Family Size in Colombia: Guessing or Planning? Intended vs. Actual Family Size in Colombia

Abstract

In this paper, we attempt to analyze the determinants of unintended births among Colombian women aged 40 years old or more using data from the Encuesta Nacional de Demografía y Salud 2005, which is Colombia's national demographic and health survey. Given the especial characteristics of the variable under analysis, we used count data models in order to test whether certain characteristics of women and their socioeconomic backgrounds such as their level and years of schooling or socioeconomic group, explain the number of unintended births. We found that women's education and the area of residence are significant determinants of unintended births. The inverse relationship between the level of education of women and the number of unintended births has key implications to social policies.

Full text

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Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia*
¿Se planea el tamaño de la familia en Colombia?
Fecundidad deseada frente a fecundidad efectiva
en las familias colombianas
Nohora Forero **
Luis Fernando Gamboa ***
Abstract
In this paper, we attempt to analyze the determinants of unintended
births among Colombian women aged 40 years old or more using
data from the Encuesta Nacional de Demografía y Salud 2005, which
is Colombia´s national demographic and health survey. Given the
especial characteristics of the variable under analysis, we used count
data models in order to test whether certain characteristics of women
and their socioeconomic backgrounds such as their level and years of
schooling or socioeconomic group, explain the number of unintended
births. We found that women’s education and the area of residence are
signicant determinants of unintended births. The inverse relationship
* We thank d h s –Macro International for allowing us to have access to their data base and
Ana Vega (p r o f a m i l i a ) for her kind help. We are also grateful to Raquel Bernal, Eden
Bolivar, Lorena Bolivar, Darwin Cortés, Luis Fajardo, Jesus Otero, Rainer Winkelmann,
Blanca Zuluaga, and two anonymous referees for their comments to a previous version of
this article. The views expressed in this paper are solely those of the authors.
** Institute of Social Studies of Erasmus University Rotterdam, e-mail: nohora.forero@gmail.com
*** Economics Department, Universidad del Rosario, Bogotá, e-mail: lfgamboa@urosario.
edu.co
This article was received May 13, 2009, modied October 5, 2009 and nally accepted
October 26, 2009.

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Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
between the level of education of women and the number of unintended
births has key implications to social policies.
Key words: family size, unintended births, schooling, count data
models.
JEL classication: C4, I21, J13.
Resumen
En el artículo se analizan los determinantes de la presencia de hijos
no planeados en Colombia. Se utiliza la información de la Encuesta
Nacional de Demografía y Salud 2005, especícamente para las mu-
jeres de cuarenta años o más. Dadas las características especiales de
la variable que se analiza, se utilizan modelos de conteo para vericar
si determinadas características socioeconómicas, como la educación
o el estrato económico, explican la presencia de hijos no deseados.
Se encuentra que la educación de la mujer y el área de residencia son
determinantes signicativos de los nacimientos no planeados. Además,
la relación inversa entre el número de hijos no deseados y la educación
de la mujer tiene implicaciones cruciales en cuanto al manejo de la
política social.
Palabras clave: tamaño de la familia, nacimientos no planeados, edu-
cación, modelos de conteo.
Clasicación JEL: C4, I21, J13.
Introduction
For several decades, most research studies have focused on the de-
terminants of the demand for children in households, measured em-
pirically by analyzing the responses to questions on desired family
size. The limitations of this approach are well known, and they are
as follows: the timing of the answer and changes in preferences over
time. In the last twenty years, the interest on the ideal family size has
grown as shown in several studies (Freedman, Coombs and Chang

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(1972), Dow and Werner (1981), Carpenter-Yaman (1982), Girard and
Roussel (1982), Gomes (1984), Isiugo-Abanihe (1994), Stash (1996),
Hagewen and Morgan (2005)). At the same time, there has been an
emerging concern about unintended pregnancy and its determinants in
less developed countries (Bongaarts (1997) Islam and Rashid (2004),
Le, Magnani, Rice, Speizer and Bertrand (2004) and, Becker and
Sutradhart (2007)). Some of these efforts are based on seminal works
of Becker (1960, 1981) and Liebenstein (1957, 1974). However, to the
best of our knowledge, the relation between the preferred number and
the actual number of children has not received similar attention in less
developed countries, where a considerable proportion of the population
still live in rural areas and do not have access to many contraceptive
programs. These facts and the persistence of income inequality in poor
countries justify the need to disseminate information and knowledge
on this phenomenon.
The purpose of this paper is to analyze the determinants of unintended
births among Colombian women aged 40 years old or more, in 2005.
Not only in less developed but also in developed countries, it is com-
mon to nd that the actual family size is bigger than the desired size.
There are many factors explaining this difference and we want to ex-
plore one of them here. We want to assess the effect of schooling on
the gap between the desired and nal number of children, particularly
in family units where there are more children than initially planned.
We excluded those cases where there were actually fewer children
than desired. The rationale for excluding them is that most of these
cases could be a consequence of biological or economic constraints
on one side and divorces or widowhood on the other side. Here, we
assume that unintended children exist, when the actual is bigger than
the desired or expected number of children.
The main contributions of our study are threefold. First, there is no
recent study on this variable in a Latin American country such as
Colombia, where there is a well known public health program in con-
traception led by Profamilia. Second, although fertility has declined
around the world, this reduction has been different in developed and
developing countries and between people at the top and at the bottom
of the income pyramid. Third, the empirical approach is novel because
we used count data models that allowed us to take into account the

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Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
discreteness condition of the number of children and to reduce the bias
in the analysis compared to bi-variate analyses and traditional ordinary
least squares (for details on this methodology see Winkelman, 2008
and Cameron and Trivedi, 2005).
The paper is organized as follows. Section I summarizes some of the
recent literature on the demand for children, unintended births and
intended family size. Section II presents the data, methodology and
results. In the last section, we have our comments and policy recom-
mendations.
I. Literature review
The study of the differences between desired and realized fertility has
received few attention in developing countries. Most of the works are
focused on family size and its determinants. However, one of its most
relevant limitations is the possible existence of changes in fertility
preferences over time. In general, demographers distinguish between
preferred or desired family size and fertility ideals.
On the one hand, Thomson (2001, p. 5347) refers to desired family
size as ‘the number of children wanted in one’s lifetime’, and can be
viewed as the demand for children. McClelland (1983, p. 288) denes
desired family size as ‘the number of children parents would have if
there were no subjective or economic problems involved in regulat-
ing fertility’. Some authors such as Lee and Bulatao (1983) classify
the factors that inuence the family-size desires in aspects such as:
income and wealth, tastes and preferences, the cost-benet analysis
of children, and the opportunity cost of childbearing and childrear-
ing. Although important reductions in total fertility have taken place
around the world and some authors nd that in many countries total
fertility is below the replacement levels (Schultz (1998); Bryant and
Zick (2005) among others), there are countries where a considerable
proportion of unintended children still exists.
As can be seen, it is a rational choice in which people try not to guess
but to plan. Certainly, we could think that in general, parents try to
control the number of children they want to have. Some of the links

