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An Empirical Investigation of Real Farm Incomes across Indian States between 1987–88 and 2011–12

Using the unit-level data from various rounds of the Employment and Unemployment Survey of the National Sample Survey Office, the first consistent time series of the average real farm income per cultivator for 18 major Indian states for 1987–88, 1993–94, 1999–2000, 2004–05, 2007–08, 2009–10, and 2011–12 is presented. Using this data, two sets of issues are studied. First, how did real farm income evolve across these 18 Indian states? Which states have high levels and growth rates of real farm incomes? Is there any evidence for convergence of real farm incomes across Indian states? Evidence for unconditional convergence is found, which suggests that the states with relatively lower farm incomes have, on average, grown at relatively faster rates. But the tendency towards convergence has not been strong enough to change relative rankings of states (by real farm income per cultivator) in any significant way. Second, did the market-oriented reforms of agricultural marketing systems increase real farm incomes? It is found that market-oriented reforms did not increase real farm incomes.

In India, a significant part of agricultural marketing has been traditionally organised through state-controlled markets (Agricultural Produce Market Committee [APMC] mandis). Since the early 2000s, there has been a move to gradually allow private capital more space in agricultural produce markets. The Bharatiya Janata Party (BJP)-led government tried to centrally legislate this move towards privatisation of agricultural produce markets with three hastily passed, controversial farm laws in September 2020. A year-long protest by farmers forced the BJP government to repeal the laws in November 2021.

Underlying such attempts to allow more space to private capital in agricultural marketing is the notion that moving away from the state-controlled marketing system, that is, market-oriented reforms of the system, is beneficial to farmers. There is surprisingly little evidence to back this widespread notion. Part of the reason for this lack of evidence is that there does not exist any consistent time series of real farm incomes at the state level over long periods of time (Chand et al 2015). The first contribution of this paper is to construct consistent estimates of average real farm incomes for 18 major Indian states between 1987–88 and 2011–12.1

The farm laws and the farmer protests against them have sparked a heated debate on whether APMC mandis and public procurement at the minimum support price (MSP) provide farmers with an income safety net or whether these regulations create inefficiencies and keep farm incomes depressed. Several commentators have argued for decades that sectors of the Indian economy like the manufacturing sector, which were not sufficiently liberalised, have performed worse than the services sector that were deregulated. In the context of agriculture, the exi­sting regulatory framework of controlling prices led to created food shortages and distorted incentives (Mehta 2013). Chand and Singh (2016) argue that the lack of reforms in the agricultural sector contributed to low and cyclical growth rates and greater concentration of poverty in this sector in comparison with the reformed non-agricultural sector. However, these comparisons are not based on causally testing the impacts of ­reforms on the incomes and growth rates ­between sectors.

Using this newly constructed data, we study the evolution of farm incomes across states, looking both at their levels and growth rates over time. To summarise the relative performance of states over the roughly two-and-a-half-decade period, 1987–88 to 2011–12, we rank states by the average level of real farm income and by growth rates. Punjab, Haryana, Kerala, West Bengal, and Tamil Nadu (TN) are the five top states in terms of the average real farm income per cultivator in 2011–12; in terms of the average annual growth rate of real farm in­come per cultivator between 2004–05 and 2011–12, the top five states are Rajasthan, TN, Kerala, West Bengal, and Haryana. We also investigate whether there has been any tendency for the convergence of real farm incomes across states. We find that there is evidence of convergence of real farm incomes across Indian states once we condition on the time-invariant state-level factors. But the tendency towards convergence has not been strong enough to change the ranking of states in terms of real farm income per cultivator drastically. The states with the highest levels of real farm income per cultivator in 1987–88 were more or less the same states that had the highest level of real farm income per cultivator in 2011–12.

The second contribution of this paper is to investigate whether market-oriented reforms of the state-controlled agricultural marketing system have led to an increase in real farm incomes. Our analysis uses a simple difference-in-difference (DD) rese­arch design. Using archival data compiled from the annual ­reports from the Ministry of Agriculture and Farmers’ Welfare (MoAFW) and academic research, we ascertain if and when each state initiated reform of the state-controlled agricultural marketing system. All states that initiated reforms are categorised as part of the treatment group; all states that did not ­undertake any ref­orms become part of the control group. By comparing the cha­nge in average real farm income before and after ref­orms bet­ween treatment and control groups, we are able to esti­mate the effect of the reforms. Our analysis shows that market-oriented reforms did not have any positive impact on real farm incomes.

