metadata: authors: 0: author: Brambilla,Maria Irene
1: author: Porto,Guido
count: Zambia
docna: 0: docna: Farm productivity and market structure :
            evidence from cotton reforms in Zambia
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Fertilizers,Pest Management,Nutrition,Food Security
teratopic: Macroeconomics and Economic Growth,Agriculture,Rural Development,Poverty Reduction
abstracts: cdata!: This paper investigates the impacts of
            cotton marketing reforms on farm productivity, a key element
            for poverty alleviation, in rural Zambia. The reforms
            comprised the elimination of the Zambian cotton marketing
            board that was in place since 1977. Following
            liberalization, the sector adopted an outgrower scheme,
            whereby firms provided extension services to farmers and
            sold inputs on loans that were repaid at the time of
            harvest. There are two distinctive phases of the reforms: a
            failure of the outgrower scheme, and a subsequent period of
            success of the scheme. The authors' findings indicate
            that the reforms led to interesting dynamics in cotton
            farming. During the phase of failure, farmers were pushed
            back into subsistence and productivity in cotton declined.
            With the improvement of the outgrower scheme of later years,
            farmers devoted larger shares of land to cash crops, and
            farm productivity significantly increased.
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repnme: repnme: Farm productivity and market structure :
            evidence from cotton reforms in Zambia
display_title: Farm productivity and market
            structure : evidence from cotton reforms in Zambia
listing_relative_url: /research/2006/05/6759729/farm-productivity-market-structure-evidence-cotton-reforms-zambia
topicv3: Agriculture and Food Security m1326205 435,Food Security m1326439 199,Crop Production m1326627 174,Fertilizers m1327354 175,Pesticides m1504403 2914
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Agriculture,Rural Development,Poverty Reduction
no_of_pages: 42
author_keys: 0: author_key: 1009083
1: author_key: 658883
model_extractions: WPS3904

# Farm Productivity and Market Structure: Evidence from Cotton Reforms in Zambia*

Irene Brambilla† Guido G. Porto‡

### Abstract

This paper investigates the impacts of cotton marketing reforms on farm productivity in rural Zambia. The reforms comprised the elimination of the Zambian cotton marketing board that was in place since 1977. Following liberalization, the sector adopted an outgrower scheme, whereby firms provided extension services to farmers and sold inputs on loans that were repaid at the time of harvest. There are two distinctive phases of the reforms: a failure of the outgrower scheme, and a subsequent period of success of the scheme. Our findings indicate that the reforms led to interesting dynamics in cotton farming. During the phase of failure, farmers were pushed back into subsistence and productivity in cotton declined. With the improvement of the outgrower scheme of later years, farmers devoted larger shares of land to cash crops, and farm productivity significantly increased.

JEL CODES: O12 O13 Q12 Key Words: state monopoly, privatization, marketing board

## World Bank Policy Research Working Paper 3904, May 2006

_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._

*We wish to thank A. Khandelwal for excellent research assistance. T. Jayne, J. Nijhoff, and B. Nsemukila allowed us access to the Post Harvest Survey data collected by the Central Statistical Office in Lusaka, Zambia. We thank J. Altonji, R. Betancourt, M. Duggan, P. Goldberg, C. Udry, and seminar participants at Di Tella, Lausanne, Maryland, Virginia and Yale for useful comments and discussion. Financial Assistance from the World Bank is greatly appreciated.

†Yale University and NBER. Department of Economics, Yale University, P. O.Box 208264, New Haven, CT 06520. Email: irene.brambilla@yale.edu.

‡ World Bank. Development Research Group, Mailstop MC3-303, 1818 H Street, Washington, D.C. 20433. Email: gporto@worldbank.org.
# 1 Introduction

In Africa, commodity markets were traditionally controlled by marketing boards, parastatal organizations that connected domestic farmers with product and input markets. Typically, these boards enjoyed monopsony power in purchases of agricultural products, and monopoly power in sales of agricultural inputs to farmers. In many countries, particularly in Sub-Saharan Africa, the public marketing boards were eliminated during the agricultural liberalization of the 1990s.

The Zambian cotton sector is a good example of this type of reforms. Until 1994, Lintco (Lint Company of Zambia) controlled the sector by selling inputs, buying cotton, giving credit, and facilitating access to technology, equipment and know-how. In 1994, the sector was liberalized, Lintco was privatized and entry into the market was encouraged. Sluggish initial entry gave rise to a phase of regional private monopolies. During this phase, the firms developed an outgrower scheme, vertical arrangements between firms and farmers whereby cotton ginners (i.e., the firms) provided inputs on loans that were repaid at harvest time. In 1998, as additional entry and competition ensued, the outgrower scheme began to fail. Farmers would take loans from one firm (for instance, an incumbent ginner) while selling to another (for instance, an entrant). As a result, credit prices increased, which made cotton production less profitable and led to increasing farmer default. Around 2000, things started to get better: further entry led to more competition and the outgrower scheme was highly perfected so that contracts between farms and firms were mostly honored. At present, the market is relatively unregulated and several firms seem to compete for locally produced cotton.

This paper investigates the dynamic impacts of cotton marketing reforms on farm productivity and crop choices in Zambia. There are several channels through which the reforms affected Zambian farmers. First, profitability of cotton production was affected, mainly through changes in input and product prices. Second, the uncertainty associated with cash cropping was affected through changes in the transparency of cotton marketing caused by the provision of extension services and technical assistance. Third, the transfer of technology (new seeds) and cotton know-how may have driven farmers to more efficient 1
methods of cultivation, increasing productivity and profitability. Further, changes in credit availability affected the cost of financing fixed capital production costs. Overall, these changes in prices, in access to inputs, and in efficiency of advice on crop husbandry led to changes in land allocation to cotton and in cotton yields. Our objective in this paper is to quantify these impacts.

The empirical analysis exploits unusual farm surveys, the Post Harvest Surveys (PHS) of the Zambian Central Statistical Office. These are repeated cross-sections of Zambian farmers covering the 1997-2002 period. Information on land allocation, yields, input use, and household characteristics across farmers in rural Zambia is collected. We use these data to set up an empirical model of cotton crop choice and cotton productivity. Our identification strategy relies on a modified difference-in-differences approach. First, we take differences of outcomes (i.e., cotton productivity) across the different phases of the reforms. Second, we use maize productivity to difference out unobserved household and aggregate agricultural year effects. Finally, since more productive cotton farmers are also more likely to allocate a larger fraction of their land to cotton production, we use cotton shares, purged of observed covariates, as a proxy for unobserved cotton-idiosyncratic productivity.

A simpler difference-in-differences model, without the correction for selection and thus without accounting for entry and exit into the agricultural cotton sector, would lead to biases in the estimates of aggregate productivity. Exit of low productivity farmers in the failure phase may bias productivity up, whereas entry of low productivity farmers in the success phase may lead to downward biases in measured productivity. The importance of these compositional effects has been emphasized in the industrial productivity literature (Olley and Pakes (1996), Pavcnik (2002)). We propose a different dynamic approach to take care of these effects when measuring productivity in agriculture and which can be applied to repeated cross-sections of farm-level data.

Our analysis provides valuable lessons on the interaction between export crops and the adoption of domestic policies in Sub-Saharan Africa. Further, by affecting market agricultural participation and cotton yields, our results have important implications for household income and consumption. These are critical issues in rural Zambia, where poverty 2
rates exceed 80 percent of the population.<sup>1</sup> Since the success of the reforms and the outgrower scheme varied from the initial phase to the final phase, we find rich dynamics in cotton markets. During the initial phase, the failure of the outgrower scheme led to a decline in the participation of households in cotton production and a decline in farm productivity of 45 to 53 percent. In contrast, the later phase of success induced farmers to increase the fraction of land devoted to cotton, and caused yields per hectare to increase by 20 to 21 percent with respect to the initial phase.

The paper is organized as follows. In Section 2, we review the main reforms in cotton markets. In section 3, we discuss the theory behind crop choices and farm productivity in Zambia. In section 4, we describe an empirical model of crop choices and farm productivity using the Post Harvest Survey farm data; and derive guidelines for the estimation of the impacts of the cotton marketing reforms. We discuss the results and assess the effects of the reforms in Section 5. In Section 6, we conclude.

# 2 The Zambian Cotton Reforms

Zambia is a landlocked country located in Southern Central Africa. With a population of 10.7 million and a per capita GDP of only 302 US dollars, Zambia is one of the poorest countries in the world. In 1998, for instance, the national poverty rate was 69.6 percent, with rural poverty at 82.1 percent and urban poverty at 53.4 percent. Nationwide, only around 4 percent of the income of rural households comes from the sales of non-food crops. Given the characteristics of the soil, cotton can only be grown in three Zambian provinces (the Eastern, Central, and Southern provinces). Where it is grown, cotton is a major source of income. Using data from the Living Conditions Monitoring Survey of 1998, we find that the share of cotton in income was 8.4 percent in the Central province, 9.5 percent in the Eastern province, and 2.8 percent in the Southern province. This makes cotton an important sector in rural Zambia.

The process of reform began in 1991, when the Movement for Multi-Party Democracy

> 1Poverty is widespread and deep in Zambia. For a comprehensive description of poverty trends, see Balat and Porto (2005).

3
(MMD) was elected. Faced with a profound recession, the new government implemented economy-wide reforms such as macroeconomic stabilization, exchange rate liberalization, trade and industrial reforms, and maize subsidies deregulation. More importantly for our purposes, privatization of agricultural marketing in cotton was also pursued.<sup>2</sup> Traditionally, the Zambian cotton sector was heavily regulated. From 1977 to 1994, cotton marketing was controlled by the Lint Company of Zambia (Lintco), a parastatal organization. Lintco set the sale prices of certified cotton seeds, pesticides, and sprayers, as well as the purchase price of cotton lint. Lintco had monopsony power in cotton purchases and monopoly power in inputs sales and credit loans to farmers.

In 1994, comprehensive cotton reforms began to take place. Most interventions were eliminated when Lintco was sold to Lonrho Cotton. Initially, a domestic monopsony developed early after liberalization. Soon, however, expanded market opportunities induced entry of private ginners such as Swarp Textiles and Clark Cotton. Because these three major firms segmented the market geographically, the initial phase of liberalization did not succeed in introducing competition, giving rise, instead, to geographical monopsonies rather than national oligopsonies.

