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primary agriculture. Expanding labor intensive industries in rural areas may be part of a rural development strategy.

The simulation is performed relative to the year 2010 baseline, and yields amongst other things changes in market prices for key commodities in China, and changes in manufacturing labor demand.
These results are fed into the village CGE model. Table 1 summarizes the shocks administered to the village model.

2.2 Linking the macro results to the village model To link the macro analysis to the village model, we follow the conceptual framework of Winters (2002), and focus on prices and labor demand. Studies of market integration of three main staple crops and fertilizer (Huang _et al._, 2003; Qiao _et al._, 2003) show integrated regional and national markets, with village level prices responding to changes at national level. These integrated commodity markets allow us to translate relative price changes derived from the macro level analysis to the village level. As in Huang _et al_ . (2003) we use a price transmission coefficient of 0.75 to link macro national level price changes to village level price changes, for commodities that are traded outside the village. This implies that a 1 percent change in the national price is modeled as resulting a 0.75 percent change in village level prices. A less than unitary transmission coefficient reflects existing frictions in domestic trade in China. These frictions are expected the persist in the future, given the findings in Poncet (2003), of a disintegration of domestic markets alongside the continuing integration of China in international markets.

In addition to the transmission of price changes, we analyze the impact of changes in employment opportunities. Studies of trade liberalization find an expansion of labor-intensive sectors in which China has a comparative advantage. We thus need to link an aggregate expansion of employment to village level changes in outside village employment. Lacking data to quantify the link between national level changes in employment and household decisions, we assume percentages changes in aggregate labor demand to be completely transmitted to the village. Percentages changes in outside village employment are thus set equal to changes in aggregate labor demand. Such a one-on-one relation between national level employment and off-farm activities of the households in the case study village seems justified given the findings in Diao _et al._ (2003). Comparing the change in rural-urban migration across regions following WTO accession, they find the fastest increase in rural-urban migration in the central provinces. The case study village is located in Jiangxi, one of these central provinces, and migration outside of the province plays an important role in the village economy, justifying the assumption of a complete transmission of the demand for labor to the village level.

2.3 The case study village The case-study village has been selected to be representative of rice producing villages in the plain areas of Jiangxi Province, one of the poorer provinces in China. Data on production and consumption of 168 households were collected for 2000, using standard household questionnaires with questions authors.

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on source and destination of commodities added to allow construction of a village SAM. These surveyed households account for about a quarter of the village population, totaling 729 households.

Differences among households are at the center of our village equilibrium model. Four groups of households are distinguished, using ownership of draught power (cattle or tractor) and access to outside province employment as grouping criteria. The resulting groups represent households with differential capacity for earning a living from agriculture and from (transitory) migration to coastal cities.

Table 2 presents the activities of each household type in terms of contribution to value-added. For all household types off-farm employment contributes a significant share of income, but there exist important differences in the nature of non agricultural income sources. Comparing the two household groups lacking a link outside the province, we find households lacking draught power oriented more towards local off-farm employment, with agricultural activities contributing about 60 percent to valued-added. The households with draught power obtain about 70 percent of value-added from agriculture, which is similar to the importance of agriculture for the other household group owning draught power. The household group with access to migration but lacking draught power derives about 40 percent of value-added from outside province migration, and only derives about 45 percent of value-added from agricultural activities. Differential access to agricultural and migration opportunities is thus reflected in the composition of to value-added.

Table 2: Activity composition (percentage of value-added) _Link outside province:_ _No link_ _Link_ _Village_ _Owning draught power:_ _No_ _Yes_ _No_ _Yes_ _N=_ _78_ _100_ _256_ _295_ _729_
**Agriculture** One season rice 9.4 10.8 8.2 10.5 9.5
Two season rice 28.5 28.2 18.6 27.1 23.9 Other crops 21.2 24.7 18.4 22.4 20.9 Cattle - 7.5 - 8.1 4.1 Pigs 0.1 0.1 0.2 0.4 0.3 Other livestock 0.1 0.1 0.0 0.0 0.0
**Village employment** Agricultural labor 1.3 2.2 - 0.1 0.4
Local business 19.4 13.5 2.4 2.7 5.2
**Outside village** Outside employment 18.0 12.9 9.1 5.4 8.9
**Migration** Inside province 2.1 - 4.2 0.9 2.3
Outside province - - 38.8 22.3 24.6 _100_ _100_ _100_ _100_ _100_ Note: ‘-‘ indicates that the household is not involved in this activity Differences in activities result in differences in income patterns across the four household groups.
Table 3 presents income per adult consumer equivalent [3] to allow a direct comparison across 3 Adult equivalent instead of per capita consumption is used to account for differences in consumption between males and females and between age groups. Lacking survey data, conversion factors were taken from detailed 11

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households. The first thing to note is that the household group with an outside link and draught power has the highest income per adult consumer equivalent. This is in line with the assumptions made when grouping households in terms of opportunities for earning agricultural and nonagricultural income.
The two household groups without an outside link have similar incomes.

Table 3: Sources of household full income (percentage of total income) and total income (yuan) _Link outside province:_ _No link_ _Link_ _Village_ _Owning draught power:_ _No_ _Yes_ _No_ _Yes_

**Labor** shadow wage income 57.4 52.8 52.6 62.1 57.4

above shadow wage income 17.5 12.3 23.3 6.0 13.5

**Land** irrigated land shadow income 12.4 13.3 12.8 11.4 12.1

irrigated land above shadow income 4.8 6.5 1.4 4.5 3.7 non-irrigated land 7.3 7.9 7.7 6.6 7.2

**Capital** Cattle - 3.3 - 3.1 1.8

Tractor - 1.6 - 0.8 0.6

**Transfer** within village transfers 0.6 0.3 0.0 0.5 0.3

receipts from outside village 0.0 1.9 2.2 5.0 3.3

**Income/adult consumer equivalent (1,000 yuan)** **4.4** **4.3** **5.0** **5.5** **5.0**

Note: ‘-‘ indicates that the household does not receive income from this source Specific features of the village SAM and equilibrium model appear in Table 3, in splitting income from labor and irrigated land between shadow income [4] and above shadow income. The household survey data reveal imperfect labor and land markets. Households are involved in a variety of off-farm activities with different wages. These wages are well above the estimated shadow wage, indicating restricted access to off-farm employment and suggesting a situation of labor surplus at the local level.
This does not come unexpected, given the high population density in China and similar findings in a study by Bowlus (2003). In the SAM and village equilibrium model the demand-constrained labor market is accounted for by valuing labor against household-specific shadow wages, estimated using the household survey data. In case of off-farm activities, labor then earns a revenue above the shadow wage which is tracked in a separate account of the SAM.

There is trade in irrigated land (paddy fields), with all four household groups renting in land. Dealing with a village level analysis we would expect the land market to balance. In total, however, households rent in an amount of irrigated land equal to about a third of their contracted land, that is not supplied by surveyed households. When conducting the survey using an official list obtained from the village administration, a number of the randomly selected households were found to have permanently left the village. These were replaced with households of which at least some members consumption data of a study in Bangladesh (Zeller _et al._, 2001). In addition to differences in age and gender, consumer equivalents were corrected for the length of absence of household members due to migration.
4 Nonseparability results in household-specific shadow prices balancing households’ demand and supply that cannot be observed. We therefore estimated a production function, explaining the total value of household output by labor, land, manure, feed and external inputs. Based on the estimated production function we computed for each household in the sample the household-specific shadow prices for household non-tradables as the marginal value product of each input. Averaging over the households constituting a household group renders a shadow price for each household nontradable that differs across household groups. These shadow prices are used in constructing the SAM and in calibrating the village equilibrium model.

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were still living in the village, resulting in a biased sample. Lacking households that have moved to the cities, our sample misses the supply of land by households that have moved to the cities. As discussed in Section 1, the combination of collective ownership with household user rights has resulted in an ambiguous land tenure system. This applies to the case study village, were according to the survey data, land reallocations are still occurring. Migrated households thus have an incentive in keeping their land cultivated by renting it to other households.

Given the ambiguous land tenure system, assuming a perfect land market seems a far stretch from the pattern observed in the data. We therefore estimated a shadow price for irrigated land, which on average was about 1.5 times the rent paid for the land. This result fits a distorted land market, where insecure land tenure prevents households leaving the village from obtaining a rent reflecting the true production value of the land. At the demand side, this implies that households can rent land for a price well below the productive value of the land. In the SAM and village equilibrium model this is accounted for by having all household groups earning a profit on the land rented in, which is equal to the difference between the estimated valued of the land and the rent paid. The amount of rented land is kept fixed in the village model, since additional supply is assumed to be associated with the migration of a complete household, which is beyond the scope of this study.

A last remark on the village SAM is the lack of data for modeling capital flows in the village. The SAM shows that the household group most involved in migration is a net supplier of money for the other three groups of households. Although the survey contains some data on the conditions on which money is provided, insufficient information is available to model a village capital market. In the analysis money flows among households are therefore assumed fixed at the level observed in the SAM. This excludes one way through which income from migration could be transmitted through village linkages, thus potentially underestimating the village-wide impact of migration.

To summarize the discussion so far, we are analyzing the response of four different types of households, distinguished on the basis of their access to agricultural income and income from outside province migration. Analyzing sources of income pointed to imperfect labor and land markets. These are accommodated by estimating household-specific shadow prices, introducing profits earned on offfarm employment and renting of land, and by modeling household production and consumption decisions as nonseparable.

