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CHAPTER FIVE

FACTORS INFLUENCING NONFARM ACTIVITIES

5.1 INTRODUCTION

The preceding chapter has unambiguously bought to the fore the significance of

the non-farm sector in rural livelihoods in Barak Valley. It is clearly borne out by the

findings of the field survey that the non-farm sector has expanded to emerge not only as

the principal source of employment for the rural workforce in the region, but it is also the

major contributor to rural incomes. However, in appreciating the importance of non-farm

activities, it must be borne in mind that while the dependence on the non-farm sector is

terms of income and employment is easily discernable from both primary and secondary

data, the underlying dynamics of the diversification process are less obvious and

warrant further investigation. This issue assumes importance in view of the fact that the

non-farm sector is an umbrella term that includes in its ambit a wide array of activities

characterized by substantial productivity differentials. Thus, our understanding of the

rural non farm economy in the region would remain partial unless the nature of the

diversification process was probed further. Therefore, it is pertinent to examine as to

whether expansion of non-agricultural activities in the region is being pre-dominantly

driven by pull or push factors. In case of the former, income and employment

diversification in favour of non-farm activities is chiefly the outcome of overall

development of the rural economy and is associated with rising demand for goods and

services produced locally. On the other, hand, push factors prevail when the rural

workforce is forced to venture into low productive non-farm activities in the absence of

gainful employment opportunities within the rural economy. In such a situation,

expansion of non-farm activities is unlikely to produce desirable results in terms of

poverty reduction and enhancement of food security. Further, even when growth

impulses exist, all households do not benefit equally from the opportunities that are

generated within the non-farm economy. This is because of the presence of entry

barriers in specific types of non-farm occupations. These entry barriers can take the

form of minimum educational requirements, ownership of assets, credit availability,

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location/place of residence and so on. This chapter uses econometric techniques to

identify the role of various endogenous and exogenous factors in determining the

participation in different types of non-farm activities by rural workers. In addition, the

impact of these factors on the composition of household incomes is also analyzed.

Besides, this chapter also investigates the pivotal question as to whether income and

employment diversification in the region is predominantly driven by demand pull or

distress push factors as this issue is of crucial importance from the point of policy

formulation. Further, the implications of non-farm income for income distribution in the

rural economy of Barak Valley are analyzed with the help of suitable statistical tools.

Finally, selected case studies representing successful and unsuccessful attempts at

diversification are presented as it was felt that such an exercise would help to improve

our understanding of the factors that expedite/ retard the growth of desirable non-farm

activities in the study region.

5.2 FACTORS DETERMINING NON-FARM EMPLOYMENT AND INCOMES:

In order to analyze the factors influencing employment in non-farm activities,

several variables which are theoretically deemed important in affecting participation in

non-farm sector were considered for the regression analysis. Many of these factors are

also considered to be relevant for explaining the proportion of household income

derived from non-farm sources. The variables included in the regression analysis and

the justification for their inclusion, are discussed below.

a. Age of workers: The age of the workers is likely to influence their choice of

occupation as younger workers are often found to be more open to the idea of trying

newer activities compared to the older workers. Moreover, younger workers tend to be

better informed about emerging opportunities within the rural economy. They are also

found to be in better possession of skills and education required for more modern types

of non-farm jobs. Hence, for the occupational choice model, age of the worker has been

incorporated as one of the explanatory variables. However, for ascertaining the

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influence of age on household income mix, average age of all workers in the household

was found to be more appropriate.

b. Education of workers: Most studies on rural income and employment diversification

focus on the role of education in affecting the shift of the rural workforce from the farm

sector to the non-farm sector. In, fact, education is considered to be the single most

important instrument for overcoming the entry barriers to more lucrative non-farm

occupations. To capture the impact of education on the occupational distribution of the

workforce, educational attainment of the workers in terms of years of completed

education was considered. Analogously, for studying the impact of education on

household income composition, the average completed years of education of all

workers in the household was used.

c. Family size: Family size constitutes an important household characteristic which can

affect the likelihood of participation in non-farm activities. However, nothing definite can

be said regarding the nature of its relation with non-farm incomes and employment on

an a priori basis. In fact, household size can be both positively and inversely related to

non-farm activity. On the one hand, large household size increases the probability that

at least one worker will get involved in non-farm vocations. On the flip side, it is pointed

out by some that often agricultural households are characterized by large families while

nuclear families are more likely to be involved in non-farm vocations.

d. Size of land holding: Land by far is the most important asset for rural families which

can have important implications on the composition of household income and

employment. It is commonly believed that ownership of large tracts of agricultural land

by a household would mean that workers in the household would naturally be engaged

in agricultural activities and that the lion’s share of household income would be derived

from agriculture. However, evidence from many studies reveals that the relation

between land ownership and rural income and employment is more complex than what

is commonly perceived. This is due to the fact that large size of land holdings increases

the value of household’s assets thereby opening opportunities for workers from those

households to acquire better education and skills to actually diversify out of agriculture.

Moreover, households owning large tracts of agricultural land find it easier to raise the

initial startup capital that is required for self employment in trade and business. On the

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contrary, landless households and marginal farmers may be forced to diversify into the

non-farm sector due to distress related factors. In sum, size of land holding is likely to

have important repercussions with regard to the composition of household employment

and income but nothing a priori can be said regarding the nature of its relationship with

non-farm activities.

e. Gender: Sex of the worker can also exert significant influence on the choice of

livelihood. This is because women are often constrained by social norms and attitudes

regarding the types of occupations that they can pursue. Moreover, women in

developing societies are usually more deprived than men with regard to their access to

education which serves to narrow their scope for employment in activities requiring

higher skills and training. Therefore, a gender dummy was introduced to assess the

impact of gender on the access to various types of non-farm activities.

f. Location: Location of the household/workers also determines the types of activities

that are available for employment which in turn affects the proportion of household

income that is derived from farm and non-farm activities. However, the likely affect of

locational factors on non-farm income and employment is theoretically vague. For

instance, if a village is located near an urban centre or the highway, it may open up

avenues for employment in varied types of non-farm activities. On the other hand,

proximity to towns and cities or improved connectivity through highways can increase

competition from urban made products thereby causing non-farm activities within the

village to wither. Therefore, to ascertain the impact of locational factors on the non-

farm sector in the region, distance of a village from the nearest urban centre was taken

as an explanatory variable.

g. Number of Income Sources: The share of the non-farm sector in the total income of

rural households is likely to be influenced by the number of income sources. However,

the nature of the relation between the percentage of household income derived from

nonfarm sources and the total number of income sources is difficult to forecast. This is

because having multiple income sources increases the probability of having at least

one source of nonfarm income which in turn would affect the percentage of income

derived from nonfarm sources. Conversely, households that specialize in one type of

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activity or the other (farm/nonfarm) would tend to have their income composition tilted

favorably towards that source.

5.3 MODELING NON-FARM PARTICIPATION: MULTINOMIAL LOGIT REGRESSION

In modeling the effect of various socio-economic factors on non-farm

participation, due attention had to be given to the fact that the rural worker is not only

faced with a choice between farm and non-farm sectors for deciding upon his primary

occupation, but he also has to simultaneously choose among different types of non-farm

activities. In other words, the dependent variable although categorical, consists of

multiple response categories (polytomous). As the effect of the explanatory variables is

likely to be different for different response groups, a multinomial logit model was found

appropriate1. In particular, 4 different occupational categories (oc) were considered

such that

oc =0, for farm

=1, for traditional unskilled non-farm activities (TUSNFA)

=2, for traditional skilled non-farm activities (TSNFA)

=3, for modern non-farm activities (MNFA)2

Here, oc =0 (i.e. farm sector) was taken as the base category. The notations of the

explanatory variables are as follows:

aw =age of the worker

ew = education of the worker(in completed years)

1. It may be noted that the multinomial logit model is used in those situations where the

dependent variable is categorical and has more than two categories which are not necessarily ordered (Greene, 2011).

2. Here, modern occupations refer to those activities which are based on higher education and/or on use of power driven machines/tools. The classification of the occupations observed during the field survey is given in Appendix I.

