POLICY RESEARCH WORKING PAPER 278 7
Do Farmers Choose to Be Inefficent?
Evidence from Bicol, Philippines
Donald F. Larson
Frank Plessmann
The World Bank
Development Research Group
Rural Development
February 2002
| POLICY RESEARCH WORKING PAPER 2787
Abstract
Farming households that differ in their ability or households to learn and apply successful available
willingness to take on risks are likely to make different technologies. The authors find evidence that
decisions when allocating resources and effort among diversification and technology choices do effect
income-producing activities, with consequences for efficiency outcomes among farmers, although these
productivity. Larson and Plessmann measure voluntary effects are not dominant. Accumulated wealth, past
and involuntary departures from efficiency for rice- decisions to invest in education, favorable market
producing households in Bicol, Philippines. They take conditions, and propitious weather are also important
advantage of a panel of household observations from determinants of efficiency outcomes among Bicol rice
1978, 1983, and 1994. The unusually long time-span of farmers.
the panel provides ample opportunities for the surveyed
This paper-a product of Rural Development, Development Research Group-is part of a larger effort in the group to study
the relationship between risk and rural household decisions. The study was co-funded by the Bank's Research Support
Budget under the research project "Dynamism of Rural Sector Development" (RPO 683-06) and by the Japanese
Government. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433.
Please contact Pauline Kokila, room MC3-604, telephone 202-473-3716, fax 202-522-1151, email address
pkokila@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The
authors may be contacted at dlarson@worldbank.org or office@plessmann.net. February 2002. (23 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Research Advisory Staff
Do FARMERS CHOOSE TO BE INEFFICIENT?
EVIDENCE FROM BICOL, PHILIPPINES
DONALD F. LARSON AND FRANK PLESSMANN
Donald F. Larson is a senior economist in the World Bank's Research Group. Frank Plessmann
is a Junior Professional visiting the World Bank under a program sponsored by the Government
of Germany. This research was sponsored in part by the Japanese Government and by a World
Bank Research Grant (RPO 683-06.) The authors would like to thank Takamasa Akiyama, Rita
Butzer, Yujiro Hayami, and Yair Mundlak for valuable comments on an earlier draft.
TABLE OF CONTENTS
1. Introduction ........................ 1
2. The household problem ........................ 2
The applied model ............................4
3. The data ........................ 5
4. Estimation Results ........................ 7
Frontier parameters ............................7
Technical inefficiency parameters ............................8
5. Measuring Impact ........................ 9
6. Alternative models ........................ 10
Comparison with least squares ........................... 11
Parameter restrictions ........................... 11
A balanced panel ........................... 12
7. Conclusions ........................ 13
8. References ........................ 13
9. Figures and tables .16
LIST OF FIGURES AND TABLES
Table 1: Household averages for selected variables, 1978, 1983 and 1994 17
Table 2: Estimation results for the base model. 18
Table 3: Simulated changes in production for selected variables. 19
Table 4: Comparison of stochastic frontier and least-squares estimates 20
Table 5: Estimation results from restricted models. 21
Table 6: Tested restrictions about model specification 22
Table 7: Estimated model parameters from full panel and from balanced panel 23
Figure 1: Area planted to rice and rice production in Bicol for 1978, 1983 and 1994 17
DO FARMERS CHOOSE TO BE INEFFICIENT?
EVIDENCE FROM BICOL, PHILIPPINES
Donald F. Larson and Frank Plessmann
1. INTRODUCTION
Research suggests that poor farming households are less able to cope with shortfalls in
production and, as a consequence, tend to diversify labor and land resources as a precaution.
This limits the adverse effects of production and market risks; however lower productivity
results as well. It is generally held that these choices are rational-- that farmers understand the
tradeoff and anticipate the consequences of ex ante production decisions. (See, for example,
Binswanger and Sillers 1983; Binswanger and Rosenzweig 1986;Walker and Jodha 1986;
Bromley and Chavas 1989; Reardon, Delgardo and Matlon 1992; Fafchamps 1992; Morduch,
1995; Dercon, 1996, and Ellis 2000.) Differing production and livelihood strategies therefore
help to explain why productivity and efficiency in farming varies internationally, nationally and
even among households living near one another. Less studied however are the quantitative
effects of household choices that lead to voluntary inefficiencies. Investigating the cost of risk-
coping strategies is worthwhile, since there are good reasons to suspect that voluntary
inefficiencies play a central role in explaining rural poverty (World Bank 2001.)
Rice farming is an important and variable source of income and nutrition in many
developing countries, especially in poor regions and among poor households. The technical
sources of production efficiency and variability for rice are well studied and well known
(Anderson and Hazell 1989). In this paper we explore why farmers often fail to achieve
outcomes that can be described as efficient and we measure voluntary and involuntary departures
from efficient rice production among rice farmers in a region of the Philippines. In particular we
measure the relative importance of household decisions about technology and diversification on
productivity. We find evidence that diversification and technology choices do effect efficiency
outcomes. At the same time, the results suggest that accumulated wealth, past decisions to invest
in education, favorable market conditions, and propitious weather are also important in
explaining efficiency outcomes among Bicol rice farmers.
Methodologically our measurement relies on a stochastic frontier model that incorporates
technical efficiency effects, as pioneered by Aigner, Lovell and Schmidt (1977) and Meeusen
and van den Broeck (1977) and further developed by Reifschneider and Stevenson (1991);
Kumbhakar, Ghosh and McGuckin (1991); Fried, Lovell and Schmidt (1993), and Coelli, Rao
and Battese (1997). Empirically, our measurements rely on a three-year panel containing 1,511
observation of Filipino rice producing households in Bicol (Bicol River Basin Development
Program 1997 and 1998; and Lanzona, 1997.)
After deriving the model in section 2, the data is described in section 3. Section 4
provides an estimation of the base model and a discussion of the empirical results. Section 5
discusses comparable measures of voluntary and involuntary departures from technical
efficiency. Chapter 6 discusses whether results from the original model are sensitive to alternate
specifications. Section 6 concludes.
2. THE HOUSEHOLD PROBLEM
We start with the household's time-separable lifetime consumption planning problem:
Maxc,x Et ItoU(ct; se )er'
subject to: dw={ I[x,; s, ]-c, - L[x,; s, ] }dt + tx,, cf, w,t; s, ]dv
w"t= wo; st"= s(; wt 2 0; c > 0
where t denotes the time period; Et denotes conditional expectations; U is an atemporal utility
function; r is a discount rate; c represents total consumption and is always positive; s is a vector
of additional exogenous state variables with an initial value of so; w represents wealth with an
initial value of wo and is bounded below; I, is a net-income function that maps household
activities, y'(x;s), and input use to household income; x is a vector of net inputs; L is an
expected loss function conditioned by the choice of inputs; v is a Wiener process with a zero
mean and a unit variance; and o(x, c, w; s) is a scaling factor conditioned by the control, (ex ante
choice) variables and the state variables, including wealth. Expected income losses are given by:
L(x) = fq5(R;x,s)dF(R),
2
where F(R) is the distribution function for random event, R.