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between that control and their success are due to the effect of school-
ing on fertility. The negative effect of schooling on fertility has been
widely studied in different literatures (Cochrane (1979); Ainsworth,
Beegle and Nyamete, (1996); Schultz (1998)). Some evidence for
Colombia has been provided recently. (Forero and Gamboa (2008)).
Families with a lower schooling level tend to have less knowledge of
contraception methods and this leads to bigger families in low-income
and less educated groups than in higher ones. Therefore, we expect the
level of education to be negatively related to the number of unintended
or guessing of children.
On the other hand, fertility ideals refer to what is desirable for popula-
tion in general, without specifying the wishes of any particular person.
(Thomson (2001) and Hagewen and Morgan (2005)). In this approach,
the concept of family size is less important.
From a microeconomic approach, the denition of unintended children
as the difference between the desired number of children and the actual
births that the woman has implies a special analysis. The presence
of unintended births may be correlated to socioeconomic status or
education variables. Therefore, this work can shed some light on the
respective policy issues.
We may nd two groups of factors that determine the presence of
unintended children. On one side, the factors that affect the desired
family size include preferences, religion, socioeconomic status among
others and on the other side, factors that determine the total (nal)
number of children include biological aspects, marital status, use of
contraception methods and cultural aspects. As we mentioned above,
the demand for children includes several dimensions and the interac-
tion of those different factors. However, it is also the couples’ joint
choice in most of the cases.
In a supply-demand framework, we can think of unintended children
as “failures” in the demand for children. These types of failures can
take place because of two reasons: rst, couples do not have infor-
mation or access to contraception methods; second, changes in pre-
ferences. If a woman is asked how many children she wants to have,
her answer could be different if she does not have any kids at the

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Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
moment of questioning, or if she has had one or more children. Thus,
preferences can change during the lifecycle, inuenced by economic
constraints; namely, when women realize that bringing up children
is ‘expensive’, then she decides not to have as many kids as she had
thought or planned.
There is an extensive literature on desired family size which gives us
some ideas for understanding of the existence of unintended children
(Leibenstein (1957); Becker (1960, 1981); Becker and Lewis (1973);
Schultz (1973) Haskell (1977); Unger and Molina (1999) and Kiriti
and Tisdell (2005), among others).
Becker (1960) and Becker and Lewis (1973) afrm that since people
from low socioeconomic backgrounds do not have wide knowledge
of birth control mechanisms, they tend to have more unintended chil-
dren. Nevertheless, setting aside access to contraceptives, they show
that couples make a cost-benet analysis when planning family size.
The question is what are the main variables that families take into
account when they make that analysis? In less developed countries
the probability of either receiving an additional income or having an
additional free worker is probably higher than the cost of childbear-
ing. Schultz (1973, p. S3) argues that parents take into account the
expected benets they can get from children and ‘equate the marginal
sacrices and satisfactions’. Nevertheless, there is no consensus about
thinking of children as consumption goods.
In a similar way, Leibenstein (1957) claims that families make a cost-
benet analysis of having another child, in order to make the decision
of having children or not, in the case when they already have one or
two children (he focuses on births of higher order). He calls it a balance
between utilities and disutilities of having an extra child. In the rst
group he includes the utility derived from consumption which means
that new sons or daughters are desired for themselves. In the second
group he mentions the disutilities associated to the costs —including
the opportunity costs— of bringing children up.
From the empirical point of view, Haskell (1977) analyzes the deter-
minants of fertility desires in 220 undergraduates of the University of
Tennessee. His results indicate that religiousness is one of the most

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important factors explaining preferred number of children. In the case
of women, factors such as being younger also affect fertility prefer-
ences. For men, having been born in a large family inuences the
desire of having a large family too. These results are intuitive, since
we could think that religion may inuence preferences of family size
through constraints on birth control mechanisms. In Latin American
countries, where most people are Catholic, cultural and religious mo-
tivations affect choices such as marriage, demand for children, and
contraception methods.
Along the same lines, family size could be affected by the gender of
the rst child. Some aspects such as male labor participation and the
desire to have continuity of the family name explain larger families in
some cases. Unger and Molina (1997) study son preferences among a
sample of 432 Hispanic women of low socioeconomic status and they
nd that these women tend to prefer sons instead of daughters (maybe
because of cultural aspects). This may explain why they do not use
contraceptives until they have had a son. According to them, women
who are 30 or more years old, less educated, divorced or widowed,
or women who have been brought up in large families, tend to desire
more sons. They argue that, there is evidence conrming that son
preference is prevalent among Hispanic women in the United States.
Similar results are found in Kiriti and Tisdell (2005) who nd that the
strong son preference in Kenya is due to husband expecting to have
male children in order to ensure the survival the family name. Conse-
quently, a possible explanation of large families among these popula-
tions is that, couples keep on childbearing until they have a son. This
negative relationship between education and the number of children
(and especially, more sons) is also found in small samples in McCarthy
and Gbolahan (1987) and Unger and Molina (1999). Although their
samples may not be representative, we could expect similar results for
the Colombian case. Williams and Pratt (1999) argue that 35% of the
births from 1983 to 1988 in the United States were unwanted. They
identify that black women are more vulnerable to this situation as a
consequence of factors such as earlier initiation of sexual activity and
lower attendance at family planning clinics.
In these studies, women’s and her partner’s education, gender com-
position and the presence of male-dominated cultures may inuence

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Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
the desired fertility (and hence, the presence of unintended children).
Accordingly, if less educated women believe that one of the reasons
to have sons is to preserve the family name, these women may: (i)
tend to have more unintended children (girls), while they keep trying
for a son; (ii) have more children due to the fact that they are less
educated and hence, have a lower opportunity cost of bringing them
up, for instance.
As it can be seen, the existence of a positive gap between realized
and desired fertility could be derived from multiple factors (internal
and external) to women preferences. Thus, we have two different
hypotheses. H-1: Observed fertility is higher than desired fertility
as a consequence of failures in family formation due to factors such
as barriers to access to contraception methods or gender preference.
H-2: The gap between observed and desired fertility is due to time
changes in preferences that could induce women to regret their initial
preference.
II. Data, methodology and results
A. Data and methodology
We use the Demographic and Health Survey (Encuesta de Demografía
y Salud, d h s ) carried out by Profamilia during 2005 with technical as-
sistance from Macro International (Maryland, u s a ). The d h s survey is
done in Colombia every ve years since 1990, but each one includes
specic questions that are not always comparable. However, d h s is
representative of the country situation and among other variables that
this survey takes into account are information about health status,
contraception methods, sexual behavior, fertility, food habits, and so-
cioeconomic status. Its design includes different segments for specic
themes. For instance, questions on body mass index were asked to
117.000 people and fertility questions were posed to 49.000 women.
In total, the sample size of the survey is about 120.000 persons from
more than 37.000 households located around the entire country. In
this study, we extracted a subset of questions for characterizing adult
women and it reduced the sample for the empirical analysis.

