Invited paper presented at the 6th African

Conference of Agricultural Economists, September 23-26, 2019, Abuja, Nigeria

Copyright 2019 by [authors]. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

Crop diversification and reduction: The role of access to road in Mahamadou Roufahi Tankaria,1 , Katrin Glatzela, Kathrin M. Demmlerb aInternational Food Policy Research Institute, Dakar, Senegal. b Centre for Environmental Policy, Imperial College London, London, UK.

Abstract Crop diversification is considered as a climate-smart agriculture practice widely adopted by smallholder farmers in Niger due to its many benefits including increased resilience, control of pests and diseases, yield stability, food diversity and . However, its impact on may be limited when farm households do not have access to basic infrastructure such as roads, which are so crucial for access to markets. This paper aims to show how crop diversification affects farm households’ poverty status and severity under access to road constraints in Niger. The estimations are based on probit and tobit models using a panel data set provided by the Surveys of Living Conditions of Households and Agriculture of Niger for the years 2011 and 2014. The identification strategy dealt with unobserved households’ characteristics which may be a source of bias following Mundlak’s (1978) approach. The findings show that the effect of crop diversification on poverty levels varies depending on the degree of a farm household’s access to roads. The higher the distance to road, the lower the reduction of poverty due to crops diversification. By removing access to road constraints, the Nigerien government could leverage the benefits of crop diversification strategies on poverty reduction. Keywords: Crop diversification, Climate-smart agriculture, road infrastructure, poverty, Niger.

1Corresponding author Email: [email protected] / [email protected]

1. Introduction Like other Sahelian countries, Niger depends strongly on its agricultural sector for food production and employment. The sector is the country’s main employer with more than 80% of the workforce employed in the agriculture sector, contributing to 40% of Niger’s GDP. However, this sector faces constraints that hinder its development. For instance, its productivity is low, and the sector is also exposed to several natural shocks including droughts, heats, floods and insects’ attacks. The climate related shocks are often those affecting more severely the agriculture sector (World Bank 2013). In recent decades, there have been a rise in temperatures and a decrease in average precipitation yielding to major droughts, affecting not only Niger but also the entire Sahel region (Druyan 2011; IPCC 2013). Therefore, the livelihoods of Niger’s rapidly increasing population, growing at a rate of 3.8%, are deteriorating, especially those of rural households depending mainly on agriculture for their food and income. Estimates show that in 2014 more than 45% of Nigerien were living in poverty (INS 2016). Ninety percent of those poor lived in rural areas. Furthermore, 48% of rural households suffered from chronic , and among them more than 20% lacked access to food and were extremely vulnerable in 2014 (Herderschee et al. 2014). One widely adopted coping strategy by farmers in Niger against production risk is crop diversification (Bello 2016). In India, evidence shows that by protecting smallholders from output price fluctuations and increasing dietary diversity at household level, crop diversification was recognized as the most environment-friendly practical way for strengthening agricultural households’ resilience against uncertainties, particularly among smallholders (Joshi 2005). Similarly, in Niger, it was found that crop diversification improves significantly the welfare of the most vulnerable households when they adopt it against climatic or market shocks (Asfaw et al. 2018). In addition, anchoring crop diversification in agricultural policy for a more positive impact of farming on households’ welfare has been widely promoted at international level. For instance, FAO recommends crop diversification to countries in order to effectively develop agriculture sustainably, improve food and nutrition security and create more jobs for poverty reduction (FAO 2012). However, to fully benefit from crop diversification, farm households need access to roads for agricultural produce transport to markets as not all the cultivated crops can be consumed at household level. In fact, roads infrastructure has the capacity to reduce poverty thanks to the transport sector perfection which enhances access to market (Gachassin et al. 2010). For example, in Kenya access to roads connects farmers to food value chains and markets in nearby towns or the capital city and enables them to negotiate higher prices for their produce (Mati 2008). In addition, by facilitating access to food markets, access to roads may increase households’ and nutrition. In Niger, distance to market was found as a determining factor for the levels of food security of households (Tankari, 2015). Therefore, access to roads may determine how crop diversification strategies can contribute to poverty reduction. Yet, in Niger, the road network is still very limited. In rural areas, the road density barely reaches 0.6 km per 1000 km2 which is the lowest in Africa due the fact that the road network represents only 7,371 km of classified road (Montaud et al. 2016).