In words, the household problem as represented in the model is to choose a consumption
path that is constrained by wealth, supplemented by generated income based on input uses and a
variety of conditioning state variables, including technology, relative prices, education, etc. The
problem is depicted as an infinite horizon multi-generation problem. For the current period,
setting to = 0, the problem can be expressed as:
rV(w; s) = Max, E[U(c)+ V., (I - c - L)+ I Vwo 2],
where the first order conditions are:
i) V.E(I, - Lx ) = °
ii) E(U) -V. = 0
iii) E(dw) =E(I - c - L)
iv) w(to = 0) = WO
To guarantee that the first-order conditions provide a maximum, V must be concave in w;
the solution values of w, c and x must be positive; and the transversality-at-infinity condition
must hold'.
The first-order conditions require that expected marginal gains and loss from additional
input use are offsetting (condition i) and that the expected marginal utility equals the shadow-
value of marginal wealth (condition ii). Expected wealth changes equal the expected savings (or
dis-savings (condition iii). In turn, the shadow value of marginal wealth depends in part on the
distribution of risks. This relationship can be expressed by applying the envelope theorem to the
value function and considering condition ii:
E(U,) V,,,(w; s) = - (Vw. v'+ I V..wr
that is, expected utility from marginal consumption must also equal the present value of the
foregone stream of future wealth. When Vwww > 0 the value of the foregone income stream
includes a "precautionary" value of wealth based partly on the variability of wealth outcomes.
Conditions iii) and iv) restate constraints on the optimum. Together, the conditions state
'In this case the traversality condition is given by lim V (t)wt)er(tfo) = o. The condition guarantees that the ending-
value of the problem diminishes with the length of the horizon. See Malliaris and Brock (1987) for a
discussion of stochastic control models and the transversality condition.
3
formally the common sense notion that the solution to one among several household activities
will be condition by constraints on the overall household problem. Operationally, this means
that, as we estimate the efficiency of a particular activity such as rice farming, we need to carry
with us as state variables the larger set of variables that define the household problem.
The solution then to a given activity, y1, is found by substituting values from the general
household problem and the solved value of L In order to derive an empirical model, we make
some additional limiting assumptions regarding I. We assume that I can be expressed as a
separable combination of activities so that at the solution value, E[I] = El yi(yi, x; s) --where
51 is a vector of outputs produced jointly with yi and x is the vector of inputs used in the joint
production activity. For the applied model, we must also make additional assumptions about the
error component of the stochastic variable y', a topic we take up in the next section.
The applied model
Following the general model, we expect that the production solution to the stochastic
optimization problem, will depend on other household activities and will be conditioned by ex
ante expectations about the distribution of random weather events as well as other initial
conditions. We make the additional assumption that we can represent the rice-producing activity
of Bicol households as a single technology frontier production function, with systematic and
accidental variation from this production frontier. That is:
yi, (x; z) = yt, (x)-uj, (z) + vi, 2.1)
where y is the frontier production function and u are random variables that depend on z, a
vector of state variables (s and w), and that denote distance from the frontier objective, where i
and t are subscripts denoting household and year2. More specifically, as is common practice in
technical efficiency models (Battese and Coelli, 1995 ), we specify a log-linear frontier
production function and expand the u linearly in the state variables so that 2.1 is specified as:
.1
ln(y1,) = ,°~ +Xfl' ln(xZj)±+vi, - u;, 2.2)
2 To be consistent with the general model, we use the price of rice as the numeraire for income so that y can be
measured as a quantity.
4
The expression ln(yi,) denotes the natural logarithm of rice production for household i in period
t; ln(x,') denotes the natural logarithm of the jth input; P are estimated parameters; and vi, are
random errors, assumed to be iid N(O, C2) . Also by assumption, the ui, are non-negative random
v~~~~~~~
variables that account for inefficiency in production, where u, = 65 + 6Zit + cit and where
the z k are K state variables and a are estimated parameters. The ui, are assumed to be
independently distributed. Additionally, the random variable, ci, is defined by the truncation of
the normal distribution with zero mean and variance, a2, such that the point of truncation is
K
_(S01 +8 (5¶Z) to insure that u;, are positive. The time-varying intercept, 60t, is included to
take into account changes in available technologies.
In addition, we follow Battese and Coelli (1995) and define the test statistic
y = oJ2 1(02 + o2 ) to check whether the ui, are deterministic. Later, we provide estimates where
the additional assumptions on u are dropped in favor of standard fixed-effects assumptions.
Finally, for some observations in the sample, farmers have chosen not to apply all inputs - this is
especially true for some fertilizers. Consequently, dummy-variables are employed. (See
Battese, 1997.)
3. THE DATA
We derived the data for this analysis from the Multi Purpose Survey (MPS), collected in
the Bicol Region in the Philippines in the years 1978, 1983 and 1994. The 1978 and 1983
surveys included farmers from three provinces Camarines Sur, Albay and Sorsogon; however, in
1994, data was collected in the Camarines Sur province only3. The MPS was collected to analyze
different social and economic aspects of households, villages and communities.
Most results reported in this paper are based on 1,511 observations from 912 rice-
planting households. The panel is unbalanced and only 144 households appear in all three
3Descriptions of the data and the survey instrument are given in: Bicol River Basin Development Program: Bicol
Multipurpose Survey (BMS) (Philippines), 1978 and 1983 as reported by the Inter-university Consortium for
Political and Social Research, Ann Arbor, 1997 and 1998; and Lanzona, Bicol Multipurpose Survey (BMS),
1994 (Philippines); Inter-university Consortium for Political and Social Research, Ann Arbor, 1998
5
surveys. Later, we discuss results based on the balanced component of the sample and compare
them to results from the unbalanced panel. Table 1 reports the mean value for key variables
from both the balanced and unbalanced panels.
For households in the sample, rice production per household averaged 84 cavan4 in 1978,
92 cavan in 1983 and 135 cavan in 1994. The differences in the averages are due partly to the
composition of the sample as the Camarines Sur households grew rice on a larger scale. In
addition, nature was more kind to rice growers in 1994 and yields for Camarines Sur households
improved from 43 cavans per hectare in 1983 to 59 cavans in 1994. Nevertheless, as can be seen
in figure 1, the spread in production and yields was generally greater among households than
among years.
In addition to production, table 1 reports averages for two other types of variables. We
associate the first group with the production frontier and the later with technical efficiency.
Inputs to the production function include land of differing type.. Land types include
upland rain-fed, lowland rain-fed, gravity irrigated and pump irrigated land. Area planted to rice
averaged about two hectares for each household and shows no clear trend over time. Irrigation
costs, seed use, fertilizer use, other chemical use, machine usage and labor comprise the
remaining input variables.
Among the variables influencing efficiency, two represent explicit short-tern choices.
The first, seed-use, includes a choice concerning technology, since rice farmers in the Bicol
region could choose to plant either high yield rice varieties or traditional ones5. Survey results
indicate three outcomes. Farmer chose to plant i) a high yielding rice variety only; ii) traditional
varieties only; or iii) a combination of high yield and traditional rice varieties. Area devoted to
other crops is another choice farmers make with potential consequences for efficiency.
