Summary InformationTF094629 Economic growth and crisis in Africa: Improving methods for measuring

povertyKCP Window Poverty Dynamics and Public Service DeliveryCountry World TTL name Kathleen BeegleProject period 05/16/2009-

05/15/2012Approving manager Peter Lanjouw

Grant amount $120,000 Disbursement $120,000

Research team

The team is led by Kathleen Beegle (DECPI) and Peter Lanjouw (DECPI). Other core members include Roy van der Weide (DECPI, primary researcher), Tomoki Fujii (Singapore Management University), Matthijs van Veelen (University of Amsterdam), and Brian Blankespoor (DECRG).

Research objectives

Summary of objectives and achievements

Objectives

The ultimate objective of this grant is to improve methods of measuring poverty and tracking its changes over time, with a focus on understanding poverty reductions over the long run. This project consists of multiple parts. One part develops survey-to-survey imputation methods that enable the modeler to impute missing data on a large scale if needed, and to combine variables that are divided over different surveys. This method may also be used to resolve the problem of incomparable consumption data, for example due to changes in the questionnaire or ambiguity concerning the price deflator, which would otherwise hamper efforts to track poverty. Another part of the project is concerned with identifying the spatial variation in consumer prices, while the third part aims to advance the knowledge on index number theory that would ultimately help the modeler to decide on how to account for variation in prices when constructing measures of real income, consumption and price deflators. Note that all parts provide important tools for poverty measurement and analysis. While the tools developed are applicable worldwide and are not restricted to poverty, they will be most critical for analysis in Africa, where these methods are urgently needed to fill in gaps in data availability.

Significant achievements

Produced research studies directly related to the objectives of this research project as outlined above.

Developed tools that are already being used by both researchers and practitioners (inside as well as outside of the World Bank).

Developed Stata Ado files that make the tools easy to use and thus accessible to a wide audience of practitioners.

Provided extensive and fully funded cross-support to World Bank projects (at no charge- yet directly linked to the KCP funded research project). Selected projects include: Malawi (using imputed poverty data to tackle problem of non-comparable consumption data), Morocco (using imputed data to track poverty), Gaza and the West Bank (impute missing data for Gaza; study of spatial consumer price variation in the West Bank; and using imputation methods to estimate spatial patterns of poverty, unemployment and education levels), Lao PDR (identifying spatial variation in rice price inflation).

Inspired a variety of future research directions.

Outputs and anticipated outputs

1. Fujii, Tomoki, and Roy van der Weide (2011), “Two-sample cross-tabulation”, mimeo, The World Bank

2. Fujii, Tomoki, and Roy van der Weide (2012), “An alternative estimator for two-sample cross-tabulations” (aka “The F2 estimator”), mimeo, The World Bank

3. Two Stata Ado files: `isum’ and `itab’, which respectively replace the `sum’ and `tab’ commands of Stata in cases where the modeler is dealing with imputed data rather than observed data (note that the `i’ stands for `imputation’); The methodology coded in `isum’ will also be implemented in the POVMAP software package (a software package developed by The World Bank).

4. Van der Weide, Roy (2011), “Real income and consumption as a function of prices, quantities and preferences”, mimeo, The World Bank

5. Van der Weide, Roy (2011), “Measuring welfare by means of intervals”, mimeo, The World Bank6. Van der Weide, Roy (2011), “The Weak Ranking Restriction and the Budget Set Axiom”, mimeo,

The World Bank7. Van der Weide, Roy (2011), “On the definition of an exact index”, mimeo, The World Bank8. Van der Weide, Roy, and Brian Blankespoor (2011), “The spatial variation of rice prices in Lao

PDR”, mimeo, The World Bank (served as a background note for a 2011 World Bank Policy Brief entitled “Lao PDR responding to rice price inflation”)

9. Van der Weide, Roy, and Brian Blankespoor (2011), “Prices and checkpoints”, mimeo, The World Bank

10. Van der Weide, Roy, and Brian Blankespoor (2011), “The geography of poverty in the West Bank”, mimeo, The World Bank (also appeared as a chapter in the 2011 West Bank and Gaza Poverty Assessment)

11. Fox, Louise M., Kilic, T. and Lanjouw, P. (2012) “Progress Report on the IHS3 Poverty Analysis”12. Van der Weide, Roy (2012) “High Frequency Poverty Data Using Labor Force Surveys in

Morocco”, mimeo, The World Bank.