Data and Methodology

The key variable for our analysis is average real farm income per cultivator at the state level. Average real farm income per cultivator gives one of the most accurate measures of the material well-being of the average farmer in a state. To construct this variable, we define farm income as the difference between value added in agriculture and the total wage bill.

Value added: We construct a consistent time series of state-level value added in agriculture at current prices in two steps.

First, we extract data on value added in agriculture from Table 6, Components of Net State Domestic Product at Factor Cost by Industry of Origin (at current prices) from the 2004–05 Handbook of Statistics on Indian Economy (available on the Reserve Bank of India [RBI] website).2 This table gives two value added series—an old series with base year 1980–81 and a new series with base year 1993–94. The unit of measurement is rupees crore. We take the 1980–81 base year series data for 1980–81 to 1993–94; we take the 1993–94 base year series data for 1993–94 to 2004–05. For each year, we compute the growth factor of value added in agriculture as the ratio of ­value added in a year and value added in the previous year. Thus, we get an annual growth factor series (for value added in agriculture) that runs from 1980–81 to 2004–05.

Second, we extract data on value added in agriculture from Table 6, Components of Net State Domestic Product at Factor Cost by Industry of Origin (at current prices), from the 2012–13, Handbook of Statistics on Indian Economy (available on the RBI website). This table gives value added series with base year 2004–05 in rupees billion. Data are provided for 2004–05 to 2012–13 for most states; for some states, data are available till 2011–12. For these latter states, we take the figure for 2012–13 from Table 6 in the 2013–14 Handbook of Statistics on Indian Economy.

Our value added series for agriculture uses the figures with base 2004–05 for 2004–05 to 2012–13, and then we project the series backward from 2003–04 to 1980–81 ­using the growth factor series that we calculated in the first step. This gives us a consistent state-level value added series for ­agriculture at current prices at an annual frequency running from 1980–81 to 2012–13.

Real farm income per cultivator: We compute the state-level farm income as the difference bet­ween value added in agriculture and the total wage bill. We construct a state-level series for the total wage bill in agriculture using unit-level data from the Employment and Unemployment Survey (EUS) conducted by the National Sample Survey Office (NSSO) for the 43rd round (1987–88), the 50th round (1992–93), the 55th round (1999–2000), the 61st round (2004–05), the 64th round (2007–08), the 66th round (2009–10), and the 68th round (2011–12). To convert nominal farm income into real or inflation-adjusted farm income, we divide the nominal magnitude by the state-level consumer price index for rural labourers (CPI-RL, published by the ­Labour Bureau of India).

Reform variable: Several states have been reforming their APMC laws and agricultural policies since the early 2000s. For instance, Madhya Pradesh (MP) introduced alternative marketing channels and inve­sted in internet-based price dissemination systems for soy farmers (Goyal 2020). The state also allowed large private corporations like ITC to procure directly from farmers (Krishnamurthy 2021). To take another example, in 2006, Bihar completely abolished the APMC mandi system and forced farmers to sell to private buyers without any price support (Kishore et al 2021).

Other states like Andhra Pradesh (AP), Maharashtra, Raja­sthan, and Haryana partially implemented the reforms where they allowed mandis to operate but also allowed farmers to sell in open markets, including trading in various e-markets across the country (Aggarwal et al 2017; Chand and Singh 2016; Ghosh 2013). Finally, at the other end of the spectrum were states like Punjab, TN, and West Bengal where none of the marketing reforms proposed by the APMC Act of 2003 were adopted.

We construct a binary reform variable that takes the value 1 if farmers could sell to private players (either exclusively or along with APMC mandis) and 0 otherwise. To construct this reform variable, we conducted archival research of state-level policies by analysing various annual reports of the MoAFW, aca­demic research, and newspaper articles. There were two main objectives of this res­earch. First, to ascertain whether farmers sold primarily in the APMC mandis or whether they were free to sell directly in the market. Second, whether the state government had refor­med the previous APMC Act, and if it did, to ascertain the year in which this change took effect? Table 1 (p 8) presents the ­reform status for each of the 18 states analysed in this paper. This ­table also lists the major source of ­archival information on the reform status and timing.

