metadata: authors: 0: author: Mcculloch,Neil Andrew
count: Nepal,Cambodia
docna: 0: docna: The impact of structural reforms on poverty
            : a simple methodology with extensions
docty: Policy Research Working Paper
subtopic: Urban Housing and Land Settlements,Labor Markets,Urban Governance and Management,Urban Housing,International Trade and Trade Rules,Municipal Management and Reform,Inequality,Transport Services
teratopic: Poverty Reduction
abstracts: cdata!: Structural reforms are often designed to
            change the prices of key goods and services. Since the
            overall intention of such reforms is the reduction of
            poverty, it is important to understand how the resulting
            price changes affect the poor. However, organizations
            seeking to provide timely advice to policymakers in
            developing countries often do not have the data and
            resources needed to undertake the most sophisticated
            approaches to such analysis. McCulloch outlines a simple
            methodology based on the analysis of household survey data
            to estimate the first-order impact of a variety of
            structural reforms. He also elaborates on the ways in which
            this methodology may be extended in a flexible way to
            account for particular features of a country in question.
            Finally, he outlines the direction of some extensions on the
            approach to tackle dynamics, risk, and qualitative poverty analysis.
geo_reg_key: 517198,517212,517191
repnme: repnme: The impact of structural reforms on poverty
            : a simple methodology with extensions
display_title: The impact of structural reforms on
            poverty : a simple methodology with extensions
listing_relative_url: /research/2003/08/2511636/impact-structural-reforms-poverty-simple-methodology-extensions
topicv3: Transport m1326661 135,Poverty m1326687 695,Trade m1326924 698,Land and Housing m1327648 817,Jobs m1503931 2670
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: Development Research Group (DECRG)
no_of_pages: 48
author_keys: 0: author_key: 660267
model_extractions: I POLICY RESEARCH WORKING PAPER 3124

# Abstract

Structural reforms are often designed to change the prices of key goods and services. Since the overall intention of such reforms is the reduction of poverty, it is important to understand how the resulting price changes affect the poor. However, organizations seeking to provide timely advice to policymakers in developing countries often do not have the data and resources needed to undertake the most sophisticated approaches to such analysis. McCulloch outlines a simple methodology based on the analysis of household survey data to estimate the first-order impact of a variety of structural reforms. He also elaborates on the ways in which this methodology may be extended in a flexible way to account for particular features of a country in question. Finally, he outlines the direction of some extensions on the approach to tackle dynamics, risk, and qualitative poverty analysis.

> This paper-a product of the International Trade Department, Poverty Reduction and Economic Management Vice Presidency-is part of a larger effort in the Bank to develop better methodologies for understanding the impact of structural reforms such as trade liberalization, privatization, and tax and subsidy reforms on poverty. Copies of the paper are available

> free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Melanie Faltas, room MC2-402,

> telephone 202-458-2323, fax 202-522-7551, email address mfaltas@worldbank.org. Policy Research Working Papers are

> also posted on theWeb at http://econ.worldbank.org. The author may be contacted at nmcculloch@worldbank.org. August 2003. (41 pages)

> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._

Produced by the Research Support Team
# **The Impact of Structural Reforms on Poverty**

**A Simple Methodology with Extensions**

Neil McCulloch PRMTR and Institute of Development Studies Sussex, UK

## **Acknowledgements**

This paper was written while the author was a consultant at PREM Trade in the World Bank. The author is grateful to Ataman Aksoy for making this work possible and for providing numerous helpful comments and suggestions and to Baris Sivri for producing several helpful tables. Thanks are also due to Burcu Duygan, Stephen Matteo Miller, Yvonne Tsikata, and other members of PRMTR and the Trade and Poverty Cluster in the World Bank Trade Department, as well as to Alessandro Nicita, Marcelo Olarreaga, and Isidro Soloaga on whose methodology this paper is based. The author can be contacted at nrncculloch()worldbank.org or neilm@ids.ac.uk.
# 1. **Introduction**

For many years there has been considerable interest in the impact of economic reforms upon poverty and more generally on the distribution of welfare within society. This interest has arisen from a number of different sources. A large number of NGO and<sup>civil</sup> society organizations, along with many developing country governments have expressed concern about the potential negative distributional impact of structural reforms.<sup>At the</sup> same time much academic work has pointed to the wide variety of outcomes resulting from reforms in different countries.' Economic reforms are typically split into two categories: macroeconomic reforms,<sup>often</sup> pursued under the auspices of the IMF; and structural or price reforms designed<sup>to</sup> improve resource allocation and increase efficiency. Although the maintenance of macroeconomic stability remains the cornerstone of effective economic development, there has been a stronger emphasis in recent years on structural reforms since these are key to achieving pro-poor growth.

Understanding the impact of structural or price reforms on poverty is key<sup>in several</sup> different areas of reform. For example, the imposition or removal of a tariff on<sup>thestaple</sup> food can have a major impact upon the incomes of the poor. The same is true of<sup>a</sup> reduction in the transaction costs faced by the poor in reaching markets, through<sup>for</sup> example, investments in rural feeder roads, policies to enhance competition<sup>in the</sup> transportation sector, or marketing reforms. Similarly utility reform and privatization often have a dramatic impact upon the prices for such services and for poverty if<sup>the</sup> purchase of such services is important for the poor. And the same is true of changes<sup>in</sup> the wide variety of taxes and subsidies which may be imposed by the government.<sup>2The</sup> central characteristic of all these reforms is that they are designed to change prices and thereby influence resource allocation to different activities. Therefore, for the<sup>purposeof</sup> understanding the impact of structural reforms upon the poor, it is essential to have a good methodology for linking price changes to changes in poverty.

The theoretical framework for linking such reforms to poverty is probably best developed in the area of trade (see Winters (2000) and McCulloch, Winters and Cirera (2001)), although the approach is generally applicable to a wide range of price reforms. The analysis of the linkages between price reforms, including trade reform, and poverty is complex and there are a large variety of different methods available (Reimer (2002)<sup>and</sup> McCulloch, Winters and Cirera (2001) provide reviews of methodologies and papers).<sup>3</sup> In an ideal world the relationship between price reformn and poverty could be accurately predicted using a general equilibrium model with a suitably disaggregated household 'See Cornia, Jolly and Stewart (1987) for an early critique of structural adjustment reformns.

> 2 See Ahmad and Stem (1991) for a comprehensive review of the issues raised by taxation in developing countries. Deaton (1997) provides a succinct statement of some of the methodological problems. _3_ See also http://wwwl.worldbank.org/wbiep/trade/poverty/papers.htni for a selection of papers. The World Bank also provide a "toolkit" of methods for conducting poverty and social impact analysis - see http://www.worldbank.org/poverty/psia.
sector. If the macroeconomic and microeconomic data required for such an approach are available and reasonably accurate, the parameters of the model are empirically estimated from the available data, and the functional forms of the behavioural relationships in the model are broadly correct, then such models can provide useful ex-ante predictions of the impact of price shocks upon different types of households and thereby upon poverty. Furthermore, even where these conditions are not completly satisfied, CGE models can provide a valuable indication of the sorts of effects which we might expect _given_ any set of assumptions about data, parameters and behavioural relationships and thereby give an indication of how sensitive the results are to particular sets of assumptions.

CGE models have now been used to examine the impact of a variety of price reforms (including trade, marketing and shifts in agricultural technology) in a large number of different countries.<sup>4</sup> However, the data requirements for such models can be considerable. Unless a recent Social Account Matrix (SAM) is already available, the construction of a useable SAM can take substantial time and expertise. Furthermore, the econometric estimation of behavioural parameters (as opposed to merely calibrating such parameters against the original SAM) can be complex and time-consuming and the choice of functional form for the behavioural relationships, although based upon plausible arguments, is essentially arbitrary (Deaton, 1997).

To reduce the data and resource requirements, many analysts have used simpler partial equilibrium techniques which can be implemented more quickly on readily available data. Such analysis has typically involved detailed microeconometric work on household survey datasets. This can yield information about the pattern of consumption and how it varies across different groups (e.g. deciles, gender of household head, type of main activity, region etc). In addition surveys sometimes have income information which can tell us the relative importance of different sources of income again disaggregated by different groups. Such work can provide a rich picture of the poverty profile of any given country and an initial indication of the likely impact of reforms.

This paper describes a simple practical methodology for estimating the poverty impact of price reforms which can be implemented in a reasonably short period of time using almost universally available household survey data. This methodology is not new, but by providing a comprehensive description of the methodology in one paper, we hope that it will become a standard "minimum" analysis of the potential impact of such reforms upon the poor. We provide some examples of the application of the methodology from the Diagnostic Trade Integration Studies conducted as part of the Integrated Framework program<sup>5</sup> to assist Least Developed Countries with integration into the world economy.

> 4 For example, IFPRI's Trade and Macroeconomic division http://www.ifpri.orEI has conducted numerous studies of this kind.

> **5** The Integrated Framework (IF) was set up by the WTO Ministerial meeting in Singapore in 1996 to help integrate LDCs into the world economy. The work programme includes the preparation of country specific Diagnostic Trade Integration Studies some of which include chapters on the linkage between trade and poverty (hilp://www.inte-ratedfraniework.org/) provides more information on the Integrated Framework. DTIS studies for Cambodia, Madagascar and Mauritania are available from

> http://if.wto.org/documents e.btm; draft DTIS studies exist for Yemen, Senegal, Malawi and Lesotho.

2
In addition, since the reforms which are<sup>likely to have thegreatestimpactupon the poor</sup> will vary from country to country, we provide<sup>some pointersto howthe analysiscan be</sup> made more sophisticated in the areas of<sup>mostimportanceto a particular country.We</sup> conclude with a discussion of areas for<sup>futureresearch.</sup>

# **2.** A **Basic Methodology**<sup>**6**</sup>

The basic methodology draws on the<sup>approachof Nicita,Olarreaga and Soloaga(2002) in</sup> their study of the impact of trade reform<sup>inCambodia.They write the income of a</sup> household as the sum of three components:<sup>own production,wage employment and net</sup> transfers. Own production includes<sup>both the value-added from farmingas well as the</sup> value-added from any other enterprises<sup>ownedby the households(e.g.smallenterprises</sup> engaged in trading or the provision of<sup>services). Wageemployment includesall</sup> payments made by those outside the<sup>household for the labourservices of membersof the</sup> household e.g. payments for working on<sup>someoneelse's farm, or the payment resulting</sup> from a job. Net transfers refers to the<sup>net payments from the government (pensions,</sup> grants and other transfers minus any<sup>feesor taxes)as wellas net transfersfrom other</sup> households e.g. net remittances.

The idea behind the methodology is that<sup>in the short run households cannot changetheir</sup> activities in response to a change in prices<sup>(inthe long run households may well change</sup> their activities as a result of the price<sup>change-indeedthis may be the intention of the</sup> reforms). In this case income from<sup>own productionand wage employment can be written</sup> as the product of a set of prices and a<sup>set of quantities.For example,income from own</sup> production is equal to the prices of the<sup>outputs producedtimes the quantity of output</sup> produced minus the prices of the inputs<sup>used timesthequantities of inputsused.</sup> Similarly income from wage employment<sup>can be writtenas wagestimes the (net) quantity</sup> of labour sold.<sup>7</sup> If households are unable<sup>tochange their activitiesimmediately when</sup> prices change, then a first approximation<sup>to the changein their income resulting froma</sup> price shock can be given by the sum of<sup>the pricechangestirnesthe originalquantities</sup> produced. Thus if the rice price increases,<sup>a firstapproximationtothe increase in income</sup> for rice farmers is given simply by the change<sup>in therice pricetimesthe quantity of rice</sup> produced. Similarly if the wage increases,<sup>a first approximationof the benefit isgiven by</sup> the change in the wage times the quantity of<sup>labour sold.</sup> However, households consume as<sup>well as produceand price changes affect consumption</sup> too. Just as with production, price<sup>changes will changethe long run consumption pattern</sup> Work has been initiated on Ethiopia, Burundi,<sup>Nepal, Guinea,Eritrea.A draft DTIS study has also been</sup> done for Armenia and work is underway<sup>in severallow income countriesincluding:Georgia,Moldova,</sup> Kyrgyz Republic and Azerbaijan).

**6** The basic methodology is based on "A<sup>simplemethodologytoassess the poverty impact of economic</sup> policies using household data. An application<sup>toCambodia. Nicita, Olarreagaand Soloaga (2002).See</sup> Annex I for details.

7 Transfer income is harder to disaggregate<sup>in this way -seethe sectionon remittancesbelow.</sup> 3
of households, causing them to consume relatively more of cheaper goods and relatively less of goods which have become more expensive. Also price changes will affect real incomes making households consume more or less of all goods. But, in the short run we can make the same assumption as for production - that the quantities of goods consumed by the household do not change. If this is the case then a first approximation of the increase in the cost associated with a price increase can be given simply by the change in the price times the quantity of the good originally consumed.

Putting the production and consumption effects together, it is possible to show that the change in welfare<sup>8</sup> can be approximated as the change in income minus the change in consumption. This makes intuitive sense: an increase in the price of a good which is both produced and consumed will increase income and also increase the cost of achieving the original level of consumption - the difference between these is therefore an approximation to the welfare change.

Note that the basic methodology is a "worst case" analysis because it assumes no quantity response at all - if households are able to substitute away from the consumption of goods whose price has risen or to substitute towards the production of such goods then it must be better off than the situation in which it could not do so. In some respects this makes the simple model more attractive because if the model points to a relatively small negative or positive impact then the incorporation of substitution effects can only make the situation better.

Finally, if one wishes to express the change in welfare as a percentage then one can divide it by the original level of welfare (given by the initial level of income). If we do this we can write<sup>9</sup> : <!-- Start of picture text --> AW o) Bk , ] I Awf -<br>E; [ 4 ( Apc > X ( (1)<br>-ZBS~cj<br><!-- End of picture text --> where W is the measure of welfare, _ISf_ indicates the value of outputj as a share of household income, (Ap° _/p7_ ) is the percentage change in the price of outputj, _BSk'_ is the budget share of input costs, _ISjw_ is the income share of net factor income from factorf (in most cases equal to the income share of wages), and _BSjC_ is the budget share of goodj in consumption.'<sup>0</sup>

> 8 Given by money metric utility - see Annex 1 for details.

> 9 See Minot and Goletti (2000) Appendix 2 for a full derivation.

> 10 We also assume no change in transfer income see Annex 1 for an expanded expression with transfer income.