Some remarks concerning the listed outputs:

Outputs (1) and (2) will be combined into one paper to be submitted to an academic journal. Outputs (4) to (7) are currently short and incomplete notes. Much of the research that will

complete these notes has already been done, but still needs to be included into the written outputs, which will subsequently all be submitted as working papers.

Output (8) is currently in the form of a collection of empirical findings that served as input for a World Bank Policy Brief. We plan to turn this note into a working paper later this year.

Output (11) is currently in the form of a policy brief presented to the Malawi National Statistical Office. We plan to turn this into a working paper and submitted journal article later this year, after receiving a report on the Malawi CPI calculations.

Output (12) is currently in the form of a power point presentation that has been presented to the Haut Commissariat Au Plan in Morocco as well as the Poverty and Inequality Measurement and Analysis Practice Group at the World Bank. It will be converted into a paper later this year.

Tomoki Fujii and Roy van der Weide have also worked on the use of imputed data into regression analysis (adopting a two-sample instrumental variables framework), which is currently not listed as an output as much of the progress made still needs to be documented.

Major difficulties

There were no major difficulties.

Surprises

Three observations come to mind:

The overwhelming demand for cross-support (funded from World Bank budget) inspired by the research funded by the KCP resulted in additional resources, but also consumed time which would otherwise have been spent on converting research into polished papers ready to be submitted to academic journals. (The research will of course still be submitted to academic journals albeit with a delay.)

The research on index number theory, see outputs (4) to (7), developed into four different directions instead of the two directions that were foreseen at the outset. While these developments are stimulating, they too have caused a delay in completing polished research papers.

An alternative method of imputation was studied, in addition to the methods outlined in the original research proposal. We refer to the new method, which we first used in a cross-support exercise where the available data excluded more conventional methods, as “spatial kernel regression” or “spatial smoothing”.

Full completion report

Background and motivation

This research project has largely been motivated by the frequently encountered problem of missing, incomplete or non-comparable data which denotes an important obstacle for tracking poverty and its determinants. Working with imputed data, where we will focus on missing consumption poverty and consumer price data in particular, offers a potential solution to this problem. The research funded by this project examines different methods of imputing data and advances the knowledge of dealing with imputed data, which entails for example an appropriate estimation of standard errors that account for sampling error as well as imputation error. While our examples will often focus on tracking poverty in Africa, the tools developed are applicable worldwide, and are not restricted to poverty.

In Africa there are expectations of possibly significant reversals in poverty reductions due to the recent global financial crisis and large swings in food prices. The demand for information on the impact of the crisis on the world’s poorest populations makes methods to reliably track poverty changes particularly important. Policy makers will want to know whether poverty is indeed increasing, if at all, by how much, and whether changes are significant and unambiguous.

The contexts in which imputation methods can be applied are wide-ranging. The tracking of welfare over time serves as our leading example. In addition to monitoring progress, it denotes an important tool in identifying leading and lagging areas. Consider two scenarios where there may be a need for imputed data: (1) the household expenditure survey is outdated and/or conducted infrequently. A viable solution may then be to impute household poverty into an alternative survey that is conducted more regularly such as a Demographic Health Survey (DHS) or a Labour Force Survey (LFS), see e.g. Stifel and Christiaensen (2007) and Christiaensen et al. (2011), and (2) even if household expenditure surveys are up-to-date, the comparison of consumption poverty between time periods may be hampered by a lack of comparability due to significant changes in the questionnaires. The Indian poverty debate denotes a well-known example, see e.g. Deaton and Dreze (2002), Deaton (2003), Kijima and Lanjouw (2003), and

Tarozzi (2007), where the offered solution is to work with imputed consumption that is deemed comparable over time. Recently, the approach was adopted in Malawi where temporal comparability was hampered by a lack of a reliable price deflator. Other examples of African countries that have had to deal with issues of comparability include Cote d’Ivoire, Zambia, Kenya, Uganda, Mali and Tanzania.

