end-of-award report · information on the timing and duration of poverty spells. recent research on...

REFERENCE No. RES000230462

Poverty dynamics and fertility in developing countries

End-of-award Report

Arnstein Aassve

Stephen Pudney

Institute of Social and Economic Research

University of Essex

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To cite this output: Aassve, A(2007). Poverty Dynamics and Fertility in Developing Countries: Full Research Report ESRC End of Award Report, RES-000-23-0462. Swindon: ESRC


1 Background

The background of the project derives from a long debate in economics and demography

on the driving forces behind high poverty and fertility (Birdsall et al. 2001). This stems

from the empirical observation that poorer countries tend to have higher population

growth rates and that larger households tend to be poorer. There is a presumption of a

negative causal relation between poverty and fertility at the national and household levels

respectively. A key shortcoming in this literature (McNicoll 1997) is that existing studies

rely on either cross sectional surveys or aggregate data sources. There seems a clear need

to re-assess the fertility-poverty relationship using longitudinal household surveys and we

have attempted to meet this need in this project. Only longitudinal surveys can provide

information on the timing and duration of poverty spells. Recent research on poverty

and fertility dynamics in industrialised countries has provided striking advances in our

understanding of poverty and policy making in general (e.g. Huff-Stevens 1999). Inspired

by this progress the aim of this study has been to analyse the relationship between

fertility and poverty dynamics in developing countries.

Sequencing of events reflected in longitudinal information, does not imply

causation. A key aim of the project, therefore, was to implement alternative

methodologies, such as treatment effect models, Instrumental Variables (IV) and

simultaneous hazard regression, to identify causal relationships, and therefore inform

policy makers about what policies may - or may not - work in reducing poverty. The

longitudinal data used in this project come from Ethiopia, Vietnam, Indonesia and

Albania.

2 Objectives

The project has had four main objectives

1. To contribute to the development economics and demography literature by using

recently-developed advanced statistical methods that will enhance our understanding

of causal relationships between poverty, fertility, education, and health. The research

will contribute to a relatively underdeveloped field with recent data sources and new

and improved techniques.

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2. To use this analysis as part of a much larger comparative project, involving

longitudinal surveys from Indonesia, Ethiopia, Albania, and Vietnam, involving

further collaborative partners in Europe.

3. To analyse and review how policies aimed at reducing poverty can be best

implemented through the related processes such as fertility, health, work and family

and fertility planning.

4. To communicate these findings to practitioners through academic journals as well as

non-academic outlets. The ultimate objective is to make improvements to policies

aimed at reducing poverty and population growth, but where this is based on

rigorous and robust empirical modelling and analysis.

3 Methods

The analysis is based on secondary analysis of longitudinal data from the four countries.

We implemented two comparative papers using data from all four countries. The first,

following a long process of data cleaning and harmonization, is descriptive in the sense

that we use simple techniques such as probit and count data models (Aassve et al 2006

[12]). In the second comparative paper we summarise the pattern of poverty transitions,

distinguishing between transitions driven by changes in economic and demographic

factors. Economic factors are defined as changes in household income or consumption

whereas demographic factors refer to changes in household size and structure. The aim

was to make a simple assessment of the extent demographic changes drive poverty

transitions compared to changes in economic factors. By using four different equivalence

scales and two poverty lines, we are able to compare how demographic changes,

compared to income changes, drive changes in poverty status (Pudney and Aassve 2007

[1]).

A third paper investigates the sensitivity for poverty dynamics from neglecting

endogenous fertility preferences (Pudney and Aassve 2007 [2]). The motivation is as

follows. The typical applied analysis of poverty dynamics works with a real equivalised

income or consumption variable as the underlying household welfare indicator. A

poverty threshold is set a priori and a binary indicator of poverty status is constructed for

each household at each observation period. The analysis then measures the frequency of

transitions between poor and non-poor status and relates these transitions to the