At that moment, Lonrho and Clark Cotton developed an outgrower scheme with the Zambian farmers. In these outgrower programs, firms provided seeds and inputs on loans, together with extension services to improve productivity. The value of the loan was deducted from the sales of cotton seeds to the ginners at picking time. Prices paid for the harvest supposedly reflected international prices. Initially, repayment rates were high (around roughly 86 percent) and cotton production significantly increased. We called this the outgrower introductory phase.

By 1998, the expansion of cotton farming attracted new entrants, such as Amaka Holdings and Continental Textiles. Instead of the localized monopsonies, entrants and incumbents started competing in many districts. As competition among ginners ensued, an excess demand for cotton seeds developed. Several concurrent factors explain why, however, farmers could not realize the full benefits of the competition phase. First, some firms that were

> 2For more details on cotton reforms in Zambia, see Food Security Research Project (2000), and Cotton News (2002).

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not using outgrower schemes started offering higher prices for cotton to farmers who had already signed contracts with other firms as outgrower. This caused repayment problems and increased the rate of loan defaults. The relationship between ginners and farmers started to deteriorate. Second, world prices began to decline, and farm-gate prices declined as a result. After many years of high farm-gate prices, and with limited information on world market conditions, farmers started to mistrust the ginners. As the relationship between farmers and firms deteriorated, default rates increased even further. In consequence, firms raised loan prices and farmers end up receiving a lower net price for their cotton production. We called this the outgrower scheme failure phase.

Partly as a result of this failure of the outgrower scheme, Lonrho exited the market in 2000. A new major player, Dunavant Zambia Limited, entered instead. Dunavant and competitors, Clark Cotton Limited, Amaka Holdings Limited, Continental Ginneries Limited, Zambia-China Mulungushi Textiles, and Mukuba Textiles, worked to improve the scheme. Farmers and firms understood the importance of honoring contracts and the benefits of maintaining a good reputation. The outgrower programs were perfected and there are now two systems utilized by different firms: the Farmer Group System and the Farmer Distributor System. In the latter, firms designate one individual or farmer as the distributor and provide inputs. The distributor prepares individual contracts with the farmers. He is also in charge of assessing reasons for loan defaults, being able, in principle, of condoning default in special cases. He is in charge of renegotiating contracts in incoming seasons. In the Farmer Group System, small scale producers deal with the ginneries directly, purchasing inputs on loan and repaying at the time of harvest. Both systems seem to work well. We call this the outgrower scheme success phase.

# 3 Determinants of Cotton Productivity

In this section we review the literature on the determinants of agricultural productivity. We define productivity as yields per hectare in physical units. Hence, our productivity definition differs from the standard definition used in industrial analysis (usually value added 5
at constant prices). A physical definition of productivity is economically more meaningful because it only reflects technology, while value added depends on market conditions via prices.

In a model with decreasing returns to fixed factors of production, a key determinant of cotton yields per hectare is the size of the plot allocated to cotton. A family farm, for instance, may obtain higher yields per unit of land if the scarce labor resources are utilized in smaller plots. Major determinants of the size of land allocated to cotton can be found in the literature on crop choice. There are different factors determining this selection process, and sometimes separate strands of literature explore the different dimensions of the problem. The theoretical problem is straightforward: endowed with a fixed amount of land, the farmer must choose the fraction of resources to be allocated to food crops (mainly maize) and cash crop (mainly cotton).

A key factor is the trade-off between profitability and risk, as in a standard portfolio allocation choice (Rosenzweig and Binswanger, 1993). Thus, relative product prices (cotton, maize) and input prices (seeds, fertilizers, pesticides) affect the choice of crops. It may be argued that cash crops show higher returns but are riskier than food crops, so that different attitudes towards risks (degree of risk aversion) can help explain the selection (Binswanger and Sillers, 1983; Dercon, 1996; Shahabuddin et al., 1982). Since direct measures of risk aversion are not available, we need to proxy them with relevant household characteristics. In particular, the attitude towards risk can be affected by variables such as household wealth, household size, and household composition.

Often times, growing cash crops requires a start-up lumpy investment that may constrain the allocation of resources (Eswaran and Kotwal, 1986; Dercon, 1996). Sometimes this takes the form of capital investment in machines, tractors, animals. In addition, there might be large initial costs of input purchases such as new seeds or expensive pesticides or sprayers. In the presence of well-developed credit markets, these fixed costs could be easily covered. When credit constraints are binding, however, the ability to borrow and the availability of collateral can be determinants of the choice of crops.

An additional important argument claims that the allocation of resources is affected by 6
missing markets (de Janvry, Fafchamps, and Sadoulet, 1991). In fact, whereas cash crops must be sold at the market price, maize can be consumed in the family to provide food security. In many less developed countries, concerns for food security are of the utmost importance. Families will want to secure the food needs of the family first, and then move to cash cropping. If food markets were well-developed, then food risk would not be an issue because households could grow cash crops, sell them at the market, and use the proceeds to purchase food. If food markets are missing or are thin and isolated (so that ex-post food prices are high and volatile), then a strategy of self-sufficiency in food production may be optimal (Fafchamps, 1992; Jayne, 1994). This suggests two sets of empirical determinants of cotton choice. First, regional characteristics, such as the availability of food markets, the number of food producers in the area, regional infrastructure, and other variables that may affect how thin local food markets are, may be relevant. Second, in the presence of food security issues, the determinants of food needs may be important. Examples include household size, household composition (so that, for instance, households with a larger fraction of children would have larger food needs), land tenure, and non-farm income.

The switch from subsistence to cotton can be interpreted as technology adoption in agriculture. There is a large literature that explores the determinants of adoption (Besley and Case, 1994; Foster and Rosenzweig, 1995; Conley and Udry, 2004). This literature identifies human capital (measured by education, gender, and age) as a major determinant of technology adoption. In addition, these authors argue that social capital, learning by doing, and learning externalities are important determinants, too. In this setting, technology adoption depends on the fraction of the neighbor farmers that have already adopted.

Many of these factors affect productivity directly as well (and not only through the cotton land allocation). For instance, the human capital of the farmer, as measured by his age, gender, and education, surely affects yields. Technology, in the form of crop know-how, high yield seeds, and efficient agricultural tools (like tractors or sprayers) may also lead to a better use of resources and to higher productivity. Similarly, if the production of the cash crops involves the initial purchase of inputs, capital goods, and machines, lack of credit and collateral (determined by land size, assets, wealth, savings, off-farm income, 7
and remittances) may hinder access to more efficient resources like better seeds, sprayers or animals (i.e., oxen). By the same token, access to local infrastructure and public goods and capital can increase yields per hectare. Further, there is an important role played by agricultural extension services and technical advice on crop husbandry, land use, and general agricultural assistance that allow farmers to achieve higher productivity. A similar role can be attached to social capital and learning externalities.

We are not only interested in the direct determinants of agricultural productivity, but also in the impacts of the marketing reforms. There are several channels that can be advanced. The provision of credit and of inputs on loan, which may allow farmers to better combine factors of production, may depend on the phases of the reform. During the collapse of the outgrower scheme, credit became more expensive to farmers thus hindering productivity. When the scheme improved, credit became cheaper, probably causing productivity to increase. Similarly, the privatization of the ginning industry may cause firms to adopt better cotton seeds (with higher yields) and more efficient pesticides and fertilizers. This would work as technological advances that firms pass-through to farmers, leading to increases in farm productivity. Finally, the outgrower scheme involved an improvement in the provision of extension services, particularly in terms of information about marketing. This could help eliminate some uncertainty about the crop. In addition, more efficient extension services, providing advice on crop husbandry and know-how, can help farmers increase yields.

# 4 Data and Estimation Strategy

In this section, we describe the data and we develop the empirical model to estimate the impacts of the cotton marketing reforms on farm productivity.

## 4.1 The Post Harvest Survey

We use farm surveys called the Post Harvest Survey (PHS). These data are collected by the Zambian Central Statistical Office (CSO). The surveys are not panel data but rather a set of repeated cross-sections. We have annual data available for the period 1997-2002.

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The survey is representative at the national level, but in this paper we only use the data pertaining to cotton producing regions: the Central, Eastern, Southern and Lusaka provinces. CSO gathers information on land tenure, land usage (allocation), output in physical units, and household characteristics such as demographic composition, age of head, and housing infrastructure. There are also limited data on farm assets and inputs.

Table 1 provides an overview of the relevant sample sizes, by year and by province. Around 600-700 households are interviewed in the Central province, around 1,200, in the Eastern province, around 800 in the Southern province, and around 200 in Lusaka. Table 2, which reports the fraction of farmers involved in cotton production, confirms that these are the major cotton producing areas. Significant percentages of cotton farmers are only observed in the Central, the Eastern, and the Southern provinces. Cotton participation is largest in the Eastern province (39 percent in 2002, for instance), then in the Central province (20 percent in 2002), and finally in the Southern province (12.6 percent). There are some, but not many, cotton producers in the Lusaka region, too. In the remaining provinces, the percentage of households involved in cotton is virtually zero.

Table 2 reveals interesting dynamic patterns that we explore below. During the introduction phase, which spans the years 1997 and 1998, cotton participation is relatively stable in all provinces (although a declining pattern may be discernible). The failure phase, which spans the 1999-2000 period, shows lower participation rates. This is particularly evident in 2000: in the Central province, for instance, cotton participation drops from 22.6 percent in 1998 to 10.3 percent. Similarly, participation declines from 32.7 to 20.4 percent in the Eastern province, from 10.7 percent to 4.3 percent in the Southern provinces, and from 3.3 percent to 0.4 percent in Lusaka. The success phase correlated with entry into cotton: the percentage of cotton growers increases significantly in all provinces.