Results

In Tables 2, 3, and 4, we present estimates of the level and growth rate of average annual farm income (agricultural inc­ome per cultivator) for 18 major Indian states in 1987–88, 1993–94, 1999–2000, 2004–05, and 2011–12.

Table 2 presents estimates of the level of farm income in nominal terms (income evaluated in current prices); Table 3 presents estimates of the level of farm income in real terms (income evaluated in 2004–05 prices).3 Both tables have ranked states by the level of income in 2011–12. In Table 4, we present estimates of the average annual growth rate of real farm income for four periods: 1987–88 to 1993–94, 1993–94 to 1999–2000, 1999–2000 to 2004–05, and 2004–05 to 2011–12. The states are ranked, in Table 4, by the average annual growth rate of real farm income over the period 2004–05 to 2011–12. While we provide estimates of the level of farm ­income in nominal terms for completeness, we will mostly comment on the level and growth rate of real farm income that is presented in Tables 3 and 4.

Before we turn to discussing our estimates of real farm inc­ome at the state level, we would like to briefly compare our esti­mates with those presented in Chand et al (2015). Since Chand et al (2015) present only the national-level estimates, we have computed national-level estimates of the total farm inc­ome. For all years, our estimate of the total farm income in current prices is lower than the corresponding figure in Chand et al (2015). Our estimates of national-level values of the CPI-RL are also slightly different from those used in Chand et al (2015).

There are three reasons behind the difference in our estimates from those reported in Chand et al (2015). First, our estimate of the total farm income adds up farm income only over the 18 major states that we include in our sample. Chand et al (2015) add up the corresponding figure for all states. This can account for the lower figure of the total nominal farm income that we report. It is not clear why our number for 1987–88 is higher than those of Chand et al (2015). Second, in a similar manner, our national-level CPI-AL numbers are the average of state-level numbers where the average is over the 18 major states in our sample. This can account for the differences in our CPI-AL estimates from those of Chand et al (2015). Third, one of the older series that we use is the CPI-rl rather than ­CPI-al. This might account for a slight difference too. But, overall, our CPI series captures price movements in the same way as the CPI series in Chand et al (2015). For ins­tance, Chand et al (2015) note that between 1983 and 2011, infl­ation averaged around 6.9%. According to our data, inflation between 1987 and 2011 is (roughly) 6.5%. Thus, while it is important to keep the differences in mind, it needs to be noted that the overall national-level trends reported in Chand et al (2015) are similar to what we have reported in this paper. What we add to the discussion, of course, is a consistent series of farm incomes at the state level, something that Chand et al (2015) do not ­report or comment on.

 

Level of real farm income: We now turn to discussing our ­results on state-level real farm incomes. From Table 3, we see that the top five states in terms of average real farm income in 2011–12 were Haryana, Punjab, Kerala, West Bengal, and Tripura. Measured in 2004–05 ­rupees, the level of average farm income in these top five states were `1,58,249 (Haryana), `1,18,902 (Punjab), `86,181 (Kerala), `81,096 (West Bengal), and `79,727 (Tripura). It is interesting to note that, other than Haryana, none of the other top states have initiated any market-oriented reforms of their agricultural marketing systems since the early 2000s. However, the Food Corporation of India (FCI) and the state government continued to procure both wheat and rice at the MSP from Haryana in significant proportions. For instance, according to the data from the FCI, around 62% of wheat and 73% rice produced by the state were procured in 2011–12.

If we turn to the other end of the distribution, we see, from Table 3, that the bottom five states in terms of average real farm income in 2011–12 were Bihar United (Bihar + Jhar­khand), Karnataka, mp United (MP + Chhattisgarh), Maharashtra, and Assam. Measured in 2004–05 rupees, the level of average farm income in these bottom five states were `19,100 (Bihar United), `26,718 (Karnataka), `27,564 (mp United), `28,277 (Maharashtra), and `30,383 (Assam). Not only have these states adopted several reforms suggested by the model APMC Act of 2003, but also none of these states witnessed any significant procurement of wheat and rice in 2011–12, with the exception of MP.4

Using the level of average real farm income in 2011–12, we can categorise these 18 states into three groups. The first group—the top group—consists of states where the average real farm income in 2011–12 was more than `75,000 per ann­um (in 2004–05 prices). This group consists of Haryana, Punjab, Kerala, West Bengal, and Tripura. The second group—the middle group—consists of states where average real farm inc­ome in 2011–12 was between `35,000 and `75,000 per annum (in 2004–05 prices). This group consists of TN, Gujarat, ap, Raja­sthan, Jammu and Kashmir (J&K) and Himachal Pradesh (HP). The third group—the bottom group—consists of states where average real farm income in 2011–12 was below `35,000 per annum (in 2004–05 prices). The members of this group are Uttar Pradesh (up) United, Odisha, Assam, Maharashtra, MP United, Karnataka, and Bihar United.