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evolving characteristics of the household. A new birth increases the equivalent size of the

household and, if total consumption or income does not rise sufficiently, this has the

effect of reducing the measure of household welfare, which may in turn drive the

household below the poverty line. From the perspective of revealed preference, this may

be a perverse outcome, since a consciously chosen action has led to an apparently worse

state. One could then argue that the poverty analysis gives a misleading result because

our empirical welfare measure is a mis-specified indicator of individual preferences. In

light of this, the third paper examines the effect of fertility preferences on estimates of

four important quantities: the initial poverty rate in year 0, the hazard rate for exiting

poverty and the joint probability of poverty in both periods, and the hazard rate for

entering poverty between periods 0 and 1. The model is implemented using the Vietnam

LSMS and we find that the estimates are indeed sensitive to fertility preferences.

A key aim of the project was to make improvements in our understanding of the

causal relationship between poverty on one hand and fertility on the other. The methods

can be grouped into two strands. The first is to use non-parametric matching models and

Instrumental Variables to avoid endogeneity bias. The second is based on multi-process

or simultaneous equation modelling.

A detailed outline of non-parametric matching and IV are provided in Aassve

and Arpino 2007 [3]. Here the interest lies in estimating the causal effect of fertility on

changes in households’ economic wellbeing expressed as equivalised consumption

expenditure. The underlying assumptions for the two methods are explained, and we

argue that the methods cannot be easily compared, simply because they rely on different

assumptions. Whereas, the non-parametric matching approach estimates the Average

Treatment Effect (ATE) and the Average Treatment Effect on the Treated (ATT), the

IV approach produces the Local Average Treatment Effect (LATE). Thus, the estimate

produced by IV relies directly on the nature of instruments used. Despite satisfying the

standard tests for relevance and validity, different instruments may produce different

estimates. The paper demonstrates this issue by considering two different instruments: 1)

availability of contraception at community level, and 2) the gender composition of

existing children. The counterfactual approach relies on the Conditional Independence

Assumption (CIA), which means that assignment to “treatment” is random once

observed variables are controlled for. Clearly CIA does not hold if assignment of

treatment also depends on unobserved characteristics. This is of course the main

motivation for employing the IV approach. But given the drawbacks of the IV approach

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as outlined above combined with the fact that valid instruments are often difficult to

find, it is useful to consider methods which assesses the sensitivity of the CIA assumption

- without having to implement the IV approach. The paper introduces a method

developed by Ichino et al (2007) to do exactly this.

A further method related to the two stage IV approach, was to consider a

reproduction function as a means to extract an exogenous measure of fertility (Rosenzweig

and Shultz 1985). The approach is as follows: estimate first a model of fertility. Fertility

outcomes depend obviously on contraceptive choice. But use of contraceptives is

endogenous with respect to fertility outcomes. Consequently we use an IV approach to

correct for the endogeneity bias (which is significant). From this regression we extract

the residuals, which are then considered as an exogenous measure of fertility. We

implemented here many different specifications, and in the final working paper we

consider the effect of the exogenous fertility measure on men and women’s labour

supply and earnings, which in any case is the main income source for households. The

analysis was applied to the Indonesian sample, as this was the only data source that

contained contraceptive calendars (Kim and Aassve, 2007 [10]).

We also developed methods for estimating poverty and fertility in a simultaneous

equations framework (Aassve et al 2006 [7]). In this setup there are two processes: 1)

poverty and 2) fertility. For both processes we included the lagged dependent variable as

regressors, implying that we are able to identify (and estimate) state dependence from

unobserved heterogeneity – for both poverty and fertility. In addition, lagged poverty

status is included in the fertility process and lagged fertility outcomes are included in the

poverty process. The model for poverty is given by a simple random effect probit:

� �piitp

itpp

itp

it kpxxp �� 11)|1Pr( where is the set of assumed

exogenous variables, is the lagged poverty status, is an indicator for child

bearing events, possibly endogenous with respect to poverty status, whereas is the

time-invariant and unobserved household effect. and are the key parameters of

interest since the former informs us about persistence or the scarring effect of poverty,

whereas the latter informs us about the effect of child bearing on poverty. The

specification of the fertility process is given by a probit model whereby the dependent

variable is binary taking the value one if a birth occurs between

waves:

pitx

1�itp 1�itk

pi�

p� p�

� �kiitk

itkk

itk

it pkxxk �� 11)|1Pr( where is the set of exogenous

covariates, which may or may not be the same as in equation (1), represents

kitx

pitx 1�itk

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variables measuring past birth events, is the lagged poverty status and potentially

endogenous with respect to childbearing, and is the time-invariant household random

effect related to childbearing. The small time dimension relative to the cross-sectional

dimension produces inconsistent maximum likelihood estimates (Heckman 1981). We

estimate the distribution of the initial conditions, together with the processes itself,

integrating out over the random effect. Though the approach is somewhat less

convenient than Wooldridge (2005), estimation of our model can be easily done in the

software package aML. The initial conditions for poverty in the initial time period is

given by:

1�itp

ki�

� �piipp

ip

i Kxxp �� 1000

00

00 )|1Pr( whereas the initial conditions for

fertility is given by a Poisson process: � �kiikk

ikK

i px �� 000

00

0 exp . Exclusion

restrictions are imposed to ensure identification. There is of course a concern that

fertility decisions are endogenous with respect to poverty and vice versa, so we allow the

random effect to be correlated across the processes. The full model is estimated by

maximum likelihood.

4 Results

Data quality and descriptive analysis. It is clear that longitudinal surveys of developing

countries are not of the same quality as the mainstream European ones, such as the

BHPS or the GSOEP say. Generally, panels from developing countries are shorter and

have higher attrition rates. Attrition was particularly high for the Ethiopian sample

(Aassve et al 2006 [7]). Data cleaning and quality checking was here an onerous task.

During the process we judged the fertility histories provided for the rural sample to be

unreliable. This means that we had to rely on the household roster to infer fertility

information. Whereas this is not a problem for recording fertility event taking place

across waves, it does pose a question on the reliability of fertility measured by the

number of children in the first wave.

Causal effects of poverty and fertility. The analysis of Ethiopia (Aassve et al 2006 [7])

shows a significant difference in the poverty and fertility relationship in urban and rural

areas. Whereas poverty is extremely high in both rural and urban areas, the fertility rates

in urban Ethiopia are considerably lower than in rural ones. In fact, the TFR in Addis

Ababa is 1.9, which is below replacement. In rural areas it is close to 6 children per

woman. There is a very strong scarring effect of poverty in urban areas. Thus, controlling

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for unobserved heterogeneity, observed covariates and initial conditions, it is clear that

experiencing poverty in time period t has a strong effect on becoming poor in time

period t + 1. New births are found to increase the likelihood of urban households

experiencing poverty, though the estimated effects are much smaller than the direct

scarring effect of poverty. We find poverty to have very small impact on subsequent

fertility. In rural areas the pattern is different. First, the scarring effect of poverty is

considerably smaller, essentially because income is more random over time. The impact

of fertility on poverty is also smaller, and there is no direct impact of poverty on fertility.

One should also add that other background variables have a relatively weak impact on

poverty and fertility in rural areas, much weaker than in urban areas. The findings are

interesting because they suggest that in urban areas, where there is more heterogeneity in

education and work, more policy levers are available. The strong scarring effect suggests

that direct poverty reduction policies will have strong long term impact. Poverty will also

be reduced through improvements in education and employment. The picture is very

different in rural areas. Here there is less (conditional) poverty persistence, very little

variation in education and the great majority work in labour intensive farming. Fertility is

high but largely unaffected by poverty status. It appears that rural Ethiopia is locked in a

poverty and fertility trap with few direct policy instruments readily available.

