Discussion Papers In Economics
And BusinessMeasuring Education Levels of Farmers:
Evidence from Innovation Adoption in Bangladesh

Masakazu Hojo
Discussion Paper 04-06

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANMeasuring Education Levels of Farmers:
Evidence from Innovation Adoption in Bangladesh

Masakazu Hojo
Discussion Paper 04-06
March 2004

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANMeasuring Education Levels of Farmers:
Evidence from Innovation Adoption in Bangladesh∗
Masakazu Hojo†
Osaka School of International Public Policy, Osaka University

Abstract
Education levels of farmers have been measured in a variety of ways inpreceding studies. In order to examine whether or not different measures of
education have different effects on the behavior of farmers, I first summarize
the measures of education and then perform an empirical analysis. Although
education measures examined in this paper have been used in many studies,
their effects are shown to differ significantly in my empirical analysis: somevariables have positive impacts on farmer’s behavior while others do not. This
result suggests we have to pay more attention to selecting measures of education in empirical investigations.

JEL classification: I20; Q12; Q16
Keywords: Education; Agriculture; Technology adoption; Bangladesh∗

The author is grateful to Akira Kohsaka, Colin McKenzie and participants of the Fall Meetingof the Japan Economic Association at Hiroshima University on 7-8 October 2001 for their helpfulcomments on a draft of this paper.
†
Address: 1-31 Machikaneyama, Toyonaka, Osaka 560-0043 Japan, Tel/Fax: +81-6-6850-5839,
E-mail address: mhoujou@osipp.osaka-u.ac.jp (Masakazu Hojo)

I. Introduction
Education has been shown to facilitate the productivity of farming activities in bothdeveloped and developing countries. A great number of empirical studies havetested the effects of farmer’s education on the efficiency of agricultural production,
as summarized in Lockheed et al. (1980) and Feder et al. (1985). Judging fromthese studies, there seems no room to throw doubt on the important role played byeducation in agricultural development.
The importance of farmer’s education has been demonstrated in many empiricalstudies, however, another problem remains: How should we measure farmer’s education? In other words, the way of measuring the level of education is not common
across studies. Of course, because the measurement of the level of education by an
researcher is subject to the availability of the data, it might be the case that only
one measure of education was available to the researcher. However, due to recent
development of the household-level data collected in developing countries, moredetailed information on farmers have become available and, as the result, we canmeasure the level of farmers’ education in several ways. Consequently, we haveseveral alternative measures of education and, if different measures have different
effects on farmers’ behavior, we have to select an appropriate measure. The purpose of this paper is to summarize measures of education used in preceding studies
and investigate whether different measures of education have different impacts on
farmers’ behavior.
Two aspects should be considered when measuring farmer education. One isthe level of education. We can measure the level of education by, say, years offormal/non-formal schooling, completion of primary/higher education, literacy, andso on. Another aspect is recently discussed by Jolliffe (2002) and known as “Whose
education matters?” To understand this, suppose our interest is in the effect ofeducation on efficiency of farm production. Because farming is typically carried out
by self-employed households in many developing countries, production efficiency
is observed at the household level. In contrast, we can observe the education level ofeach individual in the household. Thus, when we regress production efficiency on
the education variable (of course with other explanatory variables), the dependent
variable is observed at the household level while explanatory variable (education)is measured at the individual level. Therefore, we should find a variable which
represents the level of education of a household. Jolliffe (2002) proposes three-1-

variables as proxies for the education level of a household.
According to the two aspects discussed above, we select 14 representative measures of education from preceding studies. To examine whether different measures
have different impacts on farmers’ behavior, an empirical analysis is performed.
We estimate the effect of each education variable on the probability of adoption ofa new crop variety, using household data from rural Bangladesh. Estimation results
are mixed. Some variables are found to have strong positive impacts while others
have insignificant impact. This result strongly suggests that careful choice of education measures are needed before empirical investigations. Note that the purposeof this paper is not to demonstrate the validity (or invalidity) of a specific variable
among others in the general context, but to emphasize the importance of carefulchoice of education variables in individual cases.
This article is organized as follows. In section II, measures of education usedin the preceding studies are reviewed and summarized. The data used in this paper
are described in section III. Section IV presents empirical methodology. Estimation
results are reported in section V. Some concluding remarks are found in section VI.