In Table 3, we report data on the fraction of land allocated to cotton. In 2002, for instance, an average farmer in the Eastern province allocated around 17.2 percent of his land to cotton; in the Central province, the fraction is 10.5 percent. Instead, cotton adoption is less widespread in the Southern province, where only an average of 5.6 percent on land is allocated to cotton. The dynamics of cotton adoption are also revealed in Table 3. The 9
fraction of land allocated to cotton sharply declines in 1999 and 2000 and then increases in 2001 and 2002.

Finally, Table 4 describes the evolution of cotton yields per hectare. The figures are in logarithms, so that changes from one year to the other can be interpreted as growth rates. At the national level, cotton yields increased from 1997 to 1998, and then declined during the failure phase. In fact, productivity dropped by 32 percent from 1998 to 2000. However, productivity in 2000 is comparable to productivity in 1997. The success phase brought productivity up in and 2001 and 2002. Notice that there were interesting differences in regional dynamics. In the Eastern and Southern provinces, for instance, productivity changes tracked those observed at the national level. However, in the Central province, productivity increased steadily from 1997 to 2000 and then declined in the success phase of 2001 and 2002.

# 4.2 The Empirical Model

Productivity is defined in physical units. Let yht<sup>cdenote the volume of cotton production per</sup> hectare (in kilograms) produced by household h in period t. The log of output per hectare is given by Here, x<sup>c</sup> ht<sup>isavectorofhouseholddeterminantsofcottonyieldsincludingtheageofthe</sup> household head, his education, the size of the household, household demographics, input use, assets, the size of the land allocated to cotton, farm size, and district dummies. We also include cotton prices at the district level among the explanatory variables. The reason to include prices in a production function is that the labor input is imperfectly measured and does not account for hours of work and effort, for example. When prices are higher, it is likely that farmers will exert more effort, especially in weeding and irrigation, and that yields per hectare will be higher.

We model the productivity effects of the reforms with two variables, Ft<sup>1andF</sup> t<sup>2.F</sup> t<sup>1isa</sup> 10
dummy variable that captures the second period of the reform, the outgrower scheme failure phase of 1999-2000, and Ft<sup>2isanother dummy thatcapturesthethird period ofthereform,</sup> the outgrower scheme success phase of 2001-2002. The impacts of these phases of the reform are measured relatively to the excluded category, which is the outgrower scheme introductory phase of 1997-1998.

The model includes a number of unobservables, such as regional effects (included in x), year effects, It, and idiosyncratic household level unobservables ηht, φht, and �ht.

The regional effects include market access, local infrastructure, local knowledge and access to credit; they are controlled for with district dummies. The year effects It capture aggregate agricultural effects and other shocks that are common to all farmers in a given period t. In equation (1), these effects cannot be separately identified from the reform dummies F<sup>1</sup> and F<sup>2</sup> . To deal with this, we propose below to model productivity in other crops (mainly maize) to difference out time varying factors that affect productivity in agriculture.

The household level effect has three components: a farm effect, η, a cotton-specific effect, φ, and a random shock �. The farm effect η captures all idiosyncratic factors affecting general agricultural productivity in farm h that are observed by the farmer when making input and land allocation decisions but not observed by the econometrician and thus not included in x. It includes land quality, know-how, and other factors that affect productivity in all crops. The cotton-specific effect is a combination of unobserved factors that affect productivity in cotton, including ability and expertise in cotton husbandry and suitability of the land for cotton. Finally, the random shock � is unobserved both by econometrician and farmer and thus, does not affect the farmers’ decisions.

There are two problems with the household effects. First, both η and φ are observed by the farmer when making input decisions. Hence, some of the variables included in x may be correlated with these unobservables. In addition, entry and exit into cotton farming depend on these unobservables as well since farmers’ decisions on land allocation to different crops may be based on η and φ. More importantly for our purposes, this entry/exit component affects the estimates of the reform dummies by altering the composition of farmers that produce cotton in each time period.

11
A panel data set would allow us to account for both idiosyncratic effects assuming that they were fixed over time.<sup>3</sup> The Post Harvest Survey, however, is a repeated cross section of farmers. We thus need additional modeling to deal with the unobserved effects. We propose to model agricultural productivity in maize to control for η (and the year effects It, as discussed above) and to model the share of land devoted to cotton to control for φ. In what follows, we discuss these two methodological features of our empirical model.

# Productivity in Maize Farming

Our empirical analysis relies on a modified difference-in-differences approach. The simple difference in average cotton productivity after controlling for farm level variables in any two of the reform phases (i.e., the introductory, the failure, and the success phases) is not a consistent estimate of the impacts. It does not take into account the general trend or time-varying aggregate effects in agriculture, It. In addition, there are unobserved idiosyncratic farm effects that can affect inputs choices, ηt. To account for these household and agricultural effects, we perform a difference-in-differences analysis using a model of maize productivity.

In principle, the second differencing works because maize is a major food crop that is produced by virtually all cotton farmers.<sup>4</sup> Table 5, which reports the percentage of households that grow maize, provides evidence supporting this. We find that in the cotton provinces, maize is grown by virtually all households. Participation in maize production is always above 90 percent in the relevant regions. In the Eastern and Lusaka provinces, the percentage of maize producers is nearly 100 percent. Table 6 reports additional evidence that further supports our differencing strategy. We report the percentage of farmers that grow maize, conditional on being cotton growers. These shares are nearly 100 percent in the three main cotton-growing provinces.

> 3The unobservables η and φ are indexed by ht because, given the cross-sectional nature of the data, the unit of observation is a household-time period combination. However, if the data were a panel, η and φ would be indexed by h only.

> 4A key characteristic of cotton farming in Zambia is its scale: cotton is grown by smallholders, family farms endowed with small farms, usually smaller than four hectares and with an average size of around 2 hectares.

12
Yields per hectare in maize, yht<sup>m,aregivenby</sup> Maize productivity depends on covariates x<sup>m</sup> ht<sup>, including regional effects, the agricultural year</sup> effects, It, and the farm effects ηht.<sup>5</sup> By taking differences, we get Here, the observed household covariates xht included in the estimation are based on the determinants of productivity discussed in the previous section, such as household demographics, human capital, determinants of household collateral, determinants of food needs, etc. It also includes the relative price of cotton to maize at the district level and regional dummies, which are not cancelled out in the differencing because we allow the regional effects to affect productivity in cotton and maize differently. For example, to the extent that the district dummies capture local market access effects, we allow marketing conditions to affect cotton (a cash crop activity) and maize (a mostly subsistence crop) differently.

The coefficients α1 and α2 measure the impacts of the different phases of the reforms on cotton productivity. There are two important identification assumptions. First, we assume that the agricultural effects, It affect cotton and maize productivity proportionately. This is a consequence of the logarithmic specification that we adopt. In other words, the agricultural effects are assumed to have the same effect on growth of cotton and maize output per hectare.<sup>6</sup> This is an instance of the parallel trend assumption of difference-in-differences models. It means that we can use the trend in maize productivity to predict the counterfactual productivity in cotton in the absence of the reforms. Although this assumption cannot be

> 5In an alternative interpretation of this model, there are unobserved cotton and maize effects, and φht captures their relative importance.

> 6Of course, the level effect is going to be different. This is reasonable, since physical units of cotton and maize are not comparable.

13
tested directly, we can provide indirect evidence supporting it. In particular, the assumption implies that we could use productivity in other crops to difference out the agricultural effects. Under the maintained hypothesis, the trend in maize productivity and the trend in the productivity of other crops should be similar.

Figure 1 provides evidence that supports this. Each panel compares the trend in maize productivity (solid line) with the trend in alternative crops (broken line). These are sorghum, millet, sunflower, groundnuts, and mixed beans. We observe that, perhaps with the sole exception of groundnuts in 2001, the trends in all these crops are very similar. In the regression analysis, we use maize as control because, as opposed to the other crops, virtually all household produce it.

The second critical assumption of our difference-in-differences model is that the cotton reforms did not affect maize productivity. Theoretically, agricultural reforms of the type studied here could affect productivity in all crops through resource allocation (i.e., labor, effort, fertilizers, pesticides), wealth effects, and capital accumulation. In addition, there may be indirect channels, through, for example, access to credit. If the reforms affect farms by providing cotton inputs on loan, household resources to purchase seeds or fertilizers in maize may be released. To the extent that the regression includes these variables in the observed covariates x, these effects will be accounted for. In the regressions, we include measures of labor, agricultural tools, and fertilizers, land allocation.

Notice, however, that for some determinants, such as labor allocation, we only have household level data (as opposed to crop level data). This raises the possibility that the reforms affected maize productivity and that α1 and α2 are measures of the impacts of the reforms on cotton productivity relative to maize productivity. We could rule out this possibility by providing additional evidence of the trends in maize productivity in those provinces that were not affected by the cotton reforms. These trends are plotted in Figure 2. The solid line corresponds to the trend in maize productivity in reform provinces. Instead, the broken line displays the trend in non-reform provinces. It can be seen that the parallel trend holds in this case, except in 2002. Overall, this indicates that the differencing will identify the impact of the reform on cotton productivity only.

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# Entry and Exit in Cotton Farming

In most applications, the difference-in-differences approach described above would be enough. In the present case, there may be additional cotton-specific unobserved factors at the farm level, such as suitability of the land for cotton production and know-how of cotton husbandry, that affect productivity in cotton. This heterogeneity leads to different entry-exit decisions regarding cotton production, which alters the composition of the group of farmers that produce cotton in each of the reform phases. The estimates of the changes in productivity at the aggregate level comprise both the changes in productivity at the farm level and the changes in the composition of the farmers that produce cotton in each time period. The consistent estimation of the changes in productivity at the farm level requires that we control for entry and exit.

If there are fixed costs in cotton production, then cotton will only be profitable if productivity is high enough. This means that there is a cut-off (which depends on prices, market conditions, infrastructure) such that farmers with productivity above this cut-off will enter the market and farmers below the cut-off will not enter (or exit, if they were in the market already). When the reforms increase the profitability of cotton, for instance, lower productivity farmers may enter the market. Failure to control for this may lead to inconsistently lower estimates of productivity at the farm level (thus leading to a downward bias in the estimates of any productivity increases). In contrast, in periods of induced exit, farmers with lower unobserved productivity will be more likely to abandon cotton production. In consequence, measures of productivity that do not control for these dynamic effects may be artificially high (thus leading to downward biases in the estimates of productivity declines).