The available road network in Niger, in addition to allowing rural households movement, it also serves for transport of input and output between the production areas and markets and thus plays a crucial role in production and trade decisions. (Montaud et al. 2016). The capacity of rural road network to impulse agricultural sector growth is well recognized in the literature. (see for example Calderon and Serven 2010; Gwilliam 2011 or Domínguez-Torres and Foster 2011). Investment in rural road network was among the prioritized actions of the Government of Niger, to achieve its commitment for the Comprehensive Africa Agriculture Development Programme (CAADP) by repairing, upgrading or constructing up to 500km of roads annually. This paper aims to show how crop diversification affects farm household poverty status and severity, that is whether crop diversification can reduce farm household’s poverty under access to road constraints in Niger. The methodology is based on econometric estimations using probit and tobit models by controlling for unobserved heterogeneity of households. The panel data for Niger, Surveys of Living Conditions of Households and Agriculture (ECVMA) 2011 and 2014, are used for empirical evidence. The outcome variables are indicated by the poverty status and poverty gap of farm households. Crop diversification is measured as the ratio of the number of crops grown by a household in a given year to total number of different crops grown in the household vicinity or community during the same year. The constraint of access to road is measured in terms of distance from household to a main road. Following the introductory section, we review the literature on how farm household poverty is affected by crop diversification strategies and on the role access to roads. In the third section, the methodology and the data used are detailed and discussed. The presentation and discussion of the findings are conducted in the fourth section. The last section is devoted to conclusions.

2. Literature Review and theoretical framework Crop diversification is the production, in a given area, of more than one crops variety of the same or different species by rotating crop and or intercropping (Makate et al. 2016). Due some of its benefits including increase of resilience, controls for pests and diseases, yield stability, food diversity, and higher spatial and temporal biodiversity on the farm, crop diversification is also considered as a climate-smart agriculture (CSA) practice (Holling 1973; Joshi 2005; Lin 2011). Theoretically, there are two main pathways that can be used to hypothesize how crop diversification affects farm household poverty (Mazunda et al. 2018). First, crop diversification can directly alter the food intake of a family if it consumes what it produces. By adopting a crop diversification strategy, a farming family produces a more diverse set of foods providing access to a more diverse diet. However, even net consumer households benefit from crop diversification through improved access to a more diverse set of foods to choose at markets. Second, farming diversified crops including marketable higher-value crops can lead to increased income for farm families where local markets can offer producers competitive prices for those crops, which farmers then can, in turn, spend on more diverse and more nutritious foods and non-food items including healthcare and education (Mazunda et al. 2018). In northern Benin evidence shows that a group of women famers by producing more crops including, tomatoes, okra, peppers, eggplants, carrots, and other green vegetables increased their production which allowed them to keep up to 18% of their harvest to feed their families and to sell the surplus at local markets. The additional income earned from sales was used to increase the quantities of items including staples, protein and oil in the households (Burney et al. 2010). However, several factors such as characteristics of food markets, decisions on household food purchases, and household nutritional knowledge determine the extent to which agricultural income influences household poverty and malnutrition. Depending on how these factors interlink, agricultural income-generating activities can have a positive, negative, or neutral effects on poverty levels, including the household’s nutritional status (World Bank 2007). In addition, the extent of bargaining power and control that women have over choices about what a family produces and consumes and how the income made from crop sales is spent, may moderate the degree to which the above two pathways will lead to improved household welfare outcomes. A significant share of the literature has evidenced a positive association between crop diversification and household income and agricultural diversity indicating that crop diversification reduces poverty (Barrett et al. 2001; Caviglia-Harris & Sills, 2005; Ellis 1998, 2000). For instance, in Ethiopia, studies found that farmers that diversified their cropping better coped with climate change and were less subject to food insecurity (Bezabih & Sarr 2012; Di Falco et al. 2011; Di Falco & Veronesi 2013). In the rural areas of central Nigeria, it was found that crop diversification among farming households is one of the essential strategies for reducing levels and raising incomes (Ibrahim et al. 2009). Furthermore, the benefits of crop diversification may vary depending on the current income level of a household (FAO 2017). Crop diversification appears to be, for farm households having fewer resources, a good practice to cope with production and price risks. Yet, for households farming medium and large scale farms with modern equipment, specialization appears to be more profitable than diversification due to its higher return. In other words, poor households tend to benefit more