4
Equation (1) is the core of the basic methodology.<sup>The key thing to noteabout this</sup> equation is that the first-order percentage<sup>changein welfarecan be calculated usingonly</sup> information on the income shares of different<sup>income sources,the budgetshares of</sup> different items of expenditure, and the percentage<sup>changesin pricesexperienced. Such</sup> information is readily available from many<sup>householdsurveysmakingtheapplication of</sup> this methodology relatively straightforward in<sup>a largenumber of countries.</sup>

# 3. **Application of the Basic Methodology**

Given the above methodology, the impact<sup>of a price change upon a household will clearly</sup> depend on two things: which prices change<sup>(and by how much); and the natureof the</sup> household. We consider each issue in turn.

# _Determining price changes_

The easiest way to "determine" price changes<sup>is toassumethem.That is,the above</sup> methodology can be used to explore the potential<sup>impact upon different groups of</sup> households of a set of possible price changes.<sup>Thisis particularly valuable wherethe</sup> price changes likely to result from the implementation<sup>of a reformare not known with</sup> any degree of accuracy (or where the analysis<sup>of howthepolicy reforms might change</sup> prices is complex, costly or simply has not<sup>yetbeen undertaken). Evenwhere price</sup> changes have been predicted by some other<sup>model, assuminga set of exogenous price</sup> changes allows policymakers to conduct<sup>sensitivityanalysis on the poverty impact of the</sup> models predictions.

Rather than assuming exogenous changes in the<sup>pricesfaced by households,one might</sup> instead wish to assume exogenous changes in<sup>apolicy related priceand some</sup> transmission mechanism between the policy<sup>related priceand the pricefaced by the</sup> household. For example, if one is interested<sup>in the poverty impact of a 10 percent</sup> increase in the rice tariff we could write down<sup>the price of tradablegoodsas a functionof</sup> the various taxes and costs which incur<sup>between theborderand the household.i.e.</sup> _ph =_ **_pW.(1_** _-_ t) for output prices and _p = p_ **_W.(_**<sup>**_1_**</sup> _+_ t) for the price of tradeable inputs<sup>and consumptiongoods</sup> (2) where ph is the price experienced by the household,<sup>**_pW_**is the world price and t is the</sup> tariff, tax or unit cost between the border<sup>andthe household.If we knowthe world price</sup> and the tax then we can calculate the percentage<sup>change in the household pricefor any</sup> change in the tax. 1 l Alternatively one could<sup>treat the tax as endogenousand use the unit</sup>

> " In the simple case shown in equation (2) the percentage<sup>changein the householdprice will be equal to</sup> _at /(1 +_ t) where a is the percentage change<sup>in the tariff on an input;or-at</sup><sup>_/(1-_t) for anoutput.</sup>

_5_
price experienced by the household to calculate the total unit transaction cost between the border and the household and then simulate a percentage reduction in this cost.

The more detail one has regarding the transmission of prices from the policy price to the price faced by the household, the more accurately one can predict the likely percentage change in price faced by the household. For example if information on transport costs is available between different regions and one wishes to simulate the impact of a particular infrastructure development on the price of a tradeable good which is not produced domestically then we can write: _Pr_ **_=p_** *(1 **_+_** _t)(1 + tr)_ _(3)_ where ph is the price faced by household h in region _r, t_ is the tax at the border and _tr_ is the transport cost from the border to region r. One may then use the information about the different effects which the infrastructure development may have on transport costs between the border and each region to determine the likely impact of the infrastructure development on the prices faced by households in each region.

One may also simulate technological shifts in the same way. For example, Nicita, Olarreaga and Soloaga (2002) write the value of rice output as: Value of Rice Output = _q Paddy.(1-phlpaddy).A. (I -a). (l trice) rice_ _(4)_ Where _qpaddy_ is the quantity of paddy produced, _phlpaddY_ is the post harvest losses, A is the milling yield of paddy-to-rice, (1-a) is the milling unit transformation costs, _trice_ is the tax on rice _and price_ is the world price of rice. They then simulate changes in technological parameters e.g. improved storage lowering post-harvest losses, improved efficiency raising milling transformation or lowering transformation costs, as well as changes in the tax rate (see Nicita, Olarreaga and Soloaga (2002) for details).

Similarly, if the price change is the result of changing a tax or subsidy applied by the govemment on a particular good or service, then the simplest possible approach is to assume that there is a proportional increase in the consumer price, while the producer price remains fixed. If estimates of the own price elasticity of demand and supply are available, then a better approximation to the effect upon consumer and producer prices can be found by calculating the price at which the market clears when a wedge equal to the tax is placed between the consumer and producer prices.<sup>12</sup>

**12** The percentage change in producer prices will be given by _Edt/(l - Edt);_ the percentage change in consumer prices will be given by _E t/(l -E dt)_ where **_Ed = d/(s_** _-£_ **_) Es_** _= /(£_ - **_8 ) 6d_** is the demand elasticity, **_Cs_** is the supply elasticity and t is the ad valorem tax rate.

6
Thus there are a large number of ways<sup>in which one can deriveplausible price changes</sup> resulting from policy reforms. However,<sup>theimpactof these price changesupon the poor</sup> depends on the nature of the household,<sup>to which we now turn.</sup> _The Nature of the Household_ The impact of a price change upon<sup>a household will clearly dependon the relative</sup> importance of different sources of<sup>incomeand of differentgoods inthe consumption</sup> basket. For example, if the price<sup>of a staplefood rises sharply then net producers will</sup> benefit, whereas net consumers will<sup>lose, but the extentof the gain or lossdepends upon</sup> how much income depends upon<sup>theproductionof this goodand how importantthisgood</sup> is in the household's consumption<sup>basket.Consequently,the best placeto start in</sup> determining the impact of a price shock<sup>is toobtain,for different groupsin society,</sup> information about the relative importance<sup>of different sourcesof income and the relative</sup> importance of different goods in<sup>householdconsumption.Table1 showsa typical table,</sup> from Cambodia, of sources of income<sup>for differentdecilesof the consumption</sup> distribution; Table 2 shows the expenditure<sup>shares,by decile of per capita consumption.</sup>

**Table 1: Contributions to income in Cambodia**<sup>**fromdifferent sources(percent)**</sup>

|_SourceofIncome:_<br>|_1_<br>|_2_<br>|_3_<br>|_4_<br>|_D_<br>_5_<br>|_eciles_<br>_6_<br>|_7_<br>|_8_<br>|_9_<br>|**_10_**<br>|_avg_<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Selfemployment|67.8|71.4|71.7|70.3|73.6|68.5|71.2|70.7|63.3|36.3|60.9|
|*fromcultivation|27.8|29.7|31.1|30.3|31.9|32.1|30.6|32.3|19.8|3.2|22.4|
|fromricecultivation|21.4|24.3|25.3|23.6|25.7|25.0|22.7|20.4|9.7|2.1|16.3|
|fromothercrops|6.4|5.4|5.8|6.7|6.2|7.1|7.9|11.9|10.1|1.1|6.1|
|*fromlivestock|16.6|14.3|14.2|13.0|12.4|11.9|11.6|12.0|8.3|1.5|9.4|
|*fromfishgrowing,etc|6.1|7.1|5.5|8.7|6.9|6.8|8.7|7.1|5.2|2.1|5.6|
|*fromforestryandhunting|8.3|10.2|9.7|7.3|7.7|7.9|8.1|6.1|4.9|0.6|5.7|
|*fromnonfarming<br>activites|8.9|10.0|11.2|10.9|14.7|9.8|12.2|13.2|25.1|29.0|17.7|
|Other sources|32.2|28.6|28.3|29.7|26.4|31.5|28.8|29.3|36.8|63.9|39.1|
|*fromwages|19.0|15.0|15.9|14.5|14.3|18.5|17.6|16.5|21.0|30.2|20.5|
|*fromremittances|1.2|1.9|1.6|2.8|1.3|2.4|1.6|1.7|2.3|2.6|2.1|
|*other(rents,dividends,<br>etc)|12.1<br>|11.7<br>|10.9<br>|12.4<br>|10.8<br>|10.6<br>|9.6<br>|11.1<br>|13.4<br>|31.0<br>|16.5<br>|
|Total|100|100|100|100|100|100|100|100|100|100|100|

_Source:_ CSES Survey 2001 Table 1 shows that income from self-employment<sup>is the most importantsourceof income</sup> for the vast majority of the population,<sup>only falling substantiallybelow 70% of income</sup> for the top two deciles. Most of<sup>this income comes from cultivation, particularly rice</sup> cultivation, but livestock and forestry<sup>productsarealso of importanceto the poor.The</sup> 7
poorest decile obtain a higher share of their income from wages than all except the two highest deciles, probably reflecting landlessness among the poorest households in Cambodia. The most striking thing to note from Table 1 is the dramatic differences between the top two deciles and the rest of the population. The rich in Cambodia (and in many other countries) really are different, typically earning a far higher share of their income from non-farming activities, wages, and rental income than the rest of the population. Given that the top two deciles of most populations tend to live in the major urban centers and are much more closely connected to the policy process than most other groups, it is important to analyse policies to see if they are really serving the interests of the majority of the population (and in particular the poor) rather than those of the, rather different, top two deciles.

**Table 2: Expenditure shares in Cambodia by decile of per capita consumption (percent)**

|**_Consumption_**<br>**_item_**|**_1_**|**_2_**|**_3_**|**_4_**|_5_|_6_|**_7_**|**_8_**|**_9_**|**_10_**|**_avg_**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|FoodTotal|75.7|76.3|76.0|73.7|73.7|72.6|72.0|70.3|64.4|38.6|63.5|
|*Rice,all<br>varietes|28.4|23.3|22.7|20.7|20.6|18.9|17.5|15.1|12.5|5.8|15.6|
|*Fish**&**fish<br>products|9.9|11.1|10.6|10.5|10.9|10.8|10.0|10.0|9.2|5.0|8.9|
|*All other<br>consumption<br>items|37.4|41.9|42.7|42.5|42.2|43.0|44.5|45.2|42.7|27.8|39.0|
|Non FoodTotal|24.4|23.8|24.1|26.4|26.3|27.5|28.0|29.7|35.6|61.4|36.5|
|*Housing,fuel<br>and<br>transportationa|15.7|15.5|15.2|17.1|17.3|17.4|17.5|19.4|24.1|46.7|25.5|
|*Clothingb|2.9|2.8|3.0|3.0|2.8|3.4|3.4|3.2|2.9|2.3|2.9|
|*Other<br>expendituresc|5.8|5.4|5.9|6.2|6.1|6.6|7.1|7.1|8.6|12.4|8.2|

> a Includes house rent (rental value of subsidized housing, rental value of owner-occupied housing, hotel charges), house maintenance and repair, water and fuel, medical care, transportation and communication, and personal care b Clothing and footwear (tailored clothes, ready-made clothes, shoes, etc.) **c** Includes fumiture and household equipment and operation, expenditures in recreation, education , personal effects and miscellaneous items.

> Source: CSES Survey 2001.

Table 2 shows the expenditure shares on different commodity groups broken down by the same per capita consumption decile groups as above. Again we see a remarkable homogeneity across the bottom eight deciles and dramatic differences for the top two deciles. Food expenditure accounts for over 70% of total expenditure for the bottom 80% of the population, but rice consumption increases in importance for poorer households, whilst the share of all other consumption items (except fish and fish products) declines. Similarly non-food shares are quite similar for the bottom eight deciles, with the share of housing, fuel and transportation rising dramatically for the top two deciles.

8
Taken together Tables 1 and 2 give us a good indication of<sup>the likely initialimpact of a</sup> given price shock. Clearly changes in rice prices will<sup>have the largest impactgiven their</sup> large share-in both income and consumption. Of course<sup>what mattersis the households</sup> _net_ consumption position; the tables suggest that the<sup>poorest decile will be net consumers</sup> on average (28.4% of their consumption is on rice, 21.4% of their<sup>incomeisfrom rice)as</sup> are the top two deciles, whilst all other deciles are net<sup>producers.Thusa sharpincreasein</sup> the rice price may hurt the poorest, but help the not quite so poor<sup>atthe same time.The</sup> tables also give us insight into issues which might<sup>not otherwise be apparent.For</sup> example, decreases in livestock prices could hurt<sup>the poorto a significantextent,but</sup> would be of much less significance for the upper deciles<sup>of the population.Similarly</sup> shifts in wage income will matter more to the<sup>poorest decilethan toslightly less poor</sup> households in deciles 2 to 5. And increases in<sup>housing,fueland transportationcosts will</sup> hit the richest hard, but will have a much smaller<sup>effectupon the poorest80%of the</sup> population. Thus a careful examination of the income sources<sup>and expendituresharesof</sup> the population can provide much of the "story"<sup>aboutthe potentialimpact of price shocks</sup> upon the poor.

The ability to tell a relevant story about who is affected<sup>by variousreformsalsodepends</sup> upon the ways in which households are grouped.<sup>Tables1 and2 grouped householdsinto</sup> deciles of per capita consumption expenditure, which is a<sup>naturalgroupingif one is</sup> interested in determining the impact upon<sup>the poor.However,deciles of consumption</sup> expenditure are rarely a relevant group for<sup>policy purposes;rather policymakers tendto</sup> be interested in what the effects of a reform<sup>arefor functionalor geographicalgroupings</sup> e.g. rice farmers versus informal urban workers or<sup>Western provinceversus Eastern</sup> Province. Indeed there are an infinite variety of possible<sup>groupingse.g.educationallevel,</sup> gender of household head, ethnicity, location, principle<sup>activity,land ownershipetc.The</sup> particular combinations of grouping which are relevant<sup>willdependon theprecise</sup> context, but if the analysis is conducted at<sup>the householdlevel,then it is possible to put</sup> together any grouping for which the relevant variables<sup>are availablefrom the survey data.</sup> Furthermore, if one wishes to explore non-income "dimensions"<sup>of poverty(e.g.</sup> educational attainment, remoteness, health etc)<sup>and thesevariablesareavailablein the</sup> dataset, then it is possible to present the above tables in<sup>terms of deciles of these</sup> variables. Table 3 for example, breaks down households'<sup>incomesourcesin Nepal by</sup> deciles of time to get to the market. It shows for example<sup>that incomefrom own</sup> enterprises is only of real importance to households<sup>whoare relatively closeto markets,</sup> whilst consumption of own production increases as a proportion<sup>of totalincomethe</sup> further away one is from markets, rising to over half<sup>of total incomefor the most remote</sup> three deciles.