Other state variables are likely to influence efficiency, but the farmer must take these as
given - at least in the short run. Relative rice prices were included to measure economic
incentives for greater efficiency6. Because it is likely to influence the capacity to farm
efficiently, education is included. Wealth is also included since the variable potentially
4A cavan equals 44 kg. of unmnilled rice.
5Irrigation techniques represent technology choice as well, but this technology is fixed in the short-run.
6
influences the ability of the farmer to employ riskier techniques associated with higher
productivity.
Because weather influences ex-ante decisions and ex-post outcomes, several weather-
based variables are included in the estimation. Weather data were available from two official
weather stations, Deat and Lagazpi City, within the Bicol region. The data include, on a monthly
basis, average temperature and rainfall. Rice producing households are allocated to one of these
weather stations depending on proximity. Since the growing months of rice are reported in the
MPS-data, it is possible to calculate household specific indicators of weather conditions. Six
indicators are calculated. For each household, average rainfall and temperature are calculated for
the indicated growing months. Additionally, average deviations from historic mean temperatures
and rainfall are calculated. To measure variability within the growing seasons, mean squared
monthly deviations are calculated as well. Finally, time-dummy variables are included to test for
fixed year effects.
4. ESTIMATION RESULTS
In this section we discuss the parameter estimates from the base model, given in table 27.
Frontier parameters
The parameters estimated for the stochastic frontier production function indicate
elasticities for land between 0.44 for lowland gravity irrigated and 0.30 for upland. These
elasticities are similar to other production functions estimated elsewhere - for example,
Mundlak, Larson and Butzer (1999) estimated an elasticity for land of 0.47, in their cross-
country analysis of agricultural production.
The elasticity of irrigation fuel costs is positive, but not significantly different from zero.
Other inputs-- seeds, fertilizer, chemical costs and aggregated machine hours-- have typical
positive elasticities that are all significantly different from zero.
There is an inconsistency in the questionnaires concerning labor data that requires special
treatment for hired labor in 1978. Nonetheless the estimated elasticity of 0.07 associated with
6 Price incentives may be fully measured by observed input choices, including family labor. However, prices may
have an additional effect on unmeasured management.
7 The model was estimate using Frontier 4.1 (Coelli, 1996.)
7
hired labor hours in the years 1983 and 1994 is statistically significant and consistent with
Mundlak, Larson and Butzer. The elasticity of family labor hours is also significant, but
quantitatively lower with an estimated value of 0.03.
Finally, it is worth noting that, while homogeneity has not been imposed on the empirical
model, the unconstrained sums of the frontier input elasticities range from 0.82 to 0.96,
depending on the type of irrigation employed.
Technical inefficiency parameters
By convention, model parameters not included in the frontier are expressed in terms of
inefficiency - that is ui, is a subtraction from y1t. Consequently, variables with negative (positive)
coefficients will have a positive (negative) relationship with output.
The estimates associated with the two short-term choices have the expected signs and are
statistically significant. The estimated parameter for the variable "area planted with other crops"
is positive, indicating that rice productivity declines with crop diversification. The result is
consistent with the notion that rice producing households that diversify pay a price in terms of
lost efficiency in rice production. The estimates also indicates that the use of high-yielding
varieties or a combination of high and traditional varieties boosts productivity and, consequently,
that the few farmers that chose to rely exclusively on traditional varieties gave up on potentially
efficiency gains by doing so.
Longer term decisions to save and invest in education also significantly affect efficiency
according to the estimated results. The coefficient on educational obtainment is positive and
significant, as is the coefficient on wealth. The later result is consistent with the assertion that
wealthier households are better positioned to pursue strategies that are more efficient, but also
riskier. However, it is possible that wealth proxies greater managerial endowments.
The price of rice sold by the farmer also had a highly significant coefficient. High
relative prices will directly offer incentives for greater productivity; however this is potentially
fully captured in adjustments made to allocated labor and other inputs. Still, higher prices will
most likely result in added care and management, which potentially explains the result.
However, field visits indicate that some households remain remote, suggesting that low costs
may be associated with high transaction costs, the full consequences of which are not captured
8
by the other choice variables. Consequently, in addition to providing incentives for voluntary
action, prices may also reflect involuntary losses associated with poor communications and other
unmeasured factors that contribute to lower efficiency.
As mentioned, the weather variables depend on calculations based on proximity to one of
two weather stations in Bicol and on planting decisions by farmers. The estimated parameters
reflect a quadratic specification that includes weighted averages of the long-term monthly
averages for temperature and rain - which can be know ex ante - as well as ex post outcomes.
As such, little meaningful can be said about the individual weather parameters. As it turns out,
the parameters are significant, taken as a whole and that weather is significant in explaining the
range of production outcomes. We return to this topic in the next section.
The two constant terms, associated with the frontier and with the technical inefficiency
variables, are both significant. Fixed effects for panel years are also significant. The values
indicates that on average the rice farmers moved closer to the frontier with time - that is, the
inefficient measure association with the dummy value for 1978 (1.577) is greater than the value
for 1983 (1.241) and both represent greater inefficiency relative to the (excluded) 1994 dummy.
However, it is also possible that the result may reflect differences in the sample composition
since the year-effects are not significant when the balanced panel is used for estimation. We take
up the balanced panel results in section 6.
5. MEASURING IMPACT
In this section we provide comparative measures of the effects of state variables.
Technical efficiency, T, is defined for each household-time observation as Ti, = e`(ZiO) and we
present examples of how discrete and reasonable changes in state variables effect efficiency,
where AT = -6kT(z)Azk . We present a similar measure for output where Ay = -,6ky(x, )Azk 8-
Recalling from 2.2 that the technical efficiency term is multiplicative, the elasticity of y with
8For temperature and rain, quadratic terms are included in the efficiency term and consequently in the impact
measures. For example, the percentaage output change for a given deviation in average rainfall is given by:
Ay = S7Ar + c5'Ar + 269Ar2, where Ar is a given deviation from average rainfall, Ar = r - F. The change in
y
output due to a switching seed type is given by AY = exp(-34 - 1) * (See Halvorsen and Palmquist, 1980.)
y
9
respect to the efficiency variables is equivalent to the elasticity of T with respect to the efficiency
variables, that is: oyaz. _ aT -Z kZk
aZ Y aZk T kk
The results of the calculations, given in table 3, show that the gain from making use of
high-yielding seeds is large. This is a one-time gain however and the data shows that few
households in the survey relied exclusively on seeds from traditional varieties.
The results also indicate that diversification extracts a cost in foregone efficiency. The
measure calculates the average reduction in output for a given plot of land when the household
manages additional plots devoted to other crops. Though significant, the cost of foregone
specialization - estimated at 2.9% for a reduction in diversification of 0.5 hectares-- is not
especially large.