from crop diversification strategy than wealthier households for which crop specialization return is higher than crop diversification. At the same time, limited access to markets means that poor households may experience only a limited impact of their crop diversification on poverty reduction. Limited market access is one of the most significant constraints, as famers require cash to satisfy their other non-food needs, revealing the importance of access to roads. Improving market access, including access to road, has been identified as an important strategy to maximize the impact crop diversification policy among poor farm households located in harsh climatic environments (FAO 2017). Three main channels have been identified in the literature through which road access impacts on poverty: a) human capital, b) market access and c) labor activities channel (Gachassin et al. 2010). The human capital pathway relates to the ease of access to healthcare and education. By improving access to human capital facilities, access to road increase investment in human capital that is essential to escape from poverty. In other words, the effectiveness of social services provision is determined by the extent to which the beneficiaries have access to roads to reach the health and education centers providing the services. (Gannon and Liu 1997). The market access pathway is related to the increase of farmers’ ability to obtain fair prices for their produce as well as their access to important inputs at affordable prices due to transport costs reduction (Khandker et al. 2009). Furthermore, access to reliable markets to sell produce can increase farmers’ incomes largely due to new market opportunities, higher selling prices and evidence shows that farmers having access to road receive higher prices for their crops than those who don’t have access to it. (Gibson and Rozelle 2002). Finally, the labor activities channel is due the creation of employment and new job opportunities that reduce poverty thanks to this infrastructure (Jacobs and Greave, 2003; Fan and Zhang 2004). By exploring this issue, this paper contributes to the current literature on crop diversification and poverty alleviation, which still falls short of rigorous empirical evidence to support the alleged positive effect of crop diversification on farm household welfare (Michler and Josephso, 2017). Importantly, the study highlights how access to roads determines the positive effect of crop diversification on poverty. This is crucial, particularly in the case of Niger, which heavily relies on agricultural development and growth for poverty alleviation in rural areas. Furthermore, this paper adds to the emerging literature on household strategies to deal with production and price risks due to the adverse impacts of climate change on African agriculture. In fact, although crop diversification practice is part of indigenous knowledge, the expected adverse impacts of climate change on agriculture and the livelihoods of smallholders, has increased its popularity among farmers. Crop diversification is expected to significantly reduce the risks associated with agricultural production, and boost productivity, raise incomes and improve food security and nutrition outcomes in smallholder farming systems (Makate et al., 2016). Finally, this study argues that access to road is important to maximize the impact of crop diversification strategy in Niger.

3. Methodology 3.1 Empirical strategy The aim of this study is to assess how crop diversification affects farm households’ poverty under a scenario of limited access to roads. In this study, household poverty in Niger is represented by two indicators: the poverty status and poverty gap. The poverty status is a binary variable indicating whether a household is considered poor or not, whereas the poverty gap measures what a poor household needs to be pulled out of poverty, expressed as a proportion of the poverty line. The crop diversification variable is an index that reflects the extent to which a household diversifies its cropping by comparing the total number of different crops a household grew in a year, relative to the total number of different crops grown within the community during the same year (Michler and Josephso 2017). This index takes into account the agroecological constraints in the community of the household which determine the number of crops that can be grown and ensure the comparaison among the households in term of effort to diversify. The crop diversification variable is expressed as follow:

ℎ푐푖푡 푑𝑖푡 = (1) 푣푐푖푡

Where 푑𝑖푡 is the crop diversification index; ℎ푐𝑖푡 is the number of crops grown by a household and 푣푐𝑖푡 is the number of different crops grown at community or village level. Based on the standard theoretical model of multi-crop production by risk averse agents, developed by Just (1975) and following Michler and Josephso (2017), each poverty indicator is assumed to be a function of a set of regressors and the variables indicating the level of crop diversification, access to main roads and the interaction of the two variables. This allows to measure how crop diversification affects poverty and how this effect of crop diversification on poverty varies depending on a household’s ease of access to a main road. As we considered both binary and censored variables, we use a binary panel probit regression to estimate the effect of crop diversification under access to road constraints on the probability of being poor, while we use a panel tobit regression to assess the effect of crop diversification on the poverty gap under access to road constraints. The probit model can be shown as follow:

∗ 푃𝑖푡 = 훼푝푑𝑖푡 + 훽푝푟𝑖 + 훾푝푑𝑖푡 ∗ 푟𝑖 + 훿푝푥𝑖푡 + 푢𝑖 + 휀𝑖푡 (2) ∗ 1 𝑖푓 푃𝑖푡 > 0 and 푃𝑖푡 = {0 표푡ℎ푒푟푤𝑖푠푒 (3)

∗ Where 𝑖 and 푡 subscripts refer to households and time periods respectively, 푃𝑖푡 is a latent dependent variable for being in poverty, 푃𝑖푡 is the observed outcome indicating the poverty status; 푑𝑖푡 is the crop diversification index; 푟𝑖 is the household distance to roads in kilometer; 푥𝑖푡 is the vector of ∗ other time-varying and time-invariant regressors that influence 푃𝑖푡; 훼푝, 훽푝, 훾푝, and 훿푝 are the coefficients and the vector of coefficients associated with the regressors; 푢𝑖 indicates the

unobserved household-specific effects and 휀𝑖푡 is a random error, which is assumed to be normally distributed. Equation (3) shows the observed binary outcome variable. The tobit model can be shown as follow:

∗ 푔𝑖푡 = 훼푝푑𝑖푡 + 훽푝푟𝑖 + 훾푝푑𝑖푡 ∗ 푟𝑖 + 훿푝푥𝑖푡 + 푢𝑖 + 휀𝑖푡 (4) 훼 푑 + 훽 푟 + 훾 푑 ∗ 푟 + 훿 푥 + 푣 + 휀 𝑖푓 푔∗ ≤ 0 and 푔 = { 𝑔 𝑖푡 𝑔 𝑖 𝑔 𝑖푡 𝑖 𝑔 𝑖푡 𝑖 𝑖푡 𝑖푡 (5) 𝑖푡 0 표푡ℎ푒푟푤𝑖푠푒 ∗ Here, 𝑖 and 푡 subscripts refer to households and time periods respectively, 푔𝑖푡 is a latent dependent variable for the poverty gap, 푔𝑖푡 is the observed outcome indicating the poverty severity for poor while for non-poor households it takes the value of zero; 푑𝑖푡 is the crop diversification index; 푟𝑖 the household distance to roads; 푥𝑖푡 is the vector of other time-varying and time-invariant ∗ regressors that influence 푔𝑖푡, 훼𝑔, 훽𝑔, 훾𝑔, and 훿𝑔 are the coefficients and the vector of coefficients associated with the regressors, 푣𝑖 indicates unobserved household-specific effects and 휀𝑖푡 is a random error term. Equation 4 shows the observed censored outcome variable.

With respect to 푥𝑖푡, three groups of variables were included in this vector as control variables. The first group is related to socio-demographic household characteristics, such as household size, marital status, age, gender and employment status of household head, literacy, share of women, proportion of active members. The second group of variables included the household assets, such land per capita, and the number of non-farm enterprises owned or operated by the household’s members. The third group included the annual amount of rainfall in the household community or village. 3.2 Identification Strategy It is important to note that there may be some unobserved characteristics of a farm household that may influence both the level of crop diversification as well as the household poverty status or gaps. In addition, road location is not random while dwelling near a constructed road is not also random. Controlling for these factors is important for unbiased estimates. However, there are some studies that consider crop diversification to be exogenous, by providing only suggestive results about the relationship between diversification and poverty (Baird & Gray 2014; Bezu et al., 2012; Bigsten & Tengstam, 2011). To provide clear evidence that crop diversification reduces poverty, some recent studies attempted to control for the diversification potential endogeneity by using instrumental variable approaches for crop diversification (Michler and Josephso 2017; Asfaw et al., 2018). In this study, we assumed that the unobserved households’ characteristics, which may be the main source of bias, are invariant over time. Therefore, removing or controlling them could allow us to ensure that our estimates are unbiased. As we disposed of a panel data set of Niger, with a fixed effect model it is possible to remove these unobserved time invariant households’ characteristics. The fixed effects panel data model is a common method of handling such biases (Mundlack, 1978). In a linear case, the fixed effects panel data model, in essence, demeans the data, which removes all time invariant information and expresses it as part of individual household constants. By assuming that individual household effects are time invariant, this demeaning removes them from