2.4 The village equilibrium model Despite introducing nonseparability of household production and consumption decisions, the mathematical structure of the model closely resembles macro level general equilibrium models.
Consumption decisions are modeled through a linear expenditure system, while production is modeled by nested CES functions. Because of the level of detail in the village model, we do not observe twoway flows of commodities with the outside world. Households consume farm output, but do not purchase these goods from outside the village, nor from other households in the village. Household sales to outside village markets are thus equal to total production, minus household consumption.

Off-farm employment options were found to be restricted, resulting in wages exceeding the shadow price of labor. This is handled in the village equilibrium model by fixing the levels of outside village employment and having households earn a profit above labor costs on off-farm activities. Levels of 13

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village employment (agricultural and nonagricultural) cannot be fixed, although for these activities wages also exceed shadow wages. Demand for agricultural labor is therefore assumed to be demanddriven, with prices being fixed [5] . Demand for village non-agricultural labor is linked to local business activities, discussed below.

Village markets exist for traction by draught animals or tractors, and for locally produced consumption goods. Of these village markets, only animal traction has an endogenous village price in the model. The SAM indicates that only limited use is made of the tractors. This under-utilization of available tractors is therefore modeled through fixed prices for tractor services, and tractor endowments adjusting to demand (‘unemployment’ closure).

Due to lack of data on other inputs, local business activities use only labor (village non-agricultural labor), yielding a return that exceeds the shadow wages. All households are involved in local business activities and all purchase locally produced goods. This reflects a heterogeneity in goods not captured by the aggregates used in the SAM and village model. Because of lack of data we fix village prices of local goods to deal with the gap between product prices and costs of labor. Assuming fixed prices seems justified since prices of village produced goods are common knowledge, while shadow wages cannot be observed. Given the unobservable character of shadow wages, it seems unlikely that a change in labor costs will be reflected by a changed village price. A second reason for fixing prices of local business activities is the absence of a peak season. Production can therefore be shifted to times when little labor is needed in agriculture, limiting the need to increase the price when shadow wages increase.

For all demand-driven activities (local consumption goods, hired agricultural labor, tractor services), market equilibrium is established by allocating demand to suppliers based on the market shares observed in the SAM.

Summarizing, the village equilibrium model resembles macro level general equilibrium models by the way in which consumption and production are modeled. A major difference with macro and existing village level models is the household-specific production, which is affected by household consumption decisions through endogenous household shadow. Lack of data resulted in most village markets to be modeled as demand-driven. The only exception is the village market for animal traction, which is balanced through an endogenous village price. Household production and consumption decisions are calibrated on the household survey data, resulting in household-specific demand and supply functions.

3 Household production response to trade liberalization Following the analytical framework of Winters (2002) we analyze two pathways through which trade affects households: prices and employment. We first separately analyze the impact on household response these two aspects of trade liberalization, followed by an assessment of their combined effect.

5 Agricultural wages are a multitude of shadow wages. Households are therefore always willing to supply agricultural labor.

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3.1 Household level impact of price changes Trade liberalization increases prices of all agricultural outputs and some inputs. In terms of crop production, all household groups show a limited response in other crop production, and a significant change in rice production (Table 4). This difference in response is due to the fixed amount of land used in other crops. This is the only activity which uses non-irrigated land, which is a household nontradable. This fixed input limits the adjustment possibilities, although the change in shadow price of non-irrigated land (Table 5) indicates that households are adjusting to the price changes, despite their limited production response. This provides a classic illustration of the point made in de Janvry _et_ _al._ (1991) that, although market imperfections may cause sluggish production response, households accommodate price changes by making internal adjustments.

Table 4: Household production response to price shocks (% change compared to base) _Link outside province:_ _No link_ _Link_ _Owning draught power:_ _No_ _Yes_ _No_ _Yes_
**Crops** One season rice 3 84 -12 -22
Two season rice 0 -35 6 11 Other crops 0 -1 0 0
**Livestock** Pig production -11 -84 -1 -7
Other livestock 148 2420 16 104 In the case of rice, land can be allocated between two different types of rice production. One season rice consists of a single crop, while two season rice is a double-cropping system. Consisting of two crops, the latter uses more inputs, for example for transplanting which is a rather labor intensive activity. This difference in labor requirements accounts for the difference between households with no link outside the province (moving to one season rice) and those with a link outside the province (moving to two season rice).The exogenous increase in rice prices raises shadow wages, which are based on the value of their marginal product. _Ceteris paribus,_ the increase in the price of rice makes two season rice more attractive than one season rice, since it has a higher yield. This higher yield, however, is obtained through a more intensive production. Expanding two season rice thus requires more labor, increasing shadow wages. For the two household groups lacking an outside link, this increase in production costs counterweighs the exogenous increase in output prices.

Table 5: Household-specific price response to price shocks for traded commodities (% change compared to base) _Link outside province:_ _No link_ _Link_ _Owning draught power:_ _No_ _Yes_ _No_ _Yes_
**Agricultural output** Two season rice 2.0 2.2 2.0 2.0
Pig production 2.0 4.6 2.0 2.0 Feed 3.4 9.4 2.4 3.4
**Endowments** Family labor 2.7 7.2 1.2 4.3
**(shadow wages and** Irrigated land 2.6 5.0 2.0 2.7
**rental rates)** Non-irrigated land 1.0 -2.4 2.4 0.2

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As the more moderate shadow wage increases in Table 5 suggests, the two household groups with an outside link have a larger labor endowment, even after accounting for the absence of household members due to migration. As a result their shadow wage increases less, despite shifting to more intensive rice cultivation. The shadow wage increase of 7.2 percent for the household with no outside link but owning draught power, reflects that this household type has the smallest labor endowment, explaining its shift to one season rice.

If we then turn to the livestock activities, we find pig production to be reduced, while other livestock production increases. These different responses have to do with differences in production technology.
Pig production is largely based on farm-produced feed, mainly consisting of crop-residues that are relatively fixed in supply. An increase in pig production will raise the price of feed, being a household nontradable, making it a less attractive activity. Other livestock uses mainly purchased feed and a limited amount of labor. The feed is purchased outside the village against a fixed price, and other livestock production can thus be expanded as long as cash and labor are available.

The household owing draught power and lacking an outside link, shows a dramatic expansion in other livestock production. This expansion is less dramatic than it seems at first sight because of the small size of this activity in the SAM, accounting for only 0.1 percent of value added. The attraction of other livestock for this household is that it uses much more purchased feed, as opposed to farmproduced feed, than the other household types. As a result the response of this household is less affected by the increasing costs of farm-produced feed, resulting in its strong production response.

Households consume part of their own production. With the linear expenditure system, consumption of farm output is inelastic. The change in production is thus not matched by a change in consumption, resulting in a stronger change in marketed surplus than in produced output (Table 6). The strong production response of the household owning draught power and lacking an outside link, is reflected by its withdrawal from the market for two season rice and pigs. For these two activities this household limits production to its own consumption needs. Not being active in the market, the market price is no longer relevant. Instead two season rice and pigs have a household-specific shadow price which exceeds the market price that still holds for the other three household groups (see Table 5).

Table 6: Household marketed surplus response to price shocks (% change compared to base) _Link outside province:_ _No link_ _Link_ _Owning draught power:_ _No_ _Yes_ _No_ _Yes_
**Crops** One season rice 12 119 -24 -43
Two season rice -4 -100 19 39 Other crops -9 -12 1 -5
**Livestock** Pig production -11 -100 -2 -10
Other livestock 332 5123 62 284 The discussion of price changes focused on the impact of changes in agricultural prices. The scenario analyzed also includes an increase in the price of consumption goods. Given nonseparability, this 16


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price increase may affect household production decisions. The impact of the price of consumer goods on production are found to be minor, and are therefore not discussed further.

3.2 Household level impact of changes in outside village employment We now turn to analyzing the impact of labor demand, providing the second pathway through which trade liberalization affects households. Demand for agricultural labor is determined endogenously by agricultural production decisions, and we therefore focus this section on non-agricultural employment.
As discussed in the introduction, economic growth in China has concentrated in the coastal provinces, resulting in an uneven distribution of the benefits of growth. Lacking information to link national level changes to regional changes in non-agricultural sectors, we can only make a crude assessment of the impact of uneven economic development, through a two-step analysis of changes in nonagricultural employment. To capture the concentration of economic growth in the coastal cities, we start by analyzing the impact of an increase in outside province employment.

Only two of the four households groups have access to outside province employment. Comparing the production response between the household groups with and without access to outside province, we find opposite production responses, which is most pronounced for rice (Table 7). Households that have an increased opportunity for outside province migration, move from one season to two season rice, reducing demand for labor which has become more scarce. The households lacking access to outside province migration move in the opposite direction by increasing two season rice production.
Since these household are not directly affected by the increased migration opportunities, interactions in local markets cause their response. It turns out that interactions in the village market for animal traction transmit the effects of the increased migration, and therefore mainly affect the household owning draught power. The shift to one season rice by the migration households reduces the demand for animal traction, resulting in a decrease of the price for traction. The two households not involved in migration do not face a increased shadow wage, which combined with the lower costs for traction makes the more intensive two season rice production more attractive.

