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Characteristics of Cotton Farmers in Benin



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Characteristics of Cotton Farmers in Benin

19. Before estimating the impact of changing cotton prices on rural households, it is useful to describe the role of cotton in the rural economy and the characteristics of cotton growers. According to the IFPRI-LARES Small Farmer Survey, cotton is grown by roughly one third of the farm households. Cotton accounts for about 18 per cent of the area planted by farm households and 22 per cent of the gross value of crop production. In value terms, cotton is the second most important crop, after maize. Among cotton farmers, the average area planted with cotton is 2.3 hectares, producing 2.7 tons of seed cotton1. The value of this output is US$ 901 per cotton farm2.
20. Another measure of the importance of cotton in the rural economy is its contribution to cash income. Farmers in Benin are quite market oriented, selling over half the output of cowpeas, groundnuts, manioc, and sweet potatoes, and selling almost half of the output of the “staple” foodcrop, maize. Nonetheless, cotton accounts for about one-third of the value of crop sales carried out by farm households in Benin (IFPRI, 2002).

21. Who are the cotton growers in Benin and how do they differ from other farmers? As mentioned earlier, cotton production is concentrated in the north and center of Benin. About two-thirds of the farmers in the large northern department of Borgou grow cotton, as do 37 per cent of those in nearby Atacora and 64 per cent of those in the central department of Zou. By contrast, in the three departments in the south (Atlantique, Mono, and Ouémé), the percentage ranges from zero to 25 per cent. If we divide the farm households into quintiles, the proportion of farmers growing cotton does not seem to vary consistently across quintiles. If anything, the proportion of cotton growers is lower (28 per cent) in the richest quintile (see Table 1).

22. Cotton growers tend to have farms that are, on average, twice as large as those of non-growers (5.3 hectares compared to 2.3 hectares). Nonetheless, cotton growers are similar to other farmers in terms of various measures of well-being. The incidence of poverty rate is slightly lower among cotton farmers (37 per cent) than among other farmers (42 percent), but the per capita expenditure of cotton growers is about 8 per cent lower than that of others, and the budget share allocated to food is almost identical to that of non-growers (see Table 2). The reason that the larger farms do not translate into a higher standard of living is that cotton growers are concentrated in the more arid north, where the agricultural potential is lower and where there are fewer opportunities for non-farm employment.
23. To give a more concrete idea of the living standards of cotton growers, it is useful to describe some indicators of living conditions, according to the farm surveys:


  • 85% of the cotton farmers in Benin have houses with mud or mud-brick walls,

  • 62% live in houses with a dirt floor,

  • 72% have corrugated metal roofs and 28% have straw roofs,

  • 53% of the cotton farmer households get drinking water from a public well, while another 18% use water from a river or lake,

  • Less than 2 percent have electric lights,

  • On average, the nearest source of potable water is 430 m away, and the nearest paved road is 36 km away,

  • About 34% of the cotton farmers do not own a chair, 38% do not own a table, and 34% do not own a bed.

These figures are fairly typical of farmers in Benin. Thus, it is not that cotton farmers are poorer than average, but rather that almost all farmers in Benin, including cotton farmers, are quite poor.

Effect of lower cotton prices


24. In this section, we use the data from the IFPRI-LARES Small Farmer Survey to estimate the impact of lower cotton prices in Benin. First, we examine the impact of lower prices on the income and poverty of cotton farmers in the short-run, before they have an opportunity to respond to the lower prices. Next, we estimate the impact on cotton farmers in the longer run, after they have responded to the shock of reduced prices.
Short-term direct effects of lower cotton prices
25. As described earlier, we estimate the short-term change in income associated with lower cotton prices using household-level information on per capita expenditures and the volume of cotton production, combined with different assumptions about the reduction in cotton price. A 40 per cent reduction in the farm-gate price of cotton reduces the income of cotton growers 21 per cent. Taking into account the incomes of non-growers, which do not change in this simulation, the average income falls 7 per cent. Smaller reductions in the cotton price cause roughly proportionate changes in income (see Table 3).