the error term, effectively eliminating any related bias. However, with this model of fixed effects panel, time invariant variables are eliminated in the demeaning which means that by applying this approach our variable indicating access to the main road is also removed as it is invariant over the period of our study. Mundlak’s (1978) approach was suggested in the literature as an alternative to the fixed effects model allowing to control for unobserved time invariant variables in a random coefficient model, while keeping relevant time invariant variables such access to a main road here in this study, which is invariant over the period of our study. It also provides an alternative test to the Hausman test. Mundlack’s approach (1984) is based on the control of invariant unobservant effects in a random effect model. Therefore, panel-level averages of time-varying covariates are included as regressors in the model. More importantly, Mundlak’s (1978) approach applies to commonly used models, such as unobserved effects probit, tobit, and count models (Wooldridge, 2002). Therefore, in this study, we estimate binary probit and tobit random effects models combined with the additional regressors as required by the Mundlack (1978) approach. 3.2 Data source The defined methodology to answer our research questions was implemented on the socioeconomic panel data set collected during the Niger National Survey on Living Conditions and Agriculture (ECVMA). Our dataset is obtained by appending information obtained from the first survey visit in 2011 with those obtained from the second visit in 2014 to build a repeated cross section data. The ECVMA is a survey that cover many aspects of the households wellbeing in Niger, providing thus a comprehensive information on poverty levels and living conditions in Niger. The data collection was conducted by the Niger National Institute of Statistics in collaboration with the World Bank, which provided technical and financial assistance for the implementation of the surveys. Each household was visited twice: during the planting season, and during the harvest season. For each survey the data collection was conducted in two phases. During the first phase the households and agriculture/livestock questionnaires, as well as the community/price questionnaire, were filled, while during the second phase, only the households and agriculture/ livestock questionnaires were administered again to collect further relevant information. The ECVM/A surveys are representative of the country and of urban and rural areas.

4. Findings 4.1 Descriptive analysis

Table 1 displays a summary of the statistics of the variables used in the analysis for the years 2011 and 2014. The outcomes variables, poverty status and poverty gap, changed between the two years. The poverty rate decreased from 49.2% to 39.8%, while the poverty gap dropped from 0.132 to 0.112. With respect to variables used to calculate the crop diversification index, the average number of crops grown by household was 3.02 in 2011. This average decreased slightly to 2.96 in 2014. At village level, the average number of crops grown was 10.383 and 10.335 respectively in 2011 and 2014. However, in 2011 at least three crops were grown in each village, while in 2014 there were villages in which only one crop was grown indicating that some farmers in Niger are specializing over the time. In addition, in terms of access to roads the surveyed households were located on average 12.101 km indicating the continued low investment in road infrastructure in Niger.

