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On the Impact of Weather and Climate on Agriculture: Evidence from Ethiopia Abstract This paper assesses the impact of climate change and weather uncertainty in Ethiopia on agricultural productivity of households based on plot level panel data from the Amhara region. The major contribution of the analysis lies in distinguishing between weather variability and climate change that has huge significance in Ethiopian agriculture but that has not been assessed in previous related studies. The effects of the climate change and weather uncertainty are identified the modified pseudo fixed effects estimations that recognizes the possibility that climate change variables are fixed over time and varying across villages. Overall, both climate and weather variables are found to significant determinants of agricultural productivity, with farm level revenue being more responsive to seasonal climate change variables. The major policy implication of the result is that policy efforts to mitigate the permanent effects of weather uncertainty could be more important to the welfare of Ethiopian farmers than transitory measures. JEL classification: D2, Q12, Q15 Key words: farm revenue; climate change; weather variability, pseudo fixed effects method, Ethiopia 1. Introduction With their ultra weather-sensitive agrarian economy, developing countries and their economic development are likely to suffer tremendously from the threat of climate change (Mendelson and Dinar, 1999). Quantifying the impact of climate change on agricultural sector guides appropriate adaptation measures (Sachs et al. 1999; Stage, 2010) and ensures genuine participation of developing countries in climate change agreements (Cao, 2008). One critical issue in assessing the links between agricultural productivity and climate change is the actual measurement of climatic indicators. While it is a common practice to assess the potential impacts of climate change on agriculture based the long term mean values of expected climatic parameters, Roszenweig et al. (2009) argue that due consideration should be given to shorter term weather-related measures as well. Such short term measures would make visible extreme events and temporal spatial variations of more meteorological conditions that have signficant deviations from climate change. Indeed, climate figures generated from macro data for use in micro scales tend to poorly represent spatial-temporal variability and extremes (Maraun et al., 2010). The effects of such variations and extremes are likely to be captured through weather observations, as opposed to average climatic paramters (Fisher et al., 2007), implying that controlling for short term weather related factors is as important as the long term parameters in critically assessing the links between agriculture and climate change1. 1 Climate scientists emphasize the distinction between weather and climate. Weather is what occurs at a particular moment in time - typically, precipitation and temperature. Due to natural variability, weather fluctuates from one hour to another, one day to another, one month to another, and one year to another. Climate, by contrast, is the long-run pattern of weather over time. To climate scientists, therefore, climate change has a very different significance from weather change. A change in weather is inherently short-run, while climate change is a shift in the long-run pattern. Because these are different phenomena, it is not surprising that they also have different economic implications (Fischer et al, 2007). 1 Accordingly, this paper sets out to contribute to the discussion on the impact of climate change on agricultural productivity through distinguishing between long term climate measures and more short term weather measures. The effects of the climate change and weather uncertainty are identified the modified pseudo fixed effects estimations that recognizes the possibility that climate change variables are fixed over time and varying across villages. Distinguishing between weather variability and climate change has a huge significance in Ethiopian agriculture that suffers from moisture constraints and general irratic patterns even without the climate change impacts. Traditional Ricardian analyses of the impact of climate change on agriculture, introduced by Mendelsohn et al. (1994) assess the impact of future climate on farmers’ expected incomes by relying on the cross-sectional variation observed in current climate. The attractiveness of the Ricardian method is that it automatically captures adaptation, since farmers are adjusting inputs and outputs to match local conditions(Lang, 2007) and it can be employed to fairly readily available data (Felezi, 2010), as evidenced by its application in cases across the world2. These advantages of the Ricardian analysis hinge on three critical implicit assumptions. The first assumption is that average climatic parameters sufficiently capture the impact of climate change on agricultural productivity. However, as (Mendelsohn et al., 2010) argue, the model is not measuring the effects of year-to-year change in weather but a longterm change in climate. By doing so, the analysis fails to take account of change in climate variation or extreme events (Salvo, 2010). More critically, to the extent that the climate parameters capture the long term, average features of climate change, and not the short term variations and extreme events, the approach would fail to account for the full range of farmer 