26. With a 40 per cent fall in the cotton price, the average incidence of poverty, including both cotton growers and other farmers rises 8 percentage points, from 40 per cent to 48 per cent (see Table 3). In absolute terms, this implies that about 334 thousand people would fall below the poverty line as a result of a 40 per cent reduction in cotton prices.1 A 40 per cent decrease in the price of cotton results in a 40% increase in the poverty gap for all farm households in Benin, while the poverty gap squared (P2) or severity of poverty increases 61 per cent.

27. This analysis can be broken down by department to evaluate regional differences in the impact of falling cotton prices2 (see Table 3). In Atlantique and Ouémé, the reduction in cotton prices has negligible effects on income and poverty because there are virtually no cotton farmers in these departments. On the other hand, the impact on the departments of Borgou and Zou are large. In Zou, a 40 per cent reduction in cotton prices results a 15 percent fall in per capita income and a 17 percentage point increase in the incidence of poverty. In Borgou, the same decrease in cotton prices causes an 18 per cent reduction in per capita income and a 18 percentage point increase in the incidence of poverty. In fact, the department of Borgou moves from having an “average” poverty rate (greater than in two departments and less than in two others) to having the highest incidence of poverty, 62 per cent. Similarly, the poverty-gap (P1) in Borgou increases by a factor of three and the severity of poverty (P2) doubles as a result of the 40 percent reduction in cotton prices.
28. Finally, we look at the effect of falling cotton prices on the cumulative distribution of income per capita (see Figure 2). Among other things, it gives us information about the sensitivity of the results to alternative poverty lines, an important consideration given that our poverty lines is relative (set at the 40th percentile in the base distribution). The point where the cumulative distribution cross the poverty line is the poverty rate (note that the base distribution cross the poverty line at the 40th percentile). It is clear from the graph that similar results would have been obtained for higher and lower poverty lines.
Long-term direct effect of lower cotton prices

29. Because of uncertainty regarding the supply elasticity of cotton, we carry out this analysis using three elasticities: 0.5, 1.0, and 1.5. In order to simplify the discussion, we present only the impact of a 40 percent reduction in cotton prices. These results are presented with the base levels and with the short-run impact. Since the assumption behind the short-run impact is that the supply elasticity is zero (ε=0)., they are labelled as such.

30. As described earlier, the short-run impact of the lower cotton price is to reduce average per capita income by 7 per cent. If the general equilibrium supply elasticity of cotton is 0.5, the average income after the price reduction falls 6 percent from the base. At the other extreme, if the supply elasticity is 1.5, then the average income falls 5 per cent from the base (see Table 4 ).
31. In the long run, a reduction of 40 percent in the price of cotton is associated with a 6-7 percentage point increase in the overall rural poverty rate, depending on the assumption regarding the supply elasticity. The poverty gap measure (P1) rises from 0.10 to 0.12 - 0.13, again depending on the elasticity assumption. And the poverty gap squared (P2) increases from 0.036 to 0.047 - 0.058 (see Table 4). As expected, the long-run impact of the 40 per cent reduction in cotton prices is somewhat less adverse than the short-run impact. It is notable, however, that the results are not very sensitive to the elasticity assumption.
32. The long-run effects on each department are given in Table 4. For example, in Borgou, per capita income falls 18 percent in the short-run, but rebounds 4 percentage points if the supply elasticity is 1.0 and 7 percentage points if the elasticity is 1.5. Similarly, the per capita income in Zou falls 15 per cent in the short-run, but rebounds 3 percentage points in the long-run if the elasticity is 1.0.

33. The poverty rates in each department follow the same pattern in reverse. In the short-run, they rise as a result of the 40 percent fall in cotton prices, but in the long-run they fall back down part of the way. In Borgou, the poverty rate rises from 44 per cent to 62 per cent in the short run, falling back to 58-60 per cent in the long run, depending on which elasticity assumption is used. Similarly, the incidence of poverty in Zou increases from 33 per cent to 50 per cent in the short run, then falls to 47-49 per cent in the long run. As described above, there is little or no change in poverty in the three southern departments (Atlantique, Mono, and Ouémé) because there are very few cotton growers in these departments.