Table 1: Summary Statistics

Year 2011 Year 2014 Variable Mean Min Max Mean Min Max Poverty Status 0.492(0.500) 0 1 0.398(.489) 0 1 Poverty gap 0.132(0.179) 0 0.773 0.112(0.176) 0 0.791 HH crops 3.020(1.261) 1 9 2.963(1.057) 1 8 Village crops 10.383(3.215) 3 22 10.335(3.539) 1 21 Crops diversification index 0.320(0.165) 0.045 1 0.321(0.159) 0.048 1 HH distance to road 12.101(16.160) 0 124 12.101(16.160) 0 124 HH size 7.176(3.541) 1 30 7.101(3.443) 1 27 Female HH head 0.075(0.264) 0 1 0.127(0.333) 0 1 HH head age 45.169(14.407) 17 95 47.779(14.091) 18 98 HH head literacy 0.165(0.371) 0 1 0.137(0.344) 0 1 Unemployed HH Head 0.030(0.170) 0 1 0.047(0.211) 0 1 Land per capita 0.893(1.588) 0 29.703 0.723(1.618) 0 34.742 Share of women 0.512(0.172) 0 1 0.518(0.189) 0 1 Share of active member 0.459(0.189) 0 1 0.425(0.185) 0 1 Number of nonfarm enterprises 1.047(1.079) 0 6 0.878(0.988) 0 6 Rainfall amount 3.931(1.073) 0.6 8.18 3.931(1.073) 0.6 8.18 Standard errors in parentheses. In terms of households’ characteristics, households had 7.176 and 7.101 members on average in 2011 and 2014, while the average age of household head increased from 45.169 to 47.779 between 2011 and 2014. However, the household head’s literacy rate decreased from 0.165 to 0.137 over that same period. In addition, the share of unemployed household heads increased from 0.03 to 0.047. The share of women in the households was in average 0.512 and 0.518 respectively in 2011 and 2014, while the share of active members decreased from 0.459 to 0.425 over the same period.

The cultivated land per capita also decreased from 0.883 ha to 0.723 ha. Finally, it also appears that while in 2011, in average, households had ownership of at least one non-farm enterprise, in 2014, households had less than one non-farm enterprise, indicating that more households are reducing their involvement in non-farm activities. Using the definition of the crop diversification index described in the methodology, the crop diversification index was computed. Figure 1 displays crop diversification index histograms and densities for the years 2011 and 2014. The 2014 density shows that the distribution is not skewed left compared to 2011 indicating that one cannot consider that there is an increase in specialization in agricultural production. It seems that some households increased their levels of diversification while others reduced theirs in 2014, compared to 2011. Figure1: Distribution of crops diversification index in 2011 and 2014

2011 2014

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D 0 0 .5 1 0 .5 1 crops diversity index Crop diversification index Density 4.2 Econometric results kdensity crop_diversity 4.2.1 ImpactGraphs by on periode Poverty Table 2 displays the results of the panel data econometric estimation of the models expressing the effect of crop diversification on poverty status and poverty gap under access to road constraints. The assessment on poverty status through the probit regression model revealed important results. First, the variables of interest are significant including the crop diversification index and the access to road at 1% and the interaction of the two variables at 5%. The sign of the crop diversification variable is negative indicating that the increase in crop diversification was negatively associated to the probability of being poor. In other words, pursuing a crop diversification strategy may help farm households escape poverty. This finding is in line with those of similar studies conducted in other contexts, and even in Niger. More importantly, our estimates show that the effect of crop diversification on poverty varied depending on the degree of a farm household’s access to road. In

fact, the interaction of the crop diversification variable and access to road is significantly positive. Therefore, the effect of crop diversification on households’ poverty status is higher for farm households that are located closer to roads, compared to those that are based further away from main roads. Therefore, access to roads matters immensely for farm households to be able to fully benefit from crop diversification strategies including having access to reliable markets for buying inputs and selling produce as shown in the theoretical framework. Constraint to access to roads may prevent farmers to increase their yields by not being able to sufficiently access to modern inputs and make higher profits when selling produce at markets. Where prices of modern seeds, fertilizers and pesticides are out of reach due to high transport costs, farmers cannot address issues of soil fertility and plant diseases, which may keep yields well below their potential. Access to output markets may improve farm households’ incomes with the potential to reduce poverty levels substantially. Table2: Probit and Tobit model estimates