For a different application, suppose one wishes to examine the relationship between socio-economic status and health (or access to health care), see for example the studies by Sahn and Stifel (2003) (who look at child malnutrition), Schellenberg et al. (2003) (who look at child health care), and Lindelow (2006) (who looks at useage of health services more generally). Good quality data on welfare status and individual health are often collected by highly specialized surveys, such as the household budget survey (HBS) and the DHS. The HBS collects detailed household expenditure data that is commonly used to determine a household's poverty status. The DHS collects detailed health and health-care data that may include anthropometric indicators, whether the individual is HIV positive, and useage of health services. While basic demographics, education, asset ownership and dwelling unit characteristics are typically available in both types of surveys, the detailed expenditure and health data are unique to the corresponding surveys, so that an examination of the relationships between poverty and health may require the use of imputed data as a way of combining the two surveys.

Multi-dimensional poverty measurement, a field that has attracted considerable attention in recent years, denotes an application that may also benefit from survey-to-survey imputation. The study of overlapping deprivations requires that the data on the different dimensions of poverty are available for the same individuals and households, a requirement that is often not met since the best data on these variables tend to be collected by highly specialized surveys.

Breaking down estimates by region, province, and district may also require the use of imputation methods when there is no one sample that both contains the variable of interest and is representative at the desired subnational level. A frequently adopted solution is to bring in a larger survey (or census) that provides the necessary coverage, but is lacking the variable one is interested in. See the small area estimation method put forward by Elbers et al. (2003), which was followed by Demombynes et al. (2007), Elbers et al. (2008), Tarozzi and Deaton (2009), Tarozzi (2011), and Viet-Cuong et al. (2010). In these studies poverty derived from a household expenditure survey is imputed into the larger survey (or census), which is then aggregated at the small area level. Ivaschenko and Lanjouw (2010) apply the same approach to impute the probability of being tested HIV positive into a DHS (that does not collect HIV data) to obtain subnational estimates of HIV prevalence. Here the imputation model is estimated with sentinel survey data which contains HIV data for a sample of individuals that is not representative at the stratum or even national level. Fujii (2011) combines the DHS with census data to estimate child malnutrition at the small area level.

Finally, consider an application where one needs to estimate the percentage of households falling into and out of poverty, which helps to address the question of whether poverty is of a chronic or a transient nature. Using conventional methods this is only feasible when the available data have a panel component, which is the exception rather than the rule due to the high costs involved. Also here working with imputed data may offer a solution where all that is needed are two or more repeated

cross-sections (that need not have a panel component). One would then impute time t consumption into the time t+1 survey, or vice versa (see e.g. Dang et al., 2011). Similarly one may study the transitions between levels of food security and malnutrition, sectors of employment, political preferences, location of residence (urban or rural; country of origin or abroad) etc.

Original objectives along with changes and their justification

This project deals with two different but related streams of research: (1) imputation methods, and (2) index number theory. Each part came with an original set of objectives.

The first objective is getting the standard errors right of descriptive statistics, such as poverty estimates, that are based on imputed data. Note that imputation-based estimates are subject to two types of error: model error and sampling error. The added model error will generally add to the standard error of the estimates. The imputation error may also introduce a bias if the imputed data is cross-tabulated with other data with which the imputation error happens to be correlated. This dual error structure is often ignored in empirical applications leading to an over-estimation of statistical precision. As part of this objective, we wanted to get a sense of how large the different types of errors are? Does one type of error generally dominate the other?

A second objective is to derive analytic expressions for the correct standard errors that can be computed without the use of simulations. The motivation for this is two-fold. Firstly, the analytic expressions help us understand when the errors may be expected to be large or small. Secondly, the computation of analytic standard errors is instant whereas bootstrap standard errors in this case are computationally expensive.

A third objective is to develop user-friendly software (Stata ADO files) that implements the survey-to-survey imputation methods, so that the newly developed tools are readily available to a potentially wide audience of applied users.

A fourth objective is to assess whether imputation-based methods generally have merit. Do they provide reasonably accurate estimates despite the strong assumptions that may be required?