There are many different dimensions across which such tables<sup>may be constructed. For</sup> example, Table 4 shows household income share<sup>for each of the10 geologicaland</sup> economic regions of Nepal along with summary poverty<sup>and illiteracy figuresfor each</sup> region. It shows large differences in sources of livelihood<sup>betweendifferent regions.</sup> Income from own enterprises and non-agricultural<sup>wagesare key in Kathmanduand other</sup> 9
urban areas, whereas in Terai and the Hills and Mountains, own production is the most important source of income. Even within broad geographical regions there are substantial differences: in rural eastem and central Terai almost a fifth of income comes from own enterprises, whereas in mid- and far-west Terai only 4% of income comes from this source. Similarly remittances are key to those living in the rural western hills, but mostly irrelevant to those in the eastem hills.

**Table 3: Household income sources by time to market in Nepal**

||||D|ecilesofD|istanceto|Market||||
|---|---|---|---|---|---|---|---|---|---|
|ShareofIncome|Closest<br>Decile|2|3|5|6|7|8|9D<br>D|istant<br>ecile|
|cashwagesfrom<br>workinginothersfarm|2.1|3.9|8.8|8.1|6.6|7.0|8.9|6.4|5.4|
|<br>Otherwages|18.0|13.8|13.7|15.9|11.2|8.5|8.2|11.6|16.1|
|Landrent|1.8|0.6|0.7|1.1|0.6|0.1|0.1|0.2|0.1|
|netagriculturalincome|1.8|3.7|7.7|10.7|10.2|3.7|6.8|4.4|1.5|
|fromsalesofcrops<br>netlivestockincome|1.5|2.0|3.6|3.8|3.9|3.3|3.6|4.1|5.8|
|netincomefromown<br>enterprises|48.2|29.7|13.1|7.0|8.2|15.4|5.6|10.1|5.1|
|totalnetremittances|7.1|20.3|9.4|5.8|12.7|8.4|14.3|8.6|8.3|
|other income|8.1|6.0|3.2|1.4|2.9|4.6|2.2|2.6|2.6|
|consumption ofown<br>production<br>total income|11.4<br>100.0|20.0<br>100.0|39.7<br>100.0|46.2<br>100.0|43.7<br>100.0|49.1<br>100.0|50.4<br>100.0|51.9<br>100.0|55.0<br>100.0|

Source: Baris Sivri - personal communication.

**Table 4: Household income sources and poverty by region in Nepal**

|ShareofIncome<br>cashwagesfromworkingin|Urban<br>Kathman<br>du<br>Valley|Other<br>Urban|Rural<br>Eastern<br>Teral|Rural<br>Central<br>Terai|Rural<br>Western <br>Terai|Rural<br>Midand<br>Far-West<br>Terai|Rural<br>Eastern<br>Hills,<br>Mtns|Rural<br>Cental<br>Hills,<br>Mtns|Rural <br>WesternF<br>Hills,<br>Mtns|Ru<br>Mida<br>ar-Wt<br>Hil<br>Mt|
|---|---|---|---|---|---|---|---|---|---|---|
|othersfarm|2.4|1.8|10.6|10.1|8.0|10.2|3.6|4.5|6.3||
|Otherwages|12.6|20.1|12.3|12.3|8.3|9.2|12.4|12.6|9.0|_1_|
|Landrent<br>|0.0|1.7|0.9|1.5|0.1|0.6|0.2|0.0|0.1||
|net agriculturalincomefromsales|||||||||||
|ofcrops (sales-expenditures)|5.6|1.2|17.4|7.9|<br>6.0|15.8|5.1|1.3|2.0||
|netlivestockIncome|10.3|2.0|2.4|3.9|<br>3.7|0.5|6.1|3.8|1.9||
|netincomefromownenterprises<br>total netremittances(received-|40.4|44.5|19.3|17.2|10.9|4.0|5.9|31.2|10.7||
|sent)|4.3|10.0|6.1|9.8|7.8|4.4|1.1|9.5|22.0||
|otherincome|7.4|7.9|2.0|0.9|2.6|0.6|3.6|1.4|8.3||
|consumptionofownproduction|17.0|10.7|29.0|36.4|52.6|54.7|62.0|35.7|39.7|5|
|totalIncome|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|10'|
|Poverty incidence(%)*|4.0|34.0|42.0|38.0|40.0|53.0|28.0|67.0|40.0|7:|
|Povertygap|0.00|0.11|0.10|0.08|0.09|0.13|0.07|0.11|0.13|0.|
|Illiteracy(%)|24.0|45.0|62.0|77.0|69.0|72.0|59.0|66.0|54.0|7:|
|ShareinNepal Population(%)<br>Source:BarisSivri- personal|1.4<br>communica|6.8<br>tion.|12.6|16.2|7.2|6.7|9.7|13.1|13.0|1|

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Constructing such tables can thus give<sup>a goodindication of how groups of households</sup> differentiated by dimensions other than<sup>incomewill beaffected by reforms.</sup> Once such tables have been constructed<sup>the basic methodology outlined above can be</sup> applied to calculate the impact on<sup>each household.This has been done using data from</sup> several different countries and a wide<sup>variety of policy experiments.'3Table 5 provides</sup> an example of a typical set of results<sup>based on the simulation of a 10percent increasein</sup> the price of rice in Cambodia.

**Table 5: Effect of a 10 per cent increase in**<sup>**the price of rice, by docile, asper cent of**</sup> **total household expenditures, Cambodia**

|Urban|_1_|2|3|<br>_4_|_Decile(7_<br>_5_<br>|_6_<br>|7<br>|_8_<br>|_9_<br>|_10_<br>|**_TOTAL_**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|NetSellers|1.9<br>|2.8<br>|3.2<br>|3.5<br>|2.9<br>|2.9<br>|1.7<br>|2.2<br>|1.8<br>|5.7<br>|2.5<br>|
|NetBuyers|-2.2<br>|-1.5<br>|-1.7<br>|-1.5<br>|-1.5<br>|-1.3<br>|-1.2<br>|-1.2<br>|-1.0<br>|-0.6<br>|-1<br>|
|TotalUrban<br>Rural|-1.3|0.5|-0.4|-0.3|-0.5<br>|-0.1<br>|-0.1<br>|0.3<br>|-0.2<br>|-0.3<br>|-0.2<br>|
|NetSellers|2.2<br>|2.6<br>|2.4<br>|2.4<br>|2.9<br>|2.8<br>|2.4<br>|2.3<br>|1.4<br>|1.9<br>|2.4<br>|
|NetBuyers<br>|-1.9<br>|-1.4<br>|-1.1<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.0<br>|-0.7<br>|-1.3<br>|
|TotalRural|-0.4<br>|0.5<br>|0.7<br>|0.8<br>|1.1<br>|1.0<br>|0.9<br>|1.0<br>|-0.1<br>|-0.5<br>|0.6<br>|
|NetSellers|2.2|2.6<br>|2.4<br>|2.5<br>|2.9<br>|2.8<br>|2.4<br>|2.3<br>|1.5<br>|3.8<br>|2.5<br>|
|NetBuyers|-1.9<br>|-1.4<br>|-1.1<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.2<br>|-1.0<br>|-0.7<br>|-1.2<br>|
|Total|-0.5|0.5|0.7|0.7|1.0|1.0|0.8|0.9|-0.1|-0.4|0.5|

(*) Deciles by per capita total consumption _Source:_ Nicita, Olarreaga and Soloaga<sup>(2002)</sup> The results show that the main losers<sup>from a rice price increasewould be the poorest net</sup> buyers in urban areas (because they spend<sup>a large proportion of theirincome on rice),</sup> whilst the largest gainers would be the<sup>netsellers in the top decileof the urbanareas.In</sup> rural areas the gains among net sellers<sup>are moreevenly distributed,but the lossesarestill</sup> concentrated among the poor due<sup>totheir largeexpenditureshare on rice.</sup> As noted above, there are a large number<sup>of different simulationswhich can be done with</sup> the above framework. For example, Ajwad,<sup>Duygan and Sivri(2002)simulate the impact</sup> of a 25% reduction in transportation<sup>costs for farmer householdson household</sup> consumption in Armenia. They estimate<sup>thatrural households gain on average0.95%of</sup> total per capita expenditure, compared<sup>to 0. 1% for urbanhouseholds,but the gainsare</sup> reasonably evenly spread among the poor<sup>andthe non-poor.They also simulate the</sup> impact of a 10% increase in irrigation<sup>charges;this hurts both urbanand rural households</sup> by roughly the same extent on average,<sup>but poorer rural householdsare hit much harder</sup> than well off rural households because<sup>a much highershareof their expenditureis on</sup> irrigation. Similarly Ajwad, Aksoy and<sup>Sivri(2002) simulate the poverty impact of a</sup> 30% increase in tobacco and maize<sup>yields in Malawi. Thesimulatedincrease in tobacco</sup> yields raises incomes of the poorest quintile<sup>by 1.5%compared to0.99%for the top</sup>

> _1'_ See poverty chapters of the Diagnostic<sup>Trade IntegrationStudies for Cambodia,Mauritania,Madagascar</sup> and Senegal and drafts for Malawi, Lesotho,<sup>Yemen and Armenia.</sup>

11
quintile; but raising maize yields has a dramatic impact upon the poor, raising their incomes by 5.66% compared to just 0.5% for the best off.

Applying the basic methodology in this way thus provides a valuable first estimate of the potential effects of a reform. However, the results of such simulations are contentious because they do not allow producers or consumers to respond to changed prices. Possibly more serioiisly they ignore the role of labour markets; the above simulation must, by construction, giVe the result that net consumers of a product whose price falls will gain. However, there is some evidence that households who are net consumers of a staple food do not necessarily gain when the price of that food falls if they obtain much of their income from working on farms producing that good, because the reduction in profits for the farm owners results in downward pressure on wages or employment. The next section shows various ways in which the methodology can be extended to address these and other concerns.

# 4. **Extensions of the basic methodology**

The above approach to the analysis of the impact of structural reforms on the poor is attractive because its data requirements are relatively low; most reasonable income and production surveys will contain data on quantities produced, inputs used and income received from a variety of different sources.<sup>14</sup> However, it has a number of important weaknesses.

> Firstly, the model does not allow for substitution in consumption or production. In reality

> quantities will adjust (indeed the reallocation of resources may be one of the objectives of the reforms). Resources will shift into the production of goods whose prices have increased, and away from the use of inputs and the consumption of goods whose prices have gone up.

Secondly, the model does not include any markets. Prices are completely exogenous and, unless changed for the purpose of the simulation, remain fixed. Yet in many cases prices will be determined by markets; in particular changes in the demand and supply of labour will determine the real wage of unskilled labour - a key variable for understanding the poverty impact of a reform.

Thirdly, the model does not allow for growth. Yet promoting growth - particularly growth which includes the poor - is often the central aim of such reforms. Allowing the model to incorporate assumptions about growth can be important for simulating medium to long-run impacts.

14 However, even here there may be difficulties: price information is often not well recorded (often one has to rely on unit prices rather than community price surveys); similarly wage information is often absent so that wages have to be inputed from earnings and hours worked; and transfers data often gives no indication of the source of the transfer (what sector was the person working in) or the determinants of the size of the transfer.

12
# We consider each of these areas<sup>below.</sup>

## _Substitution in consumption and_<sup>_production_</sup>

To allow for substitution in consumption<sup>one needs to obtain at leastincome and own-</sup> price (and preferably also cross-price)<sup>elasticitiesof demand.The easiest way to obtain</sup> these is to draw them from existing<sup>studies of the country of interest.Alternatively,one</sup> could attempt to estimate commodity<sup>demand equationsusing one of the standard</sup> demand systems (LES, AIDS,<sup>AIDADSetc).Ideally timeseriesdata on demandsfor</sup> various goods, prices and incomes<sup>is neededto ensurea reasonableamountof variation in</sup> prices. Alternatively, one may<sup>useDeaton's method of exploitingspatial variationin unit</sup> prices to estimate own and cross-price<sup>elasticities(see Deatonand Grimard,1992;</sup> Deaton, 1997). Nicita (2002) provides<sup>an exampleof the applicationof this approach to</sup> Mexico.

Estimating these elasticities provides<sup>a partialequilibriumway of assessing the</sup> magnitude of "second round" consumption<sup>effects of price reforms. However,estimating</sup> such elasticities is not a trivial matter,<sup>especially if only cross-sectionaldataare available.</sup> Moreover, it is not clear that incorporating<sup>the ability to substitutein consumption is that</sup> important. When prices change<sup>simultaneouslyfor a large number of commodities,</sup> incorporating the ability to substitute<sup>in consumptionappears in many practical</sup> circumstances to make little difference<sup>to the overall welfareeffects.This suggeststhat it</sup> may only be worth the additional<sup>effort of estimating such elasticitiesif one has a</sup> particular reason for believing<sup>that consumption substitutioneffectsare likely tobe</sup> important. On the other hand, if reasonable<sup>consumption elasticitiesare alreadyreadily</sup> available from other studies,<sup>their incorporationwithin the above frameworkis</sup> straightforward (see below) and<sup>there is no reasonfor not doingso.</sup> To allow for substitution in production<sup>one could attempt to estimateoutput supply or</sup> factor demand equations.. As with<sup>estimatingconsumptiondemand systems,this requires</sup> one to make assumptions about<sup>the functional formof the output supply/factordemand</sup> functions, and there must be a reasonable<sup>amount of variation in pricesin the data.Again</sup> the easiest approach is to draw the<sup>resultingsupply elasticitiesfrom existing studies.If</sup> these are not available then time series<sup>data is needed on outputs,prices and factor</sup> demands. Alternatively, one may<sup>attempttoestimateelasticitiesby combiningcross-</sup> sectional and time-series data (see<sup>Mundlak(1963)),but estimatingsuch elasticities</sup> solely from cross-sectional data<sup>is particularly difficultfor agriculturalproduction</sup> because natural conditions also<sup>cause largevariationsin supply.Furthermore,estimates</sup> of supply functions tend to assume<sup>smoothsubstitutability between the production of</sup> different commodities whereas<sup>in factthe majorwelfarechangesmay occur when</sup> markets are created or destroyed<sup>as a result of reforms. Such discontinuitiesare not</sup> generally captured in traditional<sup>supply systems.</sup> 13
Does the incorporation of such supply elasticities matter? In the short-run it may be reasonable to suppose that changes in the activities undertaken by households may be relatively slow (certainly compared to changes in consumption), so that the short-run poverty impact may justifiably assume fixed quantities of production. However, data from numerous countries suggest that households tend to be much less diversified in production than in consumption, so that reforms which change the returns to different activities are likely to have much larger welfare effects than reforms which change the prices of goods which households consume. Certainly, long-run supply response is critical to long-run poverty alleviation, so the incorporation of long-run supply elasticities in calculations of the poverty impact is probably much more important in most cases than the incorporation of consumption elasticities.