Methodology and measurement issues. The original proposal envisaged the use of

alternative poverty measures, including deprivation indices. This led to methodological

work by Pudney (2007) [13] on methods for dynamic modelling of ordinal subjective

welfare measures. His identification analysis showed that analysis was infeasible for the

four panels in this project, so that strand of methodology is being pursued in other

applications, using data from developed countries. However, subjective and other

deprivation indicators are used in other project outputs, including the analysis by Pudney

and Francavilla (2006) [8], which reveals: (i) a significant discrepancy between income

and expenditure in the lower tail of the income distribution, implying substantial

misreporting of income; (ii) the value of deprivation variables (including subjective

assessments and ownership of durables) as indicators of the occurrence of misreporting.

Income misreporting is shown to have a major distortionary impact on poverty

measurement. We are currently extending this analysis using data from developed

countries.

A major difficulty in analysing these surveys is the fact that, unlike panel studies

in most developed countries, survey waves are not contiguous in time, so that there are

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many missing observations. Mattei, Mealli and Pudney [4], using a Bayesian approach,

have developed a method of jointly modelling the fertility and consumption processes

and application of this approach is in progress, with promising early results.

The issue of equivalence scales matters in any poverty analysis. Throughout we

have followed best practice, in either using a recommended equivalence scale, using a

range of equivalence scales, or to estimate the parameters of the equivalence scale

ourselves through Engle curves (Kim et al 2005 [12]). In the paper concerning state

dependence and feedback effects of poverty and fertility in Ethiopia (Aassve et al (2006)

[7]), we use the equivalence scale developed by the World Health Organisation, whereby

the scale depends on the age of children and adults and gender. This is the scale also

used by Dercon 2004 in his analysis of Ethiopia. For the comparative paper by Pudney

and Aassve 2007 [1] we use a range of scales to make sure that our findings are robust to

the choice of equivalence scale.

Endogenous fertility preferences. As outlined in the methods section, we developed an

analysis where we consider the sensitivity of the estimated effect on poverty, allowing for

different fertility preferences (Pudney and Aassve 2007 [2]). As outlined above, an

additional child will through the equivalence scale make a household more likely to

become poor if all other factors remain constant. This is a paradoxical result since

childbearing is – at least partly – down to choice. By using an approximation approach

we find that fertility preferences are indeed very important. This is an important result

raises important issues in relation to conventional analysis of poverty dynamics.

Policy findings. The collection of papers provides several important insights for

policy. The comparative papers show that the countries certainly differ in their socio-

economic and socio-political history, which in turn shape policy. The analysis considering

state dependence in poverty and fertility together with the feedback mechanisms of these

processes has important policy implications. First, policies should be differentiated for

urban and rural areas. There are very clear policy instruments available in urban Ethiopia.

Employment and education are clear alternatives. But also direct measures to reduce

current poverty will have substantial impact on future poverty rates. The situation is very

different in rural Ethiopia, which seems stuck in a poverty/fertility trap. Here it is harder

to pinpoint specific policies. Essentially efforts have to be made at every level.

The policy issues are different in Vietnam (Pudney and Aassve 2007 [1]). From

the first wave recorded in 1992/93 to 1997/98, the Vietnamese economy experienced an

unprecedented boom which reduced poverty substantially. Around 80 percent of the

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populations live in rural areas where the main activity is farming – predominantly rice

production. With the Doi-Moi (renewal policy), there was a shift from collective farming

to a system where farmers were allocated farm land. Though, still owned by the state,

farmers were given the right to farm the land. One important implication of this policy

shift was that rice production and prices increased, which accounted for much of the

income increase among farmers, and represented the most important driver behind the

poverty reduction. Thus, Vietnam is an example where substantial poverty reduction

took place without any significant increase in inequality. The paper shows that because

the variation in household income growth was very small, demographic change appeared

as a much stronger driver behind poverty transitions.

The paper by Aassve and Arpino (2007)[5] makes an important contribution to

policy analysis since it shows how multi-level models can be applied to analysis of

poverty dynamics. In particular, it demonstrates that community characteristics, many of

which can be considered as direct policy variables, matter in determining poverty status

and dynamics. Since the Vietnamese economy is dominated by rural activities, in

particular agriculture, and rural areas are the poorest, we focused our attention on farm

households. The model includes therefore random elements that allow the poverty

profiel to differ by community. Large standard deviations for these random effects show

the importance of farm location. An important benefit of the multilevel approach is that

predictions can be used to assess community and regional differences. These predictions

identify groups of communities that benefited from economic growth more than others,

and communities that suffered during the period. Given these classifications it was

straightforward to investigate differences in characteristics, which is an important tool for

policy makers to target policies. Critical characteristics of a successful community include

key infrastructural or socio-economic variables such as the availability of electricity and

daily markets as well as the presence of schools.