The fifth objective is concerned with the use of imputed data in regression analysis using a two-sample instrumental variables (IV) framework. The defining features that set the two-sample IV estimators apart from their one-sample counterparts can be shown to provide a basis for building a variety of distinct estimators. The two-best known estimators are the original estimator by Angrist and Krueger (denoted by TSIV) and two-sample two-stage least squares (TS2SLS), with virtually each and every empirical study using either one or the other (see e.g. Lusardi, 1996; Bjorklund and Jantti, 1997; Currie and Yelowitz, 2000; and Dee and Evans, 2003). Inoue and Solon (2005) provide a comparison of the two estimators, and show that TS2SLS is noticeably more efficient than TSIV. It is not difficult, however, to think of many more alternatives. These additional options will come with different finite sample size properties, and

possibly also with different asymptotic properties. (All estimators, TS2SLS and TSIV included, will tend to the same standard one-sample IV estimator when the two samples coincide.) A question that comes to mind is whether TS2SLS will also be the optimal estimator when compared to a more inclusive set of alternatives, and if so, what objective function does it optimize?

Objective six is new in that it was not included in the original research proposal. It is concerned with imputing spatial data when there is no secondary survey or census to impute the data into. The imputation method studied for this purpose is therefore very different from the imputation methods described above. We refer to this alternative method as “spatial kernel regression”. The objective in this case is to estimate the spatial distribution of poverty (for the West Bank) and food prices (for the West Bank and Lao PDR) where data is only observed for a small sample of locations. A related objective to this work is to examine the determinants of the spatial variation in consumer prices.

Objective seven addresses the question of how we should deal with the spatial variation in consumer prices (combined with the variation in consumer preferences) when measuring real consumption and income, which constitute the key building blocks of standard estimates of poverty. Indeed, note that any reasonable choice of real consumption measure will be a function of the prices that consumers face. Ignoring the spatial variation in prices when measuring consumption or income may lead to biased results. For example, note that prices are likely to show much of their variation across different distances to markets, different levels of access to markets etc. These are also the dimensions that play an important role in poverty assessments. A question that thus comes to mind is whether any variation in measured poverty across these dimensions may in part be due to variation in prices not accounted for. The challenge however is that there is no one way of accounting for differences in prices.

On a practical note, note that price indices are often computed at the region or province level (depending on the size of the country), which may not fully capture the spatial variation in prices. The spatial modeling of price data (see objective six) may help the modeler to obtain price data at a very high level of disaggregation, and at the same time average out some of the error that may have affected the observed price data (i.e. note that not all variation in observed prices reflects a genuine variation in actual prices; some of that variation may be due to noise, such as measurement error).

Assessment of the extent to which the objectives have been met

The objectives outlined above have been successfully met, except objectives five and seven which have been partially met, largely due to the limited time available. While the original research proposal was already ambitious, a new objective was added (objective six), objective seven developed into four research questions instead of the original two, and we experienced a large demand for cross-support. We still intend to fully complete these objectives acknowledging the financial support of this KCP.

Methodology and data

The objective of this research project was to develop new methods which were then applied to data from Malawi, Morocco, The West Bank and Gaza, and Lao PDR. All empirical studies focused on tracking poverty using imputed data, except for the Lao PDR study where the objective was to estimate the spatial distribution of rice price inflation.

The precise data sets that were used are:

Malawi: The Integrated Household Survey 2004/5 and 2010/11 Morocco: The Morocco Household Expenditure Survey 2001 and 2007; and the Morocco Labour

Force Survey 2001 – 2009 The West Bank and Gaza: Palestinian Expenditure and Consumption Survey 2004 – 2009; and

the Palestinian Labour Force Survey 2004 – 2009 Lao PDR: The Lao Expenditure and Consumption Survey 2003 and 2008

Results: What have we learnt so far?

Survey-to-survey imputation methods

What we have learned from this elaborate part of the research project is: (i) neither model error nor sampling error can generally be ignored when computing standard errors; (ii) the analytic standard errors are very accurate, they practically coincide with the standard errors obtained by means of bootstrapping; and (iii) descriptive statistics based on imputed data can be very precise despite the strong assumptions that may be required.