Table 7: Household production response to increase in outside province migration (% change) _Link outside province:_ _No link_ _Link_ _Owning draught power:_ _No_ _Yes_ _No_ _Yes_
**Crops** One season rice -2 -22 5 77
Two season rice 1 10 -2 -34 Other crops 0 0 -1 0
**Livestock** Pig production 0 0 3 -2
Other livestock -5 -47 -24 93 The reduction in the price of animal traction also reduces cash income of the two household groups owning draught power. For the household group with access to migration, this decrease in cash income is outweighed by an increase in remittances. A net increase in cash remains, which in combination with the higher shadow wage results in an expansion of other livestock production. The household lacking access to migration faces a reduction in the available cash due to the decreased 17


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incomes from renting animal traction services. This household group also has the most capital intensive other livestock production, resulting in a contraction of this activity.

Only shocking outside province employment thus shows that because of asymmetric access to outside province migration and village interactions, opposing production changes occur. Despite a concentration of economic growth in coastal cities, other regions benefit from trade liberalization as well. Since all household types are involved in off-farm employment, they will all benefit from an increased demand for nonagricultural labor outside the village. An increase in all types of off-farm employment, either inside or outside the province, has a similar impact as found above for the two households with an outside province link (Table 8). Migration outside the province accounts for about 70 percent of their off-farm employment income, and the analysis above about covers the impact of an increase in outside village employment.

Table 8: Household production response to increase in outside village employment (% change compared to base) _Link outside province:_ _No link_ _Link_ _Owning draught power:_ _No_ _Yes_ _No_ _Yes_
**Crops** One season rice -15 15 8 77
Two season rice 5 -6 -4 -33 Other crops 0 0 -1 0
**Livestock** Pig production 2 3 4 -1
Other livestock -44 -47 -27 78 Of the two households lacking an outside province link, the household owning draught animals shows a qualitatively different response in terms of rice production, than when analyzing outside province migration. This household type has a limited involvement in outside village employment (accounting for 12 percent of its value-added), but having the smallest labor endowment its shadow wage does increase when off-farm employment increases. At the same time, the demand for animal traction drops further, due to the twice as strong reduction in two-season rice of the household with an outside link but not owning draught power. An increasing shadow wage combined with a reduction in cash income that is only partly compensated by an increased off-farm income, the household owning draught power but not having access to migration increases one season rice production. This shift reduces demand for labor and cash, that have both become scarcer.

Summarizing, an increase in outside village employment has a differential impact on households groups, depending on their factor endowments and access to different types of off-farm employment.
Separately analyzing an increase in migration to coastal provinces shows that the access to employment by two household groups, triggers a strong production response of the households lacking such access. Local markets for animal traction provide the main link through which these changes are transmitted through the village economy. If the increased demand for labor is not concentrated in the coastal cities, all households benefit from an increase in off-farm income.
Concentrated versus evenly distributed increase in labor demand qualitatively changes the response of one household type, pointing to a need for a more disaggregated analysis of the impact of trade liberalization.

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3.3 Household level impact of trade liberalization Analyzing the two pathways through which trade liberalization affects households resulted in opposing production responses (compare Table 4 and 8). This raises the question what the net impact of these two types of shocks are. Major changes occurred with rice and livestock production, for these activities we therefore indicated whether the increase in agricultural prices or the increase in outside village employment dominates household response. From the entries in Table 9 we can conclude that the dominant aspect of trade liberalization depends on the household type.

Table 9: Household production response to trade liberalization (% change compared to base) (*) _Link outside province:_ _No link_ _Link_ _Owning draught power:_ _No_ _Yes_ _No_ _Yes_
**Crops** One season rice -32 ( _o_ ) 83 ( _i_ ) -5 ( _a_ ) 78 ( _o_ )
Two season rice 11 ( _o_ ) -34 ( _i_ ) 4 ( _a_ ) -33 ( _o_ ) Other crops -1 -1 -1 -0
**Livestock** Pig production -7 ( _a_ ) -84 ( _i_ ) 3 ( _o_ ) -9 ( _i_ )
Other livestock 84 ( _a_ ) 2398 ( _i_ ) -15 ( _o_ ) 209 ( _i_ ) Note: ( _a_ ) means the change in agricultural prices dominates; ( _o_ ) means the change in outside village employment dominates; ( _i_ ) means prices and employment have an identical impact in qualitative terms. (*) combined effect of price changes and outside village employment.

In case of the household type owning draught power and lacking a link outside the province, the two aspects of trade liberalization have the same qualitative impact. The total impact is almost identical to the impact of changing agricultural prices, which is due to the limited involvement in off-farm employment of this household type. The other household group owning draught power has a similar pattern of shifts in agricultural production. In this case the impact of outside village employment dominates, which is not surprising since this already accounts for a quarter of its value-added in the base run. Both household groups owning draught power thus shift to one season rice and other livestock production, but this is because of change in agricultural prices for one household type while being due to changes in outside province migration for the other.

The two household groups not owning draught power display an opposite shift in crop production, by moving to two season rice. Both earn a significant share of their value-added from outside village employment. Surprisingly, for the household involved in migration the impact of the increase in employment (accounting for 50 percent of value-added) does not dominate its response. The increase in rice prices combined with the decreased costs of animal traction outweighs its increased shadow wage. The importance of off-farm employment thus does not fully determine which aspect of trade liberalization dominates.

The other household type that does not own draught power earns a smaller part of its income through off-farm employment, and therefore has a more modest increase in its shadow wage (Table 10). For this household type the decreased price of animal traction following an increase in employment outweighs the rise in labor costs, resulting in a shift to two season rice.

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Table 10: Household-specific price response to price shocks for traded commodities (% change compared to base) _Link outside province:_ _No link_ _Link_ _Owning draught power:_ _No_ _Yes_ _No_ _Yes_
**Agricultural output** Two season rice 2.0 2.2 2.0 2.1
Pig production 2.0 4.2 2.0 2.0
**Endowments** Family labor 8.8 10.3 14.3 8.9
**(shadow wages and** Irrigated land 1.7 5.2 2.6 3.7
**rental rates)** Non-irrigated land -0.8 -3.9 -2.3 -0.2
Animal traction services n.a. -2.6 n.a. -2.6 Three of the households groups increase other livestock production. This activity uses a lot of external inputs and is constraint by the availability of cash needed for purchasing external inputs. The increase in output prices and outside village employment release this cash constraint, resulting in an spectacular percentage increase in other livestock production. Actual marketable amounts are more modest since it only accounts for a fraction of total output in the base run (see below). The household with an outside link and not owning draught power increases pig production while reducing other livestock production. This different response is due to the household-specific calibration of the production functions. As a result this household type gains more from increasing the amount of purchased feed in pig production than in other livestock production.

4 Village level impact of trade liberalization: supply response and income distribution The previous section focused on differences across household types. This discussion illustrated that differences in household endowment and access to off-farm employment affect household production response. Finding opposing production responses to agricultural price changes and employment changes, begs the question what the aggregate village supply response to trade liberalization is. A second question that comes to mind is which household gains most from the trade liberalization. The second part of this section therefore discusses the income and welfare impacts of trade liberalization.

4.1 Village agricultural supply response As with analyzing household response, we look at price and employment effects of trade liberalization separately before assessing the combined effect on the village marketed surplus (Table 11). We also computed the change in total marketed surplus of rice, summing one and two season rice.

The discussion of household response was cast in terms of percentage changes, ignoring the different sizes of the household groups. As indicated in Table 2, the two household groups with an outside link are larger than the two household groups lacking such a link. This difference in size affects the contribution of each household group to the village marketed surplus. The household type with an outside link and owning draught power forms the largest group (295 households), and the village marketed surplus follows the same pattern as the production response of this household group.

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Table 11: Village marketed surplus with trade liberalization _Price changes_ _Employment changes_ _Trade liberalization_ _(1)_ _(2)_ _(1) and (2) combined_
**Village marketed surplus (% change compared to base):**

One season rice -5 63 73 Two season rice 6 -51 -57 _Rice_ _2_ _-14_ _-15_ Other crops -3 -6 -8 Pig production -16 2 -13 Other livestock 1214 2 1253

**Composition of village marketed surplus (% of total surplus):**
One season rice 13 32 25 Two season rice 26 17 11 Other crops 9 11 8 Pig production 22 37 23 Other livestock 30 3 32 100 100 100 The change in agricultural prices following trade liberalization increases total exports of rice from the village, due to a shift to two season rice by the two largest household groups. This shift is induced by an increase in rice prices, with most other input prices remaining unaffected. This asymmetric price changes makes intensive rice cultivation more attractive. The increase in other livestock production is largely due to the explosion of the marketed surplus by the household owning draught power but lacking an outside province link (see also Table 6), which is only partly tempered by the relatively small size of this household group (100 households).

The increased labor demand following trade liberalization has the opposite impact of agricultural price changes. The increasing shadow wages result in a shift to less intensive rice cultivation, and total rice exports from the village drop by 14 percent. Differences across household types in marketed surplus of other livestock almost cancel each other, leaving a very modest 2 percent increase.

Combining price and employment changes, the impact of increased employment dominates the marketed surplus of crops, while agricultural price changes dominate the impact on the marketed surplus of livestock activities. The net result is a decrease in rice exports from the village, while exports from other crops also drop with 8 percent. Declines in household production response of other crops were modest for all three scenarios. But after summing over all households, and accounting for increased household consumption following the increased incomes, the reduction becomes significant at village level.