II. Measures of Education and the Related Studies
Level of Education
One aspect of measuring the education of a household is the level of education
attained by household members. Cotlear (1986) classify three types of education:formal, non-formal and informal. Formal education consists mainly of schooling;
non-formal education includes different kinds of extension, adult literacy training
and organized apprenticeships; and informal education refers to a wide definitionof learning-by-doing, which may include not only direct experience in a particular
job but also various learning processes that arise from being exposed to different
circumstances.
Among Cotlear’s classification, years of formal schooling is the most commonlyused measure in empirical studies concerning agriculture. For example, Lin (1991)
includes years of schooling of the household head in his regressions and finds a
positive effect of this variable on the probability of adoption of hybrid rice by Chinese farmers; Pitt and Sumodiningrat (1991) employ the same variable but find an
insignificant impact on the introduction of High Yielding Varieties (HYV) by Indonesian farmers.
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There are some evidence that only above a threshold level of education positively affects the probability of adoption. Jamison and Lau (1982) find that only
above a threshold level (four years) of education affects the probability of adoption of chemical inputs by farmers in Thailand. Recently, Knight et al. (2003) findthat schooling of the head of the household decreases risk-aversion and encourages
adoption of agricultural innovations in rural Ethiopia. In addition to using the yearsof schooling as such, they alternatively include dummy variables which indicate
three threshold levels: whether or not schooling of the household head exceeds zeroyears, is up to three years, and is more than four years. All these variables are foundto have significantly positive effects on reducing risk aversion and increasing theprobability of adoption of new crops and inputs.
Furthermore, considering the higher rate of dropping out from school in many
developing countries, educational attainment from formal schooling might betterbe measured by whether or not an individual completed a certain class. Foster andRosenzweig (1996) measure the schooling level of the household by creating an
indicator variable for whether or not any individual in the household had completed
primary schooling. Using this indicator variable, they show that returns to primaryschooling increased in the green-revolution period in India.
Apart from formal schooling, evidence for non-formal education has also beenpresented. Basu et al. (2002) find an important role played by a literate member ina household, using data from Bangladesh. They estimate wage of illiterate workers
on a dummy variable for whether or not the illiterate worker is living with at least
one literate person (of course other explanatory variables are also included in the
regression). Basu et al. (2002) find a significantly positive effect of this dummy
variable, that is, an illiterate person can benefit from literate persons within thehousehold. On the other hand, several researchers have addressed measuring thelevel of informal education. For example, Cameron (1999) explores the dynamic
process of innovation adoption by incorporating farmers’ past experience of HYVadoption. She finds learning is an important factor in the process of innovation
adoption by U.S. farmers.