We obtained the analytic standard errors by deriving the asymptotic distribution of the various estimators involved (which takes into account both the model- and the sampling error). To evaluate their precision we compared them to estimates of the “true” standard errors that were obtained by means of Monte-Carlo simulation. These simulation results suggest that the asymptotic standard errors are remarkably accurate even for small sample sizes. Having analytic standard errors that are easy to compute as well as accurate makes the imputation-based methods a user-friendly tool for a wide audience of applied users.

Tracking poverty in Malawi: An application of survey-to-survey imputation

In Malawi, tracking progress in poverty reduction between the 2004/5 IHS2 and the 2010/11 IHS3 household surveys has become an issue of great policy interest. There has been growing recognition in the literature that conclusions regarding the evolution of poverty can be quite sensitive to comparability

of the underlying consumption data collected in surveys such as Malawi’s IHS surveys. Close scrutiny of the consumption data in the IHS2 and IHS3 indicate that the consumption aggregates can be safely considered as comparable. However, what is less clear is that the temporal price indices available to adjust for changes in the cost of living over time are similarly reliable. When the IHS consumption data are corrected for inflation using the official CPI, poverty in Malawi is observed to decline from 52.4 percent of the population in 2004/5 to 37.7 percent in 2010/11. This is an impressive rate of poverty reduction. However, when the price information collected in the IHS surveys themselves is used to construct a temporal price index, and this index is then used to adjust consumption data for inflation, poverty is judged to have risen from 52.4 to 63.7 percent in 2010/11. Not only do different methods indicate different rates of progress, but even the direction of change is affected.

In light of these striking findings, the World Bank and the National Statistical Office have launched a careful assessment and evaluation of the data and methods that underpin Malawi’s Consumer Price Index. In the mean time, survey to survey imputation techniques have been applied in order to gain a sense of how Malawi’s poverty has evolved during the 2000’s, absent agreement as to the precise evolution of the general price level. When an imputation model is specified that does not include any explanatory variable expressed in monetary terms, then the exercise dispenses with the need to adjust for cost of living change. Applying this methodology to the Malawi IHS2 and IHS3 surveys yields an estimate of poverty in 2010/11 of 51.7%, almost identical to the 52.4% estimated for 2004/5. With this approach, the evidence thus suggests that poverty in Malawi remained broadly stable over this time period – certainly there seems little support for the notion that poverty declined dramatically.

Tracking poverty in Morocco: An application of survey-to-survey imputation

Morocco expressed a strong interest in monitoring changes of its poverty rate over time. While official statistics reveal that between 2001 and 2007 poverty has declined, recent years however have seen a global crisis unfold and large swings in international food prices which may have potentially put many households at risk of falling into poverty. By the same token, an argument could be made for a possible reduction in poverty. Recently Morocco has been fortunate with advantageous rainfall which has boosted agricultural production. Given these different possibilities our objective is to determine what has really happened to Morocco’s poverty rate in recent years. Critically, the most recent household consumption survey for Morocco does not allow us to look beyond 2007. To fill this gap we propose an alternative method for estimating poverty rates by imputing household consumption data into the Labor Force Survey (LFS) which offers representative data for each quarter of the year, and thus allows us to obtain up-to-date poverty estimates. We estimate a consumption model using consumption survey data from 2001 as well as 2007, and find that both models reveal the same trend in poverty over the last decade. Moreover by imputing consumption into the 2007 LFS using 2001 consumption model we are able to match the official poverty rate that is based on the 2007 consumption data. (The same holds when we impute into 2001 using the 2007 model.) Results obtained are very encouraging and provide support for the methodology. Interestingly, the models agree that poverty in Morocco has continued its decline beyond 2007 (poverty is estimated until the end of 2009), which confirms that the stimulus to

the agricultural sector in the form of advantageous rainfall has offset the potential malign impact of the international crisis as far as Morocco’s poor are concerned.