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sex= gender dummy(1, if female, 0 otherwise).

hs= household size.

nwhh = number of workers in the household.

pcslh= per capita size of landholding(in ha).

dist = distance (in kms) from nearest town.

C1= caste dummy (1, if SC; 0 otherwise)

C2= caste dummy (1 if belonging to tea tribe, 0 otherwise).

C3= caste dummy (1, if OBC, 0 otherwise)

5.3.1 Testing for Multicollinearity and Heteroscedasticity:

Like OLS regression, multinomial logistic regression fails to make reliable predictions,

if there is high multicollinearity among the regressors. Likewise, if disturbance variances

are not equal, MNLR will yield inconsistent results, unless corrective measures for

heteroscedasticity are adopted. In order to test for the existence of multicollinearity

among the explanatory variables, the Variance Inflating Factor (VIF) was calculated for

all the independent variables included in the model. The inverse of the VIF is called the

Tolerance Index (TI). A conventional rule of the thumb is that if the VIF for any of the

predictors is greater than 10 (or if TI is less than 0.10), than there is a problem of

multicollinearity. Table 5.1 gives the values of VIF and TI, for the predictors used in the

model. It is observed that the value the VIF is less than ten for all the regressors

(analogously TI is greater than .01). It can therefore be concluded that multicollinearity

does not pose a serious problem for the data under consideration.

Similarly, since cross section data is often characterized by heteroscedasticity, two

tests were conducted to check whether residuals have constant variance viz.

a. White's test

b. Breusch-Pagan test

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Table 5.1: VIF and TI of Explanatory Variables

Regressors VIF TI

C2 2.18 0.459144

dist 1.66 0.601098

nwhh 1.53 0.653537

hs 1.48 0.675887

ew 1.39 0.720881

c3 1.32 0.759975

c1 1.3 0.770838

aw 1.16 0.861312

sex 1.13 0.881525

pcslh 1.08 0.924318

Mean VIF 1.42

Table 5.2:Cameron and Trivedi's Decomposition of IM (Information Matrix)-Test

Source chi2 df p

Heteroskedasticity 125.24 58 0.0000

Skewness 39.06 10 0.0000

Kurtosis 4.5 1 0.0338

Total 168.8 69 0.0000

Table 5.3: Breusch-Pagan Cook-Weisberg test for Heteroscedasticity

Ho: Constant variance

Variables: fitted values of oc

chi2(1) 7.68

Prob > chi2 0.0056

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Both (White’s and Breusch-Pagan) test the null hypothesis that the variance of the

residuals is homogenous. If the p value is significant (≤ .05), there is said to be evidence

of heteroscedasticity. Since the p value in both cases is very small, we conclude that

disturbance variance is not homogenous (Table 5.2 and Table 5.3). Hence, for the

purpose of model estimation, corrective measures for heteroscedasticity were adopted.

5.3.2 Results and Discussion

1. Iteration log: The iteration log gives a listing of log likelihoods at each iteration.

Here, it may be noted that the Multinomial Logit regression uses iterative Maximum

Likelihood Estimation procedure3. The first iteration i.e. iteration 0 gives the log

likelihood of the null or the empty model i.e. the model with no predictors. In the next

iteration, all predictors are included. The log likelihood increases with each

successive iteration as the objective is to maximize the log likelihood. The model

converged after five rounds of iteration (Table 5.4).

Table 5.4: Iteration Log Iteration No log pseudolikelihood Iteration 0 -808.790 Iteration 1 -589.429 Iteration 2 -567.522 Iteration 3 -564.543 Iteration 4 -564.516 Iteration 5 -564.516

2. Wald chi2: This is a test of the null hypothesis that all of the regression

coefficients across the three models are simultaneously equal to zero. As the p

value<.0000, we reject the null hypothesis and conclude that at least one of the

regression co-efficient in the model is not equal to zero.

3. Pseudo R2: This is McFadden’s R2 which measures the goodness of fit of the

model. However, Pseudo R2 in multinomial logit regression does not have the same

interpretation as the ordinary R2 in the OLS regression and tends to be quite small.

According to a standard rule of the thumb, the model is said to give a good fit if the

3 Stata 11.0 has been used for the purpose of estimation.

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value of the McFadden R2 lies between .2 and .4. For the above model, the Pseudo

R2 value is 0.302 which may be considered to be quite satisfactory (Table 5.5).

Table 5.5: Multinomial Logistic Regression: Model Summary Number of observations 622

Wald chi2(30) 245.81

Prob > chi2 0

Log pseudo likelihood -564.5

Pseudo R2 0.302

4. Hausman test of Independence of Irrelevant Alternatives (IIA): The use of the

Multinomial Logit Regression is justified only if the property of IIA holds. The IIA

requires that the choice between two outcomes (alternatives) is independent from the

existence of other outcomes (or alternatives). Thus, if one outcome (alternative) is

omitted, the estimates for the remaining outcomes should not change significantly. If

the assumption of IIA is violated, MNLR cannot be used and other models such as

Nested Logit have to be tried (Long, 2012). However, as shown in Table 5.6, the

Hausman Test for IIA for the sample data did not provide any evidence against the

null hypothesis indicating that the assumption of IIA is valid and that the use of the

MNLM is quite appropriate.

Table 5.6: Hausman Test of IIA Assumption Ho: Odds (Outcome J vs. Outcome K are independent of other alternatives)

Omitted chi2 df P>chi2 evidence 0 -0.445 20 1 for Ho 1 -8.082 20 1 for Ho 2 12.364 20 0.903 for Ho 3 0.383 20 1 for Ho

5. Parameter Estimates: As the dependent variable i.e. occupational category

consists of four categories, the Multinomial Logit model estimated 3 sets of estimates

with agriculture/farm sector constituting the base or the referent category. In a

multinomial logistic estimation, the effect of explanatory variables on the categorical

dependent variable is reported in a probabilistic framework in terms of Relative Risk

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Ratios (RRRs). The RRR for an explanatory variable indicates how the probability of

choosing category j over the base category changes if we increase that variable by

one unit, holding other things constant. If the RRR>1, it implies that for a unit

increase in the value of the explanatory variable, the probability of observing outcome

j relative to 0 (base group) increases. The reverse is true if RRR <1. We now turn to

the discussion of the regression results (Table 5.7). A. Factors Influencing Participation in Traditional Unskilled Nonfarm Activities (TUSNFA) Relative to Agriculture: i. The regression results show that the probability of a worker participating in

TUSNFA relative to agriculture increases with an increase in the years of completed

education. In other words, an extra year of schooling would increase the probability of

a worker being engaged in TUSNFA than in agriculture. This can be explained by the

fact that TUSNFA as defined in the study not only includes unskilled non-agricultural

wage labour but also incorporates other activities such as trade and certain types of

services, which are usually associated with higher levels of education than what is

observed in agriculture. The coefficient for the education variable was found to be

highly significant.

ii. It is found that females are more likely than males to be engaged in TUNSFA than

in agriculture, other things being held constant. This can be inferred from the fact that

if the sex dummy changes 0 to 1, the RRR assumes a value of 1.9. In other words,

the probability of females being engaged in TUSNFA is 1.9 times the probability of

their being engaged in agriculture. The co-efficient for the gender dummy was found

to be significant at 10 percent. The result can be explained in view of the fact that

there is usually an underreporting of female participation in family agriculture

whereas, when they undertake employment in unskilled nonfarm activities outside the

household, their participation is better recorded.

iii. An increase in household size reduces the odds of a worker participating in

TUSNFA, other things being held constant. In other words, larger the household size,

higher is the likelihood of the worker being engaged in agricultural vocations than in

TUSNFA. The RRR for household size was found to be significant. Similarly, an

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increase in the number of workers per household reduces the probability of a worker

taking up TUSNFA compared to agriculture. However, unlike household size, the

RRR for the later variable i.e. number of workers per household was not significant.

iv. As compared to the general caste workers, a worker belonging to the category of

tea tribes is less likely to participate in TUSNFA. This is evidenced by the fact that

RRR for the caste dummy for tea tribes is less than unity and the corresponding z-

statistic is highly significant. For other caste categories, nothing definite can be said

regarding their likelihood of participation in TUSNFA compared to the base category

i.e. the general caste as the caste dummies were not found to be significant.

v. An increase in distance is likely to reduce the probability of a worker participating

in TUSNFA relative to agriculture. The z statistic for the distance variable is -3.38

which is highly significant while the RRR is .95(<1).Thus, as the distance from urban

centre increases, workers are more likely to be employed in the agricultural sector

than in TUSNFA.

vi. The probability of a worker participating in TUSNFA decreases with an increase

in per capita availability of agricultural land. Thus, when more land is available for

cultivation purposes per capita, workers are likely to stick to agriculture than shift to

TUSNFA. However, the results are statistically significant at 10 percent.

vii. Lastly, higher the age of the worker, lower is the probability of being engaged in

TUSNFA compared to agriculture. In other words, relatively younger workers are

more likely to be employed in TUSNFA than in agriculture. However, odds ratio for

age in the first model is also not significant.