Even if it is not possible to estimate a full supply or demand system, it may still be worth incorporating reasonable estimates of the own-price demand and supply elasticities for the goods which are subject to price changes since a range of plausible values is more likely to reflect reality than assuming that all elasticities are zero. These elasticities may be easily incorporated within our simple framework by amending equation (1). Each element of equation (1) consists of an income or budget share _(ISj or BSj)_ multiplied by a percentage price change (Apj /p.). Incorporating price elasticities into this equation simply involves replacing each _BS1 .(Apj_ /pi) term with <!-- Start of picture text -->
>j2<br>BS1 . +'BS(  2<br><!-- End of picture text -->

(2) where **6j** is the relevant own-price demand elasticity for goodj (and similarly for the income share terms and the own-price elasticity of supply ).15

# _Modeling the labour market_

Assuming that quantities remain fixed (and therefore that only wages adjust) is especially problematic for the labour market since it is equivalent to assuming a labour supply elasticity of zero. In reality the price and cost changes induced by reforms will make some activities more profitable and others less so; demand for labour in the benefiting sectors will rise increasing both employment and wages, whilst demand in the losing sectors will fall reducing employment and wages. If adjustment is smooth and instantaneous then there will be no net effects upon employment and the effect on wages of different types of labour will depend on the relative intensity with which they are used in the gaining and losing sectors. However, in many circumstances adjustment is far from smooth or instantaneous. Companies losing from reforms may simply be unprofitable

> _15_ The superscripts from equation (1) have been ormitted since this expression applies to all elements of that equation (except rernittances). See Minot and Goletti (2000) for a derivation.

14
and close, shedding large numbers<sup>of jobs; if employment in the affected sector</sup> dominates the local economy<sup>then this canhave large negativeexternalitieson other</sup> businesses. Enterprises in the<sup>sectors gainingfrom reformsmay be locatedin different</sup> places and draw upon a different<sup>pool of labour.Thus althoughthe nationalaggregateof</sup> employment may be little affected<sup>by reforms,localeffectscan be considerableand</sup> longer lasting.

The opposite extreme from assuming<sup>a zerowageelasticityof labouris toassumethat it</sup> is infinitely elastic implying that,<sup>if a sector expands,employmenttends torise rather than</sup> wages. In some sectors and countries<sup>this may be nearer the truth than assuminga zero</sup> elasticity (the empirical evidence<sup>is mixed -see thesurvey by Matusz and Tarr (1999);</sup> Winters (2002) presents evidence<sup>that employmentadjustedmore than wagesin India</sup> during the 1990s). In practical<sup>termsit may besensible totake intoaccount the</sup> segmented nature of many<sup>labour marketsin developingcountries,since the simplest</sup> assumption of an infinitely<sup>elastic labour supply suggestssomewhat unrealisticallythat</sup> the "wage" when out of work<sup>is exactly the sameas thewage inemployment.It may be</sup> better to assume that employment<sup>expandsin the gaining sectorsat the existingsector or</sup> location specific wage.'<sup>6</sup> If we assume that employment<sup>expandsat a fixedsectorspecificwage and wish to</sup> simulate the impact of an increase<sup>indemand then we needa way of moving peopleinto</sup> and out ofjobs. It is clearly<sup>rather unrealisticto suppose that all of these new jobs willbe</sup> filled from the ranks of the previously<sup>unemployed;in many casespeople willswitch</sup> from one job to another in<sup>order totakeadvantageof thedifferencesin sectoralwage</sup> rates. Nicita et al (2002) provide<sup>a useful approach to calculating who is likely togain</sup> from an expansion in employment.<sup>They calculatethe probability of being in different</sup> forms of employment using<sup>a multinomiallogit, rank householdsaccordingto these</sup> probabilities and then put them<sup>into jobs in theorder of their probability.Thisapproach</sup> fits nicely with the intuition<sup>that, when formalsectorsgrowit is often not the poor who</sup> get the jobs, or at least not<sup>at first. In addition,if thenew wage "allocated"toa</sup> household on getting a job is<sup>determined by a wage equation which takes intoaccount</sup> sectoral wage differentials<sup>then it will be possiblefor there to be discrete changesin</sup> income when a person gets a job.

This approach has been applied<sup>in the IntegratedFrameworkstudiesfor a number of</sup> different countries. For example,<sup>the study for the Republic of Yemen(2002)simulates</sup> the impact of a 6 percent increase<sup>in female participationinthe labour market.Since</sup> poorer households are more<sup>likely to participatein the labour market in Yemen, two-</sup> thirds of the households affected<sup>by the increase in employment opportunitiesare in the</sup> bottom three expenditure deciles.<sup>Furthermore,the averageimpactfor those households</sup> that have a new entrant to the<sup>labourforce is equivalentto 30 percent of total household</sup> expenditures. Similarly in<sup>Malawi,Ajwad,Aksoyand Sivri (2002) simulate the poverty</sup> impact of a 30% increase in<sup>employmentin manufacturing, constructionandmining.</sup> 16 Or at least that the formal sector<sup>wageis higherthan the subsistencewage.</sup> 15
> Because of the characteristics of those 'employed in these sectors they find that this large

> increase in employment has no effect upon the poorest quintile, whilst the richest quintile

> gains by 4%. In Cambodia, Nicita, Olarreaga and Soloaga (2002) show that the impact of

> an increase of 50,000 employees in the industrial sector would have a large positive impact on rural households in the lowest expenditure decile with at least one member switching to the sector. But because of the relative probability of obtaining such jobs, the overall benefit to rural households is lower than the gain experienced by urban households.

A similar but somewhat more sophisticated methodology is used by Bourgignon and others in micro-simulation models of distribution changes (Bourguignon, Foumier et al. 2000). Bourguignon et al. estimate an individual level wage equation correcting for selection bias using the Heckman method. They then estimate wage and farm labour participation equations sequentially for each member of the household (starting with the household head) as a multinomial logit choice between (i) inactivity (ii) wage work (iii) work on the family farm (iv) work in non-farm businesses and (v) a combination of (ii) & (iii). The multinomial logits incorporate variables reflecting household characteristics which may have an influence upon individual participation decisions.

> The advantage of both of the above methods of modelling employment and wage income is that they only require readily available household survey data. In particular all that is needed is information on employment by sector, wages (or employment income and the quantity of labour supplied) and household characteristics. Using only this information it is possible to "allocate" individuals to jobs and determine their likely wage, given a known increase or decrease in employment. However, the extent of the employment change is exogenous usingthe above approach. Ideally, the expansion or contraction of each sector resulting from reform could be predicted by a general equilibrium model, but,

> as noted above, the resources required to construct a CGE model may preclude this option in many countries. One therefore needs a mechanism for estimating the potential employment impact of a structural reform. There are two components to this problem: firstly, it is necessary to estimate the impact of the price change induced by reform on sectoral output; and secondly it is necessary to estimate the impact of the change in output upon employment and wages.

One ad hoc approach to the first part of the problem would be to use investment climate surveys to obtain estimates from senior managers of companies in each sector about the sort of growth rates they anticipate and what difference they might expect policy reforms to make. Such estimates are of course highly subjective at an individual level, but assuming that knowledge of the prospects for growth is reasonably common across each industry, aggregate estimates of growth expectations may be at least as accurate as historical or model based estimates. Furthermore, if detailed firm-level surveys are available it may be possible to cross-check whether growth expectations are "reasonable" given the margins and financing structure of firms within the industry.

16
Alternatively, if estimates of aggregate supply<sup>elasticitiesareavailable,then these may be</sup> used to estimate the impact of price reforms<sup>on the output of affectedsectors.By its</sup> nature this approach will not take into account<sup>generalequilibriumeffects,but if the</sup> reforms do not affect too many sectors simultaneously<sup>then this may providean adequate</sup> approximation of the output change.

Estimating the impact of the change in output<sup>upon employmentand wages canbe</sup> addressed in a number of ways without<sup>resorting to a CGE analysis.If the sector of</sup> employment is indicated in the household<sup>survey, then it is possiblesimply toscale</sup> employment up or down by the sectoral<sup>growth rate(if oneis assuming aninfinite</sup> elasticity of labour supply) or to scale wages<sup>up or down for existingemployees (if one is</sup> assuming that the labour supply elasticity<sup>is zero).</sup> If it is reasonable to assume excess capacity<sup>and unemployment so that exogenous</sup> changes can be satisfied through an increase<sup>in outputwithout having any effecton</sup> prices, then a slightly more theoretically<sup>defensible approach is given by Niimi,</sup> Vasudeva-Dutta and Winters (2002). They<sup>attempt to estimatethe employmentimpact of</sup> the growth in exports and imports in Vietnam<sup>between1993and1998using the Vietnam</sup> Input-Output matrix. They simply calculate<sup>labour coefficientsfor each sector(in terms</sup> of jobs per $ of output) by dividing the<sup>total labour cost in each sector by the value of</sup> gross output (to give labour cost per<sup>$)and then dividingby the average wage.' 7Using</sup> this approach one can simulate the employment<sup>impact of any assumed sectoralgrowth</sup> rate by simply-multiplying the change<sup>inoutput by thelabour coefficientfor the sector.</sup> For example Niimi, Vasudeva-Dutta and<sup>Winters (2002)multiply the labourcoefficient</sup> for each tradable sector by the change in<sup>exportstocalculatethe additionemployment</sup> created by the expansion of exports. Similarly<sup>the employment"destroyed"by the</sup> expansion of imports was calculated by<sup>multiplyingthe labourcoefficientsby the changes</sup> in imports. This approach provides a<sup>lower bound of the employment changesresulting</sup> from growth."<sup>8</sup> Simply using the labour coefficients from<sup>the 1-0 matrix assumes that there areno</sup> second-round expenditure effects associated<sup>with the demandfor intermediates (or that</sup> all intermediates are imported). Where the<sup>productionof intermediateinputs isan</sup> important part of the local economy<sup>onemay wish instead touse "totallabour</sup> coefficients" which take into account<sup>the effectof growthon the demandfor</sup> intermediates (see Niimi, Vasudeva-Dutta<sup>and Winters,2002).19This providesan upper</sup> bound of the employment changes. Of<sup>course thelower and upper boundsare rather</sup> crude approximations to the employment<sup>changes,but the advantageof this approachis</sup> that it only requires an Input-Output<sup>matrix (and preferablyinformationaboutsectoral</sup> wage rates).

> **17** Ideally the average sectoral wage should be<sup>used,butin their case thiswasunavailable.</sup> 18 Assuming no changes in wages.

> **9** This is similar to the technique of using<sup>SocialAccounting Matrix (SAM) multipliersto simulatethe</sup> impact of exogenous income shocks upon different<sup>householdgroups -see Decaluwe,Patry, Savard and</sup> Thorbecke (1999) for an excellent exposition.

17
# _Analysing Remittances_

> Finally, in many countries remittances form an important part of income for many groups of households. If this is the case then one needs to think about the determinants of remittance income since it is not satisfactory to treat them as exogenous. There are a

> number of ways in which this might be done. At the very least, if one has estimates of the

> sectoral growth rates likely to result from reform (or a set of scenarios about what such growth rates might be), and if the sectoral source of the remittance is indicated in the

> data, then it is straightforward to scale up or down household remittance incomes accordingly. This is similar to the inelastic labour supply assumption above in that it is

> assumed that the wage rises in the growing sector benefiting existing employees and therefore increasing remittances. To account for the idea that growing sectors will employ new people who may then remit income it may be better to estimate the

> probability of receiving remittances from different sectors and the level of remittance received/sent against a set of household characteristics (see Republic of Yemen (2002) for an example). Then one can scale up or down employment in the different sectors using the probability of involvement in exactly the same way as suggested for labour income above.

This simple approach does not take into account the fact that whether households have remittances often depends on an earlier decision by one or more household member to migrate in search of work. If data are available on sectoral employment and wage rates as well as the characteristics of those employed, then it is possible to adopt a somewhat more sophisticated approach by modeling the decision to migrate. Consider a simple model of a household with n adult members - nr are in the rural household (which is receiving the remittances), nu are in the urban area sending remittances. For simplicity say that the members in rural areas are guaranteed a return on their labour of wr, whereas those in the urban area may receive a higher return wu but with a probability that depends upon overall labour demand and their individual characteristics. Assume each household maximizes its household income (i.e. the total income of its members in both the rural and urban areas).

> In this simple model, maximizing income simply involves deciding how many people to

> send to the urban area to try and get a job. If the sectoral wage and the way in which the

> probability of getting ajob depends on characteristics is known, then each household can allocate the optimal number of people to finding employment in the urban area. In the

> case of households with members with "poor" characteristics, it may not be worth their

> while sending someone to get a job, but if, for any household member

> wU.Prob(Employment) > wr, then sending that member to obtain employment will

> increase expected household income, and, if the household is risk-neutral, its welfare.

> Finally, if a household does send one or more members to the urban area to obtain

> employment, then one has to determine how much remittance income they will send

> back. One approach is to assume (or calculate from the data) an average saving rate and assume that they remit a fixed proportion of their income. An alternative might be to

18
assume a "social compact" within the household that<sup>the migrantwould not live ata</sup> higher per adult equivalent level of consumption than the<sup>membersof the sending</sup> household i.e. that they would remit that amount which<sup>would make them as welloff as</sup> the receiving household (after taking into account the remittance).