Tracking poverty in Gaza: An application of survey-to-survey imputation

The years 2007 to 2009 were economically volatile for Gaza and the West Bank in large part as a result from Hamas coming to power after which many international donors curbed their financial support. Gaza was arguably more affected than the West Bank, although the precise magnitude of the impact and its effect on poverty could not easily be estimated as the turbulence also meant that the detailed household expenditure survey was cancelled for Gaza in 2008. The LFS was fortunately not cancelled. To reconstruct the poverty trend for Gaza we followed the same approach as for the Morocco study (see above): We estimate a model for household consumption using PECS data for the years 2007 and 2009, and then use this model to impute household consumption poverty into the LFS for 2008. While the approach does not work as well as it does for Morocco (estimates based on the 2007 model and the 2009 model do not perfectly coincide), the estimates were sufficiently precise to observe the impact of the political turbulence on poverty in the region. (We conjecture that the approach worked better in Morocco because the changes in poverty over time were to a lesser extent driven by exogenous shocks that are not well captured by the data recorded in the LFS.)

Estimating the spatial distribution of poverty and unemployment in the West Bank: An application of “spatial kernel regression” as a method of imputation

The objective here is to estimate poverty and unemployment for all populated areas in the West Bank while we only have survey data for a sample of locations and do not have a population census in which the relevant data might have been imputed. Instead we adopt an alternative imputation method that can perhaps best be described as “spatial smoothing” or “spatial kernel regression”: imputed data is obtained as a weighted average of observed data where the weights decline with the spatial distance between the observations and the target location.

Using this approach we are able to identify clear leading and lagging areas in the West Bank. In particular, we find that areas dominated by high levels of poverty also tend to be areas with severe mobility restrictions, poor access to markets, high rates of unemployment, a dominance of low-wage sectors, and a reliance on employment opportunities in Israel. The opposite holds true for the West Bank’s non-poor areas.

Study of factors that increase the spatial consumer price variation in the West Bank

The objective of this study is to identify the key determinants of the spatial variation of consumer prices in the West Bank. In particular, we test the hypothesis that road closure obstacles that are omnipresent

in the West Bank act as trade barriers that increase the transaction costs and as such widen the spatial price differences.

Our regression results confirm that the road checkpoints have a significantly positive effect on spatial price differences. This highlights the role of checkpoints as internal trade barriers. The economic effect ranges anywhere between 1 and 40 percent (added price difference), depending on the type of good. While the size of the impact is perhaps not overwhelmingly large, it is broadly comparable to the effect of crossing the U.S – Canada border (see e.g. Engel et al., 2003).

Does this affect the poor? As the economy settles to a new equilibrium, internalizing the added transaction costs, the likely outcome is that both ends, producers and consumers, will be sharing the costs. Both ends involve the poor. The agricultural sector employs many of the poor and higher food prices will arguably affect the poor more than the non-poor.

On the geography of rice price inflation in Lao PDR

In recent years the markets for rice in Lao PDR have seen large price swings, which is a relatively new phenomenon for Lao PDR. This raises the question whether the observed inflation on rice prices is domestically generated or alternatively imported from other countries in the region. To shed some light on this question a number of different approaches are adopted. The objective of this study is to visualize the spatial patterns of rice price inflation, which may reveal some of the factors that are driving the inflation. We obtain price maps for the years 2003 and 2008 using the same “spatial smoothing” method that is adopted for the West Bank study.

The geography of rice price inflation appears to support a story that the Lao rice markets follow the Thai (and Vietnamese) rice market. The period 2003 to 2008 denotes a period of high inflation. Villages where the highest levels of inflation are observed are in the area of Savankhet, which is a rice producing area that is located at an important border crossing into Thailand. This part of Laos is also believed to be most active in exporting Lao glutinous rice abroad, both to Thailand and Vietnam. The link between the rice markets of Laos, Thailand and Vietnam is also confirmed by a cointegration study, which shows that changes in the Thai and Vietnamese market cause changes in the Lao rice market, but not the other way around.

The price of rice and rice price inflation exhibit very different spatial patterns. As expected, rice prices are highest in and around Vientiane, the capital, the economic center of the country where standards of living are highest.

What do we do with observed variation in consumer prices (as well as preferences) when we measure real consumption?

This research shows that it is very difficult to make meaningful welfare comparisons between different regions if these regions face different prices as well as different preferences. At least two directions of research are explored. The first tries to identify the conditions under which comparisons can be made, which are found to be very limited (basically requires one to assume away any heterogeneity in preferences if complete orderings are demanded). The second does not demand complete orderings beforehand, but proposes analogues to `confidence intervals’ for welfare estimates. If the intervals for two regions are found to be non-overlapping, then their welfare ordering will be unambiguous.