References:

Dercon, S. (2004) “Growth and Shocks: evidence from Rural Ethiopia.” Journal of Development Economics 74(2): 309-329.

Heckman J.J. (1981) “Heterogeneity and State Dependence.” In Studies in Labor Markets, edited by S. Rosen S. Chicago: University of Chicago Press.

Huff Stevens, A. (1999). Climbing out of poverty, falling back in: measuring the persistence of poverty over multiple spells. Journal of Human Resources 34(3):557-588.

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Ichino A., Mealli F. and Nannicini T. (2007) “From Temporary Help Jobs to Permanent Employment: What Can We Learn from Matching Estimators and their Sensitivity?” Journal of Applied Econometrics, forthcoming.

Rosenzweig, M. and T.P Schultz (1985) “The Demand for and Supply of Births: Fertility and its Life Cycle Consequences”, American Economic Review, Vol 75(5): 992 - 1015

Wooldridge, J.M. (2005) “Simple solutions to the initial conditions problem in dynamic, non-linear panel data models with unobserved heterogeneity” Journal of Applied Econometrics 20(1): 39 - 54

5 Activities

Two workshops were organised during the course of the project and another is planned

for January 2008. The first was held at ISER, University of Essex in October 2004. The

participants were besides Aassve and Pudney from ISER: Alexia Fuernkranz-Prskawetz

and Jungho Kim (both from Vienna Institute of Demography), Fabrizia Mealli, Letizia

Mencarini, Alessandra Mattei and Francesca Francavilla, all from Department of

Statistics (University of Florence), and Abbi Kedir from Department of Economics

(University of Leicester). The main aim of this meeting was to undertake a careful

planning of the project and ensure coordination in the data cleaning process. The second

meeting was held at the Department of Statistics, University of Florence in April 2006,

with the same participants, apart from Abbi Kedir. In contrast to the first meeting,

details of work was presented and discussed. Moreover, a detailed plan for the remainder

of the project was set. The aim for the final workshop is to present papers ready or near

ready for being submitted to academic journals. The workshop will in this case be open

for external participants. Related to the project, the Vienna group organised a conference

on causality in population studies that was held in Vienna, Austria, December 2006.

Research visits:

As the coordinator of the overall project, Aassve undertook several research visits to

University of Florence and Vienna Institute of Demography. Professor Pudney also

visited Department of Statistics at University of Florence to work with the Fabrizia

Mealli and Alessandra Mattei.

Several of the members from the collaborating teams visited ISER during the course of

the project:

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� Bruno Arpino (University of Florence): from10th October to 18th December 2006 and

from 9th May to 24th June 2007

� Francesca Francavilla (University of Florence): from 5th to 15th July 2006 and from

25th November to 2nd December 2006

� Jungho Kim (Vienna Institute of Demography): from 18th October to 2nd November

2004 and from 10th to 21st October 2005

� Alessandra Mattei (University of Florence): from 23rd to 31st October 2004 and

from18th to 13th April 2007 (as ECASS visitor)

� Letizia Mencarini (University of Florence): from 1st to 28th November 2004 and

from 2nd to 15th November 2005

6 Outputs

6.1 Research papers

The majority of the output is produced in terms of academic working paper, many of

which are submitted for considerations in Scientific Journals. Some papers are

undergoing final revision prior to submission .