B. Factors Influencing Participation in Traditional Skilled Non Farm Activities (TSNFA) relative to Agriculture i. For this model, the RRR of the predictor aw is .998 with an associated p-value of

0.932. In other words, with an increase in age, workers are more likely to be engaged

in agriculture than in TSNFA. However, as in the first model, the coefficient is highly

insignificant.

ii. As compared to males, females are less likely to be engaged in TSNFA relative to

agriculture. This is because the odds ratio for the gender dummy is .71 implying that

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a discrete change in the sex variable from 0 (male) to 1 (female) would cause the

probability of a worker being engaged in TSNFA to fall. However, the coefficient was

not found to be statistically significant.

iii. Other things remaining constant, an increase in the education of the worker

significantly increase the probability of engagement in non-farm jobs involving

traditional skills. This is because the RRR of the education is 1.21 and the p value for

the corresponding z statistic is highly significant. Thus, workers with extra years of

schooling are more likely to shift to nonfarm jobs using traditional skills than stick to

agriculture.

iv. The decision to pursue traditional skilled jobs is found to be strongly dependent on

the availability of agricultural land per capita. This is because the RRR of the land

variable is very small and highly significant. In other words, with an increase in per

capita availability of land, workers are more likely to be engaged in agriculture than in

TSNFA. It thus appears that lack of agricultural land per capita creates a strong

incentive for shifting to nonfarm occupations involving traditional skills.

v. Caste acts as a barrier to participation in TSNFA only if a worker belongs to the

category of tea tribes. In other words, tea tribes are more likely to be engaged in

agriculture than in TSNFA. For other caste categories, the likelihood of participation

in TSNFA is not found to be significantly different from that of workers belonging to

the general caste relative to agriculture, other things remaining the same, as the z

statistic for the corresponding RRRs are not significant.

vi. An increase in distance from the nearest town significantly lowers the odds of

participation in TSNFA. This is evidenced by the negative and significant value of the

z statistic corresponding to the distance variable of the second model.

vii. Lastly, it is found that the odds of participating in TSNFA relative to agriculture are

significantly reduced by an increase in household size. On the contrary, an increase

in the number of workers in the household enhances the probability of a worker’s

employment in TSNFA compared to agriculture. However, the z statistic

corresponding to the latter variable is not significant.

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Table 5.7: Multinomial Logistic Regression of Occupational Categories

.

pcslh .3597247 .3013788 -1.22 0.222 .0696354 1.858276 dist .9167956 .0163322 -4.88 0.000 .8853375 .9493715 nwh 1.009007 .1472366 0.06 0.951 .7580277 1.343085 hs .9822799 .0533354 -0.33 0.742 .8831146 1.09258 c3 .8193097 .2693814 -0.61 0.544 .4301114 1.560685 c2 .2260781 .1190035 -2.82 0.005 .0805751 .6343311 c1 1.194364 .4977838 0.43 0.670 .5276854 2.703325 ew 1.814155 .1093129 9.88 0.000 1.612073 2.041569 sex 2.881531 1.276646 2.39 0.017 1.209214 6.866624 aw .9904938 .0113918 -0.83 0.406 .9684162 1.0130753 pcslh 6.41e-09 2.75e-08 -4.40 0.000 1.43e-12 .0000288 dist .9296906 .0216145 -3.14 0.002 .8882776 .9730343 nwh 1.084675 .1911779 0.46 0.645 .7678431 1.532241 hs .8648547 .0530414 -2.37 0.018 .7669006 .9753201 c3 .5350669 .2305631 -1.45 0.147 .2299434 1.245074 c2 .2591315 .1578801 -2.22 0.027 .0785076 .8553203 c1 1.327281 .5795009 0.65 0.517 .5640553 3.123229 ew 1.20702 .0581174 3.91 0.000 1.098321 1.326476 sex .7098632 .3880616 -0.63 0.531 .243135 2.072535 aw .9989893 .0117681 -0.09 0.932 .9761885 1.0223232 pcslh .0892005 .1129752 -1.91 0.056 .0074524 1.067677 dist .9496154 .0145421 -3.38 0.001 .9215369 .9785494 nwh .9045436 .1199106 -0.76 0.449 .6975738 1.172921 hs .8644375 .0438103 -2.87 0.004 .7826977 .9547136 c3 1.064048 .3179493 0.21 0.835 .5923974 1.911216 c2 .2987384 .1265208 -2.85 0.004 .1302545 .6851559 c1 1.534523 .556092 1.18 0.237 .7542385 3.122039 ew 1.10666 .0379632 2.95 0.003 1.0347 1.183625 sex 1.863703 .6123613 1.89 0.058 .9788031 3.548608 aw .9957074 .0094205 -0.45 0.649 .9774136 1.0143441 0 (base outcome) occ RRR Std. Err. z P>|z| [95% Conf. Interval] Robust

Log pseudolikelihood = -564.51554 Pseudo R2 = 0.3020 Prob > chi2 = 0.0000 Wald chi2(30) = 245.81Multinomial logistic regression Number of obs = 622

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C. Factors Influencing Participation in Modern Non Farm Activities (MNFA) relative to Agriculture

i. Education appears to be a very potent factor in determining the likelihood of

employment on modern non-farm activities as opposed to agriculture. The RRR for

education in the third model is 1.81 and the corresponding z statistic is highly

significant. Hence, we can say that with an increase in the level of education, workers

are more likely to be engaged in modern non-farm vocations rather than in

agriculture.

ii. In contrast, the probability of participation in modern non-farm activities is

significantly reduced if the worker is residing in relatively remote locations. This is

reflected by the low RRR for the distance variable. This is quite expected as modern

sector jobs are more likely to be concentrated in areas closest to the town.

iii. It is revealed by the estimated results that compared to the general category,

workers belonging to the category of tea tribes are highly unlikely to be engaged in

modern non-farm jobs as shown by the negative and significant value of the co-

efficient relating to the caste dummy C2. However, for other caste categories, the

corresponding caste dummies were insignificant.

iv. As compared to males, females are more likely to be employed in MNFA than in

agriculture. This is because the RRR of the sex dummy for the third model is

significant and is greater than unity. Given the existence of pervasive gender

disparities, this appears to be a somewhat surprising outcome. The probability of

females participating in modern non-farm jobs is perhaps enhanced by the fact that

there are many modern non-farm jobs which are particularly suited for females such

as teachers, Anganwadi helpers and ASHAS. It is noteworthy that under the impact

of the NRHM and SSA, employment of females in education and health sectors in

rural areas has received a boost, which explains why the log-odds of participation of

females is higher in MNFA than for males relative to agriculture.

v. An increase in per capita land holding will reduce the probability of participation

in MNFA. However, the RRR for the per capita size of land holding was not

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significant. The RRRs of the other explanatory variables such as age, household size

and number of workers per household were also not found to be significant.

In sum, it may be said that education is the single most important factor

facilitating the shift towards non-agricultural activities in rural areas. It is clearly borne

out by the regression results that workers with higher educational base are more likely

to seek non-farm employment rather than stick to agriculture. In contrast, an increase in

the per capita size of landholding is likely to lower the probability of a worker being

engaged in traditional skilled and unskilled non-farm activities relative to agriculture.