Dissemination details (include future plans)

Presentation of survey-to-survey imputation methods with an application to Morocco in the PIMA PG seminar series, February 23, 2012, Washington DC

Presentation of survey-to-survey imputation methods in the DECPI research seminar series, February 29, 2012, Washington DC

Presentation on tracking poverty in Morocco by imputing into the LFS for our client in Morocco, January 20, 2012, Rabat

Presentations on re-constructing the poverty trend for Gaza by imputing into the LFS, Palestinian Central Bureau of Statistics, May 9, 2010, Ramallah

Presentation on the geography of poverty in the West Bank using “spatial smoothing” as a method of imputation, ARIJ, October 25, 2011, Bethlehem

Presentation on the geography of poverty in the West Bank, PREM week seminar, April 21, 2011, Washington DC

Planned presentation of tools using the Stata Ado files we developed at a future PIMA PG workshop

Applications to the West Bank have become a chapter in the recent West Bank and Gaza Poverty Assessment

Application to Lao PDR features in a recent WB Policy Brief entitled “Lao PDR responding to rice price inflation”

Application to Morocco will be converted into a WB working paper (and subsequently be submitted to an academic journal)

Application to Malawi is currently a WB policy brief that has been discussed with the National Statistical Office and that serves as a background note for the team currently assessing the Malawi Consumer Price Index calculation.

Anticipated papers that come directly from KCP research

Theoretical paper on imputation-based descriptive statistics will be submitted top high-ranked academic journal (Review of Economics and Statistics)

Theoretical paper on the use of imputed data in regression analysis will when ready be submitted to the WB working paper series as well as to a high-ranked academic journal

Manual for Stata Ado files, when completed, will be submitted to the WB working paper series as well as to a journal such as the Stata Journal

Four short research notes on index number theory will all be developed into WB working papers, and will subsequently all be submitted to high-ranked academic journal

Impact within and outside the bank

The tools developed under this research project greatly help the modeler with tracking poverty over time, which concerns the bread and butter of the World Bank, and is already in high demand. The approach enables the modeler to impute household consumption data in non-consumption surveys that are available at a much greater frequency than consumption surveys are. The application to Morocco provides a great example. Using the conventional approach, which requires household consumption survey data, poverty could not be estimated beyond 2007. Adopting the survey-to-survey imputation methods enabled us to update the poverty estimates all the way to 2010, and thus allowed us to see whether the recent global crisis had increased the poverty rate in Morocco (which as it turns out did not).

The same survey-to-survey imputation methods may also be used to resolved issues with non-comparability of the original household consumption poverty data. The application to Malawi is testimony to this. We anticipate that the use of the methodology will see an expansion of applications beyond Malawi, as it is not uncommon that consumption survey data is plagued by comparability problems, especially in Africa.

The “spatial smoothing” method that continues to be under development, an alternative small area estimation method, is already found to be in popular demand within the World Bank. Note that when using standard ELL-type poverty mapping methods the number of poverty maps that can be produced is limited to the number of available censuses, see e.g. Douidiche et al. (2011) who apply ELL to obtain two poverty maps, ten years apart, for Morocco; and Viet-Cuong et al. (2011) who alternate population- with agricultural census data to obtain two poverty maps for Vietnam. In contrast, “spatial smoothing” can be applied for each survey year, which makes it ideally suited to create a panel of small area estimates. Note that we see spatial smoothing as an addition to the toolbox rather than as a replacement for ELL-type methods. There will be countries where only spatial smoothing is an option, but also countries where only ELL can be applied, as well as countries where both are viable alternatives. Countries where ELL can be applied but not spatial smoothing are cases where census data is available but where GIS data with village coordinates is missing. Also note that more research is required to fully test the statistical precision of small area estimates obtained by means of spatial smoothing approaches.

The newly created poverty data also greatly enhances the modelers options for studying the determinants of poverty, which has the potential to become a standard analysis to be included in the bank’s Poverty Assessments.