The explosion in marketed surplus of other livestock in percentage terms raises questions on the size of the other livestock sector following trade liberalization. Table 2 indicates a limited share of other livestock in production in the village SAM, which amounts to 3 percent of the value of village marketed surplus. The explosion of other livestock production results in ten-fold increase in its 21


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contribution to the value of village marketed surplus. Simultaneously rice production drops in importance, accounting for 50 percent of marketed surplus in the SAM, which drops to 36 percent following trade liberalization. In terms of total marketed surplus, the village thus can be characterized as shifting from rice-producing to a livestock producing village.

4.2 Household income and welfare The discussion so far has focused on changes in agricultural production which was found to shift considerably in response to changes in prices and employment. The impact of these changes on household income and welfare has been left untouched. Since growing income inequalities are a rising concern in China, we now turn to analyzing income and welfare. Again, we split the impact of trade liberalization in prices, before looking at their combined impact.

Table 12: Changes in full income and welfare following trade liberalization per household and village total _Trade_ _Prices_ _Employment_ _(1) and (2)_ _(1)_ _(2)_ _combined_

**Full income per household (% change):**

No draught power, no outside province link 2.5 4.2 7.0 Draught power, no outside province link 4.9 1.9 7.0 No draught power, outside province link 1.3 8.0 9.3 Draught power, outside province link 3.2 3.7 6.1
**Total** **[1]** **increase in full income (% change):** **2.7** **4.9** **7.4**

**Welfare increase per household (EV, 1,000 yuan):**
No draught power, no outside province link 0.3 0.5 0.9 Draught power, no outside province link 0.7 0.3 1.0 No draught power, outside province link 0.2 1.3 1.5 Draught power, outside province link 0.6 0.8 1.2
**Total** **[1]** **welfare increase (EV, 1,000 yuan):** **313.9** **630.0** **910.0**

1 Computed as the sum over all households.

The upper-part of Table 12 presents percentage changes in full income per household. The household lacking an outside link but owning draught power benefits most from the agricultural price changes.
This is a direct result of its activity composition (see Table 2), which focuses on agricultural production. Not surprising given its orientation on outside province migration, the household with an outside link but lacking draught power benefits least from the price change, but most from the increased employment opportunities. The total increase in full income is considerable for all households, ranging from 6 to 9 percent.

The lower part of Table 12 presents the welfare increase per household, measured by the equivalent variation. The pattern is similar as with the changes in full income. The households owning draught power are oriented towards agriculture and benefit most from the price changes, while the households involved in outside province migration benefit most from the increased employment opportunities.

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The total increase in welfare following trade liberalization varies between 900 and 1,500 yuan per household.

Table 13: Full income and cash income per adult equivalent following trade liberalization (,000 yuan) _Base_ _Prices_ _Employment_ _Trade_
**Full income per adult equivalent:**

No draught power, no outside province link 4.3 4.4 4.5 4.6 Draught power, no outside province link 4.3 4.5 4.4 4.6 No draught power, outside province link 3.8 3.9 4.1 4.2 Draught power, outside province link 4.3 4.4 4.4 4.5 _Village average_ _4.1_ _4.2_ _4.3_ _4.4_

**Full income per adult equivalent adjusted for migration:**
No draught power, no outside province link 4.4 4.5 4.6 4.7 Draught power, no outside province link 4.3 4.5 4.4 4.6 No draught power, outside province link 5.0 5.0 5.5 5.6 Draught power, outside province link 5.5 5.7 5.8 5.9

**Cash income per adult equivalent adjusted for migration:**
No draught power, no outside province link 0.8 0.8 0.9 0.9 Draught power, no outside province link 0.8 0.8 0.8 0.8 No draught power, outside province link 1.7 1.7 1.9 1.9 Draught power, outside province link 1.1 1.2 1.2 1.2 The full income and welfare per household indicate that although all households benefit from the trade liberalization, welfare gains are concentrated with the households with access to outside province migration. To assess the impact on the income distribution within the village, Table 13 presents different measures of income per capita. The upper-part presents the full income per adult equivalent, which is the household size measured in male-equivalent consumer units ignoring the absence of migrated household members. As can be expected given the egalitarian distribution of land, income differences are not huge. Note that in terms of income per adult, the household type involved in outside province migration and lacking draught power is the poorest household. Since this household benefits most from the trade liberalization, this would suggest that access to rural-urban migration reduces the income differences.

Measuring income, however, is a tricky matter. The middle part of Table 13 presents an alternative income measure, adjusting household size for the absence of migrants [6] This turns the ranking of households up-side down and the income distribution appears much less egalitarian. Using his measure, households involved in outside province migration are the richer households to start with and income differences grow with trade liberalization. Accounting for reduced consumption demand with an increase in migration thus increases both the gains from trade liberalization as the widening of income differences.

6 The household survey data only include remittances received from migrants, we thus lack data on the actual income received by the migrants. The remittances are net of the consumption expenditures by the migrants made while being away. We can thus deduct the time which migrants are absent from the consumption needs of the households.

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Through incorporating farm-household models in the village model we are able to measure the full income of households, including the consumption of farm output. Measurements of income or poverty generally need to rely on recorded expenditures, and we therefore included a measure of the cash income of households in the bottom part of Table 13. The cash income is measured as the total expenditures on consumption goods, purchased inside or outside the village. Comparing these numbers to the full income measures shows the importance of the consumption of farm produced output. Comparing across scenarios, the impact of trade liberalization appears limited when measured in terms of cash expenditures. Again, because of the increase in migrants, the household with an outside link and lacking draught power gains most from trade liberalization.

5 Conclusions In this study we employed an innovative village equilibrium model, which fully accounts for the impact of nonseparability on household production and consumption response. This allowed us to analyze the impact of trade liberalization on agricultural supply response and off-farm employment, while accounting for household consumption decisions. To our knowledge this is the first empirical village level study that accounts for nonseparability of household production and consumption decisions, both in construction of village SAM, the structure of the village model and the calibration of household production and utility functions. The village model is used to analyze the impact of trade liberalization which was quantified through a macro level GTAP model. We analyzed the impact of price changes and labor demand, the two major pathways through which international trade affects households.

Analyzing the impact of changes in agricultural prices we find that labor endowments determine the choice for more or less intensive rice cultivation. Two household groups shift to less intensive one season rice production, since an increase in shadow wages outweighs the increase in rice prices. The other two household groups move in the opposite direction moving to more intensive rice production.
Since the latter two groups are larger, the village marketed surplus of rice increases in response to the agricultural price changes. The village marketed surplus of other livestock also increases, mainly due to an explosion of this activity by one type of household which uses a lot of external inputs and little labor in producing other livestock. The rise in output prices increases the availability of cash, allowing this household to expand its production.

Analyzing the impact of an increase in employment we find that it matters whether the increased demand for labor is concentrated in the coastal cities, or spread more evenly throughout the provinces.
Comparing household production response to an increase in outside province employment, we find that households having access to migration move to less labor intensive rice production, while households lacking access to migration move to more labor intensive cultivation. The response of the latter group of households is triggered by a decreased price of animal traction services, due to the lessintensive cultivation of households involved in migration. If both inside and outside province employment increases, one type of household qualitatively changes it production response. A rise in its shadow wage combined with a further decrease in demand for animal traction, has the household owning draught power but lacking an outside province link to shift to one season rice as well. The net impact of an increase in employment is a reduction in the village-marketed surplus of rice.

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The two pathways through which trade affects households thus have an opposite impact on household production response. Assessing the combined effect at household level, the dominant aspect of trade liberalization depends on household endowments and on production activities. We furthermore found that a strong involvement in off-farm employment does not necessarily imply that the employment aspect of trade liberalization dominates household response, hampering a _ex ante_ judgment on the most relevant aspect of trade liberalization for a specific household type.

Rising income inequalities are a growing concern in China. Analyzing the income and welfare impacts of trade liberalization we find that although all households benefit, the households involved in outside province migration gain most. Whether this improves or worsens within-village income distribution depends on whether one accounts for the reduction in consumption demand when household members migrate. If the consumption impact of migration is ignored, income distribution improves with trade liberalization. Accounting for the absence of migrants leads to the opposite conclusion, with the richer households gaining most from trade liberalization. Thus although ruralurban migration can transfer benefits from economic growth in the coastal provinces to inland provinces, asymmetric access to migration implies that the rising rural-urban income differences are transferred as well.

The findings of this study clearly illustrate the advantages of using a village equilibrium model that allows for household-specific production. Opposite movements in production, reflecting different household endowments, temper the village level production response. The impact of differential household endowments would have been missed with a sector level modeling of production which separates firm production decisions from household consumption decisions. The analysis also highlights the importance of local links in transmitting shocks throughout the village economy, despite the village being well integrated in agricultural output and input markets.

Given the relatively standard data needed for the model, the most important limitation for applying this methodology elsewhere is the amount of detail at village level, which comes at the cost of coverage. A relevant question is whether the results of the case study village are representative for villages outside Jiangxi province. An interesting avenue for answering this question it to use the model as a laboratory for understanding mechanisms and for formulating testable hypotheses on the impact of trade on household incomes and production decisions. Using data collected in other villages, these hypotheses could then be tested to see whether the findings from the village equilibrium model can be generalized.

6 References Benjamin, D., and Brandt, L. (1999). “Markets and inequality in rural China: parallels with the past” in _The American Economic Review,_ Vol. 89, No. 2: 292-295.
Bowlus, A. J., and Sicular, T. (2003). “Moving Towards Markets? Labor Allocation in Rural China” in _Journal of Development Economics,_ Vol. 71: 561-583.
Cook, S. (1999). “Surplus labour and productivity in Chinese agriculture: evidence from household survey data” in _The Journal of Development Studies,_ Vol. 35, No. 3: 16-44.