Further, it is found that workers belonging to large households are more likely to be

engaged in agriculture than in non-farm vocations. Also, the probability of employment

in non-farm sector as a whole is reduced as we move towards the hinterland. Moreover,

although work participation rates among females is very low in Barak Valley, when

females do participate, they are more likely than males to be engaged in modern non-

farm than in agriculture. It can be asserted that caste acts as a barrier to non-farm

employment only for workers belonging to the category of tea tribes. In case of other

categories, the existence of caste barriers is not sufficiently corroborated. Lastly, it

appears that relatively younger workers are more likely to be employed in non-farm

vocations than in agriculture, although the outcomes were not statistically significant in

any of the models fitted above.

5.4 INTER-HOUSEHOLD VARIATIONS IN NON-FARM INCOMES: CENSORED TOBIT MODEL

In order to ascertain the factors influencing the income mix of rural households, the

percentage of household income derived from non-farm sources was regressed on a

number of independent variables representing the socio-economic characteristics of the

household and also the locational factors. However, a problem with the income model is

that there are many households for which the proportion of income obtained from non-

farm income is either zero or one. In other words, the distribution is censored at both

ends. Thus, usual OLS method is not best suited for the present analysis. Hence, for

the present purpose, a censored Tobit model was found to be suitable. Let us define

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Y= Xβ +µ

Where, Y= vector of observations on percentage of household income derived from

nonfarm sources.

X = Matrix of observations on the explanatory variables

β= vector of coefficients to be estimated.

µ= stochastic error term

The starting point of fitting a censored Tobit model is to define a new latent

variable Y* obtained by transforming the original dependent variable Y as follows:

Y*=0 if Y ≤ 0

Y*=Y if 0< Y<1

Y*= 1 if Y≥ 1

However, the Tobit regression returns the impact of the explanatory variables on the

unobserved/ latent dependent variable while the primary interest of the regression lies in

knowing the influence of those variables on Y. Hence, after running the Tobit

regression, it is customary to obtain the marginal effects of the regressors on Y

(Woolridge, 2002; Dougherty, 2012). These marginal effects are in fact routinely

computed by various Software packages.

The notations of the variables used in the income model are as follows

Y (dependent variable) = percentage of income of the ith household derived from non-

farm sources.

aage= average age of workers in the household

aedu= average education of workers in the household.

nois= number of income sources.

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Other variables have the same specification as in the occupational choice model.

5.4.1 Testing for Multicollinearity and Heteroscedasticity in the Income Model: As

the variables included in the income model are somewhat at variance from those used

in the occupational choice model, it was thought desirable to test them for the presence

of multicollinearity and heteroscedasticity. The VIFs for the variables for the income

model are presented in Table 5.8. It is observed that the VIF is well below 10 for all the

listed variables suggesting that the explanatory variables are not seriously afflicted with

a multicollinearity problem. However, both the Cameron-Trivedi IM test (Table 5.9) and

the Breusch-Pagan Cook-Weisberg test (Table 5.10) were found to be highly significant

implying the existence of heteroscedastic disturbances.

Table 5.8: Variance Inflating Factor of Explanatory Variables

VIF 1/VIF

c2 1.99 0.502826 nwhh 1.79 0.558991 nois 1.58 0.633645 dist 1.48 0.674375 hs 1.37 0.728933 aedu 1.33 0.753153 c3 1.32 0.758031 c1 1.29 0.775139 aage 1.17 0.857419 pcslh 1.11 0.899349 Mean VIF 1.44

Table 5.9:Cameron and Trivedi's Decomposition of IM-test

Source chi2 df p Heteroscedasticity 125.65 59 0.0000

Skewness 35.53 10 0.0001 Kurtosis 12.08 1 0.0005

Total 173.26 70 0.0000 Table 5.10:Breusch-Pagan Cook-Weisberg Test for Heteroscedasticity Ho: Constant variance Variables: fitted values of PNFI chi2(1) 37.42 Prob > chi2 0.0000

137

5.4.2 Model Summary: The model summary of the Tobit regression is presented in

Table 5.11. In all, 357 households of the total 377 households sampled, were

considered for the purpose of estimation as households which were entirely dependent

on remittances/transfer income were excluded from the analysis. The value of the F

statistic is highly significant showing that the coefficients of all the regressors are not

simultaneously equal to zero. Further, the pseudo R2 is .41 which implies that the model

gives a very good fit (It may be recalled that in case of models involving limited

dependent variable, a value of the pseudo R2 between .2 and .4 is considered to be

highly satisfactory).

Table 5.11:Tobit Regression- Model Summary Number of observations 357 F(10,347) 25.65 Prob>F 0.0000 Log pseudolikelihood -163.925 Pseudo R2 0.4194

5.4.3 Tobit Regression- Results: The estimated Tobit coefficients are given in Table

5.12. These coefficients are the marginal effects of the corresponding explanatory

variables on the unobserved/latent dependent variable and can be interpreted in the

same way as the OLS estimates. However, such interpretation is not useful because we

wish to know the marginal effects of the explanatory variable(s) on the observed

dependent variable. These marginal effects (dy/dx) can be obtained by running the post

estimation commands in Stata and are presented in the Table 5.13.

a. It is found that y i.e. the percentage of household income obtained from nonfarm

sources is inversely related to the number of income sources (nois) open to the

household. More specifically, a unit increase in nois would cause the value of y to fall

by .04 points. The result is found to be highly significant. It thus appears that rural

households which derive greater share of their income from non-farm sources are

relatively less diversified in terms of income sources.

138

Table 5.12: Results of Tobit Regression on Household Incomes

b. Secondly, the percentage of income derived from non-farm sources is significantly

lower for households belonging to the category of tea tribes as compared to the general

caste households. This is reflected by the negative and significant value of the caste

dummy c2. In other words, if a household belongs to the category of tea tribes, the

percentage of household income derived from nonfarm sources would tend to be .19

points lower than that derived by a household belonging to the general caste. However,

in case of SC and ST households, nothing definite can be said as the co-efficient

attached to the caste dummies were not found to be significant.

c. Thirdly, the share of income derived from the non-farm sector is markedly reduced

with an increase in the per capita availability of agricultural land. In particular, a unit

increase in the value of pcslh would on an average, cause y to decline by .32 points.

Clearly, for the sample as a whole, lower the per capita availability of land, larger is the

share of income derived from non-farm sources.

120 right-censored observations at y>=1 212 uncensored observations Obs. summary: 25 left-censored observations at y<=0 /sigma .3290354 .0193369 .2910031 .3670677 _cons 1.183646 .1293641 9.15 0.000 .9292093 1.438082 dist -.0152784 .0027125 -5.63 0.000 -.0206134 -.0099433 aedu .033724 .0052833 6.38 0.000 .0233327 .0441153 aage -.0009793 .0019595 -0.50 0.618 -.0048334 .0028747 pcslh -.7142037 .2584207 -2.76 0.006 -1.222472 -.2059356 c3 -.0459046 .043617 -1.05 0.293 -.1316916 .0398824 c2 -.3761697 .0765594 -4.91 0.000 -.5267485 -.225591 c1 .0443976 .055017 0.81 0.420 -.0638112 .1526065 nwhh .0318713 .0228992 1.39 0.165 -.0131673 .0769099 hs -.0072023 .0071113 -1.01 0.312 -.021189 .0067843 nois -.0935346 .0214081 -4.37 0.000 -.1356407 -.0514286 y Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Log pseudolikelihood = -163.92504 Pseudo R2 = 0.4194 Prob > F = 0.0000 F( 10, 347) = 25.65Tobit regression Number of obs = 357

. tobit y nois hs nwhh c1 c2 c3 pcslh aage aedu dist, ll(0) ul(1)vce(robust)

139

d. Fourthly, education has a positive impact on determining the proportion of income

derived from the non-farm sector. The regression results reveal that if the average

education attained by all workers in the household increases by one year, it would raise

the value of y by .015 points, other factors being held constant.

a. Fifthly, an increase in the distance from the nearest town has the impact of lowering

the percentage of household income obtained from non-farm sources. As can be seen

from Table 5.13, if distance from the nearest urban centre increases by one km, y

would, on an average, fall by .007 percentage points.

b. Finally, it is observed from Table 5.13, that other household characteristics such as

household size, number of workers in the household and average age of household

workers do not have any significant impact in determining the composition of the

household income.

