
Statistical Abstract published by the Central Statistical
calculated by: total state population per
Organisation, Department of Statistics, Ministry of Planning.
thousand/ total number of regulated markets.
Available on
http://sticerd.lse.ac.uk/eopp/_new/data/indian_data/default.asp
Other data source – Rural Development Statistics, National
Institute of Rural Development and Panchayati Raj (NIRD),
Land Utilization Statistics, Ministry of Agriculture;
Directorate of Agricultural Marketing and Inspection,
Ministry of Agriculture.
Market Structure
Equals 1 if composition of market committeeState APMC Act & Rules published by local state law
55

(Constitution ofMarket and Market
Structure)

Market Expansion(Channels ofMarket Expansion)take place through a provision of direct election;otherwise 0
Equals 1 if the chairman of the market
committee is agriculturalist; otherwise 0
Equals 1 if the chairman of the market
committee is an elected chairman; otherwise 0
Equals 1 if a provision to dismiss the chairmanof the market committee exists; otherwise 0
Equals 1 if a provision to suspend the market
committee exists; otherwise 0
Equals 1 if a provision to suspend the market
committee exists; otherwise 0
Equals 1 if the state marketing board has legal
status; otherwise 0
Equals 1 if a website of the state marketingboard exist; otherwise 0
Equals 1 if law allows single license to trade in
the whole State; otherwise 0
Equals 1 if law allows License to trade in morethan one market area; otherwise 0
Equals 1 if law has Provision for setting private
market yard; otherwise 0
Equals 1 if law provides Rules to procure license
for setting private market yard; otherwise 0
Equals 1 if law has Provision for private
consumer-farmers market; otherwise 0
Equals 1 if law provides Rules for establishing
Private consumer-farmers market; otherwise 0
Equals 1 if law has Provision for direct
procurement from farmers; otherwise 056

agency; obtained from respective SAMB, State Ministry ofAgriculture.

State APMC Act published by local state law agency;
obtained from respective SAMB, State Department ofAgriculture.

Market Sales nTrade

Pro-poor Market(Pro-Poor
Regulation)

Equals 1 if law has Rules for direct procurementfrom farmers; otherwise 0
Equals 1 if law has Provision of contract
farming; otherwise 0
Equals 1 if law has Rules for contract farming;otherwise 0
Equals 1 if law has Provision of Public-Private
Partnership market function; otherwise 0
Equals 1 if law permits State National Spot
Exchange; otherwise 0
Equals 1 if law mandates sale by open auction in
the market; otherwise 0
Equals 1 if law mandates payment to growerseller on the day of sales in the market;
otherwise 0
Equals 1 if law mandates issuance of sale-slip to
the seller in the market; otherwise 0
Equals 1 if law mandates sale by open auction in
the market; otherwise 0
Equals 1 if law offers provision of agricultural
inputs for sale in the regulated produce market;
otherwise 0
Equals 1 if law mandates for issuance of a saleslip (receipt) to the seller; otherwise 0
Equals 1 if law offers provision of imposinginterest on delayed payment to seller; otherwise
0
Equals 1 if law offers provision of minimum
period to settle payment to seller; otherwise 057State APMC Act published by local state law agency;
obtained from respective SAMB, State Department ofAgriculture.

State APMC Act published by local state law agency;
obtained from respective SAMB, State Department ofAgriculture.

Equals 1 if law offers provision of regulating
advance payment to agriculturalists; otherwise 0
MarketInfrastructure(Infrastructure forMarket Functions)

Road Density,(Km/1000 popu)
No. of tractors per‘000 hec land (log)No. of pumpsetsper ‘000 hec land

It uses the data on: (i) number of central
warehouse available per 1000 sq km, calculatedas (No. Of central warehouse/Land area sqkms)x1000; (ii) Central warehouse capacity
available in per 1000mt production (Central
warehouse capacity/total agricultural
productionx1000); (iii) number of state
warehouse available per 1000 sq km, calculatedas (No. of state warehouse/Land area sqkms)x1000; (iv) state warehouse capacity
available in per 1000mt production (state
warehouse capacity/total agricultural
productionx1000); (v) FCI storage capacity per
1000mt production (FCI storage capacity/total
agricultural productionx1000; (vi) number ofgrading units available per 1000 sq km,
calculated as (No. of grading units/Land area sqkms)x1000; (ii) No. of grading units available inper 1000mt production (no. of grading units/total
agricultural productionx1000)
It uses the data on: length of roads per 1000
population, calculated as ( length of roads/total
state population)
It uses data on natural logarithm of number oftractors per 1000 hectares of land
It uses data on natural logarithm of number ofpumpsets per 1000 hectares of land
58

Bulletin on Food Statistics, Directorate of Economics andStatistics, Ministry of Agriculture, India;
Directorate of Agricultural Marketing and Inspection,
Ministry of Agriculture, India;
Data on state land area was obtained from the Economic
Organisation and Public Policy Programme (EOPP, LSE)
Indian states database at the London School of Economics.
Available on
http://sticerd.lse.ac.uk/eopp/_new/data/indian_data/default.asp

Centre for Monitoring Indian Economy (CMIE) database on
Infrastructure and after 2001, updated from the online
database: IndiaStat. (http://www.indiastat.com/default.aspx )CMIE database (various annual issues) aided by online
database: IndiaStat.
CMIE database aided by online database: IndiaStat.