2 adaptations. Second, the analysis could suffer from omitted variable bias, as such cross sectional analyses insufficiently deal with unmeasured characteristics, that are important determinants of output and land values in agricultural settings, (Deschenes and Greenstone 2007). In a significant deviation from earlier cross sectional approaches, Deschenes and Greenstone (2007) assess the link between agricultural production and climate change by distinguishing between the effects of weather and climatic parameters using a panel data analysis. They argue that consistent estimation of the effect of climate change requires that the observed covariates are orthogonal to the error term which will be invalid if unobserved transitory or permanent factors co-vary with the climate variables. Accordingly, they estimate the effect of weather on agricultural profits3, controlling for county fixed effects and stateyear interactions.This enabled controlling for time-invariant idiosyncratic features of the county thereby removing long term climate variables as they are fixed over the duration of the panel using county level fixed-effects thereby. The estimated impacts of temperature and precipitation on agricultural profits are multiplied by the predicted change in climate to infer their welfare impact. While the use of panel data analysis is a significant improvement over previous analyses, Fisher et al. (2007) argue that the approach by Deschenes and Greenstone (2007) does not control for time-varying omitted variables which may be quite highly correlated with weather shocks and, therefore, it may measure something different from the impact of climate on long-run profit. As a result, their estimates may reflect the short-run response to fluctuations in weather and therefore does not allow for longer-run adaptation. 3 The inclusion of the county fixed effects necessitates two substantive differences in the use of variables from traditional Ricaridian analyses: the dependent variable, is agricultural profits, instead of land values and the climate change impact is approximated by with annual realizations of weather. 3 Similarly Masseti and Mendhalson (2010) argue that intertemporal methods that eliminate cross sectional variation and focus on year to year changes not as useful as the cross sectional variation, as interannual changes in weather are a poor proxy for climate. In addition, the coefficients of the time varying variables should not change over time, as is the case in Deschenes and Greenstone (2007)’s analysis. In an ideal panel data model, the coefficients of the time invariant variables should also not change. Accordingly, they estimate the Ricardian mode by interacting the climate variables with year dummies in order to allow the climate coefficients to change over time. These are compared with a two stage estimation in which land value is regressed on the time varying variables using the covariance method with county fixed effects followed by a regression of the time-mean residuals on the time invariant variables. Instead of a single set of coefficients for the climate variables, one has a different estimate for each year. Other recent panel data analyses of the relationship between climate change and agricultural productivity include Fezzi et al (2010) who argue that the quadratic forms that are typically used in modelling climatic variables are not restrictive and explore the relationships of interst using Additive Mixed Model (AMM) of land value, that have flexible smooth property, on a panel of farms situated throughout England and Wales (EW). Timmins (2003) argues that traditional Ricardian analysis may yield biaed results when land-use decisions depend on the climate attributes being valued and when land has unobserved attributes that differ with the use to which it is put. Our approach builds on these previous analyses of the use of panel data approaches to exploring the links between climate change and agricultural profits 4. The major departure of 4 We follow many Ricardian studies in developing countries for using agricultural profit instead of land values. the use of land values in such analyses requires land markets working perfectly such that land prices reflect the present discounted value of land rents into the infinite future (Deschenes and Greenstone 2007) which does not hold in Ethiopia lack of prices for land sales due to full state ownership of the land (Difalco et al. 2011;). 4 our approach lies in the use of (long term) climate measures along with short term (weather) measures, at the same time controlling for unobserved heterogeneity using a four-wave survey data from Ethiopia. As we argued earlier, the essence of controlling for both climate and weather factors is to ensure that the full range of adaptation options (to both weather variability and weather change) are taken into account in the Ricardian analysis. While previous studies assume that farmers do adapt to climate change but not to weather change (e.g. Masseti and Mendhalson (2010), there exists evidence that farmers in developing countries indeed adapt to weather changes as well. In this respect, we follow Difalco et al. (2011) who analyse the impact of adaptation on farm households’ food productivity using a simultaneous equations model to account for the heterogeneity in households’ decision to adapt to climate change or not. Their measure of climate/weather variable distinguishes between the long term and short term patterns of weather and climate. However, unlike our