If one were to exploit a simple "Harris-Todaro"<sup>model of this kind,estimatingthe returns</sup> to different activities and the parameters of the probability<sup>of employmentfromthe data,</sup> then it should be possible to simulate the impact of<sup>growthin a-particularsectorupon the</sup> supply of migrant labour and therefore upon the level of<sup>remittances,whilst taking into</sup> account the loss of income resulting from their departure<sup>fromruralareas.Annex2</sup> elaborates on the simple model and describes<sup>how this mightbe implementedin practice.</sup>

# _Modeling the Household_

The sections above have described ways of separately<sup>incorporatingquantity responses</sup> into each element of income and consumption<sup>i.e. productionand consumption</sup> substitution, expansions or contractions of labour<sup>employed, and shifts in remittance</sup> income. However, in practice these responses<sup>happen simultaneouslywithinthe</sup> household. It would be useful therefore to<sup>havea mechanismfor modeling the impact of</sup> price changes upon households when they are allowed<sup>tosimultaneously respond by</sup> changing production, consumption and labour sale<sup>decisionsto maximizetheiroverall</sup> welfare. This suggests the use of simple "household models" in the Singh,<sup>Squire and</sup> Strauss (1986) tradition. Such models assume<sup>that householdsare faced withan</sup> exogenous set of prices but can re-allocate their<sup>resourcesbetweendifferentactivitiesto</sup> maximize overall household welfare. IFPRI have developed<sup>a sophisticatednon-</sup> separable farm household model (Lofgren and<sup>Robinson,1999)to explain non-linearities</sup> in the supply response of households to a variety of policy<sup>reforms.Their approachis</sup> quite data and resource intensive. However, it is possible to construct<sup>a very simple</sup> version of their household model which allows<sup>one totakeaccountof householdlevel</sup> responses. Consider the following simple model:

# **Equation**

**1.** Utility

**Example** _U (qc_

2. Production function 3. Commodity balance 4. Factor balance

**_qx =a(_** _f qi +qi = qi +Eqi +qi_ qf _+qf P = lqf l + X_

19
**_EpPqq_** _+_ **_IpJqjP =Z_** _p qS +_ **_Ipqff_** _f_ **_i_** _f_

# 5. Cash constraint

Say that a household maximizes their utility (given here by a simple Cobb-Douglas function of the goods which they consume) subject to a set of constraints. The first constraint is the production technology which they have (given above by a simple CobbDouglas function of the primary factors); the next two constraints are simple commodity balances - the quantity of any commodity produced plus the amount purchased must equal the amount consumed plus the amount used as inputs and that sold - similarly the total endowment of a factor which the household may have plus that which is purchased (e.g. labour hired in) must be equal to that used in production and that sold; finally, the household is subject to a cash constraint so that its total purchases of commodities and factors cannot exceed the value of its sales of commodities and factors.<sup>20</sup> Given a set of exogenous purchase and sales prices for commodities and factors (if necessary unit prices from the household survey may be used), households will choose how to allocate their factors between different activities in order to maximize their incomes, consumption and utility. The model requires a small set of parameters (the ai, _ai,_ and i, for each commodity i and factorj) which can either be taken from existing studies or estimated from the data. It is therefore possible in principle to construct a "household level" SAM for each household in the dataset and to estimate the impact of a price shock on household behaviour for each household separately.

Table 6 gives an example of a simple stylized household SAM. This household undertakes two activities -subsistence crop production and cash crop production. These activities use labour, land and capital as well as fertliser. These activities "sell" their output to the commodity accounts (SUB-C and CASH-C) respectively. In addition the commodity accounts "import" some of the subsistence crop from the "Rest of the World" (recall that this is a Household level SAM so that the Rest of the World (ROW) here simply means all other households; "imports" in this context are simply purchases from outside the household) as well as fertliser and non-food commodity. The household also sells their output of the cash crop to the ROW - their cash constraint means that the value of their imports (subsistence crop, fertiliser and non-food) must match the value of their exports (cash crop). The household's endowment of labour, capital and land "pays" the household account which then spends its income on food and non-food.

In a practical example the household may have many more activities and there will be many more commodities. In addition, if the data allows, factors of production can be broken down (e.g. by gender, age, experience, education etc). The model may also allow some factors to be tradable (e.g. labour) whilst others may not be (e.g. land). More significantly, the determination of whether a commodity or factor is tradeable can be endogenous. Our model allowed the possibility of differences in the purchase and selling

> 20 For simplicity we have set up the commodity and factor balance constraints and the cash constraints as equalities - it is possible to specify them as inequalities so that not all factors or commodities have to be used.

20
prices arising from transaction costs.<sup>This can result in householdschoosing not totrade</sup> goods which, were such transaction<sup>costsreducedthey would trade.21</sup> It may not be realistic to construct<sup>a full household level SAMfor every household in the</sup> dataset (although this is not impossible<sup>-indeed Cogneau and Robilliard(2000) and</sup> Robilliard, Bourguignon et al. (2001)<sup>have embeddedall the householdsin thesurvey</sup> into a full country level CGE). However,<sup>it is relativelystraightforwardto constructaset</sup> of SAMs for "representative households"<sup>andestimatetheimpacton each of these</sup> representative households separately.

The household modeling approach<sup>has the attractionthat it can allowfor all the above</sup> substitution effects within a consistent<sup>theoreticalframework,whilstonly requiring</sup> information typically available<sup>from a householdsurvey.However,it has theimportant</sup> disadvantage of remaining a fixed<sup>price model. Indeed it is perfectly possibleforeachof</sup> the representative households simulated<sup>tosimultaneouslysupply more labour to the</sup> market as a result of a structural<sup>reform without thishaving any effectupon the wage.</sup> Similarly household production<sup>responseschange the supply and demandfor</sup> commodities in the economy, but,<sup>in thesehousehold modelsthis has noeffect upon the</sup> prices of these commodities. In<sup>some circumstancesthis does not matter -if pricesare</sup> likely to remain fixed or if the nature<sup>of the changes can be assumed exogenously then it</sup> is not necessary to complicate the<sup>model by incorporating assumptionsabout the ways in</sup> which markets clear and prices adjust.<sup>However,in other circumstances accountingfor</sup> the likely changes in the prices of<sup>somekey commodities(notably staple foods)and some</sup> key factors (notably the unskilled<sup>wage)is important.Oneway in which these can be</sup> accommodated without moving<sup>to a country level CGE model is toemploy a multi-</sup> market model.

> 21 More precisely, production and consumption<sup>decisions become non-separableso that the householdis in</sup> fact responding to an endogenously<sup>determined shadow price rather than to the market prices.</sup>

21
Table 6: A Stylised Household SAM

|||Activities<br>||Commodit<br>|ies<br>|**Factors**||Household|ROUTotalSu|pplylincome|
|---|---|---|---|---|---|---|---|---|---|---|
|||1<br>||3<br>|4<br>5<br>6<br>|<br>7|8|9<br>10|11||
|**Activities**<br>||SUB-A|CASH-A|SUB-C|CASH-CINPUT-C<br>NONFC|<br>LAB|CAP|LAND<br>HH|ROW||
|1.Subsistence activity<br>|SUB-A<br>|||40||||||40|
|2.Cashcropactivity<br>|CASH-A||||75|||||75|
|**Commodities**<br>|||||||||||
|3.Foodcrop<br>|SUB-C<br>|||||||60||60|
|4.Cashcrop<br>|CASH-C<br>||||||||7|75|
|5.Fertiliser<br>|INPUT-C<br>|10|20|||||||30|
|6.Non-food<br>|NONF-C|||||||25||25|
|**Factors**<br>|||||||||||
|7.Labour<br>|LAB<br>|10<br>|2|||||||30|
|8.Capital<br>|CAP<br>|10<br>|2<br>|||||||30|
|9.Land<br>|LAND|10|1|||||||25|
|**Household**<br>|||||||||||
|10.Thehousehold<br>|HH|||||30|30|25||85|
|**Otherinstitutions**<br>|||||||||||
|11.RestoftheWorld|ROW|||20|30<br>25|||||75|
|TotalDemand/Expend|iture|40|7'|60|75<br>30<br>25|<br>30|30|25<br>85|7||

Expenditures go from column to row (or equivalently, sales go from row to column). The row totals therefore represent the income of each account; column totals represent total expenditures of the accounts.

22
# _Multi-Market Models_

All the above assumes that prices are exogenous. In<sup>somecircumstancesthis may be</sup> reasonable - such as homogenous tradeable goods<sup>whose price will be determinedby the</sup> world price. But the literature on growth linkages<sup>(Haggblade,Hazell et al.1989;Hazell</sup> and Haggblade 1991) points clearly to the<sup>factthat many of the goods whichmatter to the</sup> poorest people are non- or at least not very readily<sup>tradeable.In such situations pricesare</sup> going to be determined by local market clearing.

Multi-market models model the impact of household<sup>supply and demand decisions upon</sup> the prices of key commodities and thereby<sup>on household income(see Bravermanand</sup> Hammer (1986) for an early exposition; Arulpragasam<sup>and Conway (2002) providea</sup> practical guide to the use of such models). Rich and<sup>Lundberg (2002)describethe</sup> application of a multi-market model to the analysis<sup>of policy reforms in Malawi. Their</sup> model contains explicit supply and demand<sup>equationsfor a limitedset of commodities</sup> using supply and demand elasticities estimated from<sup>previousstudies.However,the key</sup> difference from the household model above<sup>is the inclusion of a set of price equations</sup> linking domestic producer and consumer prices<sup>for eachcommodity with import and</sup> export prices given estimates of various transport<sup>and transactioncosts andtaxes.</sup> Producer and consumer prices are then made endogenous<sup>by theinclusion of a market</sup> clearing equilibrium equation which ensures<sup>that theoverallsupply and demandfor each</sup> commodity is equalized. Household agricultural<sup>incomeis determinedby the value of the</sup> commodities produced by each of four household<sup>types minus theircosts of production;</sup> non-agricultural income is exogenous to<sup>themodel.</sup> Minot and Goletti (2000) present an interesting<sup>variationof this approach by constructing</sup> a spatial multi-market model for Vietnam. This<sup>issimilar to the model of Richand</sup> Lundberg but with a number of innovations. Most<sup>significantly they estimatesupplyand</sup> demand elasticities for several different regions of<sup>the country,rather than for different</sup> household groups. Markets in their model follow rules of<sup>spatial arbitrage-that is, trade</sup> between two regions occurs when the price difference<sup>between themreaches the transfer</sup> cost (the full cost of transporting and marketing<sup>the goodfrom one region totheother).</sup> The model generates estimates of the impact of changes<sup>in transaction costsandexport</sup> quotas upon production, consumption and prices of<sup>a set of commoditiesaswellas</sup> average incomes in each region. In order to estimate<sup>the distributionaland poverty</sup> impact of these changes, the price changes predicted<sup>by the model, along with the</sup> estimated supply and demand elasticities, are applied<sup>to household data on production</sup> and consumption to predict the change in net income<sup>for eachhouseholdusing the basic</sup> methodology described above.

Multi-market models are a useful tool for the analysis of reforms<sup>if thosereformsare</sup> likely to give rise to supply and demand responses<sup>which may significantly.change prices.</sup> However, multi-market are heavily dependent on<sup>beingable to obtain reasonable</sup> 23
> estimates of income and price elasticities of demand and supply elasticities. Furthermore, multi-market models, like the household model described above, have no factor markets

> (indeed, unlike the household model above, they have no factors at all since income is determined by the value-added of own production). It is therefore impossible to examine

> the impact of supply and demand responses on the wage. Indeed non-agricultural income

> is often exogenous and models where this is the case are clearly not appropriate for the

> analysis of situations in which non-farm income is important. Given that the

> consumption of non-tradable services in rural areas can be extremely important (Hazell and Hojjati 1995) this is a particularly serious omission, although there is no reason in principle why non-tradable services cannot be added as a commodity within a multimarket model if suitable data are available. Nonetheless, multi-market models can be

> valuable if endogenous price changes resulting from responses in several different markets is likely to be the main driver of welfare changes.

# _Allowing for Growth_

The omission of growth from the basic methodology is a particularly serious omission because the size of the impact on poverty resulting from a typical simulation which does not incorporate growth tends to be rather small. Consider a large shock such as a 50% increase in the price of rice in Cambodia. Table 1 shows that rice production is 21.4% of the income of the poorest decile; Table 2 shows that rice consumption is 28.4% of their expenditure - thus their net consumption of rice is 7% of their income. Thus the percentage change in net income of the poorest decile when faced with this large shock will be at most 3.5% of their net income (7% times 50% price change). Furthermore, if poor households are able to adjust at all, either by producing more rice or consuming other foods, then the impact on their net income will be less. Of course this is an aggregate figure - individual households or groups of households may be more severely affected - but the fact remains that the aggregate impact (positive or negative) upon the poor from such simulations tends to be rather small.

The generally small value of predicted impact is in direct corntrast with the claims of two different actors in the process of reform. Activists, NGOs and organizations representing those most severely affected often claim that the negative effects from structural reforms are large; whilst governments and international organizations often claim that the benefits from such reforms are large. These differing perspectives can be understood when one allows for aggregation and growth (Kanbur, 2001). For example, claims of strong negative effects on some groups are often true - some households or groups can be badly hit by particular reforms - but when the impact on such groups are aggregated along with the smaller negative or positive impact on other households the overall effect can be quite small. Resolving this is a political rather than an economic issue - policymakers need to decide on the extent to which policy should be detennined by its impact on the worst affected as against the impact on the general population. Claims of strong positive effects from structural reforms usually rely on (sometimes correct) assumptions about the impact of reform on growth. In the medium to long-run the impact of enhanced productivity and 24
growth on poverty tend to dominate the impact of redistribution caused by reforms (see Demery, Sen and Vishwanath, 1995), and poverty can be substantially reduced if reforms actually do have the assumed impact on growth.

How might one incorporate growth within our simple methodology? In the absence of a model linking policy reform to sectoral growth rates one is forced to rely upon projections based upon historical performance. It is straightforward to incorporate a steady reduction in particular transaction costs, or productivity growth in line with that experienced in the past (see World Bank (2001)). Furthermore, if separate studies give some indication of how such parameters may be changed by policy reforms then these new values may be used instead. Certainly this approach leaves much to be desired, but it does have the advantage that, since the "growth" rates are exogenously determined, it is possible to conduct sensitivity analysis so that policymakers can see the likely distributional outcome for any set of growth rates which they deem to be plausible.

# **5. Other Ideas for Methodological Development**

In the longer term there are a number of other ways in which the analysis of the linkages between structural reforms and poverty could be improved. I have grouped these under two themes: dynamics and risk; and qualitative approaches.