 

Growth rate of real farm income: From the results presented in Table 4, we see a wide range of performance in terms of the average annual growth rate of real farm income. Using the ­average annual growth rate of real farm income from 2004–05 to 2011–12, the latest period for which we have data from the NSS employment surveys, we can divide the states into three groups.

The top group of growth performers is defined as states where the average real farm income increased by more than 10% per annum over the period from 2004–05 to 2011–12. The states that belong to this group are Haryana, Rajasthan, TN, Kerala, and West Bengal. It is interesting to note, much in line with the trend in terms of levels of real farm income, that other than Haryana, none of the other top states in terms of growth rates have initiated any market-oriented reforms of their agricultural marketing systems since the early 2000s.

The middle group of growth performers is defined by states that saw the average real farm incomes rise between 2004–05 and 2011–12 by between 5% and 10% per annum. The states that belong to this group are up United, Bihar United, Gujarat, Odisha, Punjab, Ap, MP United, Maharashtra, Hp, Tripura, and Assam.

The bottom group of states in terms of growth performance consists of states where the average real farm incomes grew by less than 5% per annum between 2004–05 and 2011–12. Two states belong to this group: Karnataka and J&K.

Are states catching up in terms of real farm income?: As we can see from Tables 3 and 4, there is a large variation across states in terms of both levels and growth rates of average real farm incomes per cultivator. A natural question that emerges in this context is whether there is any tendency for average real farm incomes per cultivator to converge across states. If states with relatively low real farm incomes were to grow, on average, at higher rates than states with relatively high real farm incomes, then the levels of real farm incomes would converge over time.

To investigate the question of convergence of average real farm incomes across states, we borrow from the standard framework used to study beta convergence in growth econo­mics. In panel data growth empirics using country-level data, the issue of beta convergence is studied in terms of a regression model where the dependent variable is the growth rate of per capita income over some period, and the key regressor is the initial level of per capita income at the start of the period. The coefficient on the level of initial per capita income is “beta” and captures the evidence of convergence. If “beta” is negative and statistically significant, it shows that countries with relatively low levels of initial per capita income have relatively higher growth rates. Hence, countries with low levels of income will have a tendency to catch up with countries with high levels of per capita income.

Using this framework, we specify the following regression model to study the convergence of real farm incomes across ­Indian states,

(1/τ) [log (yst) – log (ys,t-τ)] = β * log (ys,t-τ) + as + εst ... (1)

where yst is the level of real farm income in state s in period t, so that the dependent variable is the average growth rate of real farm income between period t – τ and period t, the key reg­ressor is the log level of real farm income in t – τ (initial period), αs are state fixed effects, and εst is a stochastic ­error term.

Our interest is in the parameter β, which will tell us if there is any evidence of convergence. If the value of β is non-­negative, then we can conclude that there is no evidence of convergence. On the contrary, if the value of β is negative and statistically significant, then it implies a tendency towards convergence. This is because a negative value of β means that states with relatively low levels of average real farm income in period t – τ (initial period) grow at relatively higher rates over the subsequent period from t – τ (initial period) to t (final ­period) than states with relatively low levels of real farm ­income in period t – τ.

Table 5 presents the estimate of β for our sample of 18 (N = 18) states with seven time periods (1987–88, 1993–94, 1999–2000, 2004–05, 2007–08, 2009–10, and 2011–12) for three different specifications. In the first specification, the only regressor is the log level of real farm income in the initial period; we do not include state or year fixed effects. In the second specification, we add state fixed effects to condition on time-invariant state-level factors; in the third specification, we add state and period fixed effects to condition on both time-invariant ­state-level factors and period-level factors common to all states.