Variables Probit : Poverty status Tobit : Poverty Gap Crops diversification index -0.372(0.114)*** -0.078(0.023)*** Log(Crops diversification index)*HH. distance to road 0.006(0.003)** 0.001(0.001)** HH. distance to road 0.014(0.005)*** 0.003(0.001)*** HH size 0.247(0.032)*** 0.062(0.006)*** Female HH head -0.204(0.233) -0.062(0.043) HH head age -0.026(0.008)*** -0.006(0.002)*** HH head literacy -0.027(0.191) -0.008(0.040) Unemployed HH Head 0.067(0.254) 0.030(0.055) Land per capita -0.024(0.029) -0.001(0.006) Share of women -0.492(0.402) -0.059(0.081) Share of active member -0.595(0.345)* -0.177(0.072)** Number of nonfarm enterprises 0.005(0.048) -0.013(0.009) Rainfall amount 0.062(0.038) 0.013(0.008) 퐿표푔̅̅̅̅̅̅(̅퐶푟표푝푠̅̅̅̅̅̅̅̅ 푑푖푣푒푟푠푖푓푖푐푎푡푖표푛̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅푖푛푑푒푥̅̅̅̅̅̅̅̅) 0.577(0.137)*** 0.119(0.027)*** 퐻퐻̅̅̅̅̅ ̅푠푖푧푒̅̅̅̅̅ -0.138(0.034)*** -0.037(0.006)*** 퐹푒푚푎푙푒̅̅̅̅̅̅̅̅̅̅̅ ̅퐻퐻̅̅̅̅ ̅ℎ̅̅푒푎푑̅̅̅̅ 0.152(0.265) 0.056(0.052) 퐻퐻̅̅̅̅̅ ̅ℎ̅푒푎푑̅̅̅̅̅ ̅푙푖푡푒푟푎푐푦̅̅̅̅̅̅̅̅̅̅ -0.608(0.224)*** -0.117(0.047)** 퐻퐻̅̅̅̅̅ ̅ℎ̅푒푎푑̅̅̅̅̅ ̅푎푔푒̅̅̅̅̅ 0.020(0.009)** 0.004(0.002)** 푈푛푒푚푝푙표푦푒푑̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅퐻퐻̅̅̅̅ ̅퐻푒푎푑̅̅̅̅̅̅̅ -0.541(0.368) -0.106(0.075) 퐿푎푛푑̅̅̅̅̅̅̅ ̅푝푒푟̅̅̅̅̅ 푐푎푝푖푡푎̅̅̅̅̅̅̅̅̅ 0.005(0.047) -0.003(0.009) 푆̅̅ℎ̅̅푎푟푒̅̅̅̅ ̅표푓̅̅̅ ̅푤표푚푒푛̅̅̅̅̅̅̅̅̅ 0.137(0.453) -0.038(0.092) 푆̅̅ℎ̅̅푎푟푒̅̅̅̅̅ 표푓̅̅̅̅ 푎푐푡푖푣푒̅̅̅̅̅̅̅̅̅ ̅푚푒푚푏푒푟̅̅̅̅̅̅̅̅̅̅ -1.514(0.412)*** -0.346(0.087)*** 푁푢푚푏푒푟̅̅̅̅̅̅̅̅̅̅̅̅ 표푓̅̅̅̅ 푛표푛푓푎푟푚̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅푒푛푡푒푟푝푟푖푠푒푠̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ -0.137(0.066)** -0.020(0.013) Constant 1.019(0.526)* 0.242(0.106)** Number of observations 2,822 2,822 Number of individuals 1,411 1,411 /lnsig2u -0.543(0.180) sigma_u 0.762(0.069) 0.178(0.010)*** /sigma_e 0.239(0.007)*** Rho 0.368(0.042) 0.356(0.031)*** Source: Authors. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 With respect to other control variables in the poverty status estimation, important results were also found. The household size appeared as a significant variable at 1%. The larger the size of a household, the higher the probability of being poor. This can be explained by the fact that households with more members have in general a higher dependency rate in Niger where the population is very young. However, a higher share of active members in a farm household decreases the household probability of being poor at 1%. In addition, the age of a household head was found to be significant at 1%, indicating that older households are less likely to be poor.

The results of the estimated tobit model, displayed in table 3, are similar to that of the probit model in terms of the effect of crop diversification under access to road constraints. The crops diversification index, the access to road variable and their interaction are significant at 1%. The sign of the crop diversification estimate is also negative, suggesting a negative impact of crop diversification on poverty gap. The positive sign of the estimated coefficient of the interaction variable highlights that the effect of crop diversification also depends on access to road constraints. Farmers who diversified and are based closer to roads benefited more from crop diversification in terms of reducing or closing the poverty gap than those farmers who are located further away from the main road. In other words, the access to road constraint decreases the positive effect of crop diversification strategies on reducing the poverty gap. With respect to other household’ characteristics, it appeared that first, the poverty gap increased with household size meaning that poverty severity is higher in households with a high number of members at the significance level of 1%. In addition, the household head age is negatively associated to poverty gap. Furthermore, the share of active members is negatively correlated to poverty gap at 5%. Finally, the response of poverty to crop diversification change is assessed through an elasticity computation. Table 3 displays the computed elasticities of poverty probability- crop diversification and poverty gap- crop diversification depending on the distance to a main road. Table3: Computed elasticities