# _Dynamics and Risk_

Poverty is not a static phenomenon. A large number of studies from both developed and developing countries show that there is a great deal of movement in and out of poverty (see Baulch and Hoddinott (2000) for a selection of papers on poverty dynamics in developing countries). However, much "poverty profile" analysis is based upon static regressions between income/consumption and a set of household and economic characteristics. Unsurprisingly we find that the poor have high dependency ratios, are poorly educated, have few assets and live in poor areas - in other words, they are poor! However, the policy implications resulting from such associations are much less clear: "removing" children or the elderly from households with high dependency ratios is not a policy option; neither in most cases is supplying significant private assets to poor households, while the problems associated with "moving" households to better areas are well known. Education _is_ a key policy option, but even here, it is not certain that, if everyone's educational level rises, the poor will be much better off, even if they are better educated.

Interventions based upon such static analysis may therefore be less effective than expected in reducing poverty. If, instead we think of poverty as a dynamic process, then the "equilibrium" poverty rate can be reduced either by reducing the probability of falling into poverty, or by increasing the probability of exiting poverty (or both). For example, if 25
the most important reason why households fall into poverty is illness of a major income earner in the household, then appropriate primary (and curative) health care can be prioritized; similarly if the principle reason for exiting poverty is obtaining a job, then employment creation can be emphasized. It is therefore important that poverty analysis should attempt to understand the most important reasons for falling into and exiting from poverty. In particular, it is important for us to know the extent to which price reforms are a cause of entries into or exits from poverty.

How might this analysis be done? When there is only one survey available for a country there is little that can be done. This said, it is astonishing that household surveys in addition to collecting large quantities of quantitative data about the _status_ of the household don't also ask the simple qualitative question "what happened?". Households that have fallen into poverty usually know why - analysis of this qualitative information would be extremely valuable and it is important that future surveys conducted should provide for a mix of quantitative and qualitative information about causality.

In some situations we do have more than one survey - usually for different years, but sometimes within year. In this situation there is a lot that can be done. Where the multiple surveys are repeated cross-sections, these are typically used to look at aggregate trends in poverty. However, they also offer the possibility of understanding something about the dynamics of poverty by examining pseudo-panels. For example, one might look at how different cohorts have fared, possibly disaggregated by region, sector or skill (see Deaton (1997) for a discussion and application of the methodology). Although the extent of disaggregation may be limited by the need to maintain reasonable standard errors, such analysis can be very useful in painting a picture of how different groups in society have actually fared as a result of known price shocks. Credible explanations of what has actually happened in previous reform episodes can be more convincing to policymakers than simulations based upon single snapshots in time.

Furthermore, in a handful of cases panel data is available. In these situations much more comprehensive analysis of the impact of economic reforrns is possible. The classic methodology is outlined by Dercon (2000) for Ethiopia. But panel data sets are available and have been analysed for Peru (Glewwe and Hall 1998), Zimbabwe (Alwang, Mills et al.; Hoddinott, Owens et al. 1999), South Africa (Carter and May, 2001), China (Jalan and Ravallion 1998), India (Walker and Ryan 1990), Pakistan (McCulloch and Baulch 2000) and Vietnam (Niimi, Vasudeva-Dutta and Winters, 2002). In other cases intraannual panels may be available (i.e. the same households were recorded at multiple times during the year, but households are different across years). Thus, in addition to exploring how poverty has changed between years, using the intra-annual data to "correct" for seasonality, it may be possible to analyze how seasonality itself moves people into and out of poverty within the year. Seasonality is well documented as one of the most important concerns of the poor (Narayan, Chambers et al. 2000) and since the seasonality of price variation arises precisely because of the lack of suitable behind-the-border trade institutions and policies (warehouse receipting systems, buffer stocks, etc), understanding 26
the way in which these price<sup>changes impactupon poverty can give valuableinsight into</sup> the poverty effectiveness of<sup>behind-the-bordertrade interventions.</sup> Closely related to the issue<sup>of poverty dynamicsand theimpact of shocks is the issue of</sup> risk and uncertainty. Sometimes<sup>economicpolicy analysisignoresthesubstantial risk</sup> aversion of poor households.<sup>However,providing security is a central pillar of the WDR</sup> 2001 approach to poverty<sup>reduction. Thereason is quiteclear;poor households notonly</sup> have less - they are much<sup>more vulnerableto being in poverty in any given year.They</sup> consequently make great<sup>effortsto minimizethis vulnerabilityand these effortscan</sup> undermine their ability to benefit<sup>from reformsand their long-rungrowth.For example,</sup> poor households sometimes<sup>plantlower yielding but more drought resistant cropsdespite</sup> the fact that they could substantially<sup>increase their incomes with higher yielding cropsif</sup> they had an effective means<sup>of dealing with the risk of crop failure. Thustackling issues</sup> of risk and vulnerability<sup>in the analysisof structural reformand poverty helpsto link such</sup> reforms more closely to<sup>the social protectionpolicies whichforma key part of many</sup> PRSPs.

There are a number of ways<sup>in which an analysis of risk may be undertaken.Firstly, in</sup> many cases surveys record<sup>both consumptionand income.In cases where there is just</sup> one survey it is useful to<sup>look at how much narrowerthe cross-sectionalconsumption</sup> distribution is than the income<sup>distribution.22</sup> Where a panel (or pseudo-panel)<sup>is</sup> available it is possible to<sup>calculate the extentof consumption smoothing as wellas the</sup> degree of persistence in both<sup>consumption and income.Studies which have done this</sup> have shown that the poor<sup>are,unsurprisingly, ratherless well insuredthan the betteroff</sup> (Jalan and Ravallion, 1999).<sup>However,this begsthe interesting policyquestion- what</sup> sorts of interventions are<sup>likely to affectthe vulnerability of households to poverty, given</sup> that domestic institutions<sup>can be importantindirect providersof social protection(e.g.</sup> boards<sup>which provideguaranteed prices, or enterprisesproviding</sup> agricultural marketing transfers to existing and former<sup>employees).In order toanswer this questionit is</sup> necessary to have a measure<sup>of vulnerability. Several people have constructed</sup> vulnerability measures (see<sup>Christiaensenand Boisvert 2000;Pritchett,Suryhadiet al.</sup> 2000; McCulloch and Calandrino<sup>2002) andthen attempted to understandthe</sup> determinants of vulnerability.<sup>An analysisof how reformsaffectvulnerabilitycould</sup> therefore enrich the analytical<sup>frameworkused above.</sup> A second, more comprehensive<sup>approachto incorporatingrisk into our analysis,would be</sup> to incorporate a stochastic<sup>elementintothe household modelingapproach discussed</sup> above. Household models<sup>provide solutionsfor the optimal allocationby householdsof</sup> the productive factors at<sup>their disposal.Including riskin suchmodels wouldallow one to</sup> capture the idea that households<sup>tend to allocateresources in order to maximize the return</sup> to and minimize the risk<sup>from theirentire "portfolio"of activities.23Such a model would</sup>

> **22** Although the difference<sup>will bea mixtureof consumption smoothingand differences in measurement</sup> error variances between consumption<sup>and income.</sup>

> **23** More precisely one might<sup>modelthem as maxirnizing theirexpected utilitytaking into account their risk</sup> aversion.

27
allow one to simulate the impact of reforms which changed the volatility of prices. For example, the removal of a variable rate tariff is likely to create greater domestic price volatility than previously (along with a change in the average price). A model which incorporated risk could analyse the impact of _both_ these effects upon poverty and vulnerability.

# _Qualitative Approaches_

The above discussion has focused on technical economic methodological issues. However, as WDR 2001 stressed, poverty is about much more than just income and consumption so it is essential to take a multi-dimensional approach to poverty analysis. A multi-dimensional approach does not necessarily mean a qualitative approach; other dimensions of poverty can be quantified and analysed using the same tools as those applied to income and consumption. For example, there is no reason why one cannot report educational level, access to medical facilities, distance from markets, extent of soil degradation etc by decile or plot their cumulative distributions just as is done for income and consumption. Furthermore, several surveys contain subjective measures of wellbeing and almost all surveys contain information on assets and durables enabling the construction and comparison of welfare indices based on these variables (see Sahn and Stifel (2000) for an example using the Demographic and Health Surveys in Africa).

However, aside from the technical literature, there is a vast amount of qualitative literature on poverty and the impact of reform on poverty in developing countries. Such material does not have the advantages of quantitative surveys in terns of providing a statistically representative picture of a whole country. But it can provide enormously valuable insights into the processes of destitution, the structural social relationships which provide the context in which economic activity takes places, the nature and functioning of key institutions, the dynamics of intra-household and community behaviour, the political processes which determine the success of reform, and much more. Of particular importance is the role which qualitative studies can place in uncovering the causal pathways through which reforms may influence different dimensions of wellbeing. For example, if a reform increases the price of a cash crop typically produced by women, but social norms dictate that women are also responsible for household maintenance and childcare then the reforms may simultaneously increase incomes and reduce the welfare of poor women due to the additional pressures on their time. Quantitative household surveys rarely capture such processes making it important to perform analysis using both quantitative and qualitative sources.<sup>2 4</sup> One key resource here is the large volume of participatory material which is now available on many countries. In situations where quantitative surveys are of debateable quality, such participatory analyses can provide key information on the relative

> 24 See (Kanbur, Chambers et al. 2001) for a collection of notes on mixing quantitative and qualitative approaches.

28
importance of different sources of<sup>incometo different groups within the society.Also,</sup> although it is difficult to obtain<sup>good measuresof the</sup><sup>_level_of welfarefrom participatory</sup> assessments making comparisons<sup>across groups difficult,such exercisesdo generally</sup> provide useful indications of the<sup>directionof</sup><sup>_changes_in welfare over time.They can also</sup> provide information about:<sup>the key causes of poverty; the dimensionsof povertywhich</sup> matter to the poor in that country;<sup>the natureand extent of the risksfaced by the poor; the</sup> policies and institutions which<sup>have thelargestimpact upon theirlives; theroleand</sup> timing of seasonal shocks; the<sup>agro-climaticconditionsfaced by differentgroupsand so</sup> forth. They can therefore provide<sup>a valuableinputintothe analysisof the impact of</sup> structural reform on poverty.

# **Summary and Conclusions**

This paper has outlined a simple<sup>methodologyfor linking structuralreforms and poverty</sup> using household survey data<sup>fromdeveloping countries.As is always the case, the nature</sup> of the appropriate model for<sup>analyzingsuch linkages must depend upon the precise</sup> question being asked and upon<sup>thedata and time resourcesavailable.If theintention is to</sup> analyse the short-run impact<sup>of exogenousprice changes,then the basic methodology,</sup> which assumes that all quantities<sup>remainfixed, can be usedto providea first-order</sup> approximation to the likely<sup>changein net income for eachhousehold. Furthermore,if the</sup> nature and size of the transport<sup>and transactioncosts determiningproducer and consumer</sup> prices are known then it is<sup>possibleto simulate the impact of exogenous changes in these</sup> costs.

If it is desired to look at the medium<sup>run outcomeof suchexogenouspolicy changesthen</sup> it is necessary to account for<sup>the possibility of substitution in both consumptionand</sup> production. Ideally this should<sup>be doneby the econometricestimationof price elasticities</sup> of supply, and income and<sup>price elasticitiesof demand if suitabledata areavailable.If</sup> this is not possible within<sup>theavailabletime or data constraints, then estimates can be</sup> selected from other studies of<sup>thesame or "similar" countries,or, at worst,parameterscan</sup> be chosen which reproduce<sup>theobservedoutputlevelsgiven anassumed functionalform.</sup> Experience from several countries<sup>suggests that many households tend to be more</sup> specialized in production than<sup>in consumption,so that the incorporationof substitution</sup> possibilities in production<sup>hasa greaterimpact upon the finalresults than the</sup> incorporation of substitution in<sup>consumption.</sup> In many circumstances, households<sup>earn animportantshareof their income from the</sup> labour market. The basic<sup>methodology'sassumptionof fixed quantitiesmay not always</sup> be appropriate here, since<sup>exogenousprice shocksresulting from reforms may give rise to</sup> changes in employment levels<sup>in differentsectorsrather than only changesin wages. If</sup> excess labour means that it<sup>is reasonableto assumethat employmentcan change without</sup> changes in wage rates and<sup>the sector of employmentis indicatedin thehouseholdsurvey</sup> data, then employment can<sup>be scaled up or down by the sectoralgrowth rate.</sup> 29
Alternatively, labour coefficients from an input-output matrix may give an indication of the employment changes resulting from exogenous changes in output.

If employment changes can be calculated or assumed, then it is still necessary to allocate (or take away) the jobs to individuals. Here it is possible to draw upon the household survey data to estimate the probability of an individual being employed in a variety of different activities based upon their characteristics and those of their household. In this way one may account for the fact that the growth resulting from a reform may not directly benefit the poor if there is no corresponding rise in the unskilled wage.

Similarly, in circumstances where remittance income is important it may be desirable to have a somewhat deeper analysis of the impact of reform from this source. If estimates are available for the sectoral growth rates likely to result from the reform and the sector source of remittance income is known then it is possible to scale up or down household remittances pro-rata. Alternatively, one may estimate the probability of receiving remittances based upon household characteristics and increase or decrease employment (and therefore remittances) on this basis. Better still, if suitable data are available, it may be possible to model the decision to migrate based upon the expected remittance returns and then simulate the change in income resulting from a shift in the probability of employment in particular sectors.

> One may also attempt to integrate the above extensions of the basic methodology in the

> form of a simple household model. Such a model can allow a household to allocate their resources between a number of different activities (including wage labour) in order to maximize their utility when faced with an exogenous set of prices. One can then simulate

> the manner in which households change their resource allocations when prices change as a result of reform.

> Alternatively, if reforms are likely to give rise to supply or demand responses with significant knock-on effects for domestic prices, then it is possible to endogenise the

> process of price formation through the use of a multi-market model. However, such models can require substantial additional data and resources and are often inappropriate for analyzing circumstances where non-agricultural income is important.

> Other potential methodological developments include greater attention to the dynamics of poverty and the ways in which reforms may cause entry into or exit from poverty. Furthermore, with suitable data it may be possible to simulate the impact of structural reforms on price volatility and the consequential impact upon household vulnerability.

Efforts may also be made to analyse the quantitative aspects of non-income dimensions of poverty. However a proper analysis of the linkages between reform and other dimensions of poverty will require an exploration of the qualitative poverty material in each country.