Figure 3 clarifies these dynamics. The graph shows relative cotton productivity y as a function of unobserved cotton-specific effects φ — for simplicity of exposition we assume that the exogenous part of x is the same for all farmers and that the only difference across farmers is given by φ. Productivity is increasing in φ since better land quality or higher cotton skills will lead to higher output (for a given usage of other inputs). The horizontal line at <u>y</u> denotes the cut-off; for simplicity, we assume here that it does not vary with the reforms. It follows that we can determine a cut-off for the unobservables, denoted φ. The line denoted 15
y0 represents the cotton productivity function before the reform. Average productivity is, say, E(y0), the average of y for levels of φ > φ.

Consider the effects of the failure of the outgrower scheme. If cotton productivity is negatively affected, the productivity curve shifts down to y1. Assuming a fixed cut-off <u>y, the</u> 0 7 cut-off for the unobservables increases to φ . This induces the “exit” of those farmers with 0 relatively low levels of φ, between φ and φ. Average productivity drops to E(y1). But the decline in individual productivity is larger. The right quantity is the average productivity, computed along the curve y1, and integrating over values of φ above the cutoff before the reform φ. This is given by E(yr).

The difference in differences model described so far estimates changes in average productivity given by E(y0) − E(y1). To estimate the true effect at the farm level, E(y0) − E(yr), we need to account for the role of unobserved cotton effects.<sup>8</sup> Entry-exit effects have been extensively considered in industrial productivity analysis.<sup>9</sup> In this paper we extend this literature by developing a method to deal with entry and exit in the estimation of agricultural production functions and crop choices. Furthermore, whereas the industrial organization literature relies on longitudinal surveys, our method can be used in repeated cross-sections.

Our solution to this problem is to construct proxies for the unobserved productivity parameter. Our method exploits the idea that since households with high φ are more productive in cotton, they are also more likely to devote a larger share of their land to cotton production. This means that we could use land cotton shares as a proxy for the unobservable φht in (3). In practice, consistent estimation requires that we purge these shares of the part explained by observed determinants of cotton choice. 7It is not necessary to assume that <u>y</u> remains fixed after the reform. Our intuition remains unchanged. 8Notice that omitting φ not only leads to inconsistencies because of the entry-exit effects, but also may induce correlation between some variables in the vector x and the error term in the difference-in-differences model. For example, the choice of inputs, such as labor or pesticide use, will depend on φ (so that higher levels of unobserved productivity may be positively correlated with input use) The model in (3) takes care of these biases.

> 9See Olley and Pakes (1996) and Pavcnik (2002) for models of industrial productivity with entry and exit with panels of firms.

16
Let a<sup>c</sup> ht<sup>bethefractionoflandallocatedtocotton.A generalmodelofthesesharesis</sup> where z is a vector of regressors which includes district effects that affect selection into cotton production. For instance, we use the district dummies to capture access to market and local infrastructure that facilitates farmer participation in market cash agriculture. The function m allows regressors z and unobservables φ to affect the shares a non-linearly.

We begin by considering the simplest model with a linear functional form Estimation of (5) is straightforward, except for the fact that the share of land devoted to cotton is censored at zero. This means that OLS may be inconsistent. A simple solution is to implement a Tobit procedure. More generally, we explore a more semi-parametric estimation of (5) by using a CLAD (censored least absolute deviation) model. Notice that, provided the right specification for the model is used, consistency follows because the regressors z are exogenous to φ. This requires that fertility, family composition, or farm size do not depend on unobservables such as cotton-specific ability or land quality. Importantly, since we use data on all households to estimate (5), this equation does not suffer from a selection problem like the one we are attempting to control for in the productivity model.

The allocation of land to cotton depends on several factors that we need to account for. In particular, the selection into cotton depends on the reform. This means that we should include F<sup>1</sup> and F<sup>2</sup> in (5). Cotton choices depend on output and input prices, too. Unfortunately, we do not have information on prices at the farm level. To the extent that prices vary by time, or by region, however, we can account for them with year or regional dummies. In practice, we estimate a different model like (5) in each of the six years from 1997 to 2002 (notice that γt is indexed by t in (5)). This means that we will not be able to separate the effects of the reforms from the effects of changes in international prices on land 17
allocation, but we will be able to control effectively for φ in the productivity model.<sup>10</sup> Finally, note that identification of φ requires that the selection into cotton is affected by the same unobservables that affect cotton productivity. In principle, it would be possible to argue that there are additional unobservable factors that affect the selection into cotton. We extend our results to the case where these unobservables in section 5.1.

Plugging in the estimates of φ in (3), the productivity model is This modified difference-in-differences approach is consistent with entry and exit into cotton farming.

# 5 Results

Our benchmark productivity results are reported in Table 7. Columns (1) and (2) report estimates of equation (1), that is, a productivity model that does not control for unobservables such as It, ηht and φht. In these regressions, we use data from the three main cotton provinces, the Central, the Eastern, and the Southern provinces. The main findings indicate that small farms are more productive; there is also evidence in favor or decreasing returns to scale in cotton since there is a negative association between the size of land allocated to cotton and cotton yields per hectare. The negative association between farm size and household agricultural productivity has long been established in the literature (Feder, 1985; Benjamin, 1994). In addition, households with male heads are more productive in cotton, as are larger households. Assets (such as ploughs or livestock) are positively associated with yields. The effects of inputs such as basal and top-dressing fertilizers are not as strong as expected.<sup>11</sup> The dynamics of cotton productivity are closely linked to the dynamics in market

> 10We also consider the possibility of estimating different selection models in different years and in different provinces. This would control for idiosyncratic provincial effects in cotton adoption. We report results in the next section.

> 11One reason for these result is that both basal and top-dressing fertilizers are actually used in maize more than in cotton. See the discussion below for more details.

18
structure: compared to the introductory phase, productivity is lower in the failure phase and higher in the success phase. The estimated magnitudes are important: in the failure phase, productivity declines by 11.5 percent (column 2) and increases by 14.7 percent in the success phase.

Columns (3) and (4) report results of the difference-in-differences model (equation (3)). The estimated impacts of the marketing reforms are significantly higher. On the one hand, productivity during the failure phase declines by 46.9 percent (column 4) instead of by 11.5 percent (column 2). This is because there is a positive trend in yields (net of the effects of covariates) from the introductory to the failure phase. On the other hand, the increase in productivity during the success phase is of around 19 percent (column 4) as opposed to 14.7 percent (column 2). This suggests a declining trend in yields from the introductory to the success phase. Interestingly, this means that, when comparing the failure and success phases, productivity in fact increases by a whopping 65.9 percent.

Table 8 reports the productivity results corrected for entry and exit.<sup>12</sup> Column (1) reproduces the estimates from column (4) of Table 7, which does not include controls for φ. Columns (2) and (5) use a Tobit model to estimate the selection equation, columns (3) and (6) use a linear model, and columns (4) and (7) use a CLAD model. Model 1 and Model 2 in Table 8 differ in the list of covariates: both models share the same regressors, but Model 1 measures assets (harrows and ploughs) in monetary units and Model 2 measures them in physical units. Notice that since the regression includes an estimated regressor, φ<sup>b</sup> , the standard errors should be corrected. We estimate them with a bootstrap model with 100 repetitions.

We confirm that productivity declines during the failure phase (i.e., α1 is negative and significant), and increases during the success phase (i.e., α2 is positive and significant). The results are robust to the selection model used to build the proxy for φ, i.e., the linear model, the Tobit model or the CLAD model. The decline of cotton yields per hectare during the failure phase ranges from 50.3 to 52.1 percent. The increase during the success phase, from 18.3 to 19.3 percent.

> 12The first-stage results of different selection models are discussed in Appendix 1.

19
Failure to control for φ can damage the estimated impacts of the reforms on productivity at the farm level, particularly during the failure phase. In column (1), we report a decline in average productivity of 46.9 percent from the introductory phase to the failure phase. When exit is accounted for, the decline in productivity is, instead, of around 50 percent. This means that although the average aggregate productivity in the economy declined by 46.9 percent, the average productivity of a typical cotton farm declined by 50 percent. In other words, average productivity is 3 percent higher than what it would be had the most unproductive farmers (in terms of φ) not exited the market.

It is interesting to notice that, during the success phase, the reforms increase yields by around 19 percent, comparable to the findings in column (1). That is, the estimated α2 do not depend on whether the regression controls for φ or not. This means that entry is not affecting the estimated changes in average productivity by much. One explanation of this finding is that entry is much more costly than exit. When unobservables φ are such that cotton becomes unprofitable, farmers may exit at no significant cost. Instead, when cotton becomes profitable, there might still be impediments to entry.

# 5.1 Further Specification Issues in Selection

So far, we have assumed that φ enters additively in the land cotton shares equation (5) and that the same combination of unobservable factors affects cotton productivity —equation (3)— and the cotton share decision — equation (5). However, there are reasons to believe that the residuals from (5) are a non-linear function of the unobservables φ, or that there are unobserved factors in addition to φ that affect the cotton share decision.

To address the first issue, we can write where ρht = ft(φht). The productivity model is 20
where gt(ρht) = ft<sup>−1(ρht).</sup> This model can be estimated using a partially linear model (Robinson, 1988). In the first stage, both ln y and all of the covariates x are regressed on ρ non-parametrically. This is done using a locally weighted linear regression (Pagan and Ullah, 1999). In the second stage, we estimate residuals for all these variables using the non-parametric estimates. Finally, a linear OLS regression between residuals is run. This procedure recovers the linear part of the model, β, α1, and α2.<sup>13</sup> The results of the partially linear model are reported in Table 9. In all our specification, we find that the non-parametric correction does not affect the estimates of the impacts of the reforms. Concretely, the failure phase leads to a decline in productivity of 50 percent, whereas the success phase leads to increases of productivity of around 19 percent.

Regarding the additional unobservables in the choice of cotton share, let us assume that the cotton land share model is given by This equation includes uht, together with φht, in the error term to capture potential additional unobservables that affect the selection into cotton but not the productivity equation. The implication of this model for our purposes is that our proxy of unobserved productivity is now estimated with error (see Altonji, 1986).