Whose Education Matters?
Another aspect of measuring education of a household is recently discussed in Jolliffe (2002) and known as “Whose education matters?” Jolliffe’s focus is on whose
education is important in determining household income in developing countries.
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He points out that, the effect of education on individual wage income can reasonably
be estimated since both education and wage income are measured at the individuallevel; On the other hand, extending the wage regression model to the householdincome regression model leads to the difficulty because household income cannot
usually be decomposed into the earning of each household member, due to the limitation of data in developing countries.
The structure of the problem raised in Jolliffe (2002) seems applicable to thecontext of agriculture. Because farming is typically carried out by self-employedfarm households in many developing countries, outcomes from farming activities
are usually observed at the household level. On the other hand, we can measure
the education level of each individual in the household. Thus, when we regress
outcomes from farming (e.g., quantity of output, adoption of new crops, etc) on the
education variable (of course with other explanatory variables), the left-hand side
is measured at the household level while the education variable in the right-handside is measured at the individual level. Therefore, we should find a variable which
represents the level of education of a household.
Jolliffe (2002) proposes three measures of education which can represent thelevel of education of a household: the minimum, average, and maximum years ofschooling within each household.1 According to Welch (1970) and Yang (1997),
Jolliffe presumed that the average level of education proxies for the worker effectwhile the maximum value of education in a household proxies for the allocative
effect.2 Jolliffe (2002) tests the effect of the three variables on household income
using data from Ghana, and finds that the maximum and average years of schoolingwithin the household have positive impacts on household income.
Except for Jolliffe (2002), the preceding studies cited above do not discuss thereason why the author employs a particular measure of education. In other words,
there may be several alternative measures and, these alternatives might have different effects on the dependent variable. As long as each education variable can havea different effect on farmers’ behavior, we should examine the validity of using aparticular measure of education. To investigate whether or not different measures
1

He additionally examines the years of schooling of the household head.
Increased education can permit a worker to produce more with the given resources (worker
effect). On the other hand, a more educated worker can acquire more information about inputs. The
increased information can reduce the cost of production and may enhance the adoption of some new
inputs (allocative effect). See Welch (1970) for the detailed concepts of these effects.
2-4-

of education have different impacts on farmers’ behavior or ability, we perform an
empirical analysis.
In the empirical analysis below, we select 14 representative measures of education from preceding studies and then carefully compare the effect of each variable
on the adoption of a new crop variety. Education measures examined in this paper are summarized in Table 1. These are calculated by the level of education of allhousehold members over 15 years old.3 First, we have four variables concerning theyears of schooling: the minimum, average, maximum and head’s years of schooling within each household. The use of years of schooling implicitly assumes thatan additional year of schooling of the household member encourages the adoptionof the new crop variety. Among the four variables, the years of schooling of thehead of the household have been most popularly used in the earlier studies. Next,
we have eight dummy variables. These dummy variables describe the thresholdyears of schooling concerning the household head and all members: whether or not
the household head or at least one member in the household have more than three,four, five and six years of schooling. The use of the threshold level of schooling is
motivated by the recognition that at least a certain level of education is needed toaffect the farmer’s attitude towards adoption and risk of new technologies.4 Last, inlieu of measuring education level by formal schooling, we examine two variables
concerning literacy. These are dummy variables which equal to one if the household head or at least one member in the household have ability to read. If farmers
needs to understand pamphlets or manuals which explain the way of growing newcrops or using new inputs before adoption, it is reasonable to employ the variable
concerning literacy.

III. Data
The data used in this paper are from four rounds of a household survey conductedby International Food Policy Research Institute (IFPRI) in three sites in Bangladesh
in 1996 and 1997 (see Bouis et al., 1998). This survey was conducted in order to3

Alternatively, we can calculate measures of education of all household members without students and children (less than five years old). I checked this calculation, but the estimation results
which will be reported in chapter V are essentially similar.
4
If we follow the method discussed in Foster and Rosenzweig (1996), we should create the indicator variable which represents whether or not a household member completes primary education. I
could not do this due to limitation of data, however, the dummy variable that whether or not at least
one member in the household have more than five or six years of schooling can be interpreted asindicating the completion of primary education.