> All of the approaches described above have focused on ex ante prediction of welfare

> effects resulting from structural reforms. This is, of course, what policymakers are most

30
interested in. However, it is worth<sup>noting thatexante analysiscanalsobe usefully</sup> informed by the application of the<sup>same techniquesex posttosee if thepredicted effects</sup> actually did occur. Such ex post<sup>analysis can point to weaknesses in the modeling</sup> methodology and provide information<sup>on the relativeimportanceof different effects upon</sup> welfare, allowing future modeling<sup>to take theseintoaccount.More generally,it is</sup> important that the effects of structural<sup>reform are monitoredduring implementationand</sup> adjustments made if it becomes<sup>clear that the predictedandactual effectsarefar apart.</sup> In conclusion, the choice of technique<sup>for modeling the link betweenstructural reforms</sup> and poverty should be determined<sup>by the precise natureof the question,the data available</sup> and the characteristics of economic<sup>activityand povertyin the country in question -there</sup> is no single "best" model. This<sup>paperhas presenteda basic methodology whichcan be</sup> applied using only household<sup>survey data and limited timeresources,along with a set of</sup> elaborations upon this methodology<sup>which may beadoptedas circumstancesand</sup> resources dictate.

# **References**

- Ahmad, E. and N.H. Stem (1991),<sup>The Theory andPractice of Tax Reformin Developing</sup> Countries, Cambridge: Cambridge<sup>University Press.</sup>

- Ajwad, M.I., A. Aksoy and B.<sup>Sivri(2002), Tradeand Poverty in Malawi, Mimeo, World</sup> Bank.

- Ajwad, M.I., B. Duygan and B.<sup>Sivri(2002), Tradeand Poverty in Armenia,Mimeo,</sup> World Bank.

- Alwang, J., B. Mills, et al. "Changes<sup>in Well-beingin Zimbabwe,1990-1996:Evidence</sup> Using Semi Non-parametric<sup>Density Estimates."</sup>

- Arulpragasam, J. and P. Conway<sup>(2002). "PartialEquilibriumand multi-market</sup> approaches" in Draft PSIA Toolkit,<sup>World Bank.</sup>

- Baulch, B. and J. Hoddinott<sup>(2000). "EconomicMobility and Poverty Dynamicsin</sup> Developing Countries." Journal<sup>of Development Studies36(6):1-24.</sup>

- Bourguignon, F., P. Fournier, et<sup>al.(2000)."Distribution,Developmentand Educationin</sup> Taiwan, 1979-1994." World<sup>Bank. mimeo.</sup>

- Braverman, A. and J. Hammer<sup>(1986)."Multi-marketAnalysisof Agricultural Pricing</sup> Policies in Senegal", in _Agricultural_<sup>_household models: extensions,applications_</sup> _and policy,_ eds. I. Singh, L. Squire<sup>and J.Strauss,Washington D.C.: The World</sup> Bank.

Carter M. R. and J. May (2001).<sup>"One kind of freedom:poverty dynamicsin post-</sup> apartheid South Africa", World<sup>DevelopmentVol. 29, No.12.</sup> Chen S. and M. Ravallion (2002).<sup>HouseholdWelfare Impactsof TradeReformin China,</sup> Mimeo, World Bank.

31
- Christiaensen, L. and R. N. Boisvert (2000). On Measuring Household Food Vulnerability: Case Evidence from Northern Mali. New York, Department of Applied Economics and Management, Cornell University.

- Cogneau, D. and A. Robilliard (2000). Growth, Distribution and Poverty in Madagascar: Learning from a Microsimulation Model in a General Equilibrium Framework. Washington DC, IFPRI, Trade and Macroeconomics Division.

- Cornia, G.A., R. Jolly and F. Stewart. (1987), _Adjustment with a Human Face,_ Oxford: Clarendon Press for the United Nations Children's Fund.

- Deaton, A. and F. Grimard (1992). _Demand Analysis for tax reform in Pakistan,_ LSMS Working Paper 85, Washington D.C., World Bank.

- Deaton, A. (1997). The Analysis of Household Surveys: A Microeconometric Approach to Development Policy. Baltimore and London, John Hopkins University Press for the World Bank.

- Decaluwe, B., A. Patry, L. Savard and E. Thorbecke (1999), "Poverty Analysis Within a General Equilibrium Framework", Working paper 9909, CREFA 99-06, University of Laval.

- Demery, L., B. Sen and T. Vishwanath (1995), "Poverty, Inequality and Growth", ESP Discussion Paper series 70, World Bank, Washington D.C..

- Dercon, S. (2000). The impact of economic reforms on households in rural Ethiopia 1989-1995, Centre for the Study of African Economies, Oxford University.

- Glewwe, P. and G. Hall (1998). "Are Some Groups More Vulnerable to Macroeconomic Shocks than Others? Hypothesis Tests on Panel Data from Peru." Journal of Development Economics 56: 181-206.

- Haggblade, S., P. Hazell, et al. (1989). "Farm-Nonfarm Linkages in Rural Sub-Saharan Africa." World Development 17(No 8): 1173-1201.

- Hazell, P. B. R. and S. Haggblade (1991). "Rural-Urban Growth Linkages in India." Indian Journal of Anricultural Economics 46(4, October-December): 515-529.

- Hazell, P. B. R. and B. Hojjati (1995). "Farm/Non-farm Growth Linkages in Zambia." Journal of African Economies 4(No 3): 406-435.

- Hoddinott, J., T. Owens, et al. (1999). revisiting forever gained: Income dynamics in the resettlement areas of Zimbabwe, 1983-1997.

- Jalan, J. and M. Ravallion (1998). Determinants of Transient and Chronic Poverty: Evidence from Rural China. Washington D.C., World Bank.

- Jalan, J. and M. Ravallion (1999). "Are the Poor Less Well Insured? Evidence on Vulnerability to Income Risk in Rural China." Journal of Development Economics 58(1): 61-81.

- Kanbur, R. (2001). Economic policy, distribution and poverty: the nature of the disagreements, World Development 29 (6): 1083-94.

32
- Kanbur, R., R. Chambers, et al. (2001). Qualitative<sup>andQuantitative PovertyAssessment:</sup> Complementarities, Tensions and the Way Forward. New<sup>York, Cornell</sup> University.

- Lofgren, H. and S. Robinson (1999). "To Trade or<sup>Notto Trade:Non-separableFarm</sup> Household Models in Partial and General Equilibrium",<sup>TMD Discussion Paper</sup> 37, IFPRI, Washington D.C..

- Matusz, S. J. and D. Tarr (1999). "Adjusting to Trade<sup>Policy Reform."World Bank</sup> Working papers 2142.

- McCulloch, N. and B. Baulch (2000). "Simulating the<sup>Impact of Policy uponChronic and</sup> Transitory Poverty in Rural Pakistan." Journal of Development<sup>Studies 36(6):</sup> 100-130.

- McCulloch, N. and M. Calandrino (2002). Poverty Dynamics<sup>in RuralSichuan between</sup> 1991 and 1995. Brighton, Institute of Development Studies.

- McCulloch, N., Winters, L.A. and Cirera, X. (2001).<sup>"TradeLiberalisationand Poverty:</sup> A Handbook", Centre for Economic Policy Research, London.

- Minot, N. and F. Goletti (2000). "Rice Market Liberalization<sup>and Poverty in Viet Nam",</sup> IFPRI Research Report 114, IFPRI, Washington D.C.

- Mundlak, Y. (1963), "Estimation of production and behavioural<sup>functions froma</sup> combination of cross-section and time-series data", in _Measurement_<sup>_in_</sup> _Economics: Studies in Mathematical Economics and Econometrics_<sup>_in memory of_</sup> _Yehuda Grunfeld,_ ed. Carl F. Christ, Stanford University Press.

- Narayan, D., R. Chambers, et al. (2000). Voices of the<sup>Poor:Crving out for Change.New</sup> York, Oxford University Press for The World Bank.

- Nicita, A. (2002). Behavioural responses of poor and<sup>non-poor households.Estimation</sup> of income, quality and price elasticities for Mexico. July<sup>2002.WorldBank.</sup>

- Nicita, A., M. Olarreaga and I. Soloaga (2002). A simple<sup>methodology to assess the</sup> poverty impact of economic policies using household<sup>data.An application to</sup> Cambodia. World Bank.

Niimi Y., Vasudeva-Dutta, P. and Winters, L.A.<sup>(2002)."Trade Liberalisationand</sup> Poverty Dynamics in Vietnam". paper presented at<sup>Globalisation, (In)Equality,and</sup> Regionalism Conference at the University of Warwick,<sup>March15-17,2002.</sup>

- Pritchett, L., A. Suryhadi, et al. (2000). Quantifying Vulnerability<sup>to Poverty:A Proposed</sup> Measure with Application to Indonesia. Washington DC,<sup>World Bank.</sup>

- Rich, K. and M. Lundberg (2002). "Multimarket models<sup>and policyanalysis:an</sup> application to Malawi", Personal Communication.

- Reimer, Jeffrey J. (2002). Estimating the Poverty<sup>Impactsof TradeLiberalization,</sup> Purdue University.

- Robilliard, A.-S., F. Bourguignon, et al. (2001).<sup>Crisisand Income Distribution:A</sup> Macro-Micro Model for Indonesia, International Food Policy<sup>ResearchInstitute</sup>

33
- Sahn, D.E. and D.C. Stifel (2000). Poverty comparisons over time and across countries in Africa. World Development Vol. 28. No. 12.

- Singh I., L. Squire and J. Strauss (1986). _Agricultural household models: extensions and applications,_ Baltimore, Md., Johns Hopkins University Press.

- Walker, T. and J. Ryan (1990). Village and Household Economies in India's Semi Arid Tropics. Baltimore, John Hopkins.

- Winters, L.A. (2000) Trade, Trade Policy and Poverty: What are the Links? CEPR Discussion Paper No. 2382. London.

- Winters, L.A. (2002) Trade Liberalisation and Poverty: Two Partial Empirical Studies, mimeo, University of Sussex, Brighton.

- World Bank (2001) Diagnostic Trade Integration Study for Madagascar. Volume 1. World Bank. Washington D.C..

- Republic of Yemen (2002). Diagnostic Trade Integration Study - Component Reports Chapter 6. Ministry of Industry and Trade.

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**Annex 1: The Basic Methodology**<sup>**25**</sup>

The basic methodology draws on<sup>the approachof Nicita,OlarreagaandSoloaga (2002)in</sup> their study of the impact of trade reform<sup>in Cambodia. Let the incomeY of a household</sup> be given by: where p,. is the price of output j; _qo_<sup>isthequantity of output j;</sup><sup>_p'_and</sup><sup>_qk_arethe</sup> corresponding input prices and<sup>quantities;wf is the wage rate for factorf,Lf is the net</sup> sale of factorfby the household;<sup>and</sup><sup>_Tmn_is the net transfer received by household</sup> member n from source m.

Note that the first term in equation<sup>(1)is the value-addedof all production (whetherfrom</sup> farming or non-farm enterprises).<sup>This includes both marketed productionand own</sup> consumption. The second term is<sup>the value of netfactorsales by thehousehold -in the</sup> case of most poor households this<sup>simply meansnet labour sales (i.e.employmentincome</sup> minus payments for hired labour)<sup>sincethe onlyfactor which most poor households can</sup> sell is their own labour. The final<sup>term representsthe net transfers received by the</sup> household.

Similarly we can write the consumption<sup>of the household simply as:</sup> where _pc_ is the buying price of good<sup>i and</sup><sup>_qc_is thequantity consumedof good i.Note</sup> that _qc_ includes own consumption as<sup>well as goodspurchasedfrom the market.</sup> It is then possible to simulate the impact<sup>on householdincome of price changesinduced</sup> by structural reforms. In the short<sup>run wecanassume thatall quantities remain fixedso</sup> that Similarly the change in consumption assuming<sup>thatquantities remainfixed is</sup>

> 25 This methodology draws heavily on "A<sup>simple methodology toassessthe poverty impact of economic</sup> policies using household data. An application<sup>to Cambodia. Nicita,Olarreagaand Soloaga (2002).</sup>

35
AC _= EApc.qc_ (4) It is possible to show that a first-order approximation of the change in money metric utility resulting from a change in the price of a commodity can be given by<sup>2 6</sup> _AMMU = Y -AC_ _(5)_ This makes intuitive sense: an increase (say) in the price of a good which is both produced and consumed will increase income and also increase the cost of achieving the original level of consumption - the difference between these is therefore an approximation to the welfare change.

Note that we can combine equations (1), (2), (3) and (4) to write equation (6): <!-- Start of picture text --> M=BS° Ao EBS' APk +ZBSw+ m _ __ B SC<br>110 k+ fi p 1<br><!-- End of picture text --> where _BSjo_ indicates the budget (or income) share of output revenue in total income, _BSj'_ is the budget share of input costs, _BSjW_ is the income share of net factor income from factorfJ and _BSjC_ is the budget share of good] in consumption. Thus the first-order percentage change in net income can approximated by the budget shares of income and expenditure on each item times the percentage changes in prices experienced.<sup>2 7</sup>

> **26** See Chen and Ravallion (2002) for an exposition of the theory.

> **27** See Minot and Goletti (2000) Appendix 2 for a full derivation.

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# **Annex 2: Modeling Remittances**

The paper outlines a number of ways in which we might model remittances.<sup>This note</sup> elaborates on the suggestion in the paper that we should attempt to incorporate<sup>a simple</sup> Harris-Todaro model of the migration process.

Imagine that a reform gives rise to growth in employment in a particular<sup>sector.Our</sup> "standard" technique for allocating people to jobs is to:

1. Estimate a probit for participation in the labour force

2. Estimate a multinomial logit for sectoral participation (including unemployment as base category) 3. Allocate individuals to the labour force according to their probability<sup>of</sup> participation from the Probit analysis (leaving in individuals who are currently participating even if their probability of participation is low) 4. Allocate new entrants to the labour force to the sector which they<sup>are most likely</sup> to participate in according to the multinomial logit (one could also<sup>allow existing</sup> employees to switch sector).

To tackle remittances we could replicate this approach using a Probit<sup>on receiving</sup> remittances, a multinomial logit to determine the sector of remittances<sup>receivedand a</sup> level of remittances regression to determine the amount. However, as<sup>argued in the</sup> paper, this ignores the fact that sometimes a household has made a<sup>priordecision to</sup> migrate in order to receive remittances - certainly there is something<sup>strangeabout just</sup> giving a household a new remittance without the household having done<sup>anythingor</sup> given anything up.