The problem resembles estimation under measurement error. In principle, these problems are corrected with instrumental variables. Notice that, in our case, we need to instrument b φht + buht. Since we do not have instruments for this variable, we follow the procedures used in Monte Carlo analysis of measurement error. If we knew the variance of the measurement error —that is, the variance of u—, then it would be in principle possible to correct the OLS estimates to get consistent estimates. The problem is precisely that the variance of u is not known. In Monte Carlo studies, the model is estimated under different assumptions about the variance. If the estimated coefficients do not change much with σu<sup>2, then there is evidence</sup> that the measurement error is not generating significant inconsistencies.

> 13The non-linear function gt(·) can be estimated with a non-parametric regression of ln y, purged of the observed covariates x, F1, and F2, on ρ.

21
Our results are reported in Table 10. We report the estimates of α1, α2 and a0 under eleven different assumptions about σu<sup>2.Weconfirmthatthecoefficientsofthephasesof</sup> the reform, and the unobserved productivity remain relatively unaffected by the potential measurement error. We believe that this is evidence that the problem can be safely discarded and that our results are not sensitive to it.

# 5.2 Robustness

Our robustness analysis follows along three lines: sensitivity to the definition of the reforms, sensitivity to the inclusion of Lusaka growers in the sample, and differences in regional analysis.

Table 11 reports estimates for different definitions of the reforms. The dynamics generated by the elimination of the marketing board are generally complex, and it may be difficult to assign different years to the different phases of the reforms. Our estimates can thus be sensitive to the definition that is being used. To examine the robustness of our results, we re-estimate the model using two additional definitions of the reforms. First, we redefine the failure phase as including only the year 2000 (dummy denoted R<sup>1</sup> ) and including 1999 in the introductory phase. As shown in section 4.1 (Tables 2, 5 and 6), the drop in the share of land allocated to cotton declines much more markedly in 2000 than in 1999. Similar observations characterize the trends in cotton yields. The success phase still includes 2001 and 2002 (with dummy defined by R<sup>2</sup> ). In our second redefinition, we measure the impacts of the reforms by including year dummies, thus allowing the effects of the reforms to vary year by year. In this model, there are six phases in the dynamics that we estimate.

We estimate two different models in Table 11. Columns (1) to (3) use a Tobit procedure and Model 1 of Table 8 (measuring assets in monetary units) for the estimation of a<sup>c</sup> ht<sup>;</sup> columns (4) to (6) also use a Tobit model, but adopt Model 2 (assets in physical units) of Table 8. Our qualitative conclusions remain unaffected. There is a decline in productivity in 2000 of around 42 percent in both specifications. Also, there is an increase in productivity in the success phase of 18 percent. More detailed patterns can be discerned when we use year dummies to measure the different phases of the reforms. Compared to 1997, we find that 22
productivity first increases in 1998 and declines in 1999 to 1997 levels. We still find a large decline in productivity in 2000, of around 37 percent. During the success phase, productivity follows an increasing trend: output per hectare is 17 percent higher in 2001 than in 1997, and 45.7 percent higher in 2002.

In Table 12, we reproduce Table 11 but we include Lusaka in the estimation. There are fewer cotton growers in Lusaka, but enough to allow us to check if results are sensitive to the inclusion of those farmers in our model. Table 12 confirms that the estimated impacts are essentially unchanged. For Model 1, for example, the coefficients of F<sup>1</sup> and F<sup>2</sup> in column (1) are −0.498 and 0.199, respectively, close to what we found before (−0.503 and 0.193 in Table 8, column 2). Similarly, the coefficients of R<sup>1</sup> and R<sup>2</sup> (column 2) are −0.421 and 0.189 (similar to −0.426 and 0.185 in Table 11, column2). Finally, the pattern of year phases are also similar to those estimated before (column 3): there is an increase in productivity in 1998, a decline in 1999 and a sharper decline in 2000, and finally significant increases in 2001 and 2002.<sup>14</sup> We have shown evidence indicating that cotton productivity followed different patterns in different regions of the country. In Table 13, we report estimates of the model that account for these differences. Concretely, we estimate a separate model for each of the three main cotton producing provinces. The first three columns of the table reproduce the benchmark results at the national level. When the original definition of the phases of the reforms is used, F<sup>1</sup> and F<sup>2</sup> , the estimated regional coefficients track the national coefficients: they are negative and significant during the failure phase and positive and significant during the success phase. Notice, however, that the magnitudes are very different. In particular, much more pronounced changes are observed in the Southern province. For example, whereas the decline in productivity during the failure phase is of around 42 and 44.5 percent in the Central and Eastern provinces, respectively, it is of 96.5 percent in the Southern province. This means that the coefficient of F<sup>1</sup> almost double (in absolute value) in the Southern province. The coefficient of F<sup>2</sup> varies from region to region as well, from 0.303 and 0.106 in the Central and Eastern provinces, to 0.554 percent in the Southern provinces.

> 14These results are robust to the specification and model used in the cotton land share (acht<sup>)equation</sup> (Model 1 or Model 2, for instance).

23
Some interesting differences are also observed when we use alternative definitions of the phases of the reforms. This is specially so under R<sup>1</sup> and R<sup>2</sup> .<sup>15</sup> In the Central province, for example, there is a large increase in productivity during the success phase, but no statistically significant changes during the failure phase of 2000. In contrast, in the Eastern province there is a significant decline in the failure phase of 2000 (R<sup>1</sup> ), but there is not any significant change during the success phase. Finally, the Southern province shows a sharp decline (of 34.8 percent) in failure phase, and a sharp increase (of 55.7 percent) in the success phase.

# 6 Conclusions

This has paper has investigated the relationship between market structure in cotton and farm productivity. We have used unique farm surveys for rural Zambia, the Post Harvest Survey, spanning the 1997-2002 period. We have exploited a marketing reform whereby the Zambian government eliminated the cotton marketing board in 1994. Entry and exit into the market and the development of the outgrower scheme gave rise to interesting dynamics in market organization. Starting with a baseline period in 1997-1998, there was a subsequent failure of the outgrower scheme in 1999 and 2000. Further entry and competition into the sector led to an improvement in the outgrower scheme in 2001-2002.

We have estimated the impacts of the different phases of the reforms by building a modified difference-in-differences estimator. The first differences are taken across the different phases of the reforms. An equation of maize productivity, a major staple produced by virtually all households, provides the second difference. In the presence of entry and exit into cotton farming, and in the presence of cotton-specific farm unobservables, the estimated average productivity can be biased. To correct for these dynamics effects, we introduce a model of selection into cotton that provides proxies for unobserved productivity. These proxies are given by land cotton shares (i.e., the shares of total land allocated to cotton) “purged” of the effects of observed covariates. This modified difference-in-differences model delivers consistent estimate of the impacts of the reforms on farm productivity.

> 15R1 includes only 2000, and R2 includes 2001 and 2002 (as does F 2). The difference is that the introductory phase now includes 1999.

24
We find interesting dynamic effects of the marketing reforms. Compared to the introductory phase of 1997-1998, the failure of the outgrower scheme caused farmers to move back to subsistence and led to significant reductions in farm productivity. The improvement of the outgrower scheme in 2001-2002, reverted these trends: farmers allocated more land to cotton, and productivity (i.e., yields per hectare) significantly increased.

# Appendix 1: Cotton Selection Models

Table A.1 reports a set of results of the selection equation. These estimates are obtained from a Tobit model. Qualitatively similar results are estimated with OLS or the CLAD models. We find that household assets are positively linked to land cotton shares. Total land and whether the household raise livestock can work as collateral perhaps allowing the household to obtain cheaper credit and to purchase inputs or to afford any initial investment. In addition, household assets may allow farms to adopt riskier (but also more profitable) agricultural activities.

The size of the family also affects cotton allocation positively. One explanation is that bigger households can take care of own-consumption needs (food security) and have additional resources needed to embark in cash agriculture. A related finding in Table A.1 is that household with higher proportion of males tend to allocated higher shares of land to cotton. This is consistent with the notion that the availability of labor supply matters in the choice of crops.

Finally, there is some evidence that male-headed households tend to grown more cotton than female-headed families.

# References

Altonji, J. (1986). “Intertemporal Substitution in Labor Supply: Evidence From Micro Data,” Journal of Political Economy vol. 94, no. 3, part 2, pp. S176-S215.

Balat, J. and G. Porto (2005). “Globalization and Complementary Policies. Poverty Impacts in Rural Zambia,” in A. Harrison ed. (2005).

Benjamin, D. (1994). “Can Unobserved Land Quality Explain the Inverse Productivity Relationship?,” Journal of Development Economics 46, 51-84.

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

25
Binswanger, H., and D. Sillers (1983). “Risk Aversion and Credit Constraints in Farmers’ Decision-Making: A Reinterpretation,” Journal of Development Studies, 20, pp. 133-140.

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

Cotton News (2002). Cotton Development Trust, Zambia.

de Janvry, A., M. Fafchamps, and E. Sadoulet (1991). “Peasant Household Behavious with Missing Markets: Some Paradoxes Explained,” Economic Journal, 101, pp. 1400-1417.

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

Dercon, S. (1996). “Risk, Crop Choice, and Savings: Evidence from Tanzania,” Economic Development and Cultural Change, vol. 33, no. 3, pp. 485-513.

Edmonds, E. and N. Pavcnik (2004). “The Effects of Trade Liberalization on Child Labor,” forthcoming Journal of International Economics.

Eswaran, M. and A. Kotwal (1986). “Access to Capital and Agrarian Production Organization,” Economic Journal, 96, pp. 482-498.

Fafchamps, M. (1992). “Cash Crop Production, Food Price Volatility, and Rural Market Integration in the Third World,” American Journal of Agricultural Economics, XX, pp. 90-99.

Fan, J. (1992). “Design-adaptive nonparametric regression,” Journal of the American Statistical Association, vol. 87, No 420, December, pp. 998-1004.

Feder G. (1980). “Farm Size, Risk Aversion, and the Adoption of New Technologies Under Uncertainty,” Oxford Economic Papers, 32, pp. 263-283.