[1] Stephen Pudney and Arnstein Aassve (2007) “Poverty transitions in developing countries: the roles of economic and demographic change”, Working Paper of Institute for Social and Economic Research, paper 2007-25. Colchester: University of Essex

[2] Stephen Pudney and Arnstein Aassve (2007) “Endogenous fertility and its impact on poverty: Evidence from Vietnam”, Working Paper of Institute for Social and Economic Research, paper 2007-26. Colchester: University of Essex

[3] Arnstein Aassve and Bruno Arpino (2007) “Estimation of causal effects of fertility on economic wellbeing”. ISER Working paper 2007-27, Colchester: University of Essex

[4] Alessandra Mattei, Fabrizia Mealli and Stephen Pudney (2007) “A discrete time model of consumption and fertility: a Bayesian approach”, mimeo.

[5] Arnstein Aassve and Bruno Arpino (2007) “Dynamic Multi-Level Analysis of Households' Living Standards and Poverty: Evidence from Vietnam”. Working Paper of Institute for Social and Economic Research, paper 2007-10. Colchester: University of Essex.

[6] Arnstein Aassve, A.rjan Gjonca and Letizia Mencarini (2006) The highest fertility in Europe – for how long? The analysis of fertility change in Albania based on Individual Data” Working Paper of Institute for Social and Economic Research, paper 2006-56. Colchester, University of Essex.

[7] Arnstein Aassve, Abbi Kedir and Habtu Weldegebriel (2006) “State Dependence and Causal Feedback of Poverty and Fertility in Ethiopia”. Working Paper of Institute for Social and Economic Research, Paper 2006-30. Colchester, University of Essex

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[8] Stephen Pudney and Francesca Francavilla (2006) 'Income Mis-Measurement and the Estimation of Poverty Rates. An Analysis of Income Poverty in Albania'. Working Paper of Institute for Social and Economic Research, paper 2006-35 (PDF). Colchester: University of Essex.

[9] Fabrizia Mealli, Sthephen Pudney, and F. Rosati Measuring the Economic Vulnerability of Children in Developing Countries, An Application to Guatemala, , University of Essex, ISER Working Paper 2006-28.

[10] Fertility and its Consequence on Family Labour Supply, J. Kim and A. Aassve, Institute of Labour Studies (IZA) Discussion Paper No. 2162.

[11] Poverty and fertility in developing countries: a comparative analysis for Albania, Ethiopia, Indonesia and Vietnam, A. Aassve, H. Engelhardt, F. Francavilla, A. Kedir, J. Kim, F. Mealli, L. Mencarini, S. Pudney, and A. Prskawetz, Population Review (December 2006).

[12] Does Fertility Decrease the Welfare of Households? An Analysis of Poverty Dynamics and Fertility in Indonesia, J. Kim, H. Engelhardt, A. Prskawetz and A Aassve, Vienna Institute of Demography Working Paper 06/2005.

[13] Pudney, S. E. (2007), The dynamics of perception: modelling subjective well-being in a short panel, Journal of the Royal Statistical Society, series A (forthcoming).

6.2. Presentations

International Union for the Scientific Study of Population (IUSSP) conference in Tours, France, July 2005: http://www.iussp.org/France2005/indexeng.php

Population Association of America (PAA) conference, Philadelphia, U.S., April, 2005: (http://paa2005.princeton.edu/)

Vienna Institute of Demography, August 2005.

Joint Empirical Social Science (JESS) Seminars, ISER, University of Essex, June 2005: http://www.iser.essex.ac.uk/seminars/jess/index.php?id=18

UNICEF Innocenti Center in Florence, February, 2006: (http://www.unicef-irc.org/)

Centre for the Study of African Economies (CSAE) Conference: Reducing Poverty and Inequality: How can Africa be included? (March; 2006): http://www.csae.ox.ac.uk/conferences/2006-EOI-RPI/default-csae.htm

Population Association of America (PAA) conference, Los Angeles, U.S., April, 2006: (http://paa2006.princeton.edu/)

European Population Conference, Liverpool, June 2006: http://epc2006.princeton.edu/

Conference on Causal Analysis in Population Studies: Concepts, Methods and Applications, held at Vienna Institute of Demography, December 2006: http://www.oeaw.ac.at/vid/caps/index.html

African Economic Research Consortium seminar series, Nairobi, Kenya, July 2007.