34. In Figure 3, we show the cumulative distribution of income in the base scenario, with a 40 per cent reduction in cotton prices in the short run (ε=0), and with a 40 per cent reduction in cotton prices in the long run (ε=1.5). Although the long-run supply elasticity used in this figure is at the upper end of what we believe is plausible, the difference between the short-run and long-run results is not very large. In other words, the long-term results are not very sensitive to the assumption regarding the supply elasticity of cotton. Even with a relatively elastic supply (ε=1.5), the response of farmers only offsets about one-third of the initial negative short-run impact.
Conclusions
35. This paper analyzes the impact of changes in world cotton prices on farmers in Benin. Both quantitative measures of per capita expenditure from household surveys and qualitative responses to a nationally representative survey suggest that rural living conditions improved over the 1990s. Furthermore, farmers tend to attribute this improvement in rural living conditions to economic factors such as crop prices, availability of food, and access to non-farm employment. Although the causal link is difficult to establish with certainty, it appears the economic reforms of the 1990s (including the 1994 devaluation) and the growth of cotton production during this period contributed to a noticeable improvement in rural standards of living.

36. The link between cotton markets and rural living conditions can, however, work against farmers as well. The analysis in this paper is motivated by the 39 percent decline in the world price of cotton between January 2001 and May 2002. We combine farm survey data from 1998 with assumptions about the decline in farm-level prices to estimate the short- and long-term direct effects of cotton price reductions on rural income and various measures of poverty. We also use the survey data to study two types of indirect effects: the impact of lower cotton production on the demand for agricultural labour by cotton growers and the impact of lower cotton prices on other households through the multiplier effect.

37. The results indicate that there is a strong link between cotton prices and rural welfare in Benin. A 40 per cent reduction in farm-level prices of cotton is likely to result in a reduction in rural per capita income of 7 per cent in the short-run and 5-6 per cent in the long-run. Furthermore, poverty rises 8 percentage points in the short-run, equivalent to an increase of 334 thousand in the number of individuals in families below the poverty line. In the long run, as households adjust to the new prices, the poverty rate settles down somewhat, remaining 6-7 percentage points higher than originally.

38. Furthermore, these estimates may well underestimate the actual effect of lower cotton prices on rural poverty in Benin. First, in an economy with unemployed resources and excess capacity, an external shock affecting income (such as a change in cotton prices) has a multiplier effect. Changes in cotton farmer income result in changes in demand for goods and services produced by their non-cotton-growing neighbours, which in turn influences the demand for goods and services these neighbours consume. Estimates for four countries in sub-Saharan Africa suggest that the multiplier is in the range of 1.7 to 2.2, meaning that the total effect on income (positive or negative) is 1.7 to 2.2 times greater than the direct impact. Second, we assume that farm prices change by the same proportion as world prices. In competitive markets with a fixed marketing margin, the percentage change in farm prices will be greater than the percentage change in world prices3. Third, our estimates do not take into account other indirect effects associated with declining cotton production. An earlier analysis of the Small Farmer Survey data from Benin indicated that cotton farmers are three times more likely to apply fertilizer to their maize crops compared to non-cotton farmers (see Minot et al, 2001). This is because growing cotton gives farmers access to fertilizer on credit, some of which they “divert” to their maize fields. The implication is that lower cotton prices will indirectly reduce the yields of food crops.

39. Overall, the results in this paper challenge the stereotype of the rural poor in developing countries as consisting of subsistence farmers that are relatively unconnected to, and thus unaffected, by swings in world commodity markets. At least in the case of Benin, to the extent that fluctuations in world cotton prices are transmitted to farmers, they will have a significant effect on rural incomes and poverty. The broader implication is that policies that subsidize cotton production in the United States and elsewhere, dampening world prices, have an adverse impact on rural poverty in Benin and (by extension) other poor cotton-exporting countries.

References

Badiane, O., D. Ghura, L. Goreaux, and P. Masson. 2002. Cotton sector strategies in West and Central Africa. Policy Research Working Paper No. 2867. The World Bank. Washington, D.C.

Centre for International Economics. 2002. Trade distortions and cotton markets: Implications for global cotton producers. Prepared for the World Bank. Canberra.