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data collected in other villages

data collected in other villages

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de Janvry, A., Fafchamps, M., and Sadoulet, E. (1991). “Peasant household behavior with missing markets: some paradoxes explained” in _Economic Journal,_ Vol. 101: 1400-1417.
Démurger, S., Sachs, J. D., Woo, W. T., Bao, S., and Chang, G. (2002). “The relative contributions of location and preferential policies in China's regional development: being in the right place and having the right incentives” in _China Economic Review,_ Vol. 13: 444-465.
Diao, X., Fan, S., and Zhang, X. (2003). “China's WTO accession: impacts on regional agricultural income - a multi-region, general equilibrium analysis” in _Journal of Comparative Economics,_ Vol. 31: 332-351.
European Commission. (2002). “The EC's Proposal for Modalities in the WTO Agriculture Negotiations.” Ref. 625/02, Directorate-General for Trade / Directorate-General for Agriculture, Brussels.
Gilbert, J., and Wahl, T. (2003). “Labor market distortions and China's WTO accession package: an applied general equilibrium assessment” in _Journal of Comparative Economics,_ Vol. 31: 774794.
Hare, D. (1999). “'Push' versus 'pull' factors in migration outflows and returns: determinants of migration status and spell duration among China's rural population” in _The Journal of_ _Development Studies,_ Vol. 35, No. 3: 45-72.
Ho, P. (2001). “Who Owns China's Land? Policies, Property Rights and Deliberate Institutional Ambiguity” in _The China Quarterly,_ Vol., No. 166: 394-420.
Huang, J., Li, N., and Rozelle, S. (2003). “Trade Reform, Household Effects, and Poverty in Rural China” in _American Journal of Agricultural Economics,_ Vol. 85, No. 5: 1292-1298.
Huang, J., and Rozelle, S. (2004). “Agricultural Development and Policies in China.” in F. W. v.
Tongeren and J. Huang (eds.) _China's food economy in the early 21st Century; Development_ _of China's food economy and its impact on global trade and on the EU._ Agricultural Economics Research Institute (LEI), Report 6.04.04, The Hague.
Jones, D. C., Li, C., and Owen, A. L. (2003). “Growth and regional inequality in China during the reform area” in _China Economic Review,_ Vol. 14: 186-200.
Löfgren, H., and Robinson, S. (1999). “Nonseparable farm household decisions in a computable general equilibrium model” in _American Journal of Agricultural Economics,_ Vol. 81, No. 3: 663-670.
Murphy, R. (2000). “Migration and Inter-household Inequality: Observations from Wanzai County, Jiangxi” in _China Quarterly,_ Vol. 0, No. 164: 965-82.
Park, A., Brandt, L., and Giles, J. (2003). “Competition under credit rationing: theory and evidence from rural China” in _Journal of Development Economics,_ Vol. 71, No. 2: 463-495.
Poncet, S. (2003). “Measuring Chinese domestic and international integration” in _China Economic_ _Review,_ Vol. 14: 1-21.
Qiao, F., Lohmar, B., Huang, J., Rozelle, S., and Zhang, L. (2003). “Producer Benefits from Input Market and Trade Liberalization: the Case of Fertilizer in China” in _American Journal of_ _Agricultural Economics,_ Vol. 85, No. 5: 1223-1227.
Rozelle, S., Guo, L., Shen, M., Hughart, A., and Giles, J. (1999a). “Leaving China's farms: survey results of new paths and remaining hurdles to rural migration” in _The China Quarterly,_ Vol., No. 158: 367-393.
Rozelle, S., Taylor, J. E., and deBrauw, A. (1999b). “Migration, remittances, and agricultural productivity in China” in _The American Economic Review,_ Vol. 89, No. 2: 287-291.
Taylor, J. E., and Adelman, I. (1996). _Village economies: the design estimation and use of villagewide_ _economic models_, Cambridge University Press, Cambridge, etc.
Taylor, J. E., and Adelman, I. (2003). “Agricultural Household Models: Genesis, Evolution, and Extensions” in _Review of Economics of the Household,_ Vol. 1: 33-58.
van Tongeren, F. W., and Huang, J. (2004). “China's food economy in the early 21st Century; Development of China's food economy and its impact on global trade and on the EU.”, Agricultural Economics Research Institute (LEI), Report 6.04.04, The Hague.

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van Tongeren, F. W. and H. van Meijl (2004). "Beyond the Uruguay Round: An evaluation of Doha Development Round proposals", in F. W. v. Tongeren and J. Huang (eds.) _China's food_ _economy in the early 21st Century; Development of China's food economy and its impact on_ _global trade and on the EU._ Agricultural Economics Research Institute (LEI), Report 6.04.04, The Hague.
Winters, L. A. (2002). “Trade, Trade Policy and Poverty: What Are the Links?” in _The World_ _Economy,_ Vol. 25: 1339-67.
Zeller, M., Sharma, M., Ahmed, A. U., and Rashid, S. (2001). “Group Based Financial Institutions for the Rural Poor in Bangladesh. An Institutional and Household Level Analysis.” Research Report _120_, IFPRI, Washington D.C.

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### **Trade Costs, Export Development and Poverty in Rwanda**

Ndiame Diop [a], Paul Brenton [a] and Yakup Asarkaya [b][*]

#### **Abstract**

For Rwanda, one of the poorest countries in the world, trade offers the most effective route for substantial poverty reduction. However, the poor in Rwanda, most of whom are subsistence farmers in rural areas, are currently disconnected from markets and commercial activities by extremely high transport costs and by severe constraints on their ability to shift out of subsistence farming. The constraints include lack of access to credit and lack of access to information on the skills and techniques required to produce commercial crops. The paper is based on information from the household survey and a recent diagnostic study of constraints to trade in Rwanda. It provides a number of indicative simulations that show the potential for substantial reductions in poverty from initiatives that reduce trade costs, enhance the quality of exportable goods and facilitate movement out of subsistence into commercial activities.

World Bank Policy Research Working Paper 3784, December 2005 _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. Policy Research Working Papers are available online at_ _http://econ.worldbank.org._

- [a] Trade Department, World Bank. [b] University of Virginia. We would like to thank Ataman Aksoy, John
Baffes, Kene Ezemenari and Waly Wane for helpful comments and Yakup Asarkaya for research assistance.

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#### **1. Introduction**

Rwanda is one of the poorest countries in the world, with a per capita income of $230 in 2003. The vast majority of the poor reside in rural areas so that rural development is crucial to sustained poverty reduction. The majority of the poor are farmers and the poorest are those that produce mainly or solely for subsistence. Farmers involved in the production of commercial crops, primarily tea and coffee, tend to be less poor and have a high propensity to spend on other products produced in rural areas. Given its small domestic market, it is trade that offers the best prospect of sustained growth and poverty reduction. But at present, handicapped by extremely high trade costs, Rwanda’s rural economy is trapped in subsistence activities. Annual exports per capita in Rwanda amount to just $18 compared to an average of $145 in Sub-Saharan Africa.

This paper shows how opening up rural Rwanda to commercialization and trade could have a substantial impact on poverty. Three major interrelated routes by which trade can enhance rural incomes, unleash the potential for specialization and reduce poverty are analyzed. First, reducing rural transport costs is central to initiate a strong growth impulse through higher farm gate prices for exportables and more specialization in commercial products. We highlight this issue by concentrating on coffee, the principal agricultural export. Transport costs facing producers in Rwanda are very high and many farmers are effectively delinked from national and international markets. High transport costs depress farm gate prices and represent an implicit tax on exportable producers. Second, a key element of the Rwanda government’s strategy for the coffee sector, as well as for other commercial crops, is to increase quality through a fully washed strategy. Fully washed coffee has been commanding prices that are more than double that of standard coffee.

Finally, reducing transport costs and the introduction of quality-enhancing techniques will increase the incentives for rural households to participate in the production of exportable products. We assess the impact on incomes and poverty of successful switching of the poorest farmers into higher return economic activities.

Our analysis, based on information from the 2001 household survey, suggests that all three mechanisms have a very strong potential for reducing poverty in Rwanda, with 2

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particularly strong impacts on small farms which support the poorest households. For example, facilitating the shift out of subsistence into coffee production for those farmers that have a propensity to produce coffee would reduce the national poverty headcount index by more than a quarter. However, substantial barriers to rural development will constrain movements out of subsistence into commercial production. The key barriers are the lack of availability of credit in rural areas to finance the transition and lack of knowledge of relevant techniques for cultivating and preparing new crops. While these constraints remain and transport costs are high the poverty reducing potential of trade in Rwanda cannot be realized. Rwanda is severely constrained in addressing these structural barriers, which should be a focus of assistance from the international community.

The rest of the paper is organized as follows. The next section analyzes the main determinants of poverty in Rwanda, emphasizing the role of trade costs. Sections 3 and 4 examine the impacts of lower transport costs and higher quality coffee on poverty, respectively. Section 5 looks at the structural implications of those two simulations on the rural economy. It highlights the poverty impact of switching from subsistence to more commercial agriculture and open trade. The main policy implications and conclusion are drawn in section 6.