The measures indicate that past investment in education and past savings are
quantitatively important. The simulations show that relatively small increased in wealth and
education lead to significant and repeated gains in efficiency.
Random shocks from market prices and weather also appear to be important determinants
of ex ante output. The quantitative results suggest that a small change in price or a small
shortfall in rainfall will result in production losses that match or overwhelm positive gains that
farmers can obtain through voluntary choices.
6. ALTERNATIVE MODELS
In this section we examine whether the results are sensitive to the choice of estimation
technique, omitted variables, or the composition of the panel used to estimate the model. We
find that results related to the frontier variables are fairly robust on all accounts. With few
exceptions, the same can be said of the direction of impact associated with efficiency variables.
However, the quantitative values and, in some cases the statistical significance of the parameters,
are effected when observations are excluded in order to balance the panel, when variables are
omitted or when assumptions regarding the composition of the error term are dropped.
10
Comparison with least squares
As mentioned, the test statistic y =o /(o, + a,) can be used to test whether the
additional restrictions on the specification of the error term in the stochastic frontier model is
justified. Specifically, the null hypothesis, y = 0, is true when the estimated stochastic frontier
model is equivalent to a traditional average response model. A one-sided likelihood ratio test
can be used to test the null; however because of asymmetries, the test statistic is, asymptotically,
distributed as an average of two chi-square distributions (Coelli, 1995.) Critical values,
appropriate for testing the null can be found in Kodde and Palm, 1986.
As reported in table 2, the estimated value of y, 0.95, indicates that the variations
association with u comprise a large portion of the overall spread of the model's error term. In
addition, a comparison of the likelihood values produced by the stochastic frontier and an
average response model estimated with least squares produces a large test statistic9. For that
reason, the least-squares version of the model can be rejected in favor of the stochastic frontier
model with a very high degree of confidence.
Setting aside for the moment the statistical comparison of the models, what are the
quantitative differences in the estimated parameters? The least-squares estimates of the frontier
parameters are similar to the stochastic model. However there seems to be a tendency that for
the land parameters to be larger in the stochastic model and for the non-land parameters to be
slightly smaller (table 4.) For the state variables, the signs - with the exception of the year
dummies - are consistent for both set of estimates, but the paramneter values of generally smaller
in the least-square estimation - especially relative to the standard errors associated with the least
square parameters.
Parameter restrictions
Table 5 reports the estimated parameters that result from applying zero-restrictions to
several sets of state-variable parameters. Table 6 presents a statistical test of the applied
restrictions. Generally, the parameter values are not overly sensitive to the restrictions.
However, omitting variables always significantly reduces the explanatory power of the model
9 The calculated value of the likelihood ratios was 238. Consequently, the null hypothesis can be rejected at a 99%
level of confidence.
11
and the restrictions can be rejected with a high degree of confidence. The single-parameter
restrictions - those for education, wealth, the price of rice and the technical efficiency intercept -
provide alternative tests for the t-scores reported earlier; the test yield identical results.
The restrictions on the rice-variety dummies, the year dummies and the weather variables
are joint. Each set of restrictions could be rejected with at a 95% level of confidence and the
restrictions on weather and varietal type at higher levels.
Finally, it is worth pointing out that all estimated frontier elasticities are positive and,
generally, significantly so. This is consistent with the expectation that the underlying production
function is strictly monotonically increasing in inputs.
A balanced panel
Mechanically, the overall variation in panel data that estimated models attempt to explain
can be decomposed along the dimensions of the panel. In practice, this means that the
composition of a sample can affect estimation results (Mundlak and Larson 1992). Separately,
for technical efficiency models, it is reasonable to expect that some sources of technical
efficiency will vary with time - for example, because of "learning" (Kumbhakar 1990; Lee and
Schmidt 1993.) With unbalanced panels, the two effects are inseparable.
In this section, we use a balanced panel to estimate the base model in order to examine
whether the significant time effects observed in the base period are due to a changing
composition in the unbalanced panel. We pay a heavy price for doing so, reducing the number
of observations from more than 1,500 to 432. Nonetheless, we find evidence that the previously
measured time-effects are due to the changing composition of the sample. Moreover, keeping in
mind that the significant changes in the two samples on which the estimates are based, the
remaining parameter estimates from the balanced panel are very similar to the results from the
unbalanced panel. The results are reported in table 7.
Except for the coefficient on upland rice, the parameters associated with the frontier
variables are similar for both sample estimates. We suspect that the value associated with upland
rice, which is large relative to the other land coefficients and relative to the unbalanced-sample
result, may be an artifact of the sample reduction since only 7 households in the balanced sample
produced upland rice.
The share of the model variance, 02, that can be attributed to the inefficiency component
of the model - as measured by y -- remains high at 0.97 in the balanced-panel results and
statistically different from zero at a very high level of confidence. In contrast to the unbalanced
panel, the year dummy variables are quantitatively smaller and statistically indistinguishable
from zero. The finding is consistent with the notion that there are no unexplained effects proxied
by time - at least in Camarines Sur. However, because the balanced panel includes only
households from Camarines Sur, it is impossible to say whether the result generalizes to
households in other provinces.
The balanced panel results do not contradict the conclusion that specialization, education
and wealth all contribute positively to technical efficiency. Quantitatively, the balanced panel
significantly larger effects for education and wealth. The technology results are unclear; they
suggest that farmers improve efficiency significantly by introducing high-yielding varieties to
their seed mix. However, the sign on the "high yield" variety is counter-intuitive and not
statistically significant.
7. CONCLUSIONS
Based on panel data from rural households in Bicol, we find evidence that farmers take
voluntary decisions of the kind normally attributed to risk coping strategies that lead to reduced
productivity. The result is not sensitive to variations in the underlying model. Although short to
medium term decisions regarding diversification and technology choice effect efficiency, these
decisions are not the only source, or quantitatively a dominant source of foregone efficiency.
Evidence suggest that small changes in weather and market outcomes are often more crucial. At
the same time, the results indicate that short-term decisions and outcomes that, in accumulation,
effect wealth and education have lasting and repeated consequences for technical efficiency.
8. REFERENCES
Aigner, D., C. Lovell, C., and P. Schmidt, 1977. Formulation and estimation of stochastic
frontier production function models. Joumal of Econometrics 6(1): 21-37.
Battese, G. and T. Coelli (1993): A Stochastic Frontier Production Function incorporating a
model for technical inefficiency effects., Department of Econometrics Working Paper 69.
University of New England, Armidale, Australia.
13
Battese, G. and T. Coelli, 1995. A Model for technical inefficiency effects in a stochastic frontier
production function for panel data. Empirical Economics 20, 325-332.
Battese, G. E., 1997. A note on the estimation of Cobb-Douglas production functions when some
explanatory variables have zero values. Journal of Agricultural Economics 48, 250-252
Bicol River Basin Development Program. Bicol Multipurpose Survey (BMS), 1978:
[Philippines] [Computer file]. ICPSR version. Camarines Sur, Philippines: Bicol River
Basin Development Program [producer], 1978. Ann Arbor, MI: Inter-university Consortium
for Political and Social Research [distributor], 1997.