In the first specification, the estimate of β is negative but statistically insignificant. In the second specification, the estimate of β is -0.090 and statistically significant at the 5% level of significance. This means that once we take account of time-invariant, state-level factors like geography, institutions, history, and politics, we see evidence of convergence of real farm incomes across states. In the third specification, when we, in addition, control for period fixed effects, that is, period-specific factors that affect all states in the same way, like developments in international trade and finance or central government policies, then the evidence for convergence becomes even stronger. The coefficient in the third specification is -0.249 and statistically significant at the 0.1% level of significance. The evidence in Table 5 shows that once we control for time-invariant factors states with relatively low levels of real farm incomes have witnessed higher growth rates of real farm income. Thus, over time, there is convergence in terms of real farm incomes ­between states.

The positive result about the tendency for convergence of real farm income per cultivator across states and over time must be tempered by the fact that this tendency has been rather weak. The tendency towards convergence has not changed the ranking of states significantly over the years. To see this, let us look at Table 6 (p 11) where states have been ranked in each year by real farm income per cultivator. From Table 6, we see that the top states in 1987–88 are more or less the same as those in 2011–12. These include Punjab, Haryana, Kerala, and West Bengal. Of course, some states like Gujarat and Karnataka, which were ranked towards the top in 1987–88, have moved down the ranked list over the years. Similarly, the group of states at the bottom of the ranked list has also remained stable, though there is more movement at the bottom than at the top. Bihar has been consistently at the bottom, but there has been some movement and change in ranking for other states also at the bottom like MP, Maharashtra, uP, and Odisha.

While this paper presents the first systematic analysis of growth rates in farm incomes between states in the period bet­ween 1987–2012, our results can be compared with existing studies that discuss regional variation in factor productivity, agricultural growth, and incomes between states. Agricultural growth started decelerating since the 1990s contrary to the expectations from economic liberalisation (Bhalla and Singh 2010). Mukherjee and Kuroda (2003) discuss trends in the total factor productivity between states during 1973–93. While they find no evidence of convergence between high productivity states like Haryana, Punjab, and West Bengal and low productivity states like Maharashtra, aP Rajasthan, and tn, they predict long-run convergence as each state’s productivity gap from the national average remains stationary over time. Given the complementarity between agricultural productivity and poverty reduction (Foster and Rosenzweig 2004; Johnson 2000), our results suggest that the trends in farm income bet­ween 1987 and 2012 follow the trends in the total factor productivity predicted by Mukherjee and Kuroda (2003). Further factors like infrastructure development, including roads and irrigation and improvements in rural literacy, have contributed convergence in per capita agricultural incomes between states during the 1967–2010 period (Chatterjee 2017).

Did reforms increase real farm incomes? We use a DD research design to probe the other main questions investigated in this paper. Did reforms of agricultural marketing increase real farm incomes? The DD research design compares treatment and control groups before and after some policy intervention or event and thereby comes up with a reliable estimate of the effect of the policy intervention. For the question of int­erest in this study, the policy intervention in question is market-oriented reforms of state-regulated agricultural markets in the early 2000s. The treatment group consists of states where such reforms occurred; the control group consists of states where such reforms have not been undertaken.

Our DD model has the following specification:

yst = β * TREATs * AFTERt + as + dt + Controlsst +εst ... (2)

where s = 1,2,...,N, and t = 1,2...,T, index states and time periods, respectively, yst denotes the level of average real farm income in state s in period t, TREATS * AFTERt is a dummy variable that takes the value 1 for state s and period t if that state undertook market-oriented reforms in period t or before, αs denotes stater fixed effects, δt denotes period fixed effects, Controls stand for control variables, and εst denotes a stochastic error term.

Our interest is in the parameter β, which provides an estimate of the effect of market-oriented reforms on real farm incomes by comparing the change in real farm incomes before and after the reforms between treatment and control groups.

Results of estimating equation (2) are presented in Table 7. We present results for four different specifications. In the first specification, in addition to the treatment variable, we include state and period-specific fixed effects to control for unobserved state-specific time-invariant factors (like historical trajectories, geographical factors, and deep cultural factors that change only very slowly) and period-specific factors that affect all states (like central government policies, and developments in the international commodity markets). In the second specification, we add the log level of real per capita net state domestic product to control for different levels of economic development across states. In the third specification, we add the state-level tax and non-tax revenue (as a share of state-level gross domestic product) to control for the possibility that differential pre-treatment trends are driven by the fiscal cap­acity of states to support agricultural growth. In the final specification, we add the crop diversification index to control for possible positive effects of diversification on farm incomes—a factor that has received quite a lot of attention in policy discussions of Indian agriculture.

