ANNEX III. ESTIMATES OF SUPPORT TO AGRICULTURE BY COUNTRY, 1991-2001 (continued)
PART 3: HOUSEHOLD LEVEL IMPACTS
195
OECD AND NON-OECD TRADE LIBERALISATION AND POVERTY REDUCTION IN SEVEN DEVELOPING COUNTRIES
Thomas W. Hertel, Paul V. Preckel, John A.L. Cranfield and Maros Ivanic*
Abstract
Poverty reduction is an increasingly important consideration in the deliberations over multilateral trade liberalisation. Within this debate, the agricultural sector has assumed a prominent role, in part due to the potential for significantly higher agricultural prices which may translate into large real income losses for low income consumers. Competing with this consumption side effect is the income side. Since most of the world’s poor live in rural areas, where agriculture is the dominant economic enterprise, higher agricultural prices following global trade liberalization serve to boost the incomes of many poor households. Which of these forces dominates depends on the particular country/household/trade policy combination in question. This paper aims to sort out the relative importance of these two competing effects for seven developing countries.
In our analysis we use the combination of a global trade model and individual household models for 100 representative household groups in each of the focus countries to assess the short run poverty impacts of both national and international trade policy changes. Our analysis is short run in nature because we assume a limited degree of factor mobility in response to the trade policy shocks. In particular, self-employed workers are not able to find wage labour if prospects in their industry (e.g., farming) turn sour. We also assume that capital cannot be reallocated to other activities over the time frame in question. Our findings suggest that trade liberalization reduces national poverty in six of the seven developing countries studied. However, these aggregate results mask substantial variation in poverty changes by individual household groups and by type of policy. When one focuses specifically on OECD trade policies, it becomes clear that agricultural policies are most important. This is due to three things. Firstly, this sector has relatively larger trade distortions than non-agriculture in OECD countries. This means that liberalisation of agriculture generates larger world price effects. Secondly, the budget share of the poor devoted to food products is very high, so they are vulnerable to food price shocks. Finally, in many of the focus economies, a number of the poor are highly dependent on agriculture for their earnings.
* Hertel and Preckel are Professors and Ivanic is Graduate Research Assistant in the Department of Agricultural Economics at Purdue University. Cranfield is Assistant Professor of Agricultural Economics at the University of Guelph. The authors acknowledge support from the Development Research Group at the World Bank. Specifically we would like to thank Will Martin for championing this work and making available the household surveys. Address Correspondence to: T. Hertel, Director, Centre for Global Trade Analysis, 1145 Krannert Building, Purdue University, West Lafayette, IN 47907-1145; [email protected].
196 Multilateral trade liberalisation and poverty reduction
Poverty reduction is an increasingly important consideration in the deliberations over multilateral trade liberalisation. Within this debate, the agricultural sector has assumed a prominent role (McCullogh, Winters and Cirera, 2001). This is due to two facts. Firstly, food is a critical item in the budgets of the poor, so anything that happens to its price will strongly affect the well being of the poorest households. In particular, a price rise following the elimination of farm support in the OECD countries could have a strong income effect – potentially forcing additional households into poverty and worsening the situation for the abject poor. On the other hand, there is a second key fact that comes into play on the income side of things - most of the world’s poor live in rural areas, where agriculture is the dominant economic enterprise. Higher agricultural prices serve to stimulate agricultural incomes and household spending in rural areas - thereby boosting non-farm, rural incomes. Which of these forces dominates depends on the particular combination of country/household/trade policy in question.
To this one must also add consideration of the impact of non-agricultural trade liberalisation. While manufacturing tariffs are generally quite low in the OECD countries, they remain high in many developing countries, and the elimination of this protection for domestic industry can also have an impact on poverty. To the extent that these protected sectors employ unskilled labour, there is the potential for increased unemployment or reduced wages, which may adversely affect the poor. On the other hand, elimination of support for capital intensive manufacturing sectors could free up scarce resources for use elsewhere in the economy, thereby potentially increasing the demand for unskilled labour and reducing poverty. Clearly, the linkages between trade liberalisation and poverty are complex and likely to vary greatly across countries and trade policies. The goal of this paper is to explore these linkages in considerable detail, for seven developing countries. By using a common methodology in each country/case study, we are able to draw some general conclusions about the impact of OECD and non-OECD trade liberalisation on poverty.