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For statistical purposes, we only included non-pregnant women who
are older than 40 years since they seem to have completed their fertil-
ity choices already1. We also excluded observations of women who
do not give numerical responses to questions about fertility and those
observations without socioeconomic information (socioeconomic
strata or education). We check in this last case to guarantee that there
is no bias in the nal sample with respect to the entire database. After
these procedures, our nal sample is about 5.567 observations (women)
distributed as follows: 79% from rural areas, 49% with basic educa-
tion or less and 11% with higher education, and 7% from Bogotá, the
capital city.
Our dependent variable is the gap between realized fertility (the nal
number of children) and desired fertility (the reported preferred number
of children of the woman). d h s asks the women who were interviewed
about their family size preferences (instead of asking about ideal)2.
The specic question is ‘If you could go back to the time when you still
did not have any children and if you could choose the exact number of
children to have in your lifetime, how many would you have?’ In the
case of women with no living children, the question is ‘If you could
choose the exact number of children to have in your lifetime, how
many would you have?
From this question, we construct the gap between realized fertility
and desired fertility, Yi. Clearly, Yi can be zero, positive or negative.
In cases in which Yi is positive, i.e., realized fertility is higher than
desired fertility, we dene Yi as the number of unintended children.
An initial research question would be to assess whether positive and
negative values of Yi are determined by the same set of factors.
The existence of positive or negative values in Yi, could be a conse-
quence of changes in preferences over time and external shocks such
as income reductions, health problems, divorces, widowhood, unem-
ployment, or domestic violence among other aspects. In order to test
whether the determinants of positive and negative values of Yi are the
1 We estimate that the percentage of women older than 40 who have an additional child is
less than 2% of the sample.
2 As we mentioned before, there is a difference between desired family size and fertility
ideals. In this sense, d h s asks about the former.

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Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
same, we estimate a multinomial logit model in which the dependent
variable is the complete support of Yi. Our ndings indicate that the
determinants of positive Yi, and negative Yi, are different3. Thus, in the
empirical analysis that follows we focus exclusively on unintended
number of children, i.e., Yi, > 0. The study of the determinants of Yi,
< 0 is, in our opinion, less interesting from the economic point of view
as it could be mainly determined by changes in health and economic
status of the household.
After this censoring process, the domain of the variable unintended
children (Yi) is non-negative which allows us to use count data mod-
els. Among the most known count data models, we have two types of
models based on the distribution of the variable and their variance4.
On the one hand, there are Poisson Regression Models (p r m ) and Nega-
tive Binomial Regression Models (n b r m ). p r m is a method intended
for cases where the variable of interest follows a Poisson distribution
function and one of its most important features is that the mean tends
to be equal to its variance (equi-dispersion). As it rarely occurs em-
pirically, the other distribution known as n b r m can be obtained from a
mixture of a Poisson and a Gamma distribution functions and it relaxes
the equi-dispersion assumption.
On the other hand, we have a particular case of truncated models
which are the Zero Inated Poisson (z i p ) and Zero Inated Negative
Binomial (z i n b ) models. z i p and z i n b are mainly used when the inci-
dence of zeros in the dependent variable is high and its use depends
on the distribution behind the data. In our case, the higher prevalence
of zeros is understood as success, because in these cases, desired fer-
tility is equal to realized one (see Table 1). However, in order to test
the robustness of the results, we estimated using all the models men-
tioned. In their simplest form, given a y count-valued random variable,
zero inated models are specied as having a probability function
3 In order to test this, we estimate a multinomial logit model, where the dependent variable
is: yi < 0, yi = 0 and yi > 0. Our ndings indicate that there is no evidence that suggests that
the determinants of the three possible outcomes of Yi are the same. These results are not
reported but are available upon request.
4 These models are employed when an important proportion of the data has zeros, when the
mean is low, and when the data are non-negative integers.

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fy ffor y
fy if y
()
=+
()()
=
()()
=


10 0
1123
*
*,,,..
..





where
∈
[]
01,
is a zero-ination parameter which allows for any
fraction of zeros. The function f(.) is a standard count probability
function. The two most common choices for f(.) are Poisson (fp) and
Negative Binomial fNB with the following expressions:
fy y
P
y
;exp
!,ll ll
()
=
()
>0
and
fy y
y
NB
y
;,,l


l
l
l l 

()
=+
()
()
+
()
+




+





10
These functional forms can be used for regression analysis. As it can
be found in the literature, in an econometric regression, it is common
to specify the mean parameter l as a function of a vector of explana-
tory variables x which could be estimated by maximum likelihood
(See also Staub and Winkelmann (2009), for details).
Table 1. Unintended children by area of residence, Colombia 2005.
Number of unexpected births Rural areas Urban areas Total
034,35 50,25 47,1
1 14,08 17,47 16,8
2 16,7 16,82 16,8
3 11,53 8,1 8,79
49,66 3,92 5,08
5 or more 13,68 3,44 5,49
Total 100 100 100
Source: d h s – Macro International, 2005.
It is important to emphasize that in contrast to ordinary least squares,
count data estimates cannot be interpreted in a straightforward way.
They contain all the relevant information and we can easily use them
to determine semi-elasticities (See, for details, Cameron and Trivedi
(2005) or Winkelman (2008)).

96
Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
However, due to the unavailability of panel data, we have to compare
events from two different points: at the beginning of fertile age and
at the end of it, which prevents us from validating hypothesis H-1 vs
H-2 mentioned above. Given that these hypotheses seem to be the
main factors explaining the presence of unintended children (besides
the variables considered in the models), we test for the existence of
changes in desired fertility over the woman’s lifecycle by using two
different strategies.
First, we run a regression of the desired number of children (desired
fertility) on a set of explanatory variables using a subsample that only
includes women between 18 and 25 years old. The regressors include
age, years of schooling, urban zone, marital status, socioeconomic
strata, and knowledge of a contraceptive method. The resulting es-
timated coefcients are subsequently used to produce out-of-sample
forecasts of desired fertility at the beginning of fertile age for all the
women in the sample (labeled “estimated” desired fertility)5.
We compare this variable with another variable extracted from the
survey which is the actual answer to the question about desired fertil-
ity, which we label “Observed” fertility.
Both “estimated” and “observed” fertility variables are shown in table 2,
for grouped ages. The average difference between observed and esti-
mated desired fertility is very small. By age range, all these differences
are positive. It means that predicted desired fertility at the beginning
of fertile age is slightly lower than reported desired fertility at each
range later in the life-cycle. In other words, the results indicate that
the reported number of desired children does not change signicantly
as women age. Consequently, these results are evidence against the
second hypothesis because there are no signs of regret in the number
of desired children over the life-cycle. If fertility preferences remain
constant over the women’s life cycle, the main reason for unintended
births should be due to H2, i.e., other failures in family formation.
5 In order to test the robustness of the results, the regression model was also estimated using
women in the following ranges of age: 18-23 years; 18-24, years and 18-26 years. The
results are qualitatively the same and therefore not reported here.