56th Session of the ISI (International Statistical Institute), August, 2007, Lisbon, Portugal. http://www.isi2007.com.pt/isi2007/

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48th Scientific Annual Meeting of the Italian Economist Society, Turin, 26-27 October2007 http://www.sie.univpm.it/ Invited address “Poverty dynamics: some conceptual and measurement problems” by Stephen Pudney to the Workshop “Dynamic Analysis Using Panel Data: Applications to Poverty and Social Exclusion”, Laboratorio R. Revelli, Centre for Employment Studies, Torino, June 2007. 6.3. Website

The project website is located at: http://www.oeaw.ac.at/vid/pdfdc/index.html, and

contains essential information about the project including access to all working papers,

description of the data sources, workshops and presentation, the participants, funding

agencies, several links, as well as contact information.

6.4 Career development

Research projects make an important contribution to the building of social science

research capacity through the career development of research staff. Staff development

has been provided in the following ways.

Project management All project staff members have been involved in formal and informal

management meetings to give them practical experience of project management. Dr

Aassve has benefited especially from Professor Pudney’s long experience in project

management.

Training in general research skills. This has mainly been on-the-job training, directed by the

principal investigator. Dr. Francesca Francavilla and Bruno Arpino were given

instruction in the use of the software package aML which is a specialised program for

multi-level and multi-process modelling. Jungho Kim, working closely with Dr. Aassve,

was given training in the methods of propensity score matching.

During his visits to Florence and Vienna, Dr Aassve worked closely with all research

assistants in how to clean and harmonize longitudinal data sets. This means that they

have been given instruction in the use of STATA, estimation of complex models based

on longitudinal data, and interpretation of results. The research assistants participated in

most of the meetings where research design and implementation was discussed. They

were also assisted in how to prepare papers for journal submission and dealing with the

review process. All team members have presented their papers either in their own

departments or in international conferences, and have gained important presentational

skills.

26



Dr Aassve has co-supervised Bruno Arpino in his work for the PhD together with

Professor Mealli of University of Florence. He has been given detailed instruction of all

sides of the research process.

27



7 Impacts

The issue of poverty and fertility in developing countries received considerable attention

in the 1980s and 1990s. Given that the great majority of existing work was based either

on macro data or cross sectional micro level data, we believe our work is making a

significant impact on academic research in both demography, and development

economics and statistics/econometrics, and consequently creating a revival of the issue.

� The research has highlighted advantages and disadvantages of longitudinal data

sources for developing countries. We have identified insights that cannot be

derived from cross-sectional or aggregate data sources.

� We have undertaken careful comparisons of different methods used for

estimating casual effects. These operate under different assumptions and

parameter estimates are not generally comparable. For the case where the interest

lies in estimating causal effects of a demographic event, we show how different

methods can be used and the meaning of their estimates.

� We have developed a simple way to assess how differences in fertility preferences

affect various poverty measures.

� Our research has demonstrated that, from a dynamic and cross country

perspective, compared to demographic household changes, income change is

generally the most important driver behind observed changes in poverty status,

the only exception being Vietnam.

� For the important question of whether fertility drives poverty or vice versa, our

research shows that the answer depends very much on the characteristics of the

country and regions considered. There is however, little evidence to suggest that

higher poverty leads to higher fertility.

8 Future Research Priorities

There are some remaining issues to be dealt with in this project. We are pursuing further

work on the use of deprivation indicators as alternatives or complements to the cash-

metric measures that underlie conventional measures of poverty. Aassve is leading this

work.

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Stephen Pudney continues to work together with Fabrizia Mealli and Alessandra Mattei

(both University of Florence) on an econometric specification of consumption and

fertility using the IFLS from Indonesia, using a Bayesian approach with MCMC

simulation and data augmentation methods. This approach is promising but requires a

large investment in computer programming for the econometric estimation. This work is

continuing beyond the end of the project and a working paper is expected to be

complete in early Spring.

[ Word count: 4937 ]

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