Feder G. (1985). “The Relationship Between Farm Size and Farm Productivity,” Journal of Development Economics, 18, pp. 297-313.

26
Food Security Research Project (2000). “Improving Smallholder and Agribusiness Opportunities in Zambia’s Cotton Sector: Key Challenges and Options,” Working Paper No 1, Lusaka, Zambia.

Foster, A. and M. Rosenzweig (1995). “Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture,” Journal of Political Economy, vol. 103, no. 6, pp. 1176-1209.

Harrison, A. (2005). Globalization and Poverty, National Bureau of Economic Research, Boston, Massachusetts.

Jayne, T. (1994). “Do High Food Marketing Costs Constrain Cash Crop Production? Evidence from Zimbabwe,” Economic Development and Cultural Change, XXX, pp. 387-402.

Olley, G. and A. Pakes (1996). “The Dynamics of Productivity in the Telecommunications Equipment Industry,” Econometrica, vol. 64, no 6, pp. 1263-1297.

Pagan, A. and A. Ullah (1999). Nonparametric Econometrics. Cambridge University Press, New York.

Pavcnik, N. (2002). “Trade Liberalization, Entry and Productivity Improvements: Evidence From Chilean Plants,” Review of Economic Studies, vol. 69, No. 1, pp. 245-276.

Robinson, P.M. (1988). “Root-N-Consistent Semi-Parametric Model, ” Econometrics, vol. 56, No. 4, pp. 931-954.

Rosenzweig, M. and H. Binswanger (1993). “Wealth, Weather Risk and the Composition and Profitability of Agricultural Investments,” The Economic Journal, vol. 103, No. 416, pp. 56-78.

Shahabuddin, Q., S. Mestelman, and D. Feeny (1986). “Peasant Behaviour Towards Risk and Socio-Economic and Structural Characteristics of Farm Households in Bangladesh,” Oxford Economic Papers, 38, pp. 135-152.

27
Table 1 Post Harvest Survey (sample sizes)

|Province|1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Central|654|674|648|795|663|701|
|Eastern|1,225|1,197|1,255|1,437|1,248|1,292|
|Southern|895|828|835|961|835|850|
|Lusaka|246|252|243|244|185|182|
|Copperbelt|370|349|379|464|367|372|
|Luapula|803|775|799|869|760|761|
|Northern|1,211|1,190|1,348|1,551|1,293|1,376|
|Nwestern|409|423|429|543|435|431|
|Western|706|648|725|835|699|733|
|Total|6,519|6,336|6,661|7,699|6,485|6,698|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

Table 2

|Perce|ntage|of Farm<br>1997|ers Gro<br>- 2002|wing C|otton||
|---|---|---|---|---|---|---|
|Province|1997|1998|1999|2000|2001|2002|
|Central|24.6|22.6|16.6|10.3|14.7|20.2|
|Eastern|35.2|32.7|31.7|20.4|32.1|39.0|
|Southern|9.9|10.7|11.7|4.3|8.8|12.8|
|Lusaka|5.4|3.3|4.7|0.4|5.1|8.2|
|Copperbelt|0.8|0.6|0.3|0.0|0.0|0.0|
|Luapula|0.0|0.0|0.0|0.0|0.0|0.0|
|Northern|0.0|0.1|0.0|0.0|0.0|0.0|
|NWwestern|0.3|0.0|0.0|0.0|0.0|0.2|
|Western|1.3|0.6|0.4|0.1|0.1|0.0|
|Total|11.0|10.4|9.4|5.4|9.0|11.6|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

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Table 3 Fraction of Land Allocated to Cotton 1997 - 2002

|Province|1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Total|9.2|9.3|8.1|4.3|7.6|9.9|
|Central|12.1|10.7|6.7|3.5|6.3|8.5|
|Eastern|12.4|13.0|12.3|7.2|11.9|14.6|
|Southern|4.1|4.2|3.7|1.3|3.2|5.1|
|Lusaka|2.4|1.4|1.7|0.1|1.9|3.3|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

||Yields|Ta<br> per He<br>1997|ble 4<br>ctare in<br> - 2002|Cotto|n||
|---|---|---|---|---|---|---|
|Province|1997|1998|1999|2000|2001|2002|
|Total|6.18|6.53|6.38|6.21|6.44|6.39|
|Central|6.33|6.67|6.72|7.04|6.98|6.73|
|Eastern|6.14|6.45|6.28|6.07|6.32|6.32|
|Southern|6.09|6.65|6.40|5.56|6.57|6.23|
|Lusaka|6.00|6.40|6.43|6.40|5.51|6.57|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

Table 5

|Percen|tage of|Househ<br>1997|olds th<br> - 2002|at Grow|Maiz|e|
|---|---|---|---|---|---|---|
|Province|1997|1998|1999|2000|2001|2002|
|Central|90.2|92.2|93.5|94.3|94.0|93.2|
|Eastern|99.9|99.5|99.2|99.5|99.7|99.6|
|Southern|93.5|92.0|94.4|96.3|97.3|97.6|
|Lusaka|100.0|99.5|98.9|100.0|98.9|99.4|
|Copperbelt|96.7|94.3|90.9|93.5|93.5|93.6|
|Luapula|28.5|24.3|31.6|35.1|31.8|41.1|
|Northern|45.1|35.9|48.8|46.9|46.9|59.3|
|NWestern|75.7|65.9|71.9|66.7|80.7|77.7|
|Western|89.9|82.2|88.5|82.6|90.1|87.2|
|Total|76.2|72.1|76.1|76.2|77.7|80.7|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

29
Table 6 Percentage of Households that Grow Maize Conditional on Growing Cotton 1997 - 2002

|Province|1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Central|96.7|95.7|95.1|100.0|97.9|99.3|
|Eastern|100.0|98.4|98.7|99.7|99.5|100.0|
|Southern|97.6|90.5|96.8|97.4|93.1|92.4|
|Lusaka|100.0|100.0|88.9|100.0|100.0|100.0|
|Total|98.8|96.6|97.7|99.5|98.4|98.8|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

30
Table 7 Basic Productivity Regression

||Simple D|iference|Diference-in-|Diferences|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Age of household head|0.006<br>0.006|0.007<br>0.006|0.014<br>0.008*|0.010<br>0.007|
|Age Squared|–8.17E–05|–9.21E–05|–1.57E–04|–1.23E–04|
||–6.18E–05|–6.12E–05|–0.77E–04**|–0.75E–04|
|Male household head|0.128|0.132|0.093|0.087|
||0.040***|0.040***|0.047**|0.047*|
|Family Size|0.103|0.078|-0.035|0.008|
||0.031***|0.031**|0.036|0.036|
|Share of males|–0.003|–0.024|–0.030|–0.034|
||0.087|0.086|0.106|0.105|
|Farm type|–0.102|–0.059|0.124|0.059|
||0.038***|0.039|0.043***|0.044|
|Size cotton plot|–0.290<br>0.023***|–0.306<br>0.023***|||
|Relative plot size|||–0.343|–0.377|
||||0.024***|0.024***|
|Livestock|0.133|0.092|–0.064|–0.060|
||0.032***|0.033***|0.038*|0.040|
|Harrows||–0.067||–0.064|
|||0.046||0.053|
|Ploughs||0.102||0.032|
|||0.019***||0.027|
|Basal fertilizer||0.075||–0.705|
|||0.108||0.256***|
|Top-dressing fertilizer||0.084<br>0.105||–0.616<br>0.281**|
|Cotton price|–0.080|–0.101|0.117|0.070|
||0.054|0.053*|0.064*|0.062|
|_F_ <sup>1</sup>|–0.126|–0.115|–0.512|–0.469|
||0.053**|0.053**|0.059***|0.058***|
|_F_ <sup>2</sup>|0.108|0.147|0.159|0.190|
||0.031***|0.032***|0.039***|0.039***|
|Constant|6.414|6.434|–1.312|–0.958|
||0.259***|0.255***|0.317***|0.309***|
|Observations|3418|3418|3418|3418|
|_R_<sup>2</sup>|0.11|0.12|0.11|0.14|

Note: Robust standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. Variables included in **x** are the age and age squared of the household head, a dummy for households where the head is male, the log of the total household size, the share of males in the household, a dummy for farms with total area smaller than 1 ha (farm type), the log size in ha. of the cotton plot, the relative sizes of the cotton and maize plots, a dummy for livestock raising households, and harrows, ploughs, basal fertilizer and top-dressing fertilizer in physical units.