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For the second strategy, we use a non parametric approach for establish-
ing the relationship between desired fertility and age. When we plot the
estimated desired fertility based on the coefcients from young women
and desired fertility reported in the d h s survey (in the vertical axis)
with respect to age (in horizontal axis), we nd the former is slightly
different6 (Figure 1). This means that desired family size seems to be
increasing over the life-cycle rather than the opposite, which lends
support to H-1. Again, this nding provides us with evidence against
the second hypothesis, therefore favoring the rst one.
Table 2. Differences between observed desired number of children and
estimated desired fertility at beginning of fertile age.
Desired fertility
(observed)
Desired fertility
(estimated)
Age Obs. Mean Std. Dev. Mean Std. Dev. Diff.
15-19 620 1,98 0,76 1,98 0,11 0,001
20-24 2.941 2,03 0,80 2,03 0,11 0,000
25-29 3.747 2,14 0,93 2,10 0,11 0,038
30-34 3.985 2,33 1,10 2,17 0,11 0,155
35-39 4.146 2,51 1,28 2,25 0,12 0,254
40-44 3.844 2,69 1,44 2,33 0,12 0,368
45-50 3.501 2,86 1,57 2,41 0,12 0,459
Source: d h s – Macro International, 2005.
To summarize, what our results tend to support is that realized fertility
is higher than desired fertility due to failures in family formation, e.g.,
access to contraception. It seems that there are no “regret effects” or
changes in preferences over time.
After these procedures that allow us to get new evidence for isolating
the two distinct hypotheses, we proceed to estimate the model in four
specications using different proxies for the women’s socioeconomic
background such as socioeconomic strata classication used by the
government to dene the level of subsidies for public utilities and an
asset index constructed by the authors. Socioeconomic strata is a good
categorical variable to proxy for income because it reects the physi-
6 These estimations are not shown but are available upon request.

98
Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
cal conditions of the neighborhood in which the house is located (the
existence of parks, main avenues, industries among others) and the
conditions of the house (wall and oor materials, the availability of
public utilities such as energy, water, and xed phone in the house).
This variable ranges from one (worst) to six (best) in the case of resi-
dential units and it is also a proxy for the price of housing. There are
some houses without this classication and they are known as “illegal
houses” because they were built without construction permits. In that
case, policy makers place them at the bottom of the income pyramid.
The variable ‘socioeconomic strata’ is one of the most common cat-
egorical variables used for classifying households in Colombia due
to its correlation with income and wealth. Our proxy to assets is an
index based on the possession of different assets in the household. The
reason why we include such a variable is that it has been documented
that physical assets —especially in agricultural societies— are related
to higher fertility. See, for example, Schultz 1998.
Figure 1. Estimated and desired fertility by age.
Source: d h s – Macro International, 2005.
Among the explanatory variables we also include some control vari-
ables (age, age squared, a dummy that indicates if the woman lives in

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rural areas, age at the birth of her rst child, marital duration in years,
and a dummy ‘marital status’ equal to one if she has a permanent or
stable relationship (marriage or similar). External effects are proxied
by two different variables. On the one hand, we include one dummy
variable ‘shock’ equal to one if the women have faced situations that
affect their long run expectations such as divorce or widowhood. We
expect that the occurrence of such shock could reduce their desired
number of children from the initial level and change their preferences.
Statistical signicance in shock implies positive evidence in favor of
the hypothesis 2. On the other hand, we include the average number of
total children in the strata and city where the women live. In this last
case, our variable can get us some information about external pressures
to have an additional child or the external control of the ignorance of
future implications of additional children (see appendix 1).
From the health point of view, we test two variables, self related health
status and Body Mass Index. Statistical signicance in these variables
can give us information about the importance of their physical health
on the difference between desired and realized number of children.
The knowledge of contraception methods is also included with two
different dummy variables: the use of contraception and knowledge
about them. However, our database has one shortcoming: the informa-
tion about use of contraceptives is only available at the time the survey
is conducted, which limits its inuence on the dependent variable.
In order to get an idea about its inuence on the dependent variable,
we include a dummy variable that is equal to one if she has used a
contraceptive method.
Two important aspects require attention. Although several works in
the literature include references to religion, we do not have this infor-
mation because the Colombian d h s does not include questions on this
matter. However, given that majority of Colombians (more than the
85% of the population) are Catholic, omitting this variable will not
have signicant implications of omitted variables. Second, we do not
have a panel data that allows us to evaluate changes in preferences for
the same observation (women) in different times.

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Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
B. Descriptive statistics
The analysis of the distribution of our variable yi, gives us some in-
teresting results. By area of residence, we nd that women living in
cities have a higher rate of success of not having unintended children
(36 out of 100 women in rural areas and 51 out of 100 in urban areas
do not have unintended children) (Table 3). However, what is more
important is that more than 10% of women in rural areas have ve or
more unintended children, (less than 3% in urban zones). In fact, a
mean comparison test indicates statistically signicant differences in
the unconditional mean of unintended children by area of residence.
Table 3. Mean of unintended children and population distribution,
Colombia 2005.
Mean of unintended births % Population
Age group
40-44 1,2 50,8
45-49 1,4 49,2
Region
Atlantica 1,32 18,34
Oriental 1,49 18,39
Central 1,28 26,76
Pacica 1,37 16
Bogota 1,02 19,46
National territories 1,48 1,05
Socioeconomic strata
No electricity 2,17 3,39
1 1,84 18,38
2 1,35 43,34
3 0,85 28,2
40,63 3,97
5 0,63 0,79
60,44 0,87
Educative level
No education 2,53 5,35
Primary 1,72 41,56
Secundary 0,92 41,15
Higher 0,51 11,95
Mate’s educative level
No education 2,03 7,01
Primary 1,68 41,98
Secundary 0,99 37,04
Higher 0,56 13,96
Total 1,29 100
Source: d h s – Macro International, 2005.