Table 5.13: Marginal effects obtained from Tobit Regression

It thus follows from the Tobit regression that an increase in the average years of

education of household workers and proximity to urban centre raises the proportion of

household income derived from non-farm sources. On the contrary, for the sample as a

whole, an increase in the per capita availability of agricultural land reduces the share of

(*) dy/dx is for discrete change of dummy variable from 0 to 1 dist -.0069157 .00119 -5.80 0.000 -.009251 -.00458 15.6807 aedu .0152651 .00255 6.00 0.000 .010276 .020255 7.22594 aage -.0004433 .00088 -0.50 0.616 -.002177 .001291 38.3296 pcslh -.3232819 .11754 -2.75 0.006 -.553662 -.092902 .066301 c3* -.0210362 .01997 -1.05 0.292 -.060186 .018114 .280112 c2* -.1906804 .04207 -4.53 0.000 -.273139 -.108222 .168067 c1* .0197544 .02411 0.82 0.413 -.027501 .067009 .196078 nwhh .0144264 .0104 1.39 0.165 -.00596 .034813 1.7535 hs -.0032601 .00321 -1.01 0.310 -.009557 .003037 5.98319 nois -.0423381 .00954 -4.44 0.000 -.061032 -.023644 2.43137 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X = .66765761 y = E(y|0<y<1) (predict, e(0,1))Marginal effects after tobit

. mfx compute, predict(e(0,1))

140

nonfarm income in total household income. Likewise, having multiple income sources

also lowers the proportion of nonfarm income in aggregate household income.

However, other household characteristics in terms of household size, number of

workers per household, average age of household workers and caste (apart from tea

tribes) were not found to be having any significant impact on determining the share of

nonfarm income in aggregate household income.

5.5 NON-FARM INCOME AND RURAL INEQUALITY

There is no clear evidence whether non-farm incomes help to increase or reduce

inequality among rural households. In order to assess the impact of non-farm income on

the distribution of income among rural households in Barak Valley, the contribution of

various income sources to the overall inequality was calculated. This was done through

the compilation of the Factor Inequality Weight (FIW) for each income source. The FIW

which gives the proportional contribution of a given income source to the overall income

inequality was calculated according to the following formula (Pyatt et.al, 1980)

i iFIW w g

Where, iiw

gi= Relative Concentration Co-efficient

ii

GRG

Where, Gi= Gini Co-efficient for the ith income source

G= Gini Co-efficient for the ith income

covcov

ii

i i

y rR

y r

Covariance between source income amount and total income rankCovariance between source income amount and the source income rank

Further, G and Gi can be calculated as follows (FAO, 2006)

141

2cov yi, rini

i

Gµ

,

Where,

n= number of rural households in the sample

µ = Average income of all households

µi= average income from the ith income source

r= series of corresponding income ranks.

Table 5.14 presents the relative concentration co-efficient of each income source and

also their contribution to overall income inequality. If the concentration co-efficient for a

particular source is positive, it shows that it has an inequality increasing effect while a

negative value reveals that it has an inequality reducing effect. Judged by this criterion,

both agricultural income and nonfarm income were found to be having an unfavorable

impact on the distribution of income among rural households. However, in case of the

former, the value of the relative concentration co-efficient is very small at 0.49. In

contrast, the non-farm sector was found to be having a large inequality enhancing effect

as the value of the relative concentration co-efficient was found to be greater than unity

for both non-farm income as well as income from residual nonfarm sources. However,

within the non-farm sector, participation in non-agricultural wage employment, other self

employed category and household industry were found to be having a favourable

impact on rural income distribution as these subsectors had a negative value of gi. On

the other hand, it was observed that income from salaries and trade and business were

the chief sources of income concentration in the rural economy as these subsectors had

a very high positive value of gi at 1.55 and 1.29 respectively. This is a pointer to the fact

that entry barriers are high in these sub-sectors of the rural non-farm economy which

prevents households without the requisite skills or capital from entering into these

occupations. Incidentally, the value of the relative concentration co-efficient for income

derived from skilled and semi-skilled labour is quite low showing that entry barriers are

relatively less potent in this group. Income from residual non-farm sources such as

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pensions and remittances are also inequality enhancing as reflected by the high positive

value of gi for this group at 1.2.

Table 5.14: Income Inequality by Source

Income Source

Relative Concentration

Co-efficient (gi)

Contribution to overall

income inequality (%)

(FIW*100)

a. Agriculture 0.49 9.15

b. Other Daily Wage Labour -0.71 -3.07

c. Salaries 1.55 51.34

d. Skilled And Semi-Skilled

Labour 0.59 4.86

e. Self-Employment in Trade

and Business 1.29 20.44

f. Other Self Employed(Thela

cart pushers, Rickshaw pullers,

petty Traders etc) -0.62 -1.61

g. Household Industry -0.23 -0.32

h. Non Farm (b to g) 1.09 71.26

i. Residual Non Farm Income 1.20 19.59

Overall Gini Index 0.47

The contribution of each income source to overall inequality in rural areas is given in

column 3 of Table 5.14. Thus, agricultural income accounts for a little over 9 percent of

the total inequality in rural areas. On the other hand, the non-farm sectors accounts for

the lion’s share of the income inequality observed in rural areas. It is found that more

than 50 percent of the income inequality among rural households in the region can be

attributed to salaries while self-employment in trade and business account for another

20 percent. Residual non-farm income also contributes nearly 20 percent to the income

inequality in rural areas. However, income from non-agricultural wage employment,

household industry and other types of self-employment serve to improve the income

143

distribution in rural areas but their impact is muted due to the insignificant shares of

these sectors in total rural incomes. The overall value of the Gini Index for rural incomes

was found to be 0 .47.

5.6 RURAL LIVELIHOOD DIVERSIFICATION: DISTRESS INDUCED OR DEMAND DRIVEN

The preceding chapters have amply demonstrated the significance of the rural non-

farm sector in rural livelihoods in Barak Valley. But while taking cognizance of the

growth of non-farm activities, it is pertinent to examine whether such growth is the

outcome of agricultural distress or has been propelled by growing rural demand for

goods and services. This issue assumes greater relevance in view of the fact that

agricultural sector in Barak Valley has been witnessing negative growth in the past

decade. Determination of the chief drivers of employment and income diversification is a

pre-requisite for purpose of policy formulation.

In order to ascertain, the nature of diversification, the type of activities pursued by

workers with diverse asset base was examined. Such an approach was found suitable

in view of the fact that certain types of non-farm activities are associated with low capital

requirements/ skill formation/returns and can be taken to be broadly pursued by those

with livelihood distress. On the contrary, there are other non-farm activities that require

larger capital investment or human capital and which yield higher returns. These

activities are largely growth induced. Thus, of the nine broad groups of non-farm

activities considered earlier, two categories namely non-agricultural labour and other

self-employed can unambiguously taken to be distress related. On the other hand,

government and semi-government jobs as well as self-employment by skilled and semi-

skilled workers can be said to be driven by growth related factors by virtue of the fact

that employment in these sectors require better education and skill formation among the

workers. On the other hand, the other salaried group comprising of private sector jobs

as well as household industries are mixed categories and can have elements pertaining

to both pull and push factors. Further, according to the criteria adopted in the present

144

study for classification of non-farm activities, self employment in trade and business can

largely said to be a demand driven phenomenon.

Fig 5.1 shows the distribution of the nonfarm workers in the sample by type of

activities. It is found that the distress driven categories i.e. ODW and OSE together

account for a little over 18 percent of the total nonfarm employment. In contrast GSS,

SSE and SSS which are representative of more secure livelihoods account for nearly 57

percent of the nonfarm employment. Further, the other salaried group and household

industry workers make up an additional 25 percent of the nonfarm workers. It may be

noted that although certain salaried jobs in the private sector are low paying, the push

factors are unlikely to dominate given the fact that the mean years of education for

workers in this group are only next to that of those employed in public sector jobs. In

other words, these workers cannot be considered to be asset poor, which is the chief

characteristic of those with livelihood distress. Likewise, although livelihood distress is

present among certain household industry workers but the entire category cannot be

considered to be distress ridden. Nevertheless, even if all HHI workers are clubbed with

the other two distress driven categories viz. ODW and OSE, it would raise the

proportion of such workers in overall nonfarm employment to just over 22 percent.