31
Table 8

# Cotton Productivity Entry and Exit in Cotton Farming

||||Model 1|||Model 2||
|---|---|---|---|---|---|---|---|
||(1)|Tobit<br>(2)|OLS<br>(3)|CLAD<br>(4)|Tobit<br>(5)|OLS<br>(6)|CLAD<br>(7)|
|Age of head|0_._01|0_._009|0_._009|0_._013|0_._009|0_._009|0_._011|
||0_._007|0_._007|0_._007|0_._007_∗_|0_._007|0_._007|0_._007|
|Age Squared|_−_0_._0001|_−_0_._0001|_−_0_._0001|_−_0_._0001|_−_0_._0001|_−_0_._0001|_−_0_._0001|
||0_._00007|0_._00007_∗_|0_._00007_∗_|0_._00007_∗∗_|0_._00007_∗_|0_._00007_∗_|0_._00007_∗∗_|
|Male hh. head|0_._087|0_._091|0_._091|0_._11|0_._091|0_._091|0_._11|
||0_._047_∗_|0_._047_∗_|0_._047_∗_|0_._047_∗∗_|0_._047_∗_|0_._047_∗_|0_._047_∗∗_|
|Family size|0_._008|_−_0_._001|_−_0_._001|0_._001|0|0|0_._004|
||0_._036|0_._036|0_._036|0_._036|0_._036|0_._036|0_._036|
|Share of males|_−_0_._034|_−_0_._048|_−_0_._048|_−_0_._059|_−_0_._048|_−_0_._048|_−_0_._005|
||0_._105|0_._105|0_._105|0_._105|0_._105|0_._105|0_._105|
|Farm type|0_._059|0_._052|0_._052|0_._04|0_._05|0_._049|0_._039|
||0_._044|0_._044|0_._044|0_._044|0_._044|0_._044|0_._044|
|Relative plot size|_−_0_._377|_−_0_._543|_−_0_._543|_−_0_._483|_−_0_._546|_−_0_._546|_−_0_._565|
||0_._024_∗∗∗_|0_._048_∗∗∗_|0_._048_∗∗∗_|0_._043_∗∗∗_|0_._048_∗∗∗_|0_._048_∗∗∗_|0_._044_∗∗∗_|
|Livestock|_−_0_._06|_−_0_._067|_−_0_._067|_−_0_._06|_−_0_._065|_−_0_._065|_−_0_._07|
||0_._04|0_._040_∗_|0_._040_∗_|0_._04|0_._04|0_._04|0_._040_∗_|
|Harrows|_−_0_._064|_−_0_._069|_−_0_._069|_−_0_._082|_−_0_._083|_−_0_._083|_−_0_._113|
||0_._053|0_._053|0_._053|0_._053|0_._053|0_._053|0_._054_∗∗_|
|Ploughs|0_._032|0_._024|0_._024|0_._028|0_._021|0_._021|0_._027|
||0_._027|0_._027|0_._027|0_._027|0_._027|0_._027|0_._026|
|Basal fertilizer|_−_0_._705|_−_0_._694|_−_0_._694|_−_0_._706|_−_0_._688|_−_0_._688|_−_0_._673|
||0_._256_∗∗∗_|0_._252_∗∗∗_|0_._252_∗∗∗_|0_._257_∗∗∗_|0_._251_∗∗∗_|0_._251_∗∗∗_|0_._253_∗∗∗_|
|Top-dressing fert.|_−_0_._616|_−_0_._611|_−_0_._611|_−_0_._602|_−_0_._613|_−_0_._613|_−_0_._625|
||0_._281_∗∗_|0_._278_∗∗_|0_._278_∗∗_|0_._284_∗∗_|0_._277_∗∗_|0_._277_∗∗_|0_._281_∗∗_|
|Cotton price|0_._07|0_._08|0_._08|0_._074|0_._082|0_._082|0_._032|
||0_._062|0_._062|0_._062|0_._062|0_._062|0_._062|0_._062|
|_F_ <sup>1</sup>|_−_0_._469|_−_0_._503|_−_0_._503|_−_0_._505|_−_0_._505|_−_0_._505|_−_0_._521|
||0_._058_∗∗∗_|0_._058_∗∗∗_|0_._058_∗∗∗_|0_._059_∗∗∗_|0_._058_∗∗∗_|0_._058_∗∗∗_|0_._059_∗∗∗_|
|_F_ <sup>2</sup>|0_._19|0_._193|0_._193|0_._19|0_._19|0_._19|0_._183|
||0_._039_∗∗∗_|0_._039_∗∗∗_|0_._039_∗∗∗_|0_._039_∗∗∗_|0_._039_∗∗∗_|0_._039_∗∗∗_|0_._039_∗∗∗_|
|_φ_||0_._009|0_._009|0_._006|0_._01|0_._01|0_._01|
|||0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|
|Constant|_−_0_._958<br>0_._309_∗∗∗_|_−_0_._951<br>0_._309_∗∗∗_|_−_0_._95<br>0_._309_∗∗∗_|_−_0_._882<br>0_._312_∗∗∗_|_−_0_._948<br>0_._309_∗∗∗_|_−_0_._948<br>0_._309_∗∗∗_|_−_0_._612<br>0_._321_∗_|
|Observations|3418|3418|3418|3418|3418|3418|3418|
|_R_<sup>2</sup>|0.14|0.15|0.15|0.15|0.15|0.15|0.15|

Robust standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. Column (1) does not include _φ_ . Tobit, OLS and CLAD refer to different models used to estimate _φ_ . See Appendix 1. Model 1: first stage includes total land tenure, family size, age, age squared, farm type, a dummy for male-headed farms, the proportion of males in the family, a dummy for livestock rasing households, and assets (harrows, ploughs) in monetary units. Model 2: first stage includes total land tenure, family size, age, age squared, farm type, a dummy for male-headed farms, the proportion of males in the family, a dummy for livestock rasing32 households, and assets (harrows, ploughs) in physical units.
# Table 9

# Cotton Productivity

Non-Linearity of Unobserved Productivity

||Mod|el 1|Mo|del 2|
|---|---|---|---|---|
||Tobit<br>(1)|Robinson<br>(2)|Tobit<br>(3)|Robinson<br>(4)|
|Age of household head|0_._009|0_._009|0_._009|0_._009|
||0_._007|0_._008|0_._007|0_._008|
|Age squared|_−_0_._0001|_−_0_._0001|_−_0_._0001|_−_0_._0001|
||0_._00007_∗_|0_._00007_∗_|0_._00007_∗_|0_._00007_∗_|
|Male household head|0_._091|0_._091|0_._091|0_._091|
||0_._047_∗_|0_._052_∗_|0_._047_∗_|0_._052_∗_|
|Family size|_−_0_._001|_−_0_._002|0|0|
||0_._036|0_._036|0_._036|0_._036|
|Share of males|_−_0_._048|_−_0_._048|_−_0_._048|_−_0_._048|
||0_._105|0_._101|0_._105|0_._101|
|Farm type|0_._052|0_._052|0_._05|0_._05|
||0_._044|0_._042|0_._044|0_._042|
|Relative plot size|_−_0_._543|_−_0_._544|_−_0_._546|_−_0_._547|
||0_._048_∗∗∗_|0_._046_∗∗∗_|0_._048_∗∗∗_|0_._046_∗∗∗_|
|Livestock|_−_0_._067|_−_0_._067|_−_0_._065|_−_0_._065|
||0_._040_∗_|0_._040_∗_|0_._04|0_._04|
|Harrows|_−_0_._069|_−_0_._069|_−_0_._083|_−_0_._083|
||0_._053|0_._048|0_._053|0_._048_∗_|
|Ploughs|0_._024|0_._024|0_._021|0_._021|
||0_._027|0_._021|0_._027|0_._021|
|Basal fertilizer|_−_0_._001|_−_0_._001|_−_0_._001|_−_0_._001|
||0_._000_∗∗∗_|0_._000_∗∗∗_|0_._000_∗∗∗_|0_._000_∗∗∗_|
|Top-dressing fertilizer|_−_0_._001|_−_0_._001|_−_0_._001|_−_0_._001|
||0_._000_∗∗_|0_._000_∗∗∗_|0_._000_∗∗_|0_._000_∗∗∗_|
|Cotton prices|0_._08|0_._081|0_._082|0_._082|
||0_._062|0_._059|0_._062|0_._059|
|_F_ <sup>1</sup>|_−_0_._503|_−_0_._504|_−_0_._505|_−_0_._506|
||0_._058_∗∗∗_|0_._056_∗∗∗_|0_._058_∗∗∗_|0_._056_∗∗∗_|
|_F_ <sup>2</sup>|0_._193|0_._192|0_._19|0_._19|
||0_._039_∗∗∗_|0_._040_∗∗∗_|0_._039_∗∗∗_|0_._040_∗∗∗_|
|_φ_|0_._009||0_._01||
||0_._002_∗∗∗_||0_._002_∗∗∗_||
|Constant|_−_0_._951|0_._0001|_−_0_._948|_−_0_._0001|
||0_._309_∗∗∗_|0_._016|0_._309_∗∗∗_|0_._016|
|Observations|3418|3418|3418|3418|
|_R_<sup>2</sup>|0.15|0.13|0.15|0.14|

Robust standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. For a description of Models 1 and 2, see note in Table 8. Columns (1) and (3) use a Tobit procedure in the selection model and OLS in the productivity model. Columns (2) and (4) use a Tobit procedure in the selection model and a partially linear, Robinson procedure in the productivity model.

33
|200|_−_0_._452|0_._188|0_._0045||
|---|---|---|---|---|
|100|0_._404|_._184|0175||
||_−_|0|0_._0|_σ_2<br>_u_.|
|odel<br>49|_−_0_._609|0_._202|0_._0379|ment error,|
|ection M<br>Error<br>36|_−_0_._546|0_._196|0_._0210|he measure|
|in the Sel<br>urement <br>25|_−_0_._525|0_._194|0_._0152|ariance of t|
|bles <br>Meas|15|4|4|the v|
|rva<br> to <br>16|0_._5|0_._19|_._012|about|
|Unobse<br>sitivity <br>9|_._509<br>_−_|193<br>|109<br>0|mptions|
|dditional <br>Sen<br>4<br>|506<br>_−_0|93<br>0_._|100<br>0_._0|iferent assu|
|A<br>1<br>|_−_0_._504<br>_−_0_._|0_._193<br>0_._1|0_._0095<br>0_._0|e reforms under d|
|0|_−_0_._503|0_._193|0_._0094|mpacts of th|
|2<br>_u_|1|2||es i|
|_σ_<br>|_F_|_F_|_φ_|Estimat|

34
Table 11

# Cotton Productivity

Sensitivity to the Definition of the Reform

|||Model 1|||Model 2||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_φ_|0_._009|0_._009|0_._011|0_._01|0_._009|0_._011|
||0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|
|_F_ <sup>1</sup>|_−_0_._503|||_−_0_._505|||
||0_._058_∗∗∗_|||0_._058_∗∗∗_|||
|_F_ <sup>2</sup>|0_._193|||0_._19|||
||0_._039_∗∗∗_|||0_._039_∗∗∗_|||
|_R_<sup>1</sup>||_−_0_._426|||_−_0_._425||
|||0_._048_∗∗∗_|||0_._048_∗∗∗_||
|_R_<sup>2</sup>||0_._185|||0_._183||
|||0_._039_∗∗∗_|||0_._039_∗∗∗_||
|Dummy 1998|||0_._326|||0_._328|
||||0_._058_∗∗∗_|||0_._058_∗∗∗_|
|Dummy 1999|||_−_0_._143|||_−_0_._141|
||||0_._104|||0_._103|
|Dummy 2000|||_−_0_._368|||_−_0_._369|
||||0_._064_∗∗∗_|||0_._064_∗∗∗_|
|Dummy 2001|||0_._172|||0_._168|
||||0_._061_∗∗∗_|||0_._061_∗∗∗_|
|Dummy 2002|||0_._457|||0_._457|
||||0_._057_∗∗∗_|||0_._057_∗∗∗_|
|Constant|_−_0_._951|0_._706|0_._252|_−_0_._948|0_._708|0_._253|
||0_._309_∗∗∗_|0_._331_∗∗_|0_._534|0_._309_∗∗∗_|0_._331_∗∗_|0_._534|
|Observations|3418|3418|3418|3418|3418|3418|
|_R_<sup>2</sup>|0.15|0.15|0.16|0.15|0.15|0.17|

Robust standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. Model 1: first stage includes total land tenure, family size, age, age squared, farm type, a dummy for male-headed farms, the proportion of males in the family, a dummy for livestock rasing households, and assets (harrows, ploughs) in monetary units. Model 2: first stage includes total land tenure, family size, age, age squared, farm type, a dummy for male-headed farms, the proportion of males in the family, a dummy for livestock rasing households, and assets (harrows, ploughs) in physical units.