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We also nd incidence of unintended children more often among adult
women (Table 3). For a deeper understanding of the relationship be-
tween the age and the number of unplanned children, we have to take
into account changes in preferences or in socioeconomic backgrounds
(education, income, marital status, among others). For instance, if we
had information from the same woman in different time periods, by
asking her the same question, If you could go back to the time you still
did not have any children and if you could choose the exact number
of children to have in your lifetime, how many would you have, the
response would suggest if the woman has some regrets on her number
of children If we can state that in fact she regrets it, we could afrm
that a change in her preferences has taken place. Differences between
the number of unintended children by age ranges, could also be a
consequence of changes in socioeconomic situation faced or expected
by the woman or her partner. In our database we only have informa-
tion in one point and it prevents us from directly evaluating changes
in fertility preferences.
Table 3 also indicates that women who live in Bogotá —which is
the capital and most populated city in the country with more than six
million people— have the smallest average difference between the
preferred and the actual number of children. By socioeconomic strata,
the rate of success is higher among women of upper socioeconomic
position.
We also conrm that unintended children are negatively related to the
mother’s as well as the father’s education; Women whose partners have
no education have more than two unplanned children while this number
falls to 0.55 when their partners have attended the university. Women
with no education have on average 5,2 times more unintended children,
with respect to women with higher education (see Table 3). As can be
seen from Figure 2, women’s years of schooling and the number of
unintended children are negatively related. Moreover, the opportunity
cost of childbearing is evident in that more educated women wish to
have fewer children, in comparison to the less educated women in our
sample. While 2,8% of non-educated women do not want to have chil-
dren, this value is 3,8% in the case of women with higher education.
The proportion of women that who would like to have more than ve
children, decreases as the level of education increases: 15% of women

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Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
without education want to have ve children or more, while in the case
of the most educated women, this value is less than 1%.
Figure 2. Unintended number of children and years of education in
Colombia.
Guessing children
Years of schooling
0
0,5
1
1,5
2
2,5
5101520
Source: d h s – Macro International, 2005.
Finally, the distribution of unplanned children per educational level
indicates success (Yi = 0) in 70% of women with higher education
and 30% in women without education (Figure 3). The inverse rela-
tion between unintended children and schooling denotes high success
among highly-educated women. The possible causes will be studied
in the next section.
C. Empirical results
Our empirical approach begins with the estimation of our model by
four different econometric methodologies (p r m , n b r m , z i p and z i n b ) (see
detailed results in appendix 2). As it was mentioned above, Poisson
Regression Model (p r m ) and Negative Binomial Regression Model
(nbrm) differ from the Zero Inated models (zip and zinb) in that
the latter appear more suitable in the presence of excess zeros. All the

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models estimated exhibited the same sign in the estimated coefcients
and similar levels of signicance. We use two approaches for selecting
the best econometric specication. First, we compare the plots of the
differences between the observed and predicted values of Yi for each
model (Models 1 to 4) and for each specication (p r m , n b r m , z i p and
z i n b ). The results, which can be seen in appendix 3, indicate that z i p and
z i n b exhibit a better t. Second, we use both the Akaike and Schwartz
Bayesian information criteria (a i c and b i c , respectively) and Voung´s
likelihood ratio test, (see appendix 4). Our ndings suggest that the
z i n b and z i p models are the preferred model specications7.
Figure 3. Unintended number of children and women’s education level in
Colombia.
0
012345omore
No education Primary Secondary Higher
10
20
30
40
50
60
70
80
Source: d h s – Macro International, 2005.
Table 4 summarizes four different specications for the z i p and z i n b
models. The rst two models include educational levels by using
dummy variables, but the two models differ in the use of the socio-
economic variable; the former uses the assets index and the latter uses
the ‘socioeconomic strata’. The last two models include years of
education instead, and as in the two previous cases, they use distinct
socioeconomic variables.
7 Appendix 4 summarizes these tests: Panel i compares p r m vs. the rest of methodologies. Panel
ii does it for n b r m vs Zero Inated Models and panel iii compares z i p vs. z i n b models.

104
Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
Table 4. Results of the Poisson and negative binomial regression models
(semi-elasticities).
Dependent variable:
Yi= Realized – Desired
fertility
Model 1 Model 2 Model 3 Model 4
zip zinb zip ZINB zip ZINB zip ZINB
Education in single years - - - - -4 -4 -4,5 -4,5
- - - - -(9,46) -(9,37) -(10,78) -(10,63)
Primary -6,5 -6,6 -9,6 -9,7 - - - -
-(1,61) -(1,6) -(2,43) -(2,39) - - - -
Secondary -25,4 -25,5 -29,1 -29,3 - - - -
-(5,9) -(5,82) -(7,0) -(6,86) - - - -
Higher -39,8 -40 -45 -45,2 - - - -
-(5,34) -(5,29) -(6,32) -(6,24) - - - -
Current age 13,9 13,8 10,6 10,3 17,7 17,7 15 14,9
(0,78) (0,76) (0,6) (0,57) (0,97) (0,96) (0,83) (0,81)
Age squared -0,1 -0,1 -0,1 -0,1 -0,2 -0,2 -0,1 -0,1
-(0,63) -(0,61) -(0,46) -(0,43) -(0,84) -(0,82) -(0,7) -(0,68)
Live rural 18,4 18,6 15,7 16 17,5 17,7 14,9 15
(5,51) (5,44) (4,76) (4,68) (5,27) (5,23) (4,53) (4,47)
Asset index -18,8 -18,9 - - -17,3 -17,3 - -
-(6,87) -(6,8) - - -(6,24) -(6,19) - -
Socio-economic strata - - -4,6 -4,7 - - -3,6 -3,7
- - -(1,38) -(1,36) - - -(1,09) -(1,08)
Marital duration -1,0 -1,0 -1,0 -1,0 0 0 -0,1 -0,1
-(0,03) -(0,05) -(0,07) -(0,09) -(0,01) -(0,02) -(0,05) -(0,07)
Married 1,6 1,6 0,4 0,4 1,2 1,2 0 0
(0,48) (0,46) (0,13) (0,11) (0,36) (0,35) (0,01) -(0,01)
External shock
(divorce or widowhood)
-1,4 -1,4 -2,1 -2 -2,3 -2,2 -2,9 -2,9
-(0,24) -(0,23) -(0,36) -(0,34) -(0,38) -(0,37) -(0,49) -(0,48)
Knowledge of
contraception 16 16,1 14,7 14,8 16,5 16,6 15,1 15,2
(0,76) (0,75) (0,7) (0,68) (0,78) (0,78) (0,72) (0,71)
Use of contraception -16,3 -16,3 -17 -16,9 -15,4 -15,4 -15,9 -15,8
-(2,58) -(2,51) -(2,7) -(2,59) -(2,43) -(2,38) -(2,5) -(2,43)
Continued