#### **2. Trade Costs as Key Determinants of Poverty in Rwanda**

The first step in putting in place policies and programs to reduce poverty is to gain an understanding of its key determinants. We investigate here the role of trade costs, among other key determinants, in allowing individuals to escape poverty in Rwanda. To do this, we estimate the determinants of the probability of not being poor in Rwanda through a probit regression, using the Rwanda Household Living Conditions Survey (EICV). The EICV was carried out between October 1999 and July 2001 and it contains community characteristics indicators together with information from 6,420 households distributed among Rwanda’s 12 provinces. [1] The overwhelming majority of the households interviewed live in rural areas, where the incidence of poverty is highest. Indeed, the 1 The sample design contains three main strata: (i) rural prefecture; (ii) urban Kigali and (iii) other prefecture urban centers. The rural prefecture stratum is further subdivided into 11 sub-strata corresponding to each rural prefecture.

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headcount index in rural Rwanda stands at 47 percent, against only 13 percent in urban areas [2] (Table 1). The EICV also shows that poverty varies substantially across provinces.

The poverty incidence is highest in Gikongoro (57 percent) and lowest in Kigali Ville (10 percent).

Denoting _y_ as the poverty status (incidence index) of observed individuals within the sample, and _X_ as a vector of major individual, household, community and regional characteristics, the probability of escaping poverty is defined by the probit model: Pr( _y_ = 1 _X_ ) = Φ ( _Xb_ ) (1) Φ is the standard cumulative normal probability distribution and _Xb_ is the probit score or index. The probit coefficient, _b_, shows the impact of a one-unit increase in the predictor on the probability of not being poor. A one-unit increase in the predictor leads to an increase in the probit score by _b_ standard deviations. We model the probability of being out of poverty as a function of the following predictors: market access community variables (such as, access to roads, access to electricity, proximity of markets), household and individual characteristics (demographic profile, head of household education, land size, livestock, etc.), and a set of control variables such as provincial dummies.

Table 2 shows the results of the probit estimation. A key feature of these results is the central role of community variables in escaping poverty. Access to year-round roads, credit and electricity increase significantly the chances of escaping poverty. Of particular interest in the context of this paper is that all of the market access variables significantly influence the chance of escaping poverty. Households that do not have easy access to a road and/or have to use impracticable earth roads to connect to secondary or main roads have limited or no access to product and factor markets, substantially reducing the opportunities for escaping poverty. Regional disparities in the probability of being poor partly reflect this result. Residents of Kibuye, Kigali, Gikongoro, Byumba, and Butare that are more likely to be in poverty have poor access to community infrastructures, rural 2 For our analysis the poverty line is conventionally defined as the per capita income of the final individual in the 4 [th] decline of the income distribution. The headcount index, P0, is the proportion of population with per capita expenditure below the poverty line. The poverty-gap, P1, estimates the average distance separating the poor from the poverty line, as a proportion of that line.

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transport in particular. In contrast, residents of Gisenyi and Kibungo, better endowed in infrastructures, are more likely to be out of poverty.

Among the household characteristics that affect poverty, occupation appears to be the single most important variable. The results suggest that someone who earns a wage in the non-farm sector has a substantially higher chance of not being poor and that a self employed non-farm worker is also much more likely to be non-poor. On the other hand, being an agricultural worker (4 percent of rural households) implies a higher probability of being poor. However, these three categories represent only 4 and 8.5 percent of rural households, respectively. About 76 percent of household heads are farmers. Being a farmer helps many households escape from poverty but only insofar as the comparator category is households without occupation (7 percent are in other sectors or have no occupation). [3] The other household characteristics that significantly affect poverty include household size, education and the level of wealth, reflected in terms of land and livestock holdings.

Households with a low dependency ratio, large land and livestock holdings and a more educated head are more likely to be out of poverty. The result regarding land is interesting given a proposed revision to the land law in Rwanda that seeks to increase the feasibility of larger land plots. Also of importance here is the role of cooperatives, which can successfully bring small scale farmers together to allow for more efficient utilization of land, improved access to credit and better dissemination of information. The key message here is that a reorganization of the rural sector, which leads to the more effective use of agricultural land, is likely to have a substantial effect in reducing poverty.

#### **3. The Poverty Impacts of Lower Rural Transportation Costs**

_Direct short run narrow effects_ The analysis in this section shows that allowing and improving access to markets through better rural infrastructure, especially rural transport, has a strong potential for reducing 3 These results are consistent with those of Dabalen, Paternostro and Pierre (2004) on the returns to participation in the nonfarm sector in Rwanda.

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poverty. [4] The analysis concentrates on coffee, the main exportable crop and the main vehicle for raising cash incomes in rural areas and for stimulating a switch from subsistence to more market oriented agriculture. Beyond coffee farmers, reductions in rural transport costs will benefit all producers and consumers in rural areas and will be integral to a policy that seeks to increase the role of markets and market oriented production in rural areas. Thus, the impact of policy and investment initiatives to improve access to rural transport will by far exceed the more narrow simulations on poverty that we investigate here.

As a small coffee exporter, Rwanda is a price taker in the world markets. Hence in the analysis below we take world prices as exogenous and assume that changes in coffee supply in Rwanda have no impact on the world price. For all coffee farmers, regardless of the quality of their crop, one of the key determinants of the producer price that they receive in Rwanda will be the transport cost.

Transport costs incurred by exporters both within Rwanda and on the main corridor routes to ports in neighboring countries are very high. It is estimated that the transport cost from the farm-gate in Rwanda to the port in Mombassa is about 80 percent of the producer price. Rural transport costs alone, that is, of transport from the farm gate to the capital, Kigali, is estimated at 40 percent of the farm gate price! [5] These costs represent an implicit tax on coffee (and other exportable) producers.

Porto (2005) shows how reducing trade costs in Moldova could have a substantial impact on poverty. We follow a similar approach simulating lower rural and international transport costs leading to higher prices for farmers, which encourage greater production and raise the incomes of rural farmers. Here, we look specifically at the effect of a 50 percent cut in rural transport costs on coffee farmers’ income. This is not unrealistic.

Most of the roads in rural areas are unpaved. Vehicle operating costs are estimated to be 4 Access to rural transport is also a key element in providing access to other key services related to poverty and standards of living, such as, participation in education and access to health services.
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about 50 per cent higher on poor quality roads compared to good quality roads. [6] For many farmers roads are not accessible or totally inadequate and head carrying is the only available mode of transport. Given the uncertainty of the magnitude of such a reduction in transport costs on producer prices, we perform the simulations using two pass-through assumptions that is the extent to which the fall in transport costs is reflected in higher producer prices for farmers: a 100 percent pass-through and a 50 percent pass-through.

We follow Deaton (1989, 1997) whereby the short-run effects of price changes are directly proportional to income shares. In particular, holding all the other components of household income constant and assuming that coffee produced in Rwanda is entirely exported (there is almost no domestic consumption of coffee), the direct short-term welfare impact of a price shock on household _i_ is given by: Δ _Y_ ) _i_ = φ _icoffee_ - ( Δ _Pcoffeecoffee_ ) (2) Δ _YY_ ) _i_ = φ _icoffee_ - ( Δ _PPcoffeecoffee_ ( ) = φ _coffee_ - ( ) 0 _i_ _i_ _P_ 0 _coffee_ Where _Y_ 0 is initial income of household _i_, Δ _Y_ is the change in income following a change in the price of coffee _,_ [φ] _icoffee_ is the share of coffee income (sales) in total household income as provided by the EICV survey and _P_ 0 _coffee_ the initial average producer price of coffee _._ Table 3 shows the different sources of income for Rwandan households, including income derived from coffee sales. At the national level, close to 57.6 percent of households’ income comes from labor market activities and almost 26 percent of income is derived from the sale of agricultural products. In rural areas, agricultural income accounts for close to 42 percent of total income, most of which is derived from the sale of food crops (33 percent) while a smaller portion comes from the sale of commercial crops, 6 A World Bank study found that lack of maintenance on the road between Gitarama and Kibuye raised vehicle operating costs from a 1989 level of $1.00 per kilometer to almost $3.40 per kilometer in 1996. The recent rehabilitation of the road has resulted in a fall of more than 50 percent in vehicle operating costs and to a reduction of overall transport costs of about 40 percent. As a result, agricultural surpluses in the area can now be sold in markets throughout the country, and a general shift is taking place from subsistence agricultural to production for the market (World Bank (2005)).

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mainly from the sale of coffee (3 percent). Labor market income constitutes about 43 percent of the income of rural households, but agriculture contributes little to this (8 percent), most of the income coming from collective services (16 percent).

Since the direct effect of an increase in the farm gate price of coffee is on coffee farmers, we restrict the sample to coffee growers (those that produce a nonzero quantity of coffee in the year before the administration of the questionnaire). This allows us to see more clearly the contribution of coffee to the total income of a group that represents 10 percent of rural households and about 400,000 people. The share of income derived from coffee varies between 14.4 percent for coffee farmer households in the 4 [th] decile and 1.79 percent for the top decile (table 4). Due to different agro-ecological conditions and infrastructure availability across regions, there is a wide disparity in households’ ability to engage in coffee production and, therefore, in the contribution of coffee to total income. The share of coffee sales in total income is the greatest in Cyangugu province with 14 percent, while coffee is not a viable option in Umutara and represents a minor activity in Ruhengeri and Buyamba (table 5).