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Table 1
Description of the Education Measures
Definition
Years of Schoolinghead of the household
maximum member
average of the household members
minimum member
Dummy = 1 if Schooling of the Headmore than 3 years
more than 4 years
more than 5 years
more than 6 years
Dummy = 1 if Schooling of One Member
more than 3 years
more than 4 years
more than 5 years
more than 6 years
Literacy (Dummy)
head of the householdat least one member in the household

Variable

Mean

Std. Dev.

yrs headyrs max
yrs avg
yrs min

2.496
4.732
2.242
0.358

3.823
4.115
2.338
1.216

mt3 head
mt4 head
mt5 head
mt6 head

0.325
0.301
0.276
0.220

0.470
0.460
0.449
0.416

mt3 all
mt4 all
mt5 all
mt6 all

0.634
0.593
0.528
0.415

0.484
0.493
0.501
0.495

lit head
lit all

0.390
0.691

0.490
0.464

Note: Number of observations = 123.

evaluate the impacts of new agricultural technologies being disseminated throughnon-governmental organizations (NGOs). In Saturia, one of the surveyed sites,
commercial vegetable (CV) production technology was disseminated, and we use
the data from this site.5 Each household was surveyed four times within approximately twelve months: middle 1996, later in 1996, early 1997 and middle 1997. The
dataset provides literacy, enrollment status, detailed agricultural production module,
demographic compositions, and so on.
Bangladesh administrative units are division, district, thana, union, village andpara. There are six divisions in the country. A division is then divided into districts, which comprise several thanas. Thanas are divided into unions, which arecomposed of villages. A para is a subunit of a village. Sampling methods can besummarized as follows: In Saturia thana, five paras are selected from all paras
where the CV production technology had been disseminated by NGOs. This gavea total of 916 adopting and non-adopting households. All households were eligible
5

In the other two sites, Jessore and Mymensingh, group and individual fishponds technologies
were disseminated.

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for sample selection, although with unequal probabilities: 110 households were selected at random from 128 households which adopted CV production technology,
and 55 out of 788 non-adopting households were selected randomly. This gave a
total of 160 adopting and non-adopting households. In the empirical analysis below,
however, thirty-seven households are excluded due to missing observations. As aresult, we have a sample of 123 farm households in five paras in Saturia thana.
Note that the population of this sample is not the farmers in Bangladesh, but thosein Saturia thana.6
Descriptive statistics for the measures of education are shown in Table 1. The
average years of schooling of the head of the household is about 2.5 years, while theaverage of the most (formally) educated member is about 4.7 years.7 The percentageof the household head who has more than three years of schooling is 32.5%. In
63.4% of the sample households, years of schooling of the most educated memberin the household exceeds three years. The percentage of literate household head is39%, and in 69.1% of the sample households, at least one member is literate.

IV. Hypothesis and Methodology
Given that education facilitates the adoption of agricultural innovations by farmers,
we face, as discussed above, the choice of education measures in empirical investigations. So the hypothesis tested here is: whether or not different measures of
education have different effects on the adoption of commercial vegetables by sample households.
To test the hypothesis, we estimate the determinants of adoption of commercial
vegetables. Because our interest is in the choice of education measures in empirical formulation, we employ a simple model. Following the earlier studies such as
Lin (1991) and Knight et al. (2003), the decision of innovation adoption by a farmhousehold i is modeled as follows. The utility function of a household i is
Ui (T ) = Xi γT + εT i(1)where T is an indicator of technology adoption (T = 1 when CV is adopted and
T = 0 otherwise), Ui (T ) is the utility gain from adopting technology T , Xi is avector of education variables and characteristics of household i, γT is a vector of
unknown parameters, and εT i is a household-specific shock independent of X. We
6
7

See Bouis et al. (1998) for detailed information on the sampling frame of this data set.
The head of the household is determined by the respondent.