Implication for future research

The work funded by this KCP offers inspiration for a number of different directions for future research:

A. The use of survey-to-survey imputation makes it possible to construct a rich panel of poverty data, which may in turn be used to estimate dynamics panel data models to capture the (subnational) dynamics of poverty. This has two obvious applications. One is to study the determinants of poverty reduction. The dynamic model may potentially also be used to forecast future poverty rates (an application that has until recently proven to be particularly challenging). Note that the imputed data is not observed data, which has to be taken into consideration for proper inference. There is a considerable (and relatively young) literature on dealing with measurement error in Vector Autoregressive Models that we may draw from (see e.g. Deistler and Anderson, 1989; Chanda, 1996; Verbeek and Vella, 2002; Staudenmeyer and Buonaccorsi, 2005; Antman and McKenzie, 2007; Hassler and Kuzin, 2008; Patriota et al. 2009; and Komunjer and Ng, 2010, 2011).

B. The tools developed under this research proposal are also seen to be an asset in future research on multi-dimensional poverty measurement, a field that is attracting considerable attention in recent years. Note that overlapping deprivations can only be depicted by means of, say, Venn Diagrams if information on the various dimensions of well-being is contained in the same household survey dataset. Because the best information on different individual and household characteristic is often to be found in different surveys, an important methodological innovation will be to explore the imputation of key poverty indicators across different survey data sets. Rather than relying on proxies that happen to be available within a single survey, the idea is to apply survey-to-survey imputation techniques to impute the best available indicators on disparate dimensions of wellbeing into a single “host” survey. These multiple dimensions can then be jointly assessed at the household level.

C. Another direction for future research is to further develop “spatial smoothing” as an alternative method of small area estimation. This research is necessary to assess the statistical precision of the approach. One way to proceed is to compare small area estimates obtained with the standard ELL approach to estimates obtained by means of a “spatial smoothing” approach. By repeating this exercise for a number of different countries we would gain an understanding of the circumstances under which the two approaches yield very similar or rather different estimates. If the data permits, one could also consider combining estimates obtained with ELL and spatial smoothing into one new estimate. Taking a weighted average seems an obvious choice. Assuming a spatial autoregressive model the weight can arguably be estimated from the data. Given optimal weights, the combined estimator is expected to be more accurate than

either of the “stand-alone” estimators. The ELL approach is sometimes criticized for underutilizing available survey data (see e.g. Molina and Rao, 2011). An efficient merge between ELL and spatial smoothing will deal with this critique.

Capacity Building

This project has largely been focused on methodology. But the goal has been to develop methods that allow one to confront problems that are very commonly encountered when attempting to track poverty changes over time. Three empirical applications have been undertaken during the project, aimed specifically at the question of how poverty has evolved in a context where there are gaps in the available consumption data (Morocco, Gaza and the West Bank), or concerns about the appropriate price deflator (Malawi). These three empirical studies have been launched in coordination and close collaboration with the counterpart statistical organizations in each of the respective countries. In all three settings the clear goal is not only to produce an “answer” to the specific question posed in the respective setting, but to transfer the know-how to the counterparts. Training has been provided in the three countries, and as these empirical applications get completed the counterpart teams will also be in a position to independently apply these techniques and undertake similar analyses with other data configurations. Additional training efforts have also been applied within the Poverty Course that DECPI offers each year as part of its suite of courses on Household Survey Data Collection and Analysis. A special module on imputation was offered in this year’s poverty course, and it is expected that this will leverage new applications of these methods in a variety of partner countries. Such efforts invariably involve counterpart participation from the respective statistical organizations.

Leveraging of Additional Resources

The KCP project itself has not financed the capacity building component of this work. Instead cross-support funding has been used to transfer the know-how developed here to counterparts in the respective partner countries. As such this project has already leveraged significant resources from the World Bank’s operational units, and it is expected that such expressions of effective demand will grow considerably over the coming year. To date, we are aware of a number of ongoing efforts to apply survey-to-survey imputation methods by operational colleagues in collaboration with in-country counterparts. Examples include projects to gauge comparability of household surveys during the 2000s in Mali, Afghanistan, Iraq, Nepal and Vietnam.

Annex 1: Background Papers and Policy Briefs(Please see attached)

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