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Table 4. Results of the Poisson and negative binomial regression models
(semi-elasticities).
Dependent variable:
Yi= Realized – Desired
fertility
Model 1 Model 2 Model 3 Model 4
Age at the rst son -6,8 -6,8 -6,8 -6,9 -6,6 -6,6 -6,6 -6,6
-(13,28) -(13,06) -(13,33) -(13,04) -(12,84) -(12,66) -(12,83) -(12,6)
Peer effect 13 13,1 10,8 10,8 12,3 12,3 10,9 10,9
(6,01) (5,92) (2,48) (2,42) (5,69) (5,63) (2,52) (2,47)
Body mass index -0,5 -0,5 -0,6 -0,6 -0,5 -0,5 -0,6 -0,6
-(1,66) -(1,63) -(2,12) -(2,08) -(1,65) -(1,64) -(2,07) -(2,04)
Constant -81,8 -81,5 -54,7 -52,2 -90,6 -90,6 -80,9 -80,6
-(0,45) -(0,44) -(0,21) -(0,19) -(0,63) -(0,62) -(0,44) -(0,43)
N5181 5181 5181 5181 5181 5181 5181 5181
Ll
-7824,34 -7823,86 -7854,42 -7853,31 -7810,32 -7810,08 -7835,24 -7834,60
Source: d h s – Macro International, 2005. t-statistic in parentheses.
Semielasticities are obtained by (exp(BX)-1*100).
All the specications used are equally robust and the sign of the co-
efcients are the same. From these, we can extract some interesting
ndings.
As we expected, after controlling for all the variables mentioned, we
nd a negative (and statistically signicant) relationship between
education and the unintended children. Table 4 summarizes the semi-
elasticities obtained from the estimated coefcients. High human
capital (measured by levels or years of schooling) is negatively related
to the number of unintended children. Higher success in achieving the
desired fertility among most educated people arises from their wider
knowledge of the future cost of children. As we expected, the value of
the semi-elasticity is greater in absolute value in women with higher
education than in women with basic education. The fact of having
achieved primary education reduces in 6,5% the number of unintended
children; this percent in the cases of secondary and higher education
are 25,4% and 39,8% respectively.
The relationship between unintended births and the mother’s education
may be explained by the interaction of different factors: rst, more

106
Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
educated women tend to postpone motherhood. Thus, in the case of
women with the high education levels, it would be reasonable to nd
that they start to have children later (in comparison to less-educated
women), which reects the fact that they have less time to have chil-
dren and hence, lower likelihood of making a mistake in the preferred
number of children. Besides —as we mentioned above— more edu-
cated women would like to have fewer children (as a consequence
of the effect of education on women’s preferences).Second, the op-
portunity cost of having children is higher for more educated women,
which could explain not only their preference for smaller families, but
also their use of birth control methods in order to achieve the exact
number of desired children. Third, these facts may be reinforced tak-
ing into account that educational levels of women and their partners’
tend to be similar.
People living in rural areas seem to be more exposed to risk than those
in urban populations. According to the semi-elasticities estimated,
living in these zones increases unintended children by about 16,7%
under both methods (z i p and z i n b ). This may be explained taking into
account cultural conditions in rural areas: rst, in rural areas we nd
a male dominated culture where the woman’s role is different than in
urban zones. Second, in rural areas women tend to have more children
since kids are seen as inputs in the home’s production function (i.e.
daughters help with domestic chores and sons help with land labor). As
a result, even if a rural woman preferred fewer children, her expecta-
tions may not t the effective number of children because of factors
associated to the culture in those zones.
The set of variables used for controlling the socioeconomic level of
respondents (socioeconomic strata or assets index) have the expected
sign but they are not always statistically signicant in the case of
socioeconomic strata. This can be a result of small differences in
the stratication. For example, there are no considerable differences
between physical conditions and neighborhoods from strata 6 or 5. In
Colombia, the population in the highest quintile of income belongs to
strata 5th and 6th. As we expected, the fact of being in a higher socioeco-
nomic position could contribute to the reduction in unintended births,
but this effect is possibly captured by the knowledge of contraceptive
methods and higher access to them.

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One interesting nding is that women who start their motherhood later
reduce their risk of having more unintended children. This may lead
to some policy recommendations because if some kind of program
is designed in order to delay the age at which motherhood starts, we
can reduce the risk of unintended children. It is common to nd that
women from low income deciles have on average more children and
starts their motherhood earlier than higher income ones. Here it is im-
portant to note that neither marital duration nor marital status explain
our dependent variable.
In order to assess the effect of adverse external shocks on prefer-
ences, we include our dummy ‘shock’ as we have previously dened.
None of the external shocks included (divorce or widowhood) seem
to be signicant. These variables could also give us some ideas about
changes in preferences over women’s lifecycle. However, it is not pos-
sible to know the timing of the event, which prevents us from deriving
conclusions that the existence of that shock induces women to change
their demand for children.
Finally, in order to isolate the effect of education on our dependent
variable from the knowledge and use of contraception, we include two
variables, the answer to questions about the use and the knowledge
of contraception methods. We nd that people who report that they
have used some contraception method, have more success in their
nal family size.
III. Concluding remarks
Our ndings conrm the hypotheses that the more educated the women
are, the smaller the number of unintended children they will have. Our
ndings give us some important policy implications. As we mentioned
in the previous section, risk exposure is higher in women from rural
areas and with lower human capital levels. Thus, public policy should
focus on programs that give more information about consequences
and implications of reproductive behavior for those out of the formal
educational system. The challenge is to delay the motherhood in young
women by increasing the available information that can help them
make decisions using cost-benet analysis. These should be comple-

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Family Size in Colombia: Guessing or Planning?
Intended vs. Actual Family Size in Colombia
Nohora Forero and Luis Fernando Gamboa
mented with the use of information and communications technologies
such as internet, mobile and television to provide assistance to remote
populations who do not have access to formal education. Although
fertility rates have decreased during the last decades, it is important
to provide more information about the public and private initiatives
that try to help adult women who have already nished their basic
education. This point is especially important if we take into account
the fact that there is not enough coverage of higher education in remote
areas. Our ndings are starting points in the study of the implications
of unintended children on poverty and economic inequality.
In this sense, the conjunction of more public focalized programs and
more efforts that enhance, for instance, school attendance could not
only reduce the number of unintended children in rural areas, but it
could also help to improve public health. Given the positive exter-
nalities of education, we could expect this kind of policies to have
intergenerational effects. As a result, less educated women who can
be beneted by these policies not only would have less unintended
births, but also would be able to afford better conditions for their
offsprings.
However, these types of efforts face at least one considerable constraint.
Since most Colombians are Catholic, their beliefs can impede them to
use some contraception methods.
Special attention is needed in the young population because of the
possible intergenerational effects that unintended births may have on
their standard of living. Young women with unintended children quit
studying and since they do not study, they cannot afford a better qual-
ity of life for their children. This fosters a vicious circle of poverty for
their children, which should be broken.
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number 064240.