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assume that a household adopts commercial vegetable if Ui (1) > Ui (0), whereas if
Ui (1) ≤ Ui (0), the household does not adopt. Thus, defining Ui∗ (T ) = Ui (1) − Ui (0),we have a familiar latent variable model:Ui∗ (T ) = Xi β + εi ,T = 1 if Ui∗ > 0 and T = 0 otherwise(2)

where β = γ1 − γ0 is a vector of unknown parameters and εi = ε1i − ε0i is assumed a continuously distributed variable independent of X. We also assume thatthe distribution of εi is symmetric about zero. Thus, the probability of adoption ofcommercial vegetables can be expressed as follows.
Pr(T i = 1) = Pr(Ui∗ > 0) = Pr(εi > −Xi β) = 1 − F(−Xi β) = F(Xi β)(3)where F(·) is the cumulative distribution function for εi evaluated at Xi β.
The distribution of F depends on the distribution of εi . If εi is normally distributed, then F(Xβ) = Φ(Xβ) where Φ(·) is a cumulative normal distribution, andthe probit estimator for β would be consistent. Given the small sample size of thedata (123 households), however, some readers might concern about the distributionof εi . Actually, non-normality in the latent error εi means that F(Xβ) , Φ(Xβ),
and therefore Pr(T i = 1) , Φ(Xβ). In this case, the probit estimator for β will
be inconsistent. But, recall that our interest here is estimating and comparing theeffects of various education variables. Therefore, we should not pursue the consistent estimation of β as such, but pay attention to the partial effect of the education
variable on the probability of adoption of commercial vegetables, ∂ Pr(T = 1)/∂x,where x is an education variable included in Xi . The small sample size is certainly
undesirable, but I think the probit provides good estimates of the partial effects ofeducation variables.8
The explanatory variables, Xi , are composed of the measures of education andthe characteristics of the household. As discussed in section II, we consider 14
representative education variables and compare the effects of them. Table A in the
Appendix reports the correlation coefficients between the education variables. As
might be expected, the 14 variables seem highly correlated with each other. In addition, most of the equations estimated in the preceding studies include only one
education variable. Therefore, in the estimations below, we run one regression for
one education variable, that is, 14 equations in total are independently estimated,
8

For the problem of non-normality in the latent variable model, see Wooldridge (2002, chapter

15).

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and each regression includes one measure of education. This procedure, I expect,
can provide pure effect of each variable rather than including other education measures which are highly correlated.9
As the result, the equations to be estimated in this paper can be summarized asfollows.
T i = β0 + β1 educationi + β2 characteristicsi + β3 villagedummyi + εi(4)where education measures (education) are those discussed above, and the characteristics of the household (characteristics) are composed of: age of the head of thehousehold; whether or not the head of the household is female (dummy variablewhich equals one if female headed); the job of the head of the household (dummy
variable which equals one if the primary occupation of the head is farming); thedemographic structure in the household (the number of adults (15-60 years of age),young (0-14) and old (60-)); and the characteristics of the land owned by the household (area per adult, irrigation status and the soil type of the land).10 Descriptive
statistics for these variables are reported in Table B in the Appendix. We also include village dummies (villagedummy) to account for the clustered nature of thedata.

V. Estimation Results
Table 2 summarizes the marginal effects of education variables estimated fromequation (4) by probit. As discussed in Section III, the sample households are selected with unequal probabilities. We adjust this by including sampling weights.
Clustered nature of the data is also taken into account.11 Recall that each measureof education is separately included in the regression, therefore 14 equations in total
are estimated independently. To concentrate on the effect of education, results onother explanatory variables and village dummies are not reported in table 2.
9

I also run regressions which include multiple education variables. However, the results of these
regressions are fairly unreliable. For example, a variable which is positively significant when only
this variable is included becomes negatively significant when included with other education measures.
10
The area of owned land is measured in Decimal (1 Decimal=435.6 Sq.Ft.). Irrigation status isdescribed as a dummy variable which equals one if the land is well irrigated. Soil types are clay,
loam, sandy, clay-loam and sandy-loam, and these are also expressed as dummy variables (clay-loamis excluded).
11
The dprobit command of Stata version 7.0 with pw and cl options is used to estimate all themodels in this paper.