- Instead we may build a little Harris-Todaro module into our standard methodology<sup>as</sup> follows - the first three steps are the same as above i.e.: 1. Calculate the probability of getting employment (in any sector) using a Probit model

- 2. Estimate a multi-nomial logit to get the probability of being employed in each sector (relative to the base category of being unemployed)

3. Estimate sectoral wage equations (or at least one wage equation with sectoral dummies). (Alternatively use information on sectoral wages or unit eamings if wages are unavailable, but wages are preferable).

In addition we then: 4. Do a regression of household (NB not per capita) income on household characteristics including household size (or other demographic categories);<sup>predict</sup> the household income, and then predict the new household income when you remove one household member - do this for each adult household<sup>member.Take</sup> the percentage change in income resulting from the removal of the household member and multiply this by the actual household income<sup>-</sup> this<sup>is an estimateof</sup> the income loss associated with that individual migrating for employment.

5. Calculate the expected income of the migrant individual as: 37
_E[ymig ]_ (1 _- Pr(Job))wj_ (A2. 1) where Pr(s) is the probability of being employed in sector j; Pr(so) is the probability of being employed in the base category (the multinomial logit predicts this ratio); Pr(Job) is the probability of having a job in any sector (predicted from the Probit regression); and wj is the estimated wage for sector j.

Now let us define the following variables:

- _yn_ **0** is the original household income of the household with n members _yn 1l_ is the household income reduced by the estimated loss of income resulting from the migration of one individual

- Yn 1l+ is the household income reduced by the estimated loss of income resulting from the migration of one individual, but with the remittance received by the household added

- Yn l+ is the household income reduced by the estimated loss of income resulting from the migration of one individual and with the whole value of the income received by the migrant added (NB the subscript is n indicating that it is the income of n individuals)

The original income is known; step 4 above estimates yn11 ; and step 5 allows us to calculate Ynl+ (by simply adding the expected income of the migrant onto yn 1). The only remaining question is how to calculate yn.

One simple approach is to estimate a savings rate ax. An alternative approach is to assume a "social compact" between the household sending the migrant and the migrant such that they will live at the same per capita income level. If we assume that the migrant sends a share a of their income then we have: <!-- Start of picture text --> Yn-l n-I + a m 1a)yg (A2.2)<br><!-- End of picture text --> which implies that 38
<!-- Start of picture text --> a =n1) - n (A2_3)<br><!-- End of picture text --> In either case Yn can then be calculated as: Once the above y variables have been<sup>calculatedit is possibleto calculatethelevelof</sup> household welfare with migration. However,<sup>this depends upon our unit of analysis.</sup> There are two values which we could take: a) if the household includes the welfare<sup>of the migrantthen householdwelfarecan be</sup> calculated as: _y-_<sup>_1_</sup> _+In_ b) if the household only considers the welfare<sup>of theremaining membersthen welfarecan</sup> be calculated as: _y-'+ /(n -1)_ Rather than choose between these options<sup>wesimply refertothe welfarelevelof the</sup> household with and without migration<sup>as Wland W° respectively.Wemight therefore</sup> expect households to send a migrant out for employment<sup>if E[W']> W°.</sup> Note that we can calculate both with and<sup>without migrationvalues</sup><sup>_before_any reform</sup> takes place as well as after any reform. To<sup>makethedistinctionclear we thereforerefer</sup> to:<sup>2 8</sup> Without migration With migration Before reform After Reform **_WWR_** _WIR_ Since **_WOR_** and _WBR_ can both be calculated<sup>beforereform it is possibleto putthe</sup> households in the survey into four groups:

- i. Households who don't now receive remittances<sup>and would not benefitfrom</sup> migration _(_ **_WBR_** _< WBR)_

- ii. Households who don't now receive remittance<sup>and would benefit from migration</sup>

- **_(WBR_** _>_ **_WBR)_**

- iii. Households who do now receive remittance<sup>and would not benefit from migration</sup> **_(WBR_** _<_ **_WBR)_**

> **28** For notational convenience we drop the<sup>expectationsymbol on the W'variables.</sup>

39
- iv. Households who do now receive remittance and would benefit-from migration

_(WBR_ **_>_** _WBR)_ In the case of group i, households behave in exactly the way we predict since it is not worth their while sending a migrant in search of work. Similarly the third group also behaves as predicted - these are households who are already receiving remittances but for whom it is not worth sending a migrant. This might be because the characteristics of the remaining family members make their expected income from migration less than their income loss resulting from migration; or it may be that the existing source of remittance is not due to migration but from some other source. However, for groups ii and iv we predict that it would make sense for households to send a migrant and yet they do not do so. This suggests that there may be unobserved costs or constraints to such migration for these households. It will be necessary to take into account these constraints when analyzing the impact of reform below.

# **The Impact of Reform on Poverty**

We can now consider the same two types of reform which were described in the paper under modeling labour incomes namely exogenous wage increases and exogenous employment increases. As before, the choice of whether wages or employment adjusts to reform depends upon ones assumption about the elasticity of labour supply.

# _Wage changes:_

As before the impact of exogenous wage changes is easy to calculate by simply applying the assumed percentage increase (say) in wage income to the individuals who work in the affected sector. Similarly a wage increase for migrant workers can be simulated by increasing the remittance income for households receiving remittances from the affected sector.

However, this simple approach doesn't give the full story when we consider the migration model. Wage increases increase the expected value of income for a migrant as shown in equation (A2. 1). This will increase the number of households for whom sending a migrant will beneficial. If we simply simulate the impact of a wage increase by increasing the wages of those currently in employment assuming that there is no increase in unemployment then all of these additional migrants will, by assumption, be unsuccessful in obtaining employment. This could even lead to a fall in welfare as migrants are lured by higher wages into unemployment in the urban areas. One way of ensuring that outcomes for migrants are consistent with their expectations is to allow all jobs in the urban areas to be reallocated to existing employees and migrants according to their probabilities of getting ajob in the sector. This will have the result that some of those currently employed will become unemployed, but will ensure that some of the migrants obtain jobs.<sup>29</sup>

> 29 This is done using the original Probit and multinomial logit distributions and allocating the fixed number of jobs according to the probability of getting a job in the sector as before. One could then re-estimate the Probit and multinomial logit to obtain a new distribution which will accurately reflect the lower standards required to obtain a job (or the higher probability of getting a job with any given set of characteristics). If

40
# _Employment changes:_

Employment changes can be tackled in a<sup>very similar manner to theway in which they</sup> are dealt with when modeling labour incomes.<sup>Specifically we use the following</sup> procedure:

1. As for wage changes, calculate the<sup>expectedvalues of welfareafter reformwith</sup> and without migration (W1R and _WOR)._ The<sup>householdsends a migrantif the</sup> expected value of welfare with migration<sup>**WAR**</sup><sup>**_>_**</sup><sup>_WAR._</sup>

2. Calculate the probability of each<sup>localindividualandall the migrants sent being</sup> employed in each sector and allocate<sup>individualsto jobsin order of their</sup> probability until all the available jobs have<sup>beenfilled.31</sup> 3. If the individuals employed are local,<sup>then this simply contributesto the labour</sup> income of their household. If however,<sup>they are non-local then theirincome</sup> contributes to the remittances received by<sup>the household accordingto the fixed</sup> saving or social compact rule described<sup>above.</sup> 4. Calculate the household's ex post<sup>welfare</sup><sup>_WA._</sup> This is the level of welfare when one knows whether the migrant actually<sup>obtaineda job.</sup> In this way we can incorporate rational<sup>decisionsby households regardingmigration and</sup> remittances into our simple model of<sup>the impactof reformupon poverty.</sup> jobs were then reallocated to all households<sup>according to their probabilityof employmentineachsector the</sup> new distribution should (on average) generate<sup>the correctfixed numberof jobs.Of course,thereduced</sup> probability of obtaining a job will reduce<sup>the number of migrantsin the long run -we are interested here in</sup> relatively short run effects so we assume that<sup>information aboutthewageincreasetravelsquickly and</sup> therefore influences migration decisions<sup>first, whereasinformation on the graduallyincreasingprobability</sup> of obtaining employment travels more slowly.

**30** Altematively one may wish to take<sup>into considerationthe unobservedcosts of migration mentioned</sup> before. This can be done by assuming<sup>that households whowould have benefitedfrom migration (groupsii</sup> and iv) but who did not do so, are unable<sup>to send migrants,so theirwelfare level remainsat</sup><sup>_WO_</sup> **_AR_**<sup>even if</sup> **_>_** _W_<sup>_0_</sup> A further issue is who within the household<sup>is sent.It is assumed that the householdmember</sup> resulting in the highest expected value<sup>of</sup><sup>_WAR_is sent -thismay not necessarily be themember with the</sup> highest probability of employment,<sup>sincethehousehold willalso take intoaccountthe loss of income</sup> resulting from their departure.

**3'** A choice will need to be made here<sup>betweenallocating the new jobssimply to people whodo not</sup> currently have jobs accordingly to<sup>their probabilityof employment;or allocatingpeoplewith thehighest</sup> probability to jobs (if they are made<sup>betteroff by switching)and thenfilling the jobs which they leavewith</sup> others accordingly to the same process.<sup>Also,asfor wagechanges,onemay wish to re-estimatethe</sup> probit/multinomial logit once the additional<sup>employeeshave been added to reflect theincreased probability</sup> of obtaining a job. Here we assume<sup>that employment expansions draw from the existing labourforcewhilst</sup> information about the increased probability<sup>of employment takes time tofilter back to migration decisions.</sup>

41
I
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**forpaper**|
|---|---|---|---|
|WPS3101Portfolio PreferencesofForeign<br>InstitutionalInvestors|Reena Aggarwal<br>LeoraKlapper<br>PeterD.Wysocki|July2003|A.Yaptenco<br>31823|
|WPS3102 InvestinginInfrastructure: Whatis<br>Neededfrom 2000to 2010?|MarianneFay<br>TitoYepes|July2003|M.Fay<br>87200|
|WPS3103OwnershipStructureandInitial<br>PublicOfferings|ReenaAggarwal<br>LeoraKlapper|July2003|A.Yaptenco<br>31823|
|WPS3104DoesStrictEmploymentProtection<br>DiscourageJobCreation?Evidence<br>from Croatia|JanRutkowski|August2003|J.Rutkowski<br>84569|
|WPS3105Further EvidenceontheLinkbetween<br>FinanceandGrowth:AnInternational<br>AnalysisofCommunityBankingand<br>Economic Performance|AllenN.Berger<br>Iftekhar Hasan<br>LeoraF.Klapper|August2003|A,Yaptenco<br>31823|
|WPS3106 GovernanceMattersIlIl:Governance<br>Indicatorsfor1996-2002|DanielKaufmann<br>AartKraay<br>MassimoMastruzzi|August2003|A.Bonfield<br>31248|
|WPS3107MoreFavorableandDifferential<br>TreatmentofDeveloping Countries:<br>TowardaNewApproachinthe<br>World TradeOrganization|BernardHoekman<br>Constantine Michalopoulos<br>L.AlanWinters|August2003|P.Flewitt<br>32724|
|WPS3108StabilizationandAssociation Process<br>intheBalkans: IntegrationOptions<br>andtheirAssessment|BartlomiejKaminski<br>ManueldelaRocha|August2003|F. Sy<br>89750|
|WPS3109TheImpactofChina's WTO<br>AccessiononEastAsia|Elenalanchovichina<br>TerriWalmsley|August2003|S.Lipscomb<br>87266|
|WPS3110GovernanceofPublic PensionFunds:<br>LessonsfromCorporateGovernance<br>andInternationalEvidence|DavidHess<br>GregorioImpavido|August2003|P.Braxton<br>32720|
|WPS3111International TradeandWage<br>Discrimination: Evidencefrom<br>EastAsia|GunseliBerik<br>YanavanderMeulenRodg<br>JosephE.Zveglich,Jr.|August2003<br>ers|M.Ponglumjeak<br>31060|
|WPS3112Onthe TimingofMarriage,Cattle,<br>andWeatherShocksinRural<br>Zimbabwe|JohannesHoogeveen<br>BasvanderKlaauw<br>GijsbertvanLomwel|August2003|P.Sader<br>33902|
|WPS3113 WhatDrivesBankCompetition?<br>Some International Evidence|StijnClaessens<br>Lucleaven|August2003|R.Vo<br>33722|
|WPS3114The DynamicsofForeignBank<br>Ownership:Evidence fromHungary|GiovanniMajnoni<br>RashmiShankar<br>LvaVarhegyi|August2003|H.Issa<br>30154|
|WPS3115IntegratingHousingWealthintothe<br>SocialSafetyNet:TheElderlyin<br>Moscow|RobertBuckley<br>KimCartwright<br>Raymond Struyk<br>EdwardSzymanoski|August2003|0.Himid<br>80225|
**Policy Research Working Paper Series**

||**Title**|**Author**|**Date**|**Contact**<br>**forpaper**|
|---|---|---|---|---|
|WPS3116|DollarizationoftheBanking System:<br>GoodorBad?|GianniDeNicol6<br>Patrick Honohan<br>AlainIze|August 2003|A.Yaptenco<br>38526|
|WPS3117|PolicyResearchonMigrationand<br>Development|David Ellerman|August2003|B.Mekuria<br>82756|
|WPS3118|ToShareorNottoShare:Does Local<br>ParticipationMatterforSpilloversfrom<br>ForeignDirectInvestment?|BeataSmarzynska<br>Javorcik<br>Mariana Spatareanu|August2003|P.Flewitt<br>32724|
|WPS3119|EvaluatingtheImpactofConditional<br>CashTransferPrograms:Lessons<br>fromLatinAmerica|LauraB.Rawlings<br>GloriaM.Rubio|August 2003|M.Colchao<br>38048|
|WPS3120|LandRights and Economic<br>Development:EvidencefromVietnam|Quy-ToanDo<br>Lakshmilyer|August2003|P.Sader<br>33902|
|WPS3121<br> <br>|DoBilateralInvestmentTreaties<br>AttractForeignDirect Investment?<br>OnlyaBit...andTheyCouldBite|MaryHallward-Driemeier|August2003|A.Bonfield<br>31248|
|WPS3122<br>|Individual AttitudesToward<br>Corruption:DoSocialEffects Matter?|RobertaGatti<br>StefanoPaternostro<br>JameleRigolini|August 2003|N.Obias<br>31986|
|WPS3123 <br> <br>|ProductionandCostFunctionsand<br>TheirApplicationtothe PortSector:<br>ALiteratureSurvey|BeatrizTovar<br>SergioJara-Diaz<br>LourdesTrujillo|August 2003|G.Chenet-Smith<br>36370|