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Appendix
Appendix 1. Descriptives.
Variable Denition Obs. Mean Std. Dev. Min. Max.
Age Age in years 5567 44,34 2,87 40 49
Rural Dummy equal to one if she lives in
rural areas 5567 0,22 0,41 01
Years of
schooling 5567 6,83 4,30 019
Peer efect Average of total children in their
environment (socioeconomic strata
and region)
5567 3,87 0,69 2,25 6,28
Asset index Index from 0 to 6 of total of durable
goods owned by the household (t v ,
telephone, radio, refrigerator, car)
5567 0,48 0,50 01
Socioeconomic
strata Index from 1 (worst) to 6 (best) 5567 1,95 0,95 05
Primary ed. Dummy equal to one if her highest
education level is primary 5567 0,43 0,50 01
Secondary ed. Dummy equal to one if her highest
education level is secondary 5567 0,40 0,49 01
Higher ed. Dummy equal to one if she /he
has attended a tertiary education
institution
5567 0,11 0,32 01
Marital duration
(years)
Number of years since she got
married 5567 23,80 5,95 1 37
Marital status Dummy equal to one if she is
married or living together 5567 0,72 0,45 01
External shock Dummy equal to one if she is
widowed or divorced. 5567 0,06 0,24 01
Know and use
contraception
Do you know and use any
contraception method? 5567 2,92 0,48 03
Use
contraception Do you use contraception methods? 5567 0,97 0,16 01
Age at the rst
son years 5567 21,31 4,25 11 43
b m i Body mass index 5567 27,34 4,93 15,48 50
Source: d h s – Macro International, 2005.

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Appendix 2. Estimated coefficients of count data models.
Model 1 Model 2 Model 3 Model 4
Variable p r m n b r m z i p z i n b p r m n b r m z i p z i n b p r m n b r m z i p z i n b p r m n b r m z i p z i n b
Current age 1,21 1,16 1,14 1,14 1,20 1,13 1,11 1,10 1,24 1,21 1,18 1,18 1,23 1,18 1,15 1,15
1,34 0,77 0,78 0,76 1,25 0,63 0,6 0,57 1,51 0,95 0,97 0,96 1,43 0,84 0,83 0,81
Age squared 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00
-1,15 -0,63 -0,63 -0,61 -1,08 -0,50 -0,46 -0,43 -1,35 -0,82 -0,84 -0,82 -1,28 -0,72 -0,70 -0,68
b m i 1,00 1,00 1,00 1,00 1,00 1,00 0,99 0,99 1,00 1,00 1,00 1,00 1,00 1,00 0,99 0,99
-1,61 -0,98 -1,66 -1,63 -2,25 -1,49 -2,12 -2,08 -1,59 -1,01 -1,65 -1,64 -2,20 -1,48 -2,07 -2,04
Rural 1,18 1,20 1,18 1,19 1,14 1,16 1,16 1,16 1,17 1,19 1,18 1,18 1,13 1,15 1,15 1,15
6,13 4,64 5,51 5,44 4,91 3,77 4,76 4,68 5,74 4,40 5,27 5,23 4,55 3,55 4,53 4,47
Years of
schooling - - - - - - - - 0,95 0,95 0,96 0,96 0,94 0,94 0,96 0,96
- - - - - - - - -15,00 -11,25 -9,46 -9,37 -17,02 -12,73 -10,78 -10,63
Primary ed. 0,94 0,93 0,94 0,93 0,90 0,89 0,90 0,90 - - - - - - - -
-1,73 -1,31 -1,61 -1,60 -2,63 -1,93 -2,43 -2,39 - - - - - - - -
Secondary ed. 0,69 0,69 0,75 0,75 0,65 0,65 0,71 0,71 - - - - - - - -
-8,30 -5,79 -5,90 -5,82 -9,81 -6,78 -7,00 -6,86 - - - - - - - -
Higher ed. 0,52 0,52 0,60 0,60 0,47 0,47 0,55 0,55 - - - - - - - -
-9,38 -7,18 -5,34 -5,29 -10,92 -8,35 -6,32 -6,24 - - - - - - - -
Asset index 0,78 0,78 0,81 0,81 - - - - 0,80 0,80 0,83 0,83 - - - -
-9,66 -7,27 -6,87 -6,80 - - - - -8,79 -6,57 -6,24 -6,19 - - - -
Socioeconomic
strata - - - - 0,94 0,92 0,95 0,95 - - - - 0,95 0,93 0,96 0,96
- - - - -2,09 -1,94 -1,38 -1,36 - - - - -1,78 -1,68 -1,09 -1,08
Marital duration
(years) 1,00 0,99 1,00 1,00 1,00 0,99 1,00 1,00 1,00 0,99 1,00 1,00 1,00 0,99 1,00 1,00
0,09 -0,42 -0,03 -0,05 -0,07 -0,46 -0,07 -0,09 0,19 -0,38 -0,01 -0,02 0,03 -0,42 -0,05 -0,07
Continued

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Appendix 2. Estimated coefficients of count data models.
Marital status 1,05 1,04 1,02 1,02 1,03 1,02 1,00 1,00 1,04 1,04 1,01 1,01 1,03 1,02 1,00 1,00
1,54 1,00 0,48 0,46 0,95 0,53 0,13 0,11 1,44 0,93 0,36 0,35 0,91 0,49 0,01 -0,01
External shock 1,07 1,07 0,99 0,99 1,07 1,07 0,98 0,98 1,07 1,06 0,98 0,98 1,06 1,06 0,97 0,97
1,30 0,97 -0,24 -0,23 1,22 0,89 -0,36 -0,34 1,22 0,86 -0,38 -0,37 1,17 0,78 -0,49 -0,48
Know and use
contraception 1,17 1,18 1,16 1,16 1,15 1,16 1,15 1,15 1,19 1,20 1,17 1,17 1,16 1,18 1,15 1,15
0,92 0,70 0,76 0,75 0,81 0,61 0,70 0,68 1,00 0,74 0,78 0,78 0,90 0,67 0,72 0,71
Use
contraception 0,88 0,91 0,84 0,84 0,85 0,89 0,83 0,83 0,89 0,92 0,85 0,85 0,87 0,90 0,84 0,84
-2,05 -0,96 -2,58 -2,51 -2,48 -1,18 -2,70 -2,59 -1,84 -0,86 -2,43 -2,38 -2,19 -1,06 -2,50 -2,43
Age at the rst
son 0,91 0,91 0,93 0,93 0,91 0,91 0,93 0,93 0,92 0,91 0,93 0,93 0,92 0,91 0,93 0,93
-21,12 -16,03 -13,28 -13,06 -21,31 -16,00 -13,33 -13,04 -20,31 -15,52 -12,84 -12,66 -20,41 -15,43 -12,83 -12,60
Peer efect 1,21 1,21 1,13 1,13 1,17 1,15 1,11 1,11 1,20 1,20 1,12 1,12 1,17 1,15 1,11 1,11
10,39 7,36 6,01 5,92 4,17 2,62 2,48 2,42 9,85 7,09 5,69 5,63 4,09 2,64 2,52 2,47
Constant 0,03 0,08 0,18 0,19 0,07 0,23 0,45 0,48 0,02 0,04 0,09 0,09 0,04 0,09 0,19 0,19
-1,06 -0,57 -0,45 -0,44 -0,85 -0,32 -0,21 -0,19 -1,21 -0,73 -0,63 -0,62 -1,02 -0,53 -0,44 -0,43
Statistics
alpha
-0,54 - - - 0,56 - - - - - -
N
5.181 5.181 5.181 5.181 5.181 5.181 5.181 5.181 5.181 5181 5181 5181 5181 5181 5181 5181
ll
-8.415,78 -8.033,85 -7.824,34 -7.823,86 -8.461,02 -8.058,16 -7.854,42 -7.853,31 -8.393,72 -8.022,93 -7.810,32 -7.810,08 -8.431,32 -8.042,98 -7.835,25 -7.834,61
bic
16.968,41 16.213,10 15.922,37 15.929,97 17.058,88 16.261,72 15.982,53 15.988,86 16.907,18 16.174,15 15.860,12 15.868,19 16.982,38 16.214,26 15.909,97 15.917,24
aic
16.863,57 16.101,70 15.712,69 15.713,73 16.954,04 16.150,32 15.772,84 15.772,62 16.815,44 16.075,85 15.676,64 15.678,16 16.890,64 16.115,96 15.726,49 15.727,21
t-statistic is shown under the coefcient
Note: For each model, it was estimated four specications. p r m : Poison Regression Model, n b r m : Negative Binomial Regression Model,
z i p : Zero Inated Regression Model, z i n b : Zero Inated Negative Binomial