Bicol River Basin Development Program. Bicol Multipurpose Survey (BMS), 1983: Philippines
[Computer file]. ICPSR version. Camarines Sur, Philippines: Bicol River Basin
Development Program [producer], 1983. Ann Arbor, MI: Inter-university Consortium for
Political and Social Research [distributor], 1998.
Binswanger, H. and D. Sillers, 1983. Risk aversion and credit constraints in farmers' decision-
making: a reinterpretation. Journal of Development Studies 21(1): 5-21.
Binswanger, H. and M. Rosenzweig, Mark R., 1986. Behavioural and material determinants of
production relations in agriculture. Journal of Development Studies 22(3): 503-539.
Bromley, D. and J.P. Chavas. 1989. On risk, transactions, and economic development in the
semiarid tropics. Economic Development and Cultural Change 37(4): 719-736.
Coelli, T., 1995. Estimators and hypothesis tests for a stochastic frontier function: a Monte Carlo
analysis, Journal of Productivity Analysis 6(3), 247-268.
Coelli, T., 1996. A Guide to FRONTIER Version 4.1: A computer program for stochastic
frontier production and cost function estimation. Centre for Efficiency and Productivity
Analysis Working Paper 96/07. Center for Efficiency and Productivity Analysis, University
of New England: Armidale, Australia.
Coelli, T. J., D. S. P. Rao and G. E. Battese, 1997. An introduction to efficiency and productivity
analysis. Boston, Dodrecht and London: Kluwer Academic Press.
Dercon, S., 1996. Risk, crop choice, and savings: evidence from Tanzania. Economic
Development and Cultural Change 44 (3): 485-513.
Ellis, F., 2000. The determinants of rural livelihood diversification in developing countries.
Journal of Agricultural Economics 51 (2): 289-302.
Fafchamps, M. 1992. Cash crop production, food price volatility, and rural market integration in
the third world. American Journal of Agricultural Economics 74(1): 90-99.
Fried, H. O., C. A. K. Lovell and S. S. Schmidt, 1993. The measurement of productive
efficiency: techniques and applications. New York: Oxford University Press.
Halvorsen, R. and R. Palmquist, 1980. The interpretation of dummy variables in semi
logarithmic equations. American Economic Review 70, 474-475.
Hazell, P. and J. Anderson, 1989. Variability in grain yields. Baltimore: Johns Hopkins
University Press.
Kennedy, P., 1998. A Guide to Econometrics, Fourth Edition, Cambridge, Massachusetts: MIT
Press.
14
Kodde, D. A., and F. C. Palm, 1986. Wald Criteria for jointly testing equality and inequality
restrictions. Econometrica 54, 1243-48.
Kumbhakar, S., 1990. Production frontiers, panel data, and time-varying technical inefficiency
Journal of Econometrics 46(1), 201-211.
Kumbhakar, S., S. Ghosh, and J. McGuckin, 1991. A generalized production frontier approach
for estimating determinants of inefficiency in US dairy farm. Journal of Business and
Economic Statistics 9, 279-286.
Lanzona, Leonard. Bicol Multipurpose Survey (BMS), 1994: Philippines [Computer file]. ICPSR
version. New Haven, CT: Yale University [producer], 1994. Ann Arbor, MI: Inter-university
Consortium for Political and Social Research [distributor], 1997.
Lee, Y. and P. Schmidt, 1993. A production frontier model with flexible temporal variation in
technical efficiency in H. Fried, C. Lovell, and S. Schmidt, eds. The measurement of
productive efficiency: Techniques and applications. Oxford: Oxford University Press.
Malliaris, A. and W. Brock, 1982. Stochastic methods in economics and finance. Amsterdam:
North Holland.
Meeusen, W. and J. van-den-Broeck, 1977. Efficiency estimation from Cobb-Douglas
production functions with composed error. International Economic Review 18, 435-444.
Mundlak, Y. D. Larson and R. Butzer, 1999. Rethinking within and between regressions: the
case of agricultural production functions. Annales d'econome et Statistique 55-56, 475-501.
Mundlak, Y. and D. Larson, 1992. On the transmission of world agricultural prices. The World
Bank Economic Review 6, 399-422.
Morduch, J. 1995. Income smoothing and consumption smoothing. Journal of Economic
Perspective 9(3), 103-114.
Pitt, M. M. and M. F. Lee, 1981. The measurement and sources of technical inefficiency in the
Indonesian weaving industry. Journal of Development Economics 9, 43-64.
Reardon, T., C. Delgado and P. Matlon. Determinants and effects of income diversification
amongst farm households in Burkina Faso. Journal of Development Studies. 28 (1): 264-
296.
Reifschneider, D. and R. Stevenson, 1991. Systematic departures from the frontier: A framework
for the analysis of firm inefficiency. International Economic Review 32, 715-723.
Walker, T. and N Jodha, 1986. How small farm households adapt to risk in P. Hazell, C.
Pomareda and A. Valdes (eds.) Crop insurance for agricultural development. Baltimore:
Johns Hopkins University Press.
World Bank, 2001. World development report 2000/2001: Attacking poverty. Oxford and New
York: Oxford University Press.
15
9. FIGURES AND TABLES
Figure 1: Area planted to rice and rice production in Bicol for 1978, 1983 and 1994.
1,000
; 800-
600-
0
Area planted to rice in hectares
16
Table 1: Household averages for selected variables, 1978, 1983 and 1994
All observations Balanced panel
Survey year 1978 1983 1994 1978 1983 1994
Output measures
Rice production (cavans) 83.60 92.03 135.24 128.53 125.49 157.37
Rice yield (cavans/hectare) 43.68 45.78 59.06 47.33 46.48 60.10
Input measures
Area Planted (hectares) 1.91 2.01 2.29 2.72 2.70 2.62
Type of land (hectares)
Upland rain fed 0.07 0.07 0.02 0.02 0.02 0.02
Other rain fed 0.73 0.62 0.86 0.91 1.16 1.02
Gravity irrigated 0.69 0.94 0.63 0.84 0.78 0.58
Pump irrigated 0.42 0.38 0.78 0.95 0.74 1.00
Seeds (cavans) 3.53 3.96 5.51 5.48 5.74 6.34
Irrigation costs (1994 pesos) 56 75 104 101 155 115
Fertilizer costs (1994 pesos) 701 832 982 750 990 1,018
Other chemical input costs (1994 pesos) 774 954 2,100 1,284 1,351 2,473
Machine hours 62 45 107 130 70 140
Hired labor
1978 definition, (hours) 2,053 - - 2,164 - -
1983 and 1994 definition(hours) - 991 950 - 1,113 1,167
Family labor (hours) 400 493 318 468 642 348
Efficiency variables
Price of rice per cavan(1994 pesos) 286 211 220 294 215 220
Area planted to corn and coconuts 1.35 0.92 0.76 0.76 0.54 0.54
Use of high yield varieties (share of 0.78 0.86 0.85 0.88 0.92 0.85
households)
Use of traditional seeds (share of households) 0.22 0.14 0.15 0.13 0.08 0.15
Use of mixed high yield and traditional seeds 0.07 0.03 - 0.08 0.04 -
(share of all households)
Education of household head (years of 5.97 6.41 6.86 6.38 6.65 6.91
schooling)
Wealth proxy (value of home in 1994 pesos) 22,338 27,648 49,278 19,775 33,433 49,784
Weather variables
Average weighted monthly rainfall (mm)l 265 262 317 272 260 326
Average weighted difference in rainfall from (9.61) (12.43) 48.81 (7.22) (16.65) 53.33
historic mean (mm)
Weighted quadratic mean difference in rainfall 26.37 20.75 22.50 27.74 19.94 23.61
from historic mean (thousand mm2)
Average weighted monthly temperature (C 0) 23.54 23.43 23.78 23.55 23.23 23.78
Average weighted difference in temperature (0.03) 0.16 0.35 (0.02) 0.09 0.36
from historic mean (C°)
Weighted quadratic mean difference in 0.11 0.45 0.36 0.11 0.47 0.37
temperature from historic mean (C2,)
Source: Authors' calculation from survey and weather data
17
Table 2: Estimation results for the base model.