Table 6 shows the results of the simulations. A 50 percent reduction in rural transportation costs that translates into a 20 percent increase in coffee producer prices reduces the poverty incidence among coffee farmers by almost 6 percent (from 42 to 39.6 percent), and the poverty gap of coffee farmers by 7 percent (from 0.136 to 0.126) [7] . This corresponds to about a half percent reduction in national poverty incidence, or about 16400 people [8] . This reduction in the number of poor is not trivial given that no second round effect is incorporated in the analysis (see below). The impact of reductions in transport costs on coffee farmers’ incomes is fairly linear, smaller or larger changes in transport costs will have proportionate effects on incomes. Here we have assumed a 50 percent fall in rural transport costs alone. If international transport costs were also to fall by a similar magnitude the impact on coffee farmers incomes would be approximately 7 As a starting point, note that coffee farmers are located in the middle section of the income distribution.
They are neither extremely poor nor rich. Their poverty gap for coffee producers is of less than 0.14 against 0.16 for non-coffee farmers also clearly indicates that poverty is less deep among coffee farmers than noncoffee farmers.
8 Calculated as: 0.0053*0.43*7,200,000.

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double that presented in table 6. There is substantial potential for lower transport costs on the main trade routes. It is estimated that the cost per ton of transport between Mombasa and Kigali can be more than 70 percent higher than the cost of transport between Mombasa and Kampala (World Bank 2005).

Furthermore, reducing rural transport costs has a positive impact on the distribution of income – the poor benefit (those in the first four deciles) proportionally more than their wealthier counterparts (table 7). The effect on the distribution of income (ranked in deciles) mirrors the distribution of the share coffee income in total income. Coffee farmers in the 4 [th] decile, for whom coffee provides 14 percent of total income, would see a 3 percent rise in their income. In contrast, coffee farmers at the top of the income distribution would only enjoy a 0.36 percent increase in income. In terms of the different regions, the largest welfare gain accrues to Cyangugu (+2.7 percent), Kigali Ngali (+1.8 percent) and Kibuye (+1.8 percent) whilst in regions where coffee is less important as a source of income, Umutara, Ruhengeri and Buyamba, there is little increase in income (table 7).

_Potential second-round effects_ .

While the direct effect of changes in coffee producer prices falls on the welfare of coffee farmers, other farmers and non-farmers may reap significant indirect benefits from higher coffee incomes. The two traditional channels through which spillover effects work are through the labor market and the markets for food and other local non-farm goods. In Rwanda, only 8 percent of coffee farmers employ paid labor, implying that greater returns to coffee farming may have little employment impact in the short run.

Furthermore, since farmers use sub-optimal levels of fertilizer, they may be more inclined to use some of their extra income to apply more fertilizer and boost productivity, rather than increasing employment. Finally, given Rwanda’s very high labor to land ratio (with a population density of 350/ km2), a land constraint may prevent high incremental employment while at the same time, a large pool of landless people may suppress the potential increase in wage rates (in other words, it is unlikely that a neoclassical closure rule --full employment and flexible wage rate- applies in rural Rwanda).

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In contrast, significant spending spillover effects are likely to arise from the higher incomes for coffee farmers. On average, some 40 percent of coffee farmers’ income is spent on food products. The share of income spent on food is actually higher is some regions, mirroring the geographical disparities in coffee production in Rwanda. Food expenditures have the highest share in consumption in Cyangugu and Kibuye, accounting for about 54 percent of total consumption and the lowest share in Umutara and Kibungo, with 38-40 percent (table 8). The marginal spending of extra incomes on local goods and services can have strong multiplier effects in the rural economy, especially in Cyangugu and Kibuye [9] . Given the constraint on land however, provinces where a growing number of farmers engage in producing exportable crops will increasing rely on other regions and the international market for their food and other goods imports. Once again lower transport costs are important, as is an open trade regime [10], to reap the benefits of specialization.

#### **4. The Poverty Impacts of Higher Quality Coffee**

Enhancing the quality of agricultural products produced in Rwanda’s rural economy is another vehicle for poverty reduction. A key element of the government’s strategy for the coffee sector is to increase the proportion of washed or quality coffee in output and exports. Fully washed coffee has been commanding prices that are more than double that of standard coffee. Here we investigate the potential impacts of this strategy on poverty.

It is worth noting that improvements in rural transport logistics are crucial to the success of the fully washed coffee strategy since the coffee cherries must be washed within 6 hours of being harvested or the quality starts to depreciate. Clearly farmers that are currently more than 6 hours distant from a washing station will be unable to benefit from this strategy.

9 The magnitude of multiplier effects depends on supply elasticities of local goods, as demonstrated by Delgado et al. (1998), after Haggblade et al. (1991). If capital constraints or high transaction costs result in inelastic supply of non-tradable goods, then costs of production will quickly rise with expanded demand, and the growth in production of local goods and services will be dampened.
10 For example, tariffs on imports of rice are currently high at more than 25 percent.

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Using the same methodology as above, we explore a number of scenarios. First, we simulate the impact of half of coffee farmers receiving double the price for their coffee as a result of producing higher quality coffee. Second, we assess the impact when only large farms are able to increase quality and receive double the price for their coffee, whilst the third simulation constrains the impact of higher quality to medium sized farms. Next, we allow for both large and medium farms to receive the 100 percent price increase for higher quality coffee. A final scenario assumes that only small farms raise quality and receive double the price for their coffee. Small farms are defined as farms with less than 0.5 hectare, medium sized farms are those with between 0.5 and 1 hectare, while large farms have more than 1 hectare.

The results suggest that the coffee quality program has a strong poverty-reducing potential. For instance, doubling the price received by half of coffee farmers reduces the incidence of national poverty by about 5 percent (table 9). Further, the poverty impact of the quality program is inversely proportional to the level of income. Targeting small farms, through technical assistance and access programs, could reduce the incidence of national poverty by 6 percent, compared to a reduction of 2 percent if only large farms adopt the quality program. This reflects that most of the large farmers are not poor. On the other hand, they are more capable of adopting the quality program and their resulting increase in income could indirectly benefit the poorest farmers.

#### **5. Welfare Gains from Switching from Subsistence to Coffee Farming**

Lower transport costs and the introduction of quality-enhancing techniques will boost the incentives to produce coffee in Rwanda. Existing farmers will seek to raise productivity and will be better able to purchase fertilizer and invest in new coffee trees and improved techniques. Higher returns to coffee will also lead to an increase in the number of farmers who wish to switch from subsistence to coffee production. In this section, we explore the potential gain to farmers from switching from subsistence agriculture to coffee farming in Rwanda and then assess the impact on national poverty if such switching were to occur.

The purpose of this analysis is to stress that initiatives to reduce transport costs and raise 11


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the quality of exported crops will have a much greater impact and return if constraints to farmers moving out of subsistence and into commercial crops are also addressed.

To do this analysis, one cannot just compare the average observed income of coffee farmers with that deduced for subsistence farmers since these two groups of farmers have different characteristics, some of which are correlated with the probability of belonging to one or other category. It is important to take into account the propensity of farmers to move out of subsistence and into coffee production. Here this is based on the characteristics of those already producing coffee.

The propensity score matching (PSM) methods, pioneered by Rubin (1977), Rosembaun and Rubin (1983), and Heckman, Ichimura and Todd (1997), allow one to build a counterfactual and estimate the average effects of participating in coffee markets on monetary household incomes. In other words, they help determine how much extra income is associated with switching from subsistence to coffee, assuming a binding land constraint. The method consists of first estimating the probability of producing coffee (probit model) that defines a propensity score p( **x** ), for a given vector of observable characteristics **x** . In a second stage, one can use either traditional (one-to-one) or more recent matching methods to pair participants (coffee farmers) and non-participants (subsistence farmers). Traditional PSM methods (Rubin 1977, Rosembaun and Rubin 1983) pair each participant with a single non-participant, the matching being based on the degree of similarity in the estimated probabilities of being a participant. More recently developed non-parametric matching estimators use a weighted average over multiple observations to construct the matches (see Heckman et al, 1997). We apply both methods in this analysis. Finally, the outcome differential, here the per capita income gain, is estimated using kernel methods.

More formally, denote _[Y]_ 1 [ the outcome (income) conditional on participation in coffee ] farming and _Y_ 0 the outcome conditional on non-participation. The impact of participation is Δ = _Y_ 1 - _Y_ 0 (3) 12


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Let _D =_ 1 for individuals who participate and for whom _[Y]_ 1 [ is observed and ] _[D]_ [ = 0 for ] non-participants, for whom _Y_ 0 is observed. Finally let Z denote a vector of observed characteristics of individuals used as conditioning variables. Our task is to estimate the mean impact of participation (“the mean treatment effect on the treated”), given by:

## TT = E ( Δ Z, D = )1 = E { ( Y 1 − Y 0 ) Z, D = 1 }= E ( Y 1 Z, D = )1 − E ( Y 0 Z, D = )1 (4)

The parameter _TT_ estimates the average impact among participants. However, while observed data on _[Y]_ 1 [ allows a computation of ] _E_ ( _Y_ 1 _Z_, _D_ = )1, the counterfactual mean, _E_ ( _Y_ 0 _Z_, _D_ = )1, cannot be directly estimated, hence the recourse to matching estimators.

Denoting P, the propensity score, i.e., the probability of participation given Z, _P_ = Pr( _D_ = 1 _Z_ ), one key assumption made is: _E_ ( _Y_ 0 _P_, _D_ = )1 = _E_ ( _Y_ 0 _P_, _D_ = 0,) _P_ < 1 (5) In other words, the outcome for non-participants matched to participants using the closeness of their propensity score (characteristics) is a proxy for the outcome for participants had they not participated.