-9-First, we consider the years of formal schooling of household members. Amongthe four variables, the average and minimum years of schooling attained by thehousehold members are found to have significantly positive effects on the probability of adoption of commercial vegetables. Especially, An additional year of
the minimum schooling in the household increases the probability of adoption by
12 percent. On the other hand, the maximum and head’s years of schooling are
insignificant. In addition, as the next four rows suggest, dummy variables whichindicate the threshold years of schooling of the head of the household are all insignificant. This is somewhat surprising because the years of schooling of the headof the household have been most frequently used in the preceding studies.
In contrast, a household in which at least one member has more than three orfour years of schooling is found to adopt CV with significantly higher probability,
as shown in the next two rows. This effect, however, seems to be decreasing. Fiveor more years of schooling in a household do not have any significant effects.12 A
household in which at least one member has three or more years of schooling adopts
commercial vegetables with higher probability (15.9 percent) than a household inwhich all members have less than two years of schooling. These results may indicate the important role of primary (or fundamental) education in the decision oftechnology adoption by sample households. The last two rows report the results onliteracy. As might be expected from the above results, literacy of the head of thehousehold do not have any significant impacts. By contrast, the probability of adoption significantly increases by about 15.5 percent when a household has at least one
literate member. This result seems to correspond to the empirical evidence foundby Basu et al. (2002), in which they show that literacy of one member is shared with
the illiterate members within the household using data from Bangladesh.
Estimates on the characteristics of the household are not reported in the table,
but can be summarized as follows. The characteristics of the head of the householdsuch as age, sex and primary job are all insignificant. This seems to correspond to
the insignificant effects of head’s education reported above. The coefficients of thenumber of adults and young members, which can be interpreted as the availabilityof family labor, are significantly positive in most of the regressions. A household
which has well-irrigated land is found to adopt CV with significantly higher probability, and this reflects the importance of the stable supply of water in growing up
12

To account for the completion of the secondary and tertiary education, the author also examine
the dummy variables which indicate at least one member in a household has more than nine or twelve
years of schooling. These variables are both insignificant (results are not shown).

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Table 2
The Probit Marginal Effects of the Education Variables
Variable∂ Pr(T = 1)/∂x
Years of Schoolinghead of the household-0.001
maximum member
0.005
average of the household members
0.015 **
minimum member
0.120 ***
Dummy = 1 if Schooling of the Headmore than 3 years-0.015more than 4 years-0.045more than 5 years-0.023more than 6 years-0.078Dummy = 1 if Schooling of at least One Member
more than 3 years
0.159 *
more than 4 years
0.113 **
more than 5 years
0.058
more than 6 years-0.013
Literacy (Dummy)
head of the household-0.027at least one member in the household
0.155 ***

Std. Error
0.008
0.008
0.006
0.045
0.056
0.054
0.074
0.120
0.077
0.053
0.060
0.070
0.033
0.021

Note: Number of observations = 123. Each education variable is separately estimated with common explanatory variables and village dummies (results are notshown). Clustering robust standard errors are reported. Stars indicate significance as follows: *** = 0.01; ** = 0.05; * = 0.10.

the commercial vegetables. On the other hand, the effect of farm size (the area ofland owned by the household) is found negative. This is somewhat controversial,
but the negative sign of this variable is also reported in the earlier studies such as
Hayami (1981).
After all, our estimation results can be summarized as follows: The educational
attainments of the head of the household are found to have insignificant effect irrespective of the way of measuring the level of education. In contrast, the household
average, minimum and one member’s education (especially primary or fundamental
level) have positive effects on the probability of adoption of commercial vegetables.
Thus, it is shown that different measures of education have different impacts on
farmers’ behavior. We should consider the implications of this result. One possibility is that the results may reflect the structure of decision making in the sample
households: The head of the household may have relatively lower bargaining powerin the decision of farming activities. Another possibility is the characteristic of the- 11 -

new technology, commercial vegetables: considering the importance of fundamental education such as three or four years of schooling and literacy, the introductionof CV may require only fundamental knowledge or skills. In any case, the resultsof this paper throw doubt on the arbitrary choice of education variables in the preceding empirical studies.