(log)
Irrigated % of gross
cropped area

It uses data on percentage of land irrigated of thetotal gross cropped area

Land Utilization Statistics published by the Ministry ofAgriculture, Government of India (GOI), New Delhi obtained
from CMIE.

Fertilizers (kg/hec)(log)
Agricultural
workers per’000
GCA (log)
Total gross cropped
area (‘000 hec)(log)
Averagelandholding (hec)(log)
Cropping Intensity(log)(GCA-NCA)
Literacy rate

It uses data on natural logarithm of use offertilizer in kilograms per hectare of land;
It uses data on natural logarithm of number ofagricultural workers per 1000 hectare of gross
cropped area
It uses data on natural logarithm of per 1000
hectare of gross cropped area

CMIE database (various annual issues) aided by online
database: IndiaStat.
Various issues of the Agricultural Statistics at a Glance andIndian Agricultural in Brief, published by GOI, New Delhi
and online database IndiaStat.
Land Utilization Statistics, Ministry of Agriculture, New
Delhi.

It uses data on natural logarithm of average landholding (farm size)

Agriculture at a Glance, Ministry of Agriculture/ CMIE
database (various annual issues) aided by online database:
IndiaStat.
Land Utilization Statistics, Ministry of Agriculture, GoI, New
Delhi.

Agricultural
Expenditure percapita (INR)

It uses data on per capita expenditure on
agriculture sector as a share of total expenditure
in Indian rupees.

Average Actual
annual Rain (mm,It uses data on natural logarithm of annual
average rainfall in millimetres

It uses data on natural logarithm of gross
cropped area subtracted by net cropped area
(number of times land is sown in a year)
It uses data on percentage of population who isliterate in the state

The demographic data (population, literacy rate) from thedecennial Census of India and have been interpolated toobtain annual data.
Collected from the Reserve Bank of India Bulletin – State
Budget, online database Macroscan and the Public Finance
Statistics published by the Ministry of Finance, accessed fromthe library of National Institute of Public Finance and Policy(NIPFP), New Delhi.
CMIE database – Agricultural Harvest, New Delhi.

59

log)
Aggregate Yield(kg/hec) (log)

It uses data on natural logarithm of aggregate
yield in kilogram per hectare of landCompiled from the CMIE database, New Delhi, from various
issues of Agricultural Situation in India, Season and Crops
Reports and Statistical Abstracts published by the Directorateof Economics and Statistics (DES) of the Ministry ofAgriculture, Government of India, New Delhi.
CMIE database (various annual issues) aided by online
database: IndiaStat.

proportion of highIt uses data on proportion of land under HYV ofyielding varieties
Rice production(HYV) of riceproportion of highIt uses data on proportion of land under HYV of CMIE database (various annual issues) aided by online
yielding varieties
Wheat production
database: IndiaStat.
(HYV) of Wheat
Note: Data was collected during the field visit to India between January-July 201160

Table A4: Progress of Reforms in Agricultural Markets (APMC Act) as on 31.10.2011
Sl. No.
1.

2.

3.

Stage of Reforms
States/ UTs where reforms to
APMC Act have been done for
Direct Marketing; Contract
Farming and Markets in
Private/ Coop Sectors
States/ UTs where reforms to
APMC Act have been done
partially

States/ UTs where there is no
APMC Act and hence do not
require reforms
4.
States/ UTs where APMC Act
already provides for thereforms
5.
States/ UTs whereadministrative action is
initiated for the reforms* APMC Act is repealed w.e.f. 1.9.2006.

Name of States/ Union Territories
Andhra Pradesh, Arunachal Pradesh,
Assam, Goa, Gujarat, Himachal Pradesh,
Jharkhand, Karnataka, , Maharashtra,
Mizoram, Nagaland, Orissa, Rajasthan,
Sikkim and Tripura.
a) Direct Marketing:
NCT of Delhi, Madhya Pradesh andChhattisgarhb) Contract Farming:
Chhattisgarh, Haryana, Punjab,
Chandigarh. Madhya Pradeshc) Private Markets
Punjab and Chandigarh
Bihar*, Kerala, Manipur, Andaman &Nicobar Islands, Dadra & Nagar Haveli,
Daman & Diu, and Lakshadweep.
Tamil Nadu

Meghalaya, Haryana, J&K, Uttrakhand,West Bengal, Pondicherry, NCT of Delhi
and Uttar Pradesh.

Status of APMC Rules
The status of APMC reforms in different states is given below:—a) States where Rules have been framed completely :Andhra Pradesh, Rajasthan, Maharashtra, Orissa, Himachal Pradesh, Karnataka,b) States where Rules have been framed partially:i) Mizoram only for single point levy of market fee;ii) Madhya Pradesh for Contract Farming and special license for more than one
market;iii) Haryana for Contract Farming.

Source: State of Indian Agriculture, Government of India, 2011-12

61