Missing-value
dummies
Frontier variables Estimate t-score Estimate t-score
f30 Constant 2.929 21.381
,BI Gravity irrigated area planted 0.437 12.952 0.623 8.674
,2 Pump irrigated area planted 0.356 7.425 0.553 6.750
P3 Lowland rain-fed area planted 0.394 13.129 0.391 5.780
04 Upland rain-fed planted 0.303 5.614 0.196 2.321
f5 Irrigation fuel costs 0.009 0.276 -0.080 -0.350
,6 Seeds 0.142 5.993
,37 Fertilizer costs 0.093 5.138 -0.448 -3.471
,8 Other chemical costs 0.106 6.077 -0.328 -3.079
09 Machine hours 0.076 4.467 -0.029 -0.296
010 Hired labor, 1978 0.087 1.508 -0.435 -0.959
,ll Hiredlabor, 1983 and 1994 0.068 3.694 -0.257 -2.167
f312 Family labor 0.025 1.770 -0.245 -2.140
Technical inefficiency variables
80 Constant -17.434 -11.047
81 Diversification 0.058 4.010
82 Education -0.101 -3.786
83 Wealth -0.590 -4.405 4.344 3.801
Seed types
84 Mixed varieties -0.444 -1.257
85 High yielding varieties -0.162 -0.834
86 Price -0.004 -23.588
87 Average weighted monthly rainfall 0.003 2.044
88 Average weighted difference in rainfall from -0.007 -3.305
historic mean
89 Weighted quadratic mean difference in rainfall 0.017 2.910
from historic mean
810 Average weighted monthly temperature 0.605 21.528
811 Average weighted difference in temperature -1.253 -4.982
from historic mean
812 Weighted quadratic mean difference in 1.218 1.500
temperature from historic mean
813 Year-effect, 1978 1.577 2.856
814 Year-effect, 1983 1.241 3.035
2r = 2. + 2.245 4.709
r 0.954 90.852
Source: Bicol MPS data and authors estimation.
18
Table 3: Simulated changes in production for selected variables.
Assumed change Impact on production elasticity
cavans %
Production decisions
Switch to high-yield seeds 16.5 17.5% 0.18
Reduce diversify by an additional 0.5 ha. 2.7 2.9% 0.05
Investment decisions
One-year increase in education level 9.5 10.1% 0.65
1,000 peso increase in wealth 2.0 2.1% 0.59
External shocks
20 peso fall in the relative price of rice -7.6 -8.0% 0.99
Rain level averages 10 cm below normal -3.7 -3.9% -10.62
Temperature averages 0.020 C below normal -1.3 -1.4% -0.33
19
Table 4: Comparison of stochastic frontier and least-squares estimates
Stochastic frontier Ordinary least squares
Dummies on missing values Dummies on missing values
Estimate t-score Estimate t-score Estimate t-score Estimate t-score
Frontier variables
Constant 2.929 21.38 2.010 4.07
Gravity irrigated rice land (hectares) 0.437 12.95 0.623 8.67 0.356 8.60 0.657 7.82
Pump irrigated rice land (hectares) 0.356 7.42 0.553 6.75 0.264 4.62 0.586 6.13
Lowlandrainfedriceland(hectares) 0.394 13.13 0.391 5.78 0.324 8.98 0.370 4.63
Upland rice land (hectares) 0.303 5.61 0.196 2.32 0.295 4.47 0.224 2.17
Irrigation fuel costs (pesos) 0.009 0.28 -0.080 -0.35 0.015 0.33 -0.130 -0.40
Seeds (cavans) 0.142 5.99 0.162 5.56
Fertilizer (pesos) 0.093 5.14 -0.448 -3.47 0.097 4.37 -0.453 -2.90
Other Chemicals (pesos) 0.106 6.08 -0.328 -3.08 0.102 4.64 -0.323 -2.54
Aggregated machine hours 0.076 4.47 -0.029 -0.30 0.090 4.56 -0.132 -1.17
Hired laborproxy for 1978 0.087 1.51 -0.435 -0.96 0.153 2.17 -0.877 -1.59
Hiredlaborinhours for 1983 and 1994 0.068 3.69 -0.257 -2.17 0.084 3.82 0.264 1.51
Family labor inhours 0.025 1.77 -0.245 -2.14 0.068 4.02 -0.218 -1.55
Technical efficiency variables
Constant -17.434 -11.05
Area planted to other crops (hectares) 0.058 4.01 0.013 3.03
Schooling of rice farmer (years) -0.101 -3.79 -0.013 -2.59
Wealth (In pesos) -0.590 -4.41 4.344 3.80 -0.080 -6.54 0.592 4.39
Dummy for high and traditional rice varieties -0.444 -1.26 -0.006 -0.07
Dummy for high yield rice varieties -0.162 -0.83 -0.004 -0.06
Selling price for rice (pesos) -0.004 -23.59 -0.001 -3.08
Average monthly rainfall in mm per growing months 0.003 2.04 -0.000 0.43
(mm(
Aver. monthly difference in rainfall from the long-term -0.007 -3.31 -0.001 -1.52
mean in mm
Aver. quadratic difference in rainfall from long-term 0.017 2.91 0.004 2.26
mean in mmn/1000
Average temperature in C° of the growing months 0.605 21.53 0.023 1.26
Aver. monthly difference in temperature from the long- -1.253 -4.98 -0.099 -1.77
term mean in C°
Aver. quadratic difference in temperature. from long- 1.218 1.50 0.105 0.82
term mean in CX
Dummy for the year 1978 1.577 2.86 -0.394 -2.69
Dummy forthe year 1983 1.241 3.03 0.155 2.57
Note: In order to comply with technical efficiency conventions, the signs on the OLS fixed-effect technical-efficiency parameters have been reversed.
Consequently, a negative sign indicates that technical inefficiency (efficiency) increases (decreases) with an increase in the value of the associated variable.