Distance to road in kilometer Elasticity poverty probability- Elasticity poverty gap- crops crops diversification. diversification. 0 -0.288(0.088)*** -0.078(0.023)***

5 -0.261(0.084)*** -0.071(0.022)***

10 -0.235(0.082)*** -0.064(0.022)***

15 -0.210(0.081)*** -0.057(0.022)***

20 -0.185(0.080)** -0.051(0.022)**

25 -0.162(0.081)** -0.044(0.023)*

30 -0.139(0.083)* -0.037(0.024)

35 -0.117(0.086) -0.030(0.026)

40 -0.095(0.089) -0.023(0.027)

45 -0.075(0.092) -0.017(0.029)

50 -0.055(0.095) -0.010(0.031)

Source: Authors. standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

The results indicate that the magnitude of the elasticities decreases with the degree of constraint to access to road. The higher the distance to road, the lower the effect of crop diversification on poverty. It was also found that where the distance to a main road is less than 1 km, increasing the diversification by 50% reduces the probability of being poor by 14.4% while for a household located at 25km away from the main road, the probability is only 8.1%. However, beyond 35km the benefit of crop diversification is not significant completely neutralized by the constraint to road effect. For poverty gap- crop diversification elasticities, it appears that increasing crop diversification by 50% reduced the poverty gap by 3.9% and by 2.85% respectively for households living less than 1km and 15km away from a main road. Beyond 25 km, crop diversification has no

effect on the scale of poverty. This result has important policy implication in terms of poverty reduction strategies among farm households and maximizing road investments. To fully benefit from crop diversification programs, which is being promoted as a strategy for poverty reduction, farmers need to have easy access to roads. With the adverse impacts of climate change threatening to affect farming households in Niger (Montaud et al., 2016), more farm households may recourse to crop diversification to cope with production and market risks. Access to roads is crucial for poverty reduction agendas. By overcoming access to road constraints, governments can maximize the benefit of crop diversification strategies as adopted by farmers in Niger.

Conclusion Access to road is important in rural areas particularly for farming communities to fully benefit from crop diversification and move out of poverty. this study analyzed whether diversification can lift poor households out of poverty and reduce the poverty severity, under access to road constraints in Niger. The empirical evidence was provided using on econometric estimations of probit and tobit models. As the unobserved households’ characteristics, which may be the source of bias, binary probit and tobit random effects models combined with the additional regressors following the Mundlack (1978) approach have been estimated based on the assumption that these unobserved characteristics are invariant over time. The methodology was implemented on the socioeconomic panel data set obtained from the Niger National Survey on Living Conditions and Agriculture (ECVM/A). Our dataset is obtained by appending information obtained from the first survey visit in 2011 with those obtained from the second visit in 2014 to build a repeated cross section data. It is found that the increase in crop diversification was negatively associated to the probability of being poor indicating that pursuing a crop diversification strategy may help farm households escape poverty in Niger. More importantly, our findings show that the effect of crop diversification on poverty varied depending on the degree of a farm household’s access to roads. The effect of crop diversification on households’ poverty status is higher for farm households that are located closer to roads, compared to those that are based further away from main roads. Access to roads matters immensely for farm households to be able to fully benefit from crop diversification strategies. With respect to poverty severity, it is also highlighted a positive impact of crop diversification on reducing the poverty gap and farmers who diversified and are based closer to roads benefited more from crop diversification in terms of reducing or closing the poverty gap than those farmers who are located further away from a main road.

This study findings have important policy implication in terms of poverty reduction strategies among farm households. By overcoming access to road constraints, government can maximize the benefit of crop diversification strategies as adopted by farmers in Niger.

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