Therefore, although the existence of distress driven diversification cannot be ruled out,

the overall composition of nonfarm employment does not lend credence to the

7.09

15.44

20.25 18.73

22.78

11.39

4.30

0.00

5.00

10.00

15.00

20.00

25.00

ODW GSS OS SSS SEE OSE HHI

Fig 5.1: Intra-Sectoral Distribution of Rural Non Farm Workers

145

hypothesis that livelihood distress is the principal driving force behind the growth of

nonfarm activities in the study region.

Fig 5.2 depicts the occupational distribution of workers belonging to landless

households. As noted earlier, employment in non-farm vocations is very high for this

group due to the obvious reason that these workers do not possess the most important

asset required for crop production. Hence it is important to assess whether landless

workers are being pushed into low productive non-agricultural employment. However,

classification of landless workers according to the type of activity pursued again does

not give any clear indication of diversification among landless workers being chiefly the

outcome of economic distress. Thus, 6.6 percent of these workers were engaged in

casual wage employment while another 8.91 percent were employed in low productive

self employment activities (OSE). Distress related activities therefore accounted for 15.5

percent of the total employment of the landless workers. Interestingly, skilled and semi-

skilled work was the most important source of employment for this category of workers

followed by salaried employment in the private sector which indicates that skills and

education can substitute for the lack of what has been traditionally the most valued

asset for rural households namely agricultural land. Further, approximately 12 percent

of the landless workers were engaged in trade and business while a little more than 4

percent were dependent on household industries.

5.286.60

14.85

6.60 7.59

16.83 17.16

11.88

8.91

4.29

0.00

5.00

10.00

15.00

20.00

CUL AL AAA ODW GSG OS SSS SEE OSE HHI

Fig 5.2:Occupational Distribution of Landless Workers

146

Fig 5.3 presents the occupational distribution among workers belonging to the other

spectrum i.e. the medium and large land owning class where more than 90 percent of

the workers are found to have diversified out of agriculture. However, distress related

non-farm employment is clearly absent for this category. On the contrary, government

and semi-government jobs are the primary source of employment for this group as

31.82 percent of the workers were found to be engaged in public sector employment.

Self employment in trade and business accounted for another 27 percent of the

employment among the medium and large land owning class while 18 percent and 14

percent of the workers were found to be engaged in skilled and semi-skilled activities

and other salaried jobs respectively. Only, 9 percent of the workers in this group

reported cultivation as their primary activity.

Another way of ascertaining whether diversification into non-farm activities is

predominantly led by pull or push factors, is to analyze the mix of activities that are

pursued by workers with different educational bases. However, classification of sample

workers by level of education reveals that scope for diversification into non-farm

employment is limited for workers with low educational base. Thus, less than 30 percent

of the illiterate workers were found to be venturing into non-farm activities while seventy

percent of the workers with no education were sticking to agriculture based activities

9.09

0.00 0.00 0.00

31.82

13.64

18.18

27.27

0.00 0.000.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

CUL AL AAA ODW GSG OS SSS SEE OSE HHI

Fig 5.3:Occupational Distribution among Large and Medium Land Owners

147

(Fig 5.4). Needless to say that majority of the illiterate workers entering the non-farm

sector were engaged in low productive activities such as casual wage employment and

other self employment activities. The upshot is that avenues for employment in the non-

farm sector are restricted for illiterate workers and further whatever diversification is

observed among these workers is primarily a distress phenomenon.

While agriculture is the mainstay for illiterate workers in the region, with a rise in

education level, a progressive shift of workers to the non-farm sector is clearly

discernable. Thus, education up to the primary level causes the share of non-farm

workers to rise up to 50 percent. Further increases in education levels are associated

with a rapid decline in agricultural employment and a concurrent rise in non-farm

employment. Therefore, while 52 percent of the workers with education up to middle

school were dependent on non-farm employment while the figure rises sharply to more

than 73 percent for workers with high school education. For the highest educational

category that is HSSLC and above, involvement in agricultural activities is negligible as

almost 96 percent of the workers in this group were found to have shifted to non-

agricultural employment. Moreover, leaving aside the illiterate workers, among the other

educational groups there is no clear evidence of diversification being entirely distress

led (Table 5.15).

70.7150.50 48.00

26.874.04

29.2949.50 52.00

73.1395.96

0.00

20.00

40.00

60.00

80.00

100.00

120.00

Illiterates Primary Middle School

High School

HSSLC and Above

Fig 5.4:Classification of Main Workers by Education and Employment

Non Farm

Farm

148

Table 5.15 :Occupational Classification of Non Farm Workers by Education

Illiterates Primary MS HS HSSLC and Above

ODW 9.09 6.93 6.00 2.64 0.00

GSG 0.00 1.98 1.00 13.66 26.26

OS 1.01 5.94 11.00 14.10 29.29

SSS 4.04 9.90 11.00 17.62 8.08

SEE 3.03 7.92 9.00 17.62 30.30

OSE 12.12 12.87 8.00 4.41 2.02

HHI 0.00 3.96 6.00 3.08 0.00

Total Non

Farm 29.29 49.50 52.00 73.13 95.96

In sum, it may be concluded that the study has provided no conclusive evidence

to suggest that diversification into non-farm activities by the rural population in the study

region is predominantly a distress led phenomenon. True, there are instances of

workers being pushed into low productive non-farm activities owing to the lack of assets

or adequate education and skills. However, such workers do not account for an

overwhelmingly large percentage of the total employment in the non-farm sector. In a

similar vein, it cannot be argued that employment diversification in rural Barak Valley is

an out and out growth propelled scenario. The fact of the matter is that there are

elements of both growth induced and distress led diversification. Those workers who

are in possession better physical, financial and human capital assets are able to venture

into highly productive non-farm jobs while those lacking the essential capital base are

forced to eke out a living from low return activities in the non-farm sector. However, the

finding that employment diversification into non-farm activities in Barak Valley is not

predominantly distress led holds hope for the rural economy of the region as it shows

that the distress in agriculture is not overwhelmingly spilling over into the non-

agricultural sector in the form of casual/petty non-farm jobs and that guiding the

expansion of non-farm sector along desirable lines can actually serve to counteract the

crisis gripping the agricultural sector. This calls for a proper understanding of the factors

149

that facilitate/hinder desirable forms of non-farm employment. It is to this discussion that

we turn now.

5.7 FACTORS FACILITATING/ IMPEDING NON-FARM EXPANSION

The regression results have highlighted some of the factors that facilitate/retard the

growth of the non-farm sector on desirable lines. In keeping with the last objective of the

study i.e. in order to ascertain the factors impeding the growth of non-farm activities in

the study region, we present some case studies which serve to draw a contrast between

the instances of successful diversification vis-à-vis unsuccessful ones. For this purpose,

five cases each of successful and unsuccessful diversification were chosen from the

surveyed households and their background conditions analyzed. In order to protect the

identity of the respondent, their real names have not been reported.

5.7.1 Nonfarm diversification: Success Stories

Case study 1: Afzal Mohammad of Berakhal Village (Tarapur Part III) retired from

service in the State government almost fifteen years ago. He owns around 20 Bighas

(3.2 ha) of agricultural land most of which has been leased out. Being a graduate

himself, Mr. Mohammad prioritized the education of his children. As the family lives in

the close vicinity of Silchar town which enjoys the advantage of having a University as

well a National Institute of Technology, higher education was well within the reach of his

children. His eldest son, an M. Phil in Computer Science is employed as a lecturer in a

college in Silchar. His wife armed with a graduate degree in Computer Application runs

a Computer Training Centre just outside the town. Mr. Mohammad’s second son, a

graduate in Pharmacology, works as the Regional Manager of a Pharmaceutical

Company besides also being the proprietor of a Medicine Shop, located on the main

road close to the village. His third son is a B.Tech in Electronics from NIT, Silchar and at

the time of the survey, was employed with a leading mobile company in the town. With

his children well settled, Mr. Mohammad leads a peaceful retired life, minding the family

farm and playing with his grandchild. This case study shows how proximity to an urban

centre not only facilitates the acquisition of skill based education for those who can

afford it, but also opens up opportunities of employment in modern non-farm activities.