Table 5: Estimation results from restricted models.
Frontier variables param. t-score param. t-score param. t-score param. t-score param. t-Score param. t-score param. t-score param. t-score param. t-score
Constant 2.93 21.38 2.93 21.64 2.91 22.05 2.93 21.88 2.95 22.27 2.90 22.04 2.88 22.20 2.91 21.54 2.93 20.79
Gravityirrigatedricelandinha 0.44 12.95 0.44 13.10 0.43 13.07 0.43 12.62 0.45 13.13 0.44 13.18 0.44 13.12 0.44 12.49 0.44 12.87
Pumpirrigatedricelandinha 0.36 7.42 0.36 7.36 0.35 7.30 0.35 7.24 0.36 7.35 0.36 7.47 0.36 7.42 0.36 7.22 0.36 7.26
Lowlandrainjfedricelandinha 0.39 13.13 0.39 13.00 0.39 13.13 0.39 12.89 0.40 13.16 0.40 13.33 0.40 13.30 0.39 12.69 0.39 12.97
Upland rice land in ha 0.30 5.61 0.30 5.63 0.29 5.28 0.31 5.69 0.31 5.73 0.31 5.72 0.31 5.64 0.31 5.73 0.30 5.62
Irrigation fuel costsin pesos 0.01 0.28 0.01 0.27 0.01 0.21 0.01 0.35 0.01 0.37 0.01 0.24 0.01 0.22 0.03 0.77 0.00 0.16
Seeds incavan 0.14 5.99 0.14 5.96 0.14 6.00 0.14 5.92 0.15 6.14 0.14 6.03 0.14 6.03 0.13 5.43 0.15 6.22
Fertilizer in pesos 0.09 5.14 0.09 4.88 0.09 5.20 0.09 5.14 0.10 5.26 0.09 5.17 0.09 5.32 0.09 4.80 0.09 4.97
OtherChemicalsinpesos 0.11 6.08 0.11 6.13 0.11 6.02 0.11 5.99 0.11 6.12 0.10 5.86 0.10 5.91 0.10 5.86 0.11 6.18
Aggregated machine hours 0.08 4.47 0.08 4.52 0.08 4.41 0.08 4.52 0.07 4.33 0.07 4.46 0.07 4.47 0.07 4.15 0.08 4.62
Hired laborproxy for 1978 0.09 1.51 0.09 1.49 0.09 1.34 0.09 1.48 0.10 1.58 0.08 1.31 0.08 1.47 0.07 0.63 0.09 1.50
Hired labor in hours for 83 & 94 0.07 3.69 0.07 3.68 0.07 3.81 0.07 3.67 0.07 3.83 0.07 3.51 0.07 3.57 0.07 3.85 0.06 3.30
Family labor in hours 0.02 1.77 0.03 1.83 0.03 1.96 0.02 1.67 0.02 1.55 0.02 1.76 0.02 1.74 0.03 1.85 0.02 1.54
Efficiency variables
Constant 80 -17.43 -11.05 -16.53 -2.63 -16.49 -2.84 -20.10 -2.88 -17.53 -3.08 -17.91 -3.85 0.22 0.44 -15.40 -2.56
Area planted to other crops 0.06 4.01 0.05 4.54 0.05 4.78 0.05 4.84 0.06 5.02 0.06 5.16 0.03 2.88 0.06 4.98
Schooling of rice farmer -0.10 -3.79 -0.10 -3.72 -0.09 -3.99 -0.14 -5.06 -0.09 -4.16 -0.10 -4.52 -0.04 -2.28 -0.10 4.18
Wealth -0.59 -4.41 -0.54 -5.60 -0.55 -5.56 -0.56 -6.02 -0.56 -5.58 -0.57 -6.53 -0.26 -6.35 -0.58 -5.87
Dummy for mixed rice varieties -0.44 -1.26 -0.51 -1.49 -0.46 -1.30 -0.49 -1.50 -0.73 -1.58 -0.44 -1.05 -0.16 -0.51 -0.31 -0.89
Dummy for high yield rice -0.16 -0.83 -0.34 -1.69 -0.21 -1.09 -0.18 -0.92 -0.52 -2.40 -0.18 -0.86 -0.07 -0.42 -0.08 -0.50
varieties
Selling price for rice 0.00 -24.15 0.00 -21.43 0.00 -25.60 0.00 -23.40 0.00 -25.69 0.23 0.87 0.00 -3.60 0.00 -25.36
Averagemonthlyrainfall 0.00 2.04 0.00 1.26 0.00 2.03 0.00 1.98 0.00 2.34 0.00 2.10 0.00 2.19 0.00 2.30
Difference in rainfall from long- -0.01 -3.31 0.00 -2.38 -0.01 -2.91 -0.01 -2.89 -0.01 -3.76 -0.01 -2.96 -0.01 -3.23 -0.01 -4.13
term mean
Quadraticdifferenceinrainfall 0.02 2.91 0.01 2.60 0.02 2.82 0.02 2.82 0.02 3.27 0.01 2.66 0.02 2.89 0.02 3.16
Average temperature 0.60 21.53 -0.07 -1.88 0.60 2.62 0.57 2.77 0.70 2.79 0.59 2.89 0.61 3.64 0.59 2.62
Differenceintemperaturefrom -1.25 4.98 -0.45 -2.20 -1.13 -2.89 -1.14 -3.05 -1.42 -3.31 -1.14 -3.23 -1.17 -3.70 -1.34 -3.47
average
Quadratic difference in 1.22 1.50 0.79 1.09 0.93 1.06 1.03 1.34 1.11 1.78 0.94 1.30 1.00 1.45 0.82 1.86
temperature from average
Dummy forthe year 1978 1.58 2.86 1.24 2.96 1.48 3.27 1.51 3.63 1.95 3.86 1.14 2.74 1.20 2.80 1.01 3.51
Dummy for the year 1983 1.24 3.03 0.92 2.75 1.15 3.70 1.17 3.90 1.60 3.86 1.21 3.74 1.25 3.71 0.74 2.99
o2 (Sigma-squared) 2.25 4.71 2.00 4.57 1.94 5.60 1.90 5.80 2.25 5.97 1.91 6.08 1.97 7.23 1.07 11.03 2.01 5.48
y(Gamma) 0.95 90.88 0.95 84.28 0.95 96.68 0.95 93.33 0.95 113.87 0.95 101.93 0.95 105.10 0.91 75.99 0.95 93.28
21
Table 6: Tested restrictions about model specification
Omnitted variables tests x2- statistic
Technical inefficiency constant, 6o = 0 10.45
Area diversification, 6, = 0 7.69
Education, 62 = 0 6.79
Wealth, 63 = O 38.01
Rice varieties, 64 = 65 = 0 14.99
Price, 66 = 0 14.08
Weather, 67 =68 = 69 610 = 61I = 812 = 0 32.77
Year effects, 613= 614 0 6.56
Note: The test statistic, calculated as a likelihood ratio, is based on a
mixed x2 - distribution (Kodde and Palm, 1986). The null hypothesis
could be rejected in all cases with a 95% degree of confidence.