VI. Conclusion
Many researchers have shown the important role of education on farming activities
in both developed and developing countries, however, the way of measuring thelevel of education is not common across studies. If we have several measures of
education, we have to select an appropriate measure from them. In this paper, I
first summarized measures of education used in recent empirical studies and thenperformed an empirical analysis, in order to investigate whether or not different
measures of education have different effects on farmers’ behavior.
Empirical results are mixed. Education of the head of the household do not haveany significant effects on the adoption of commercial vegetables, irrespective of theway of measuring the level of education. On the other hand, the household average,
minimum and one member’s education (especially primary or fundamental level)
are found to have significantly positive impacts. Therefore, it is found that different measures of education have different effects on the behavior of farmers. This
result strongly suggests we should pay more attention to selecting the appropriate
education measures. Also, if an education variable which is significant in one data
set becomes insignificant in another data set, it is fruitful to investigate the implication of this change. This paper contributes to this area of research using a data
set on commercial vegetables in rural Bangladesh. Future researches using data set
on other countries, other kinds of technology, or other contexts (e.g., effect of education on productivity of farming) should clarify the appropriate way of measuring
the level of farmer education.

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References
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Appendix
Table A reports the correlation coefficients between the education variables. Table
B contains descriptive statistics for the explanatory variables other than education.

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- 15 1.000
0.477
0.563

mt6 all

1.000
0.535

lit head

1.000

lit all

mt4 head

1.000
0.942
0.809
0.498
0.543
0.549
0.599
0.820
0.439

mt3 head

1.000
0.945
0.890
0.764
0.527
0.539
0.552
0.578
0.868
0.464

Note: Number of observations = 123. See Table 1 for the definition of the variable.

mt5 all
mt6 all
lit head
lit all

mt5 all
1.000
0.795
0.455
0.708

Table A
Correlation Coefficients between the Education Variables
yrs head yrs max yrs avg yrs min
yrs head
1.000
0.688
1.000
yrs max
yrs avg
0.794
0.894
1.000
yrs min
0.466
0.359
0.616
1.000
mt3 head
0.903
0.638
0.723
0.397
0.916
0.653
0.733
0.377
mt4 head
mt5 head
0.918
0.653
0.744
0.403
0.901
0.605
0.674
0.314
mt6 head
mt3 all
0.471
0.844
0.704
0.224
mt4 all
0.504
0.863
0.736
0.245
0.538
0.872
0.748
0.252
mt5 all
mt6 all
0.619
0.868
0.779
0.283
lit head
0.802
0.549
0.642
0.369
lit all
0.420
0.751
0.629
0.198
1.000
0.858
0.470
0.512
0.584
0.624
0.773
0.413

mt5 head

1.000
0.403
0.439
0.501
0.630
0.663
0.355

mt6 head

1.000
0.918
0.804
0.639
0.435
0.880

mt3 all

1.000
0.876
0.697
0.459
0.808

mt4 allTable B
Descriptive Statistics for the Explanatory Variables other than Education
Variable
Mean Std. Dev.
Characteristics of the headAge
46.122 12.858
Dummy =1 if female headed
0.033
0.178
Dummy =1 if primary occupation is farming
0.577
0.496
Demographic Composition
Number of Adults (15-60 years of age)
3.179
1.499
Number of Young (0-14 years of age)
1.870
1.101
Number of Old (60 or more years of age)
0.268
0.513
Land Owned
Area per adult (in Decimal)
28.974 25.282
Dummy =1 if well irrigated
0.715
0.453
Soil Type of the land (Dummy)
Clay
0.366
0.484
Loam
0.683
0.467
Sandy
0.122
0.329
Sandy-loam
0.732
0.445
Note: Number of observations = 123. 1 Decimal = 435.6 Sq. Ft.

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