35
Table 12 Cotton Productivity Sensitivity to the Sample

|||Model 1|||Model 2||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_φ_|0_._009|0_._009|0_._011|0_._009|0_._009|0_._011|
||0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|
|_F_ <sup>1</sup>|_−_0_._498|||_−_0_._499|||
||0_._058_∗∗∗_|||0_._058_∗∗∗_|||
|_F_ <sup>2</sup>|0_._199|||0_._197|||
||0_._039_∗∗∗_|||0_._039_∗∗∗_|||
|_R_<sup>1</sup>||_−_0_._421|||_−_0_._42||
|||0_._048_∗∗∗_|||0_._048_∗∗∗_||
|_R_<sup>2</sup>||0_._189|||0_._187||
|||0_._039_∗∗∗_|||0_._039_∗∗∗_||
|Dummy 1998|||0_._321|||0_._323|
||||0_._058_∗∗∗_|||0_._058_∗∗∗_|
|Dummy 1999|||_−_0_._157|||_−_0_._155|
||||0_._102|||0_._102|
|Dummy 2000|||_−_0_._366|||_−_0_._367|
||||0_._064_∗∗∗_|||0_._064_∗∗∗_|
|Dummy 2001|||0_._156|||0_._152|
||||0_._060_∗∗∗_|||0_._060_∗∗_|
|Dummy 2002|||0_._472<br>0_._056_∗∗∗_|||0_._473<br>0_._056_∗∗∗_|
|Constant|_−_0_._822|0_._816|0_._452|_−_0_._82|0_._818|0_._456|
||0_._310_∗∗∗_|0_._331_∗∗_|0_._524|0_._310_∗∗∗_|0_._331_∗∗_|0_._525|
|Observations|3462|3462|3462|3462|3462|3462|
|_R_<sup>2</sup>|0.15|0.15|0.17|0.15|0.15|0.17|

Robust standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. Model 1: first stage includes total land tenure, family size, age, age squared, farm type, a dummy for male-headed farms, the proportion of males in the family, a dummy for livestock rasing households, and assets (harrows, ploughs) in monetary units. Model 2: first stage includes total land tenure, family size, age, age squared, farm type, a dummy for male-headed farms, the proportion of males in the family, a dummy for livestock rasing households, and assets (harrows, ploughs) in physical units.

36
|0.003<br>0.003<br>0.859<br>0.211***<br>0.37<br>0.190*<br>-0.588<br>0.314*<br>0.754<br>0.251***<br>1.066<br>0.213***<br>410<br>0.24<br>d the share of<br>olds where the|
|---|
|Southern<br>-0.001<br>0.002<br>-0.348<br>0.156**<br>0.557<br>0.158***<br>410<br>0.18<br>ld members an<br>my for househ|
|0.002<br>0.003<br>-0.965<br>0.287***<br>0.554<br>0.144***<br>410<br>0.2<br>er of househo<br>e that a dum|
|0<br>0.001<br>0.302<br>0.062***<br>-0.076<br>0.07<br>-0.373<br>0.074***<br>0.123<br>0.060**<br>0.221<br>0.060***<br>2339<br>0.15<br>og of the numb<br>ughs owned (not|
|Eastern<br>-0.001<br>0.001<br>-0.333<br>0.052***<br>0.033<br>0.043<br>2339<br>0.13<br>ld head, the l<br>rows and plo|
|0<br>0.001<br>-0.445<br>0.066***<br>0.106<br>0.040***<br>2339<br>0.13<br>nt at 1%.<br>of the househo<br>d value of har|
|0.008<br>0.002***<br>0.213<br>0.112*<br>0.187<br>0.133<br>-0.327<br>0.159**<br>0.143<br>0.134<br>0.585<br>0.128***<br>669<br>0.2<br>; *** signifca<br> age squared <br>ising farms, an|
|Central<br>0.006<br>0.002***<br>-0.111<br>0.105<br>0.351<br>0.097***<br>669<br>0.18<br>gnifcant at 5%<br>ng the age and<br>for livestock ra<br> province.|
|All Provinces<br>0.002<br>0.001<br>0.002<br>0.008<br>0.001**<br>0.001<br>0.001***<br>0.002***<br>-0.513<br>-0.42<br>0.060***<br>0.138***<br>0.2<br>0.303<br>0.036***<br>0.092***<br>-0.298<br>0.045***<br>0.162<br>0.039***<br>0.339<br>0.054***<br>0.025<br>0.059<br>-0.401<br>0.068***<br>0.203<br>0.055***<br>0.399<br>0.054***<br>3418<br>3418<br>3418<br>669<br>0.15<br>0.14<br>0.16<br>0.19<br>ndard errors in parentheses: * signifcant at 10%; ** si<br>tion equations (frst stage) are run by province includi<br> log of total farm area, a farm type dummy, a dummy <br>le is not included, Tobit did not converge).<br> 3 columns (all provinces), Tobit regressions are run by|
|_φ_<br>_F_1<br><br>_F_2<br><br>_R_1<br>_R_2<br>1998<br>1999<br>2000<br>2001<br>2002<br>Obs<br>_R_2<br>Robust sta<br>Tobit selec<br>males, the <br>head is ma<br>In the frst|

37
Table A.1 Determinants of Land Cotton Shares

||1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Age of household head|_−_1_._00|_−_0_._21|_−_0_._86|_−_0_._45|_−_0_._08|_−_0_._68|
||0.51|0.57|0.51|0.70|0.56|0.49|
|Age squared|0.004|_−_0_._003|0.004|_−_0_._001|_−_0_._004|0.0006|
||0.005|0.006|0.005|0.007|0.006|0.005|
|Family size|2.53|4.77|0.01|0.26|_−_4_._87|2.37|
||2.42|2.72|2.62|3.11|2.70|2.49|
|Male household head|9.79|10.70|6.59|5.87|11.54|7.90|
||3.56|3.88|3.69|4.45|3.76|3.04|
|Share of males|18.02|11.72|12.87|_−_0_._11|6.13|_−_9_._04|
||6.82|7.47|7.41|8.76|7.02|6.42|
|Total land|29.30|33.07|34.51|34.20|31.69|23.53|
||2.11|2.40|2.51|3.35|2.43|1.94|
|Farm type|1.26|_−_0_._63|3.06|23.41|4.32|_−_0_._27|
||3.03|3.82|3.80|5.41|3.45|3.10|
|Livestock|_−_1_._67|3.61|6.64|9.33|3.80|3.18|
||2.71|2.99|3.07|3.59|3.05|2.80|
|Value of harrows|0.36|3.09|2.72|1.00|-|1.73|
||2.51|2.40|1.65|2.53|-|1.39|
|Value of ploughs|_−_2_._04|_−_4_._32|_−_3_._15|0.21|-|0.43|
||1.11|1.05|0.84|0.74|-|0.57|
|Constant|_−_28_._43|_−_59_._26|_−_51_._40|_−_82_._93|_−_51_._00|_−_21_._36|
||14.01|15.52|14.50|18.43|15.43|13.57|

Tobit estimates of land cotton shares. Includes dummy district variables. A separate regression is run in each year to account to macro shocks, prices, and the reforms. Since no information on assets was collected in 2001, the Tobit specification for that year does not include the value of harrows and the value of ploughs.

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Figure 1 Trends in Agricultural Productivity Maize, Mixed Beans, Millet, Sorghum, Sunflower, and Groundnuts <!-- Start of picture text --> mixed beans millet<br>7.5 7.5<br>7<br>7<br>6.5<br>6.5<br>6<br>5.5 6<br>1997 1998 1999 2000 2001 2002 1997 1998 1999 2000 2001 2002<br>year year<br>sorghum sunflower<br>7.5 7.5<br>7<br>7<br>6.5<br>6.5<br>6<br>6 5.5<br>1997 1998 1999 2000 2001 2002 1997 1998 1999 2000 2001 2002<br>year year<br>groundnuts<br>7.5<br>7<br>6.5<br>6<br>5.5<br>1997 1998 1999 2000 2001 2002<br>year<br><!-- End of picture text --> Note: The graphs compare the trend in maize productivity with the trends in productivity in alternative crops. Starting at the top-left, the panels represent the cases of Mixed Beans, Millet, Sorghum, Sunflower, and Groundnuts, respectively. In each panel, the solid line represents the trends in maize productivity and the broken line, the trend in the productivity in the alternative crops.

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Figure 2 Trends in Maize Productivity Reform Provinces versus Non-Reform Provinces <!-- Start of picture text --> 7.2<br>7<br>6.8<br>6.6<br>1997 1998 1999 2000 2001 2002<br>year<br><!-- End of picture text --> Note: The graph reports the trends in maize productivity in reform provinces (solid line) and non-reform provinces (broken line). Estimates based on the Post Harvest Survey.

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Figure 3 Average Productivity Entry and Exit into Cotton Farming <!-- Start of picture text --> y<br>y0<br>y1<br>E(y0)<br>E(y1)<br>E(yr)<br>y<br>φ<br>φ φ '<br><!-- End of picture text --> 41