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Nohora Forero and Luis Fernando Gamboa
Appendix 3. Goodness of fit. Differences between observed-predicted values.
-.1 -.05 0.05 .1
Observed-Predicted
0 1 2 3 4 5 6 7 8 9
Count
PRM NBRM
ZIP ZINB
Note: positive deviations show underpredictions.
-.1 -.05 0.05 .1
Observed-Predicted
0 1 2 3 4 5 6 7 8 9
Count
PRM NBRM
ZIPZINB
Note: positive deviations show underpredictions.
Model 1
(a)
(b)
Model 2
Continued

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Appendix 3. Goodness of fit. Differences between observed-predicted values.
-.1 -.05 0.05 .1
Observed-Predicted
0 1 2 3 4 5 6 7 8 9
Count
PRM NBRM
ZIP ZINB
Note: positive deviations show underpredictions.
-.1 -.05 0.05 .1
Observed-Predicted
0 1 2 3 4 5 6
Count
PRM NBRM
ZIP ZINB
Note: positive deviations show underpredictions.
789
Model 3
Model 4
(d)

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Family Size in Colombia: Guessing or Planning?
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Nohora Forero and Luis Fernando Gamboa
Appendix 4. Information criteria test.
Model 1 Model 2
PRM BIC = -27343.40 AIC = 3.25 Prefer Over Evidence BIC = -27252.93 AIC = 3.27 Prefer Over Evidence
vs NBRM BIC = -28098.72 AIC = 3.11 NBRM PRM Very strong BIC = -28050.10 AIC = 3.11 NBRM PRM Very strong
(i) vs ZIP BIC = -28389.44 AIC = 3.03 ZIP PRM Very strong BIC = -28329.28 AIC = 3.04 ZIP PRM Very strong
Vuong = 16.39 prob = 0.00 p = 0.000 Vuong = 16.56 prob = 0.00 Prefer Over p = 0.000
vs ZINB BIC = -28381.85 AIC = 3.03 ZINB PRM Very strong BIC = -28322.95 AIC = 3.04 ZINB PRM Very strong
NBRM BIC = -28098.72 AIC = 3.11 Prefer Over Evidence BIC = -28050.10 AIC = 3.12 Prefer Over Evidence
(ii) vs ZIP BIC = -28389.44 AIC = 3.03 ZIP NBRM Very strong BIC = -28329.28 AIC = 3.04 ZIP NBRM Very strong
vs ZINB BIC = -28381.84 AIC = 3.03 ZINB NBRM Very strong BIC = -28322.95 AIC = 3.04 ZINB NBRM Very strong
Vuong = 11.58 prob = 0.00 ZINB NBRM p = 0.000 Vuong = 11.44 prob = 0.00 ZINB NBRM p = 0.000
ZIP BIC = -28389.44 AIC = 3.03 Prefer Over Evidence BIC = -28329.28 AIC = 3.04 Prefer Over Evidence
(iii) vs ZINB BIC = -28381.84 AIC = 3.03 ZIP ZINB Strong BIC = -28322.95 AIC = 3.04 ZIP ZINB Strong
Model 3 Model 4
PRM BIC = -27404.633 AIC = 3.246 Prefer Over Evidence BIC = -27329.436 AIC = 3.260 Prefer Over Evidence
vs NBRM BIC = -28137.669 AIC = 3.103 NBRM PRM Very strong BIC = -28097.560 AIC = 3.111 NBRM PRM Very strong
(i) vs ZIP BIC = -28451.696 AIC = 3.026 ZIP PRM Very strong BIC = -28401.846 AIC = 3.035 ZIP PRM Very strong
Vuong = 16.278 prob = 0.000 ZIP PRM p = 0.000 Vuong = 16.411 prob = 0.000 ZIP PRM p = 0.000
vs ZINB BIC = -28443.624 AIC = 3.026 ZINB PRM Very strong BIC = -28394.573 AIC = 3.036 ZINB PRM Very strong
NBRM BIC = -28137.669 AIC = 3.103 Prefer Over Evidence BIC = -28097.560 AIC = 3.111 Prefer Over Evidence
(ii) vs ZIP BIC = -28451.696 AIC = 3.026 ZIP NBRM Very strong BIC = -28401.846 AIC = 3.035 ZIP NBRM Very strong
vs ZINB BIC = -28443.624 AIC = 3.026 ZINB NBRM Very strong BIC = -28394.573 AIC = 3.036 ZINB NBRM Very strong
Vuong = 11.654 prob = 0.000 ZINB NBRM p = 0.000 Vuong = 11.540 prob = 0.000 ZINB NBRM p = 0.000
ZIP BIC = -28451.696 AIC = 3.026 Prefer Over Evidence BIC = -28401.846 AIC = 3.035 Prefer Over Evidence
(iii) vs ZINB BIC = -28443.624 AIC = 3.026 ZIP ZINB Strong BIC = -28394.573 AIC = 3.036 ZIP ZINB Strong
Panel i compares p r m vs. n b r m , z i p and z i n b . Panel ii does it for n b r m vs Zero Inated Models and panel iii compares z i p vs. z i n b models.
b i c : Schwartz Bayesian Information Criterion. a i c : Akaike information Criterion. Voung: Voung Test for nonnested models.