There is now a great deal of work being undertaken to analyse the links between trade and poverty.
Some of this focuses on the consumption side of the problem (e.g. Levinsohn, Barry and Friedman, 1999; Case, 1998), while abstracting from the income effects of trade on poverty. Others have used single-region computable general equilibrium (CGE) models to bring in the income side of the picture (Devarajan and van der Mensbrugghe, 2000; Harrison, Rutherford and Tarr, 2000; Löfgren, 1999;
Taylor, 2002). A final group of studies have used a combination of multi-region, CGE models and single region models and household surveys to analyse the link between multilateral trade liberalisation and poverty (Evans, 2001; Friedman, 2001; Ianchovichina, Nicita and Solaga, 2000).
One of the key findings to date is that, while factor markets are critical to determining the trade- poverty linkage, they are relatively neglected in much of the poverty research. This point is also emphasised in the recent paper by Decaluwé, Patry, Savard and Thorbecke (1999), as well as in the pathbreaking work of Adelman and Robinson (1978). Part of the problem stems from the tendency of poverty researchers to focus their attention on the expenditure side of household surveys due to its greater reliability for purposes of measuring poverty. This may be fine for poverty measurement, however, when it comes to counterfactual analysis of policies and poverty, it is impossible to proceed without proper treatment of the factor markets.1 CGE modellers are fundamentally constrained by data obtained from the household surveys, since this is the only way to identify the mapping from factor 1. By way of example, Coxhead and Warr (1995) report that substantially more of the poverty reduction from technological change in agriculture is transmitted through the factor markets than through the consumer goods markets. In a more recent paper utilizing CGE models of archetype economies from Africa, Asia and Latin America, De Janvry and Sadoulet (2002) also emphasize the importance of factor markets for transmitting poverty impacts of changes in agricultural technology – particularly in the cases of Asia and Latin America.
197
earnings to specific household groups (e.g. how heavily reliant are the poor on unskilled wages?). In light of this state of affairs, we have chosen to focus the present paper squarely on the factor earnings effects of trade liberalisation and their role in determining the subsequent poverty impacts.
To date, there has been a great deficit in the area of multi-region trade policy analysis and poverty.2 Such studies are very difficult to accomplish, due to the country-specificity of the household surveys.
So, when it comes to multi-country, or global trade liberalisation analyses, researchers are typically forced to focus only on average, or per capita effects (e.g. the paper by Beghin et al., 2002, in the present volume). This severely limits the potential for addressing the poverty question.
In this paper, we extend the typical multi-country trade analysis in a direction that permits us to assess the likely impacts of trade liberalisation on the incidence of poverty. The approach builds on a combination of seven national household surveys available through the World Bank, and multi- country data sources, including: the International Comparisons Project (ICP) database on per capita consumption (Kravis, Heston, and Summers 1982), the Deninger and Squire income distribution data set (Deninger and Squire, 1996), and the Global Trade Analysis Project (GTAP) database (Dimaranan and McDougall, 2002). The proposed approach is flexible enough to incorporate improved national databases as they become available.
The ideal approach to analysing the poverty implications of multilateral trade liberalisation would incorporate a highly disaggregate set of households directly into a multi-region general equilibrium model, which could then be used for policy simulations. In a major research undertaking supported by the World Bank, Harrison et al., (2002) have done so for one country – Brazil – and a modest number of household groups (20). However, incorporation of disaggregated household data into a global model on a larger scale (multiple countries and hundreds of households) has yet to be accomplished.
Therefore, the present analysis is conducted in two parts. First, we simulate a global model to determine regional price changes owing to the policy experiment. Then we utilise a second, household model to conduct the detailed analysis of household incidence and implications for poverty.