150

Case Study 2: Mridul Debnath of Tupkhana village under Hailakhandi block is

employed as a Khalasi in a State government Department. His being educated up to the

Higher Secondary level coupled with the fact that he belongs to a Backward Community

facilitated the way for his absorption in a government job. However, Debnath maintains

that given the current consumption trends, after meeting all family expenses, his income

by way of salary is not enough to ensure a secure future for his children. Debnath has

therefore encouraged his wife, an under matriculate, to start a self-employment venture

in the form of setting up of a bakery within the household premises to add to the family

coffers. The initial capital for the business came in the form of a loan from Bandhan, a

local self help group for women. Debnath also pumped in some of his own savings to

get the project moving. Besides, he also advises his wife on matters relating to

business. While his wife supervises the business, the baking work is done by hired

workers. After one year of initiating the business, Debnath says that demand for bakery

products within the village is quite satisfactory. He attributes this primarily to the high

population density within the village. Also, the village being located in close proximity of

Hailakhandi town, the unit has also started supplying confectioneries to the shops in the

town. Debnath is optimistic that the business prospects will improve in future and is

happy that he is able to generate employment for at least a few unemployed youths of

the village.

Case Study 3: Mrs. Sarabala Das, also of Sadarashi Pt II village heads an extended

family comprising of 19 members. The household does not own any agricultural land

nor does it lease in any land for the purpose of cultivation. The family is therefore

entirely dependent on non-agricultural sector for their livelihood. However, five of Mrs.

Das’s six sons started working early as apprentices in various jobs which facilitated their

quick absorption in the workforce. While her eldest son trained as a scooter mechanic

after six years of schooling, her second son took to carpentry after completing school.

Her third son, a matriculate, is an assistant in a Pharmacy. Apart from the salary he

receives from his employer, he supplements his income by offering his services to

villagers when they are in need of his assistance. Her, fourth son also a mechanic is

engaged with a bike repairing centre while her fifth son is employed as a driver. Her

youngest son, aged 22 is still unemployed although he has completed 10 years of

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schooling. Although the family cannot be considered to be affluent, at the same time

they are not livelihood insecure. This is a classic example how early acquisition of skills

can enhance employability rather than acquiring mere literary education.

Case study 4: Ahmed Afzal, aged 65 of Masly Part II village under Badarpur

Subdivision had completed 5 years of school. Though he was originally engaged in

cultivation, Afzal was encouraged by the activities of his fellow villagers to try his hand

at areca nut trading with neighbouring states of Mizoram and Meghalaya. The

opportunity for such trade was expedited by the fact that the village is located along

National Highway connecting Karimganj and Mizoram. Although Afzal initiated his

business as experimental attempt, he found that returns were actually lucrative, which

prompted him to plough back his profits and invest further in business expansion. Soon,

Afzal found it worthwhile to lease out his land and devote himself entirely to his new

task. Today, all three of Afzal’s sons are engaged in the business which has brought

considerable prosperity to the family. Afzal and others in the village owe their good

fortune to the booming demand for areca nut in the neighbouring states where chewing

of betel nut is common.

Case Study 5: Nabendu Paul of Narshingpur Pt II Village is a retired school teacher.

He owns around 12 Bighas of agricultural land which he cultivates with the help of hired

labour. His two sons aged 38 and 35 are graduates. However, despite their best efforts,

the two brothers were unable to secure government jobs, thereby compelling them to

take up self-employment ventures. Availability of a loan from the local Commercial Bank

expedited their efforts. Thus, while the eldest of the two brothers runs a commercial

Sumo service from Silchar to Bhaga, the younger brother owns a Pharmacy in

Kabuganj, which is the main market area in the block. Income from both avenues is

sufficient to ensure reasonable profit even after paying the monthly installments to the

bank. Thus, financial inclusion and provision of startup capital by the local bank paved

the way for successful nonfarm diversification for the two brothers.

The review of the above cases highlights the factors that individually/collectively

promote successful diversification. These are

152

a. Initial physical asset base of the household (primarily in terms of land holding):

The role of assets is bought out by the first and fifth case studies. In the first case,

availability of additional income from agriculture enabled the respondent to

supplement his income from his salaried job and afford tertiary education for his

children. In the fifth case, ownership of agricultural land facilitated the process of

securing a respectable loan from the bank for initiating both the businesses, by

serving as collateral.

b. Education and skill base of the workers: The role of tertiary education in

promoting modern nonfarm jobs is clearly borne out by the first case. Thus, even

though belonging to a village, technical education enabled Mr. Afzal Mohammad’s

sons to secure lucrative employment in the town (Case study 1). Likewise, early

training in vocational skills in the third case ensured that, despite the lack of

agricultural land, Mrs. Sarabala Das’ children could still find quick employment.

c. Proximity to an urban centre- Proximity to an urban centre can facilitate the

growth of successful nonfarm activities through rural-urban linkages as in the second

case where the bakery was found to be supplying confectionaries to the shops in

Hailakhandi town. Linkage of this type also helps to expand the scale of operations of

rural enterprises, thereby generating more employment for the local youths.

d. Improved transport system: Improvement of the transport system opens up

markets and strengthens the rural-urban linkages. As in the case of Ahmed Afzal

(Case study 4), improved transport system may throw up opportunities of profitable

trade with other areas, thereby providing an incentive to diversify out of agriculture.

e. Prevailing demand conditions: The long term survival of a rural enterprise

depends on the conditions of demand which in turn is influenced by the economic

condition of the village and also on the population density. In the second case study,

high population density in the village and the stable demand for confectionary

products was the chief reason behind its survival.

f. Financial Inclusion: Often the ability of a prospective entrepreneur to enter into

profitable nonfarm activities is determined by the provision of the necessary startup

capital from financial institutions. As in the last case study, it was the loan given by

the local commercial bank that helped the brothers to become gainfully employed in

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profitable ventures. In addition to traditional sources of finance, micro-finance can

also play a vital role in meeting the needs of finance of rural enterprises thereby

propelling their growth.

5.7.2 Nonfarm diversification: Instances of Insecure Livelihoods Case Study 6: Monotosh Deb, aged 50 is the youngest of three brothers residing in

Durganagar village of Udharbond Block. While his elder brothers are Matriculates and

are employed as teachers in the local primary school, Monotosh was able to

complete only six years of school education. Following the division of ancestral

agricultural land among the three brothers, Monotosh has only .08 acres of land to

call his own. Although he does lease in some extra land, the value of the crop

received after settling the claim of the lessor as well as the yield raised from his own

marginal plot is clearly inadequate to support the family of four comprising of his wife

and two children besides himself. Moreover, with single cropping being the norm in

the village, Monotosh has no assured source of employment during slack agricultural

period. Consequently, during the lean agricultural season, he has to take up casual

daily wage employment in the village such as carrying construction materials or

repairing of homestead. Even then, he finds it difficult to make both ends meet as frail

health often prevents him from working regularly. His children aged ten and twelve,

being too young can’t work either. In difficult times, the family has to fall back on

financial assistance doled out by their relatively wealthier relatives. Monotosh’s case

highlights how lack of assets in the form of education or adequate land forces rural

workers into unskilled manual work. While access to such casual non-farm income

provides a safety net and prevents rural households from slipping further into poverty,

at the same time they are not instrumental in helping families emerge from their

poverty trap.

Case Study 7: Sukhen Das aged 40 is a resident of Durganagar Village under

Udharbond Village. His household comprises of five members including his wife, two

children and his aged mother. Being the son of a landless agricultural labourer,

Sukhen had no opportunity of attending school. He supports his family by ferrying

construction materials on his Thela cart which yields an income of around Rs100-

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Rs150 per day. However, on days when he cannot work, he earns nothing. He faces

a particularly precarious situation during the monsoons when poor condition of the

village roads makes it exceedingly difficult to carry out his task. The family is housed

in a dingy unventilated house without electricity and drinking water which has to be

procured from a distance. On being questioned, Sukhen’s wife revealed that they

were perennially food insecure.