If we see the result in the first column of Table 7, we note that the coefficient on the interaction of TREATs and AFTERt is negative (-4.437) but not statistically significant. This means that once we control for unobserved state- and period-specific factors, there is no statistically significant effect of the market-oriented reforms. If anything, the effect is negative, though that effect is not statistically distinguishable from zero. Even after we control for per capita net state domestic product, tax and non-tax revenues, and the degree of crop diversification, the coefficient remains negative and statistically insignificant as can be seen from columns 2, 3, and 4 in Table 7. Taken tog­ether, the evidence for the DD research design presented in ­Table 7 shows that market-oriented reforms of agricultural marketing systems across Indian states did not ­improve real farm incomes per cultivator.

The validity of the DD research strategy rests on the parallel trends assumption. We test the parallel trends assumption ­following the method in Muralidharan and Prakash (2017) by estimating the following regression model:

yst = β * TREATs × TRENDt + as + Controls +ust ... (3)

where s = 1, 2... N, and t = 1, 2...T, index states and time periods, respectively, yst denotes the level of average real farm inc­ome in state s in period t, TRENDt is a linear time trend, and TREATis a treatment dummy equal to 1 if the state enacted market-friendly reforms, αdenotes state fixed effects, Controls stand for control variables, and ust denotes a stochastic error term.

To test the parallel trends assumption, we estimate model (3) for all pre-market reform years, that is, for 1987–88, 1993–94, and, 1999–2000. The estimate of β in model (3) can be seen in Table 8. The coefficient is -0.435 but its magnitude is statis­tically insignificant. Thus, once we condition on unobserved time-invariant state-level factors, state-level per capita net state domestic product, and the share of tax and non-tax revenue in state-level gross domestic product, we see that the evolution of real farm incomes per cultivator was no different in treatment than in control group states.

Conclusions

In this paper, we have analysed the first consistent time series of average real farm income per cultivator for 18 major Indian states for 1987–88, 1993–94, 1999–2000, 2004–05, 2007–08, 2009–10, and 2011–12. We find that Punjab, Haryana, Kerala, West Bengal, and TN are the top five states in terms of real farm income per cultivator in 2011–12. In terms of average annual growth rate of real farm income per cultivator between 2004–05 and 2011–12, the top five states are Rajas­than, TN, Kerala, West Bengal, and Haryana. While we do see some evidence for convergence of real farm incomes across states, this tendency has not been strong enough to change state rankings drastically, especially at the top of the ranked list of states. Other than Haryana, none of the states at the top of the ranked list have initiated market-oriented reforms of the agricultural marketing systems. This suggests that the market-oriented reforms might not have been beneficial to the average farmers.

The year-long farmer protests may have succeeded in protecting the existing APMC mandis and ensuring that farmers continue to sell foodgrains at the MSP. However, the debate over AMPC reforms is far from over. The estimates and trends in farm incomes presented in this paper suggest that structural challenges faced by India’s agricultural sector need urgent ­attention. Table 2 shows that the average annual farm income in the top five states in 2011–12 was `1,04,831 in real terms. The corresponding figure for the bottom five states was only `26,408. Further, the results presented in this paper suggest that sweeping reforms, including the abolition of AMPCs and dismantling of the price support offered by government procurement at the MSP may not raise farm incomes for ­cultivators.

Based on our understanding of the evidence, we offer three policy recommendations emerging from this analysis. First, the MSP coupled with actual procurement is pivotal in providing farmers with a price floor necessary for price negotiations. Most states with high farm incomes like Punjab, Haryana, and West Bengal have robust procurement systems. Second, the number of APMC mandis need to be expanded and not dismantled. This would address issues of low accessibility, storage and low farm gate prices faced by small and marginal farmers. Finally, asymmetry in the bargaining power of farmers vis-à-vis corporations should be acknowledged and addressed in any new policy reform aimed at improving farmer incomes in India.

Notes

1 The complete time series data can be found here: https://doi.org/10.7275/qfz9-pm47.

2 We focus on agriculture and not the agriculture and allied activities sector.

3 More details about variable construction and the full-time series of farm income and related variables, both at current and constant (2004–05) prices can be found here: https://doi.org/10.7275/qfz9-pm47.

4 The authors’ calculations based on data from the FCI.

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Updated On : 11th Jul, 2022
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