Delgado, C., J. Hopkins, and V. Kelly with P. Hazell, A. McKenna, P. Gruhn, B. Hojjati, J. Sil, and C. Courbois. 1999. Agricultural Growth Linkages in Sub-Saharan Africa. Research Report No. 107. International Food Policy Research Institute. Washington, D.C.

Delgado, C. and N. Minot. 2000. Agriculture in Tanzania since 1986: Follower or Leader of Growth. World Bank Country Study. The World Bank and the International Food Policy Research Institute. Washington, D.C.

Dercon, S. 1993. Peasant supply response and macroeconomic policies: Cotton in Tanzania. Journal of African Economies 2 (2):157-193.

Dorosh, P. and S. Haggblade. 1993. Agriculture-led growth: Foodgrains versus export crops in Madagascar. Agricultural Economics. 9 (August): 165-180.

Foster, J., J. Greer, and E. Thorbecke. 1984. A class of decomposable poverty measures. Econometrica 52 (3): 761-66.

Hazell, P. 1984. Rural growth linkages and rural development strategy. Paper presented at the Fourth European Congress of Agricultural Economics, September 3–7, Kiel, Germany.

Hazell, P. and A. Roell. 1983. Rural growth linkages: Household expenditure patterns in Malaysia and Nigeria. Research Report 41. International Food Policy Research Institute. Washington, D.C.

Haggblade, S., J. Hammer, and P. Hazell. 1991. Modeling agricultural growth multipliers. American Journal of Agricultural Economics 73 (May): 361–374.

International Food Policy Research Institute (IFPRI) and Laboratoire d’Analyse Régionale et d’Expertise Sociale (LARES). 2001. Impact des Réformes Agricoles sur les Petits Agriculteurs au Bénin. Volume 1. Report prepared for the Deutsche Gesellschaft Für Technische Zussammenarbeit (GTZ). Project 97.7860.6-001.00.

Just, R., D. Hueth, and A. Schmitz. 1982. Applied welfare economics and public policy. Prentice-Hall, Englewood Cliffs, N.J.

Minot, N., M. Kherallah, B. Soulé, and P. Berry. 2001. Impact des réformes agricoles sur les petits agriculteurs au Bénin. Volume 1: Résultats des Enquêtes des Petits Agriculteurs, des Communautés, et des Groupements Villageois. Washington, D.C.: International Food Policy Research Institute, Washington, D.C.

Mushtaq, K. and P. Dawson. 2000. Supply response of wheat, cotton, and sugarcane in Pakistan. Presented at International Food and Agribusiness Management Association conference, 26-28 June 2000, Chicago, Illinois.

Oxfam. 2002. Cultivating poverty: The impact of US cotton subsidies on Africa. Briefing Paper No. 30. Washington, D.C.

République du Bénin. 1997. Rapport sur l’Etat de l’Economie Nationale – Développements récents et perspectives à Moyen Terme. Cellule Macroéconomique de la Présidence de la République du Bénin. Cotonou, Bénin.

Sumner, D. 2003. “A quantitative simulation analysis of the impacts of US cotton subsidies on cotton prices and quantities.” Prepared for DS 267 Brazil vs US Cotton Subsidies, World Trade Organization, Geneva.

United Nations Development Programme and Ministère du Développement Rural (UNDP-MDR). 1996. Profil de la Pauvreté Rurale et Caractéristiques Socio-Economique des Ménages. United Nations Development Programme and the Ministère du Développement Rural. Cotonou, Bénin.

US Department of Agriculture. 2001. Cotton and Wool Situation and Outlook Yearbook. Economic Research Service. CWS-2001 (November 2001). Washington, D.C.

US Department of Agriculture. 2002a. Cotton and Wool Outlook. Economic Research Service. CWS-05-02 (June 2002). Washington, D.C.

US Department of Agriculture. 2002b. Briefing room: cotton policy Economic Research Service. http://www.ers.usda.gov/briefing/cotton/policy.htm. Accessed on 12 September 2002.

US Department of Agriculture. 2002c. Farm Bill 2002 (http://www.usda.gov/farmbill/). Accessed 25 October 2002.

World Bank. 2000. World Development Report 1999/2000 – Entering the 21st Century. The World Bank. Washington, D.C.