Case Study 8: Abdur Laskar also of Narshingpur village dropped out from school

after the eight standard as his family was unable to finance his education further.

Later he got engaged as an apprentice in the cycle repair shop in the village. Having

worked as a mechanic in the same shop for a number of years, he started his own

cycle repair business in a small rented space near the main road leading to Kabuganj

Baazar. However, Laskar can scarcely make both ends meet as his income from the

repairing business is not sufficient to meet the requirements of his family. He laments

that if he could expand his business by selling cycle spare parts in addition to offering

repairing services, his returns would increase significantly. Unfortunately, he does not

have any capital to invest in his business nor has he been successful in securing a

loan from any source. He stares into an uncertain future.

Case study 9: Mrs. Apurba Dey of Sadarashi Pt II Village under North Karimganj

Block is the widow. Her late husband who was serving in the Army expired while still

in service. Her eldest son, aged 34 years has studied up to the 12th standard. After

the death of her husband, Mrs. Dey had tried to secure a job for her son in the Army

in lieu of her husband on compensatory grounds but was unable to do so. Having

failed to secure a government job, her son turned to business and invested a part of

his late father’s savings in a stationeries shop. However, the enterprise did not run

well on account of lackluster demand for stationary products in the village. Faced with

accumulating losses, he was forced to close shutters. He has since migrated to

Chennai on a small paid job which his relative has secured for him. Mrs. Dey,

however, complaints that his job is purely temporary and the salary too meager. Her

younger daughter aged 26 was unable to complete her graduation. She is employed

in a private school in exchange of a monthly salary of Rs600 only. With her son not

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into permanent employment and her daughter neither married nor properly employed,

Mrs. Dey is worried about the future of both her children.

Case Study 10: Tiken Roy of Umapati village sold off his agricultural land as he was

unable to bear with the losses inflicted by frequent flooding from the neighbouring

Longai River. Since then, Tiken, who is 42years old and has only four years of

education, has moved to working as a daily wage worker. However, as the village is

accessible mainly by the river way, finding work within the village is a remote

possibility. He therefore has to cross the river to make himself available for work in

the adjoining town. Tiken says that majority of the workers in the village have taken to

wage based employment, as agriculture offers no hope due to the recurrent flooding.

Apart from the natural calamity, occurrence of which is almost a certain event, Tiken

blames the poverty in his village chiefly to its lack of communication. The village does

not have a motor able approach road due to the absence of a level crossing over the

defunct Karimgamj- Mahisasan rail route at Longai forcing all vehicles to terminate

several kilometers away from the village. Tiken says that opportunities of trade and

commerce are non-existent in the village due to the poor communication forcing even

the younger lot of workers into casual non-agricultural employment.

From the above case studies it appears that non-farm activities fails to produce

desirable outcomes in terms of income and livelihood security on account of the

following factors.

a. Lack of assets primarily land and education: Lack of physical and human capital

often forces workers into low productive nonfarm jobs as is evident from sixth

and seventh case studies. In both cases, in the absence of education and assets

forced the household heads to fall back on unskilled manual work which failed to

ensure either security of employment or sufficient earnings.

b. Non-availability of credit: When internal source of financing is not available, the

non-availability of credit stands in the way of diversification and expansion or

rural enterprises which results in low returns from the business, as in the case of

Abdur Laskar (Case study 8). Sometimes lack of credit can stand in the way of

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entrepreneurs choosing activities that are lucrative but at the same time require

higher capital investment.

c. Sluggish demand: It is found that lack of demand may sound the death knell of

rural enterprises. As in the case of Mrs. Apurba Dey, the stationary store started

by her son failed to take off due to the poor demand for cosmetic products in the

village. Hence, the question of demand mapping assumes significance.

d. Poor infrastructure: The growth of successful nonfarm activities is retarded by

poor infrastructure. As in the last case, absence of road connectivity prevented

businesses and other activities from mushrooming thereby compelling the flood

affected inhabitants of the village to seek employment as unskilled manual

workers in the non-agricultural sector.

e. Lack of training: Mere acquisition of degrees and the lack of vocational training

lowers the employment potential of workers. As is evident from the case of Mrs.

Apurba Dey, although both her children had studied beyond high school, finding

employment proved elusive as, given their educational backgrounds, they were

suited mostly for white collar jobs which were not readily available.

The approach of pitting the successful instances of nonfarm diversification against

unsuccessful ones provides interesting insights into factors that determine the success

or failure of such diversification. It clearly emerges that higher education opens up

avenues of highly remunerative occupations while lack of it forces workers into

unskilled/ manual nonfarm jobs. Further, acquisition of vocational skills ensures quick

absorption into the labour force. Education also serves to substitute for the lack of

physical assets such as land although in many cases the capacity to acquire an

education on the part of the workers is itself dependent on the initial asset position of

the household. In addition to the quality and quantity of soft infrastructure of which

education is a crucial component, hard infrastructure primarily in the form of roads also

exerts a significant bearing on the type of occupations that are open to rural workers.

Thus improved connectivity enables rural traders to emerge from the confines of the

local markets and engage in profitable exchange with other areas. Enhanced

connectivity with urban areas also facilitates the spread of skill based education and

paves the way for the growth of modern nonfarm jobs. The success or failure of trade

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and business is also critically linked to the conditions of demand. This is because lack of

demand often leads to the closure of many local enterprises. Therefore, given that self

employment in trade and business constitutes an important avenue of nonfarm

employment and earnings, assessing the market potential of various types of

businesses is of crucial importance for ensuring profitability and sustainability of the

enterprises. Financial inclusion is another important factor which ensures successful

diversification as it enables self employed persons to choose activities that are lucrative

but at the same time involve a higher startup capital. Micro-finance institutions and Self

Help Groups like Bandhan also appear to be of relevance in promoting rural micro-

enterprises in the region. However, the most important point that needs to be

emphasized is that often all of the above factors are required to be present

simultaneously in some proportion for ensuring vibrant nonfarm growth. Lastly, it is

observed that in chronic flood prone areas, lack of alternatives force local residents to

take up low productive manual jobs outside the agricultural sector resulting in distress

diversification.

5.8 CONCLUSION

The econometric analysis carried out in this chapter has helped to identify the role of

various socio-economic factors in influencing nonfarm employment and earnings. The

powerful message emanating from the econometric exercise is that education is the

single most important factor that influences the shift of workers from the primary sector

to secondary and tertiary activity. Other factors such as household size, distance of the

village from the nearest urban centre, size of land holdings etc are also of relevance in

explain income and employment diversification of rural households. In particular, it was

found that households that derive a higher proportion of their income from the nonfarm

sector tend to be more specialized in terms of the number of income sources i.e. their

income is derived from relatively fewer sources. Further, the analysis has provided no

conclusive evidence to suggest that the growth of the nonfarm sector in Barak Valley is

either predominantly demand induced or distress driven. In fact one finds the presence

of both growth and distress related elements and the objective of any policy of nonfarm

growth should be to strengthen the growth related factors and at the same time address

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the cause(s) of distress diversification. Further, it has been found that nonfarm incomes

are the chief cause of income inequality in rural areas. Income from salaries in particular

is highly skewed and accounts for over 50 percent of the inequality in rural incomes.

This primarily occurs due to the existence of high entry barriers in salaried jobs as the

analysis of primary data in the preceding chapter has shown that educational

accomplishments are the highest among salaried workers compared to other groups.

Earnings of self employed entrepreneurs also contribute significantly to the

concentration of rural incomes. Lastly, it was found that while income and employment

diversification is widespread among rural households, the economic outcome critically

depends on a number of associated factors. Thus, higher education and skills among

rural workers, improved rural connectivity, growing markets and availability of financial

coverage were found to be positively influencing the returns obtained from nonfarm

activities. Where these associated factors were absent, nonfarm activities failed to

deliver in terms of providing sufficient returns and ensuring sustainable livelihoods.

Hence, policy directed towards promoting nonfarm employment in the region should

strengthen the supporting institutional framework and at the same time seek to ensure

that the entry into more remunerative nonfarm occupations becomes feasible for all

sections of the rural population. This in turn would automatically secure a more even

distribution of nonfarm incomes in rural Barak Valley.

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