The mean impact of participation thus becomes [11] :

## φ p = E ( Y 1 D = )1 − E P D = 1 { EY ( Y D =,1 P ) }= E ( Y 1 D = )1 − E P D = 1 { EY ( Y D =,0 P ) }   (6)

Applying this method to our purpose, φ _P_ is the average extra income that an individual would gain from switching from subsistence to coffee farming.

To model the decision to engage in coffee farming, we build on the large literature on the determinants of adoption of new techniques in agriculture (Foster and Rosenzweig 1995, 11 For an in-depth discussion of the matching techniques and assumptions, see Smith and Todd (2001).

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Conley and Udry 2004, Besley and Case 1994). This literature identifies human capital (measured by education, gender, and age), learning by doing and learning externalities as major determinants of technology adoption. We model the decision to engage in coffee farming as a function of market access community variables, household characteristics (head of household education, demographic profile, level of wealth, etc.), and a set of control variables such as provincial dummies and availability of extension services. The probit regression is performed for heads of households. The switch from subsistence to coffee can be interpreted as technology adoption in agriculture.

Community characteristics strongly influence the probability of participating in coffee farming with access to electricity, road and extension services all being important (table 10). Extension services are a set of technical supports, training and information about markets provided to farmers to increase productivity and profitability through the promotion of new crops, new techniques and new quality-enhancing practices. Other studies on African economies also show the importance of extension services. For instance, Balat et al. (2004) find that households in Zambia that have received extension services are on average more productive in market agriculture than households that have not. Clearly, provision of high-quality extension services is central to facilitating a structural transformation of agriculture in Rwanda.

Demographic profile is the most important household characteristic that significantly influences the likelihood of engaging in coffee farming. Households with many dependents and headed by a male are more likely to be involved in coffee farming in Rwanda. Coffee is a labor-intensive activity and in a situation where labor markets are imperfect, having a large pool of family labor appears to be an advantage. This is important because our earlier broad analysis of the determinants of poverty showed that a high number of dependents is positively associated with the probability of being poor.

This suggests that, after controlling for community characteristics, those households currently involved in subsistence activities who have a relatively strong propensity to produce coffee are more likely to be poor. Hence, initiatives that facilitate the movement 14


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of these households into coffee production are likely to have a very strong impact on poverty.

Education and land and livestock holdings affect positively the probability of participating in coffee farming. This is not surprising. Engaging in a tradable sector presents different challenges to farmers, compared to those that arise in producing non marketable products. These include substantial initial investments, the capacity to adjust to frequent changes in prices as well as adoption of new technologies and marketing skills, all of which are helped by education and wealth reflected in terms of land and livestock. [12] Thus, in the absence of effective financial markets in the rural area, it is only relatively wealthy farmers who have the capacity to move into coffee production. Finally, agro-ecological conditions are an important endowment for some provinces and districts (Cyangugu, Butare, Gitarama) and a serious constraint to engaging in coffee farming for others (Umutara, Ruhengeri and Buyamba). Farmers in the latter provinces are less likely to gain from a switch from subsistence to coffee farming because of natural constraints.

Having examined the main determinants of the probability of being able to participate in coffee farming, we now look at the potential gain from participating in coffee farming by estimating the average agricultural income subsistence farmers would have earned had they participated in coffee production. We find that farmers who have a propensity to produce coffee who actually shift out of subsistence agriculture into coffee production would have earned between 17,000 and 22,000 RwF more (depending on the particular matching method that is used). This translates into a difference in income of between 23 and 30 percent (table 11). [13] If the subsistence farmers identified as having a propensity to produce coffee (40 percent of our sample) actually shifted into coffee production then the national poverty headcount index would fall by more then 25 percent and the poverty gap would drop by 12-13 percent.

12 Access to credit did not appear significant in the regression, but this is very likely to relate to how this variable was defined in the questionnaire. The question asked is whether credit is available in the community without precision of the type of credit (micro-credit, credit for farm investment, etc.).
13 Balat and Porto (2005) find similar quantitative effects in Zambia, where a shift out of subsistence into cotton production leads to gains of around 20 percent of average expenditure for the representative poor farmer.

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These average gains are substantial and raise the question of why farmers are not already making this shift if there is a strong incentive in terms of higher incomes. There must be constraints which are preventing farmers in rural areas from moving away from subsistence agriculture even though they have a propensity to produce coffee. There are a number of issues here that must be addressed including the costs that are incurred in shifting into coffee, in terms of the investment in plants and so on, the lack of income whilst the new plants become harvestable, and the higher risks associated with cash crops. Cadot et al (2005) suggest that these adjustment costs can be very large. They estimate that the cost of moving out of subsistence for farmers in Madagascar can amount to around 250 percent of the value of a subsistence farmer’s annual production. These costs and investments all necessitate the effective provision of credit to rural communities, something which is severely lacking at present. In addition, there is also the need for information on techniques for producing different crops, and so a need for widely available extension services, as well as access to transport.

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**6. Policy Implications and Conclusions**

Increasing incomes in rural areas is necessary for poverty reduction in Rwanda since the majority of the poor live in rural areas. Improvements in returns to exportable commercial crops are the most direct and probably the most effective way of increasing the flow of cash into rural areas and so must lie at the heart of poverty reduction in Rwanda. This paper has shown that reducing trade costs can have a profound impact on poverty. The benefits of lower transport costs, if reflected in higher producer prices, will be realized by all coffee farmers, and indeed all commercial farmers. Reducing transport costs and providing access to transport to those who are currently remote is therefore a crucial element in the poverty reduction strategy. Similarly, our analysis shows that the government’s strategy of increasing returns by raising quality and therefore the market price of Rwandan produce will have a significant impact on poverty, particular if small farmers are targeted and assisted in improving the quality of their coffee crop. However, the success of this strategy requires effective rural logistics services and transport capacity, to ensure that coffee is washed in good time before its quality starts to deteriorate. This capacity is currently lacking.

We also show the substantial impact on poverty that could arise if higher returns to commercial crops encourage larger numbers of farmers to shift from subsistence activities into the production of coffee or other commercial crops. We find that a key characteristic linked to the probability of being poor in the overall sample, a large dependency ratio, is associated with a strong propensity to produce coffee. This suggests that measures that facilitate switching into coffee will have an especially strong impact on the incomes of the poorest families in rural areas. Nevertheless, there are substantial barriers to this process of switching into commercial crops in terms of access to credit to finance the adjustment away from subsistence and access to knowledge and skills to successfully cultivate commercial crops. Unless these are addressed, the role of trade in reducing poverty in Rwanda will be severely hampered.

We have found significant impacts on poverty while only considering the most direct reactions to changing prices. However, the effects will be considerably larger, since 17


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coffee farmers typically have a high propensity to spend on other products produced in the rural communities. Increasing returns to commercial farmers could have substantial multiplier effects within rural communities. The challenge is to provide an infrastructure and framework by which rural markets can develop and flourish. Given the current level of development in Rwanda it is clear that this will require a significant response and engagement from the international institutions and donors.

#### **References**

Balat, J. and G. Porto (2005) ‘Globalisation and Complementary Policies: Poverty Impacts in Rural Zambia’, NBER Working Paper 11175.

Besley, T. and A. Case (1994). “Diffusion as a Learning Process: Evidence From HYV Cotton,” mimeo, Princeton University.

Cadot, O., L. Dutoit and M. Olarreaga (2005) ‘Subsistence Farming, Adjustment Costs and Agricultural Prices: Evidence from Madagascar’, mimeo, World Bank.

Conley, T. and C. Udry (2004). “Learning About a New Technology: Pineapple in Ghana,” mimeo, Yale University.

Dabalen A., S. Paternostro and G. Pierre (2004). “The Returns to Participation in the Nonfarm Sector in Rural Rwanda” World Bank Policy Research Working Paper 3462.

Deaton A. (1989). Rice prices and Income Distribution in Thailand: A non parametric Analysis”. _Economic Journal_ 99 (395) (supplement), 1-37.

Deaton, A. (1997). _The Analysis of Household Surveys. A Microeconometric Approach to_ _Development Policy_, The World Bank and The Johns Hopkins University Press.

Delgado, C. J. Hopkins, and V. Kelly (1998). _Agricultural Growth Linkages in Sub-_ _Saharan Africa._ Research report 107, IFPRI.

Foster, A. and M. Rosenzweig (1995). “Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture,” _Journal of Political Economy_, 103, 1176-1209.

Haggblade S, J. Hammer and P. Hazell (1991). “Modelling Agricultural Growth Multipliers”, _American Journal of Agricultural Economics_, 361-378.

Heckman, J., H. Ichimura, and P.E. Todd (1997) "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Program." _Review of_ _Economic Studies_, 64, 605-654.

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Porto, G (2005) ‘Informal Export Barriers and Poverty’, _Journal of International_ _Economics_, 66, 447-470 Rosenbaum, P., and D. Rubin (1983). “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” _Biometrika_, 70, 41-55.

Rubin D. (1977) “Assignment to a Treatment Group on the Basis of a Covariate,” _Journal of Educational Statistics_, 2, 1-26.

Smith, Jeffrey and Petra Todd. 2001. “Reconciling Conflicting Evidence on the Performance of Propensity Score Matching Methods,” _American Economic Review_ _Papers and Proceedings_, 91, 112-118.

Winters L.A.; McCulloch N.; McKay A. (2004) “Trade Liberalization and Poverty: The Evidence So Far” _Journal of Economic Literature_ ; 42, 72-115.

World Bank (2005). _Rwanda: Diagnostic Trade Integration Study_ . May 2005. The World Bank.

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