World Bank. 2002. Subsidies hurt cotton producers. http://web.worldbank.org/WBSITE/EXTERNAL/NEWS/0,,contentMDK:20054420~menuPK:34459~pagePK:34370~piPK:34424~theSitePK:4607,00.html. Accessed 14 October 2002.







Maize

Cotton

Department







Atacora

76

37

Atlantique

100

0

Borgou

96

68

Mono

83

25

Ouémé

91

4

Zou

95

64

Quintile







Poorest

91

35

2nd

93

30

3rd


90

44

4th

88

38

Richest

90

28

Benin

89

34

    Source: IFPRI-LARES Small Farmer Survey.

    Table 2. Characteristics of cotton growers and other farmers



      Cotton

      Other





      growers

      farmers

      Total

      Household size

      10.1

      8.1

      8.8

      Dependency ratio

      49


      48

      48

      Sown area (ha)

      6.5

      3.2

      4.4

      Farm size (ha)

      5.3

      2.3

      3.3

      Expenditure (FCFA/person/year)

      99,437

      108,315

      105,203

      Food share



      57

      56

      57

      Home production share

      35

      24

      28


      Percent growing cotton

      100

      0

      35

      Cotton area (ha)

      2.3

      0

      0.8

      Cotton output (kg)

      2,559

      0

      897

      Cotton yield (kg/ha)

      1,084



      1,084

      Cotton sales (FCFA)

      505,584

      0

      177,217

      Poverty measures







      P0

      0.37

      0.42

      0.40

      P1

      0.095

      0.103

      0.100

      P2

       

       



      0.033

      0.037

      0.036

    Source: IFPRI-LARES Small Farmer Survey.




    Table 3. Short-run direct impact of reductions in cotton prices by department



      Atacora


      Atlantique

      Borgou

      Mono

      Ouémé

      Zou

      Total

      Per capita expenditure













      Base



      84,672

      139,290

      94,803

      88,034

      116,479

      110,108

      105,203

      10% reduction

      83,559


      139,290

      90,455

      87,547

      116,414

      106,115

      103,388

      20% reduction

      82,446

      139,290

      86,106

      87,060

      116,349

      102,123

      101,574

      30% reduction

      81,333

      139,290

      81,758

      86,573

      116,284


      98,130

      99,759

      40% reduction

      80,219

      139,290

      77,409

      86,086

      116,219

      94,137

      97,944

      Incidence of poverty (P0)













      Base



      0.54

      0.14

      0.44


      0.50

      0.44

      0.33

      0.40

      10% reduction

      0.55

      0.14

      0.46

      0.50

      0.44

      0.37

      0.42

      20% reduction

      0.56

      0.14

      0.53

      0.50

      0.44

      0.43

      0.44

      30% reduction


      0.56

      0.14

      0.58

      0.52

      0.44

      0.47

      0.46

      40% reduction

      0.57

      0.14

      0.62

      0.53

      0.44

      0.50

      0.48

      Poverty gap (P1)













      Base



      0.161

      0.034

      0.098

      0.131

      0.110

      0.071

      0.100

      10% reduction

      0.166

      0.034

      0.114

      0.134

      0.110

      0.081

      0.106

      20% reduction

      0.172

      0.034


      0.137

      0.137

      0.111

      0.097

      0.115

      30% reduction

      0.178

      0.034

      0.167

      0.140

      0.111

      0.118

      0.126

      40% reduction

      0.185

      0.034

      0.202

      0.143

      0.111

      0.144

      0.138


      Severity of poverty (P2)















      Base



      0.065

      0.012

      0.031

      0.046

      0.042

      0.022

      0.036

      10% reduction

      0.068

      0.012

      0.039

      0.048

      0.042


      0.025

      0.038

      20% reduction

      0.070

      0.012

      0.052

      0.050

      0.042

      0.031

      0.042

      30% reduction

      0.074

      0.012

      0.071

      0.052

      0.042

      0.041

      0.049

      40% reduction

      0.078


      0.012

      0.100

      0.055

      0.042

      0.057

      0.058

    Source: IFPRI-LARES Small Farmer Survey.




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