22
Table 7: Estimated model parameters from full panel and from balanced panel
Full panel Balanced panel
Missing-value dummies Missing-value dummies
Estimate t-score Estimate t-score Estimate t-score Estimate t-score
Frontier variables
Constant 2.929 21.38 2.511 6.80
Gravity irrigated rice land in ha 0.437 12.95 0.623 8.67 0.396 7.62 0.498 5.15
Pump irrigated rice land in ha 0.356 7.42 0.553 6.75 0.299 4.45 0.299 4.45
Lowland rain fed rice land in ha 0.394 13.13 0.391 5.78 0.358 7.83 0.276 3.07
Upland rice land in ha 0.303 5.61 0.196 2.32 0.933 2.69 0.145 0.80
Irrigation fuel costs in pesos 0.009 0.28 -0.080 -0.35 0.019 0.55 -0.121 -0.49
Seeds in cavan 0.142 5.99 0.050 1.60
Fertilizer in pesos 0.093 5.14 -0.448 -3.47 0.070 2.54 -0.382 -1.82
Other Chemicals in pesos 0.106 6.08 -0.328 -3.08 0.206 7.56 -0.339 -1.04
Aggregated machine hours 0.076 4.47 -0.029 -0.30 0.066 2.54 0.259 1.16
Hired labor proxy for 1978 0.087 1.51 -0.435 -0.96 -0.003 -0.04 0.144 0.23
Hired labor in hours for 83 & 94 0.068 3.69 -0.257 -2.17 0.056 1.80 -0.248 -1.15
Family labor in hours 0.025 1.77 -0.245 -2.14 0.059 2.90 -0.483 -3.28
Technical inefficiency influencing variables
Constant -17.434 -11.05 -3.755 -0.41
Area planted to other crops in ha 0.058 4.01 0.466 2.17
Schooling of rice farmer in years -0.101 -3.79 -0.115 -1.70
Wealth, natural log of family home value -0.590 -4.41 4.344 3.80 -0.832 -1.91 5.700 1.88
Dummy for high and traditional. rice varieties -0.444 -1.26 -1.636 -1.01
Dummy for high yield rice varieties -0.162 -0.83 1.776 1.35
Selling price for rice -0.004 -23.59 -0.012 -1.78
Average monthly rainfall in mm per growing months 0.003 2.04 -0.004 -1.05
Aver. monthly difference in rainfall from the long-term mean in mm -0.007 -3.31 -0.011 -1.36
Aver. quadratic difference in rainfall from long-term mean in 0.017 2.91 0.061 2.01
mm2/1 000
Average temperature in C° of the growing months 0.605 21.53 0.107 0.31
Aver. monthly difference in temperature from the long-term mean in C° -1.253 -4.98 -1.610 -1.53
Aver. quadratic difference in temperature from long-term mean in C02 1.218 1.50 2.994 1.28
Dummy for the year 1978 1.577 2.86 -0.233 -0.24
Dummy for the year 1983 1.241 3.03 -0.166 -0.39
o2 (Sigma-squared) 2.245 4.71 2.130 1.81
y (Gamma) 0.954 90.85 0.967 55.42
23
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS2765 Inequality Aversion, Health Adam Wagstaff January 2002 H. Sladovich
Inequalities, and Health Achievement 37698
WPS2766 Autonomy, Participation, and Learning Gunnar S. Eskeland January 2002 H. Sladovich
in Argentine Schools: Findings and Deon Filmer 37698
Their Implications for Decentralization
WPS2767 Child Labor: The Role of Income Rajeev H. Dehejia January 2002 A. Bonfield
Variability and Access to Credit in a Roberta Gatti 31248
Cross-Section of Countries
WPS2768 Trade, Foreign Exchange, and Energy Jesper Jensen January 2002 P. Flewift
Policies in the Islamic Republic of David Tarr 32724
Iran: Reform Agenda, Economic
Implications, and Impact on the Poor
WPS2769 Immunization in Developing Countries: Varun Gauri January 2002 H. Sladovich
Its Political and Organizational Peyvand Khaleghian 37698
Determinants
WPS2770 Downsizing and Productivity Gains Martin Rama January 2002 H. Sladovich
In the Public and Private Sectors Constance Newman 37698
of Colombia
WPS2771 Exchange Rate Appreciations, Labor Norbert M. Fiess February 2002 R. lzquierdo
Market Rigidities, and Informality Marco Fugazza 84161
William Maloney
WPS2772 Governance Matters II: Updated Daniel Kaufmann February 2002 E. Farnand
Indicators for 2000-01 Aart Kraay 39291
Pablo Zoido-Lobat6n
WPS2773 Household Enterprises in Vietnam: Wim P. M. Vijverberg February 2002 E. Khine
Survival, Growth, and Living Jonathan Haughton 37471
Standards
WPS2774 Child Labor in Transition in Vietnam Eric Edmonds February 2002 R. Bonfield
Carrie Turk 31248
WPS2775 Patterns of Health Care Utilization in Pravin K. Trivedi February 2002 R. Bonfield
Vietnam: Analysis of 1997-98 31248
Vietnam Living Standards Survey Data
WPS2776 Child Nutrition, Economic Growth, Paul Glewwe February 2002 E. Khine
and the Provision of Health Care Stefanie Koch 37471
Services in Vietnam in the 1990s Bui Linh Nguyen
WPS2777 Teachers' Incentives and Professional Gladys L6pez-Acevedo February 2002 M. Geller
Development in Schools in Mexico 85155
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS2778 Technology and Firm Performance Gladys L6pez-Acevedo February 2002 M. Geller
in Mexico 85155
WPS2779 Technology and Skill Demand Gladys L6pez-Acevedo February 2002 M. Geller
in Mexico 85155
WPS2780 Determinants of Technology Adoption Gladys L6pez-Acevedo February 2002 M. Geller
in Mexico 85155
WPS2781 Maritime Transport Costs and Port Ximena Clark February 2002 E. Khine
Efficiency David Dollar 37471
Alejandro Micco
WPS2782 Global Capital Flows and Financing Ann E. Harrison February 2002 K. Labrie
Constraints Inessa Love 31001
Margaret S. McMillan
WPS2783 Ownership, Competition, and Geroge R. G. Clarke February 2002 P. Sintim-Aboagye
Corruption: Bribe Takers versus Lixin Colin Xu 37644
Bribe Payers
WPS2784 Financial and Legal Constraints to Thorsten Beck February 2002 A. Yaptenco
Firm Growth: Does Size Matter? Asli Demirguc-Kunt 38526
Vojislav Maksimovic
WPS2785 Improving Air Quality in Metropolitan The Mexico Air Quality February 2002 G. Lage
Mexico City: An Economic Valuation Management Team 31099
WPS2786 The Composition of Foreign Direct Beata K. Smarzynska February 2002 P. Flewitt
Investment and Protection of 32724
Intellectual Property Rights: Evidence
from Transition Economies