Stratification of households
In our analysis of poverty, we find it useful to stratify the population into five groups, depending on their primary source of income. Otherwise one is left with the impression that all households are diversified in their income sources, with the composition of their earnings reflecting the average for their income level. We believe that in the short run, which is the time frame for our analysis, household incomes will be differentially affected depending on their reliance on sector-specific factors of production. For example, a household which earns all of its income from a family run farm will be heavily dependent on the prices of agricultural products. If prices rise, they will likely have a hard time gaining access to land and credit with which to expand production in the short run. If prices fall, they may eventually be able to find other employment, but this is likely to be difficult in the short run – particularly if they are not currently employed off-farm. Therefore, our first stratum identifies self- employed households specialising in agricultural production (95% or more of income). Any approach which aggregates households across this specialisation dimension will miss much of the impact, by giving an artificial impression of diversification. Similarly, the second stratum comprises households specialising in non-agricultural enterprises, (i.e. income from profits for non-agricultural enterprises).
The third and fourth strata are comprised of households that work for others, and are specialised in wages/salaries (95% or more), and those relying almost exclusively (95% or more) on transfers (both public and private) for their income, respectively. The fifth stratum represents the remaining 2. See, for example, the recent survey of papers in the trade and poverty area authored by Reimer (2002).
198
“diversified” households. Note that this final category comprises all those households that get less than 95% of their income from any single one of the four sources: transfers, agricultural profits, non- agricultural profits and labour – hence the label “diversified”.
The sources for the earnings data are national household surveys for seven countries for which such surveys are readily available, and which are also representative of diverse income, and geographic and trade policy circumstances: Brazil, Chile, Indonesia, Philippines, Thailand, Uganda, and Zambia.3 Before conducting an analysis of trade policy, household earnings and poverty, we first had to reconcile the household income data with the macroeconomic data on earnings. The most important part of this process is obtaining an accurate split between self-employed earnings and wage income.
This division is often ambiguous in national accounts data. We also rely on the household surveys to adjust the aggregate composition of skilled and unskilled labour earnings, as estimated in the GTAP data base.
Given the likely differential impact of trade liberalisation on the diverse household groups, it is also important to examine the relative importance of each stratum in overall poverty. The bottom row of Table 1 reports the percentage of the total population living on less than $1/day as reported in the World Development Report: 2000/2001.4 These figures range from lows of 2% in Thailand, 4% in Chile and 5% in Brazil, to a high of nearly 73% in Zambia. The body of Table 1 reports the estimated rate of poverty, by stratum, as a percentage of the total population. Referring to Brazil (the first column), we see that, of the 5.1% of the population living in poverty, about one-third (1.58% of the entire population) are dependent on wage labour. Agriculture-specialised households follow in importance (1.22% of the population). Of course, this understates the importance of agriculture in the poverty picture since many of the poorest wage labour households work in agriculture, as do many of the diversified households. A similar situation applies in Chile. By contrast in Indonesia, one-third of the poor households are specialised in agricultural earnings (presumably reflecting returns to small- holdings of land and family labour). The relative importance of agriculture-specialised households in the total poverty picture also applies in Philippines, and Zambia, whereas in Thailand and Uganda, most poor households are diversified.
3. The sources of these surveys are as follows: Pesquisa Nacional por Amostra de Domicilios (1998), Brazilian Institute of Geography and Statistics (IBGE); Characterisation Socioeconomica Nacional, 1998, Ministerio de Plantificacion y Cooperacion, Santiago, Chile; SUSENAS: Indonesia’s Socio- Economic Survey (1993) Biro Pusat Statistik, Jakarta, Indonesia; Annual Poverty Indicator Survey (1999) National Statistics Office, Manila, Philippines, World Bank Mission and the United Nations Development Programme; Thailand Socio-Economic Survey (1996) National Statistics Division, Bangkok, Thailand; National Household Survey 1999, Uganda Bureau of Statistics, Entebbe, Uganda; and the Living Conditions Monitoring Survey II (1998) Central Statistical Office, Lusaka, Zambia.
4. We have calibrated the poverty level of utility in each country to reproduce this total. An alternative would be to use the AIDADS model and the $1/day definition for 1996 ICP dollars to predict poverty in each country. However, this appears to result in an over-estimate of poverty – likely due to the presence of under-reporting of income in the household surveys. Therefore, we prefer to benchmark the model to independent estimates of the poverty level.