approach, their analysis is based cross section data, and hence does not enable controlling for unobserved heterogeneity. This paper is concerned with empirically assessing the differential impacts of weather variability and climate change on productivity of Ethiopian farmers. Plot level panel data from Ethiopia combined with 30 year monthly rainfall and temperature data are employed in the analysis. Both moving average and time invariant long term mean measures are used as climate variables while the weather variables were constructed as both averages for the years corresponding to the survey years as well as lags from the survey years. Both annual and seasonal precipitation and temperature measures are used in the analysis. The study makes two major contributions to the existing literature on the impact of climate change on productivity. First, while the effects of annual and seasonal weather patterns are likely to differ from the long term patterns of climate change, these possible differentials 5 have not been thoroughly investigated. To the extent that the pattern of climate change mimics weather uncertainty, policy measures aimed at mitigating the impacts of climate change could also serve the same purpose as those as weather uncertainty. This distinction is highly relevant in a setting like Ethiopia where both seasonal and yearly variations in rainfall are huge and rainfall is hugely erratic. Secondly, the study makes an important methodological contribution to the analysis of the links between climate change and agriculture by applying a modified pseudo fixed effects analysis that enables keeping the time invariant effects of climate change, the time variant effects of weather and at the same time controlling for unobserved effects at a household level that potentially lead to omitted variable bias in cross sectional Ricardian studies. The rest of the paper is organized as follows. Section 2 presents an overview of the roles of climate change and weather uncertainty in Ethiopia. In Section 3, the data employed in the empirical analysis is presented. The econometric methodology employed is discussed in section 4. Section 5 discusses the empirical findings and section 6 concludes the paper. 2.Weather variability, climate change and agricultural productivity in Ethiopia: a Background Agriculture remains one of the most important sectors in the Ethiopian economy for the following reasons: (i) it directly supports about 83% of the population in terms of employment and livelihood; (ii) it contributes over 40% of the country’s gross domestic product (GDP); (iii) it generates about 85% of export earnings; and (iv) it supplies around 73% of the raw material requirement of agro-based domestic industries (MEDaC 1999; AfDB 2011). It is also the major source of food for the population and hence the prime contributing 6 sector to food security. In addition, agriculture is expected to play a key role in generating surplus capital to speed up the country’s overall socio-economic development (MEDaC 1999). Ethiopia has a total land area of about 112.3 million hectares. Of this, about 16.4 million hectares are suitable for producing annual and perennial crops. Of the estimated arable land, about eight million hectares is used annually for rainfed crops. The country has a population of about 80million with a growth rate of about 2.6% (AfDB 2011). Small-scale farmers who are dependent on low input and low output rainfed mixed farming with traditional technologies dominate the agricultural sector. The present government of Ethiopia has given top priority to this sector and has taken steps to increase its productivity. However, various problems are holding this back. Some causes of poor crop production are declining farm size; subsistence farming partly due to population growth; land degradation due to inappropriate land use such as cultivation of steep slopes; overcultivation and overgrazing; and inappropriate polices. Other causes are tenure insecurity; weak agricultural research and extension services; lack of appropriate agricultural marketing; an inadequate transport network; low use of fertilizers, improved seeds and pesticides; and the use of traditional farm implements. However, the major causes of low level of production are drought, which often causes famine, and floods. These climate related disasters make the nation dependent on food aid. With agriculture almost completely dependent on rainfall, rain rules the lives and well-being of many rural Ethiopians. It determines whether they will have enough to eat and whether they will be able to provide basic necessities and earn a living. Indeed, the dependence on rainfall and its erratic pattern has largely contributed to the food shortages and crop crises that farmers are constantly faced with. Even in good years, the one-time harvest or 7 crop may be too little to meet the yearly household needs; as a result, the majority of Ethiopia’s rural people remain food insecure (Devereux 2000)5. Rainfall contributes to poverty both directly, through actual losses from rainfall shocks, and indirectly, through responses to the threat of crisis. The direct impacts particularly occur when a drought destroys a smallholder farmer’s crops. Under such circumstances, not only will the farmers and their families go hungry, but they also will be forced to sell or consume their plough animals in order to survive. They are then significantly worse off than before because they can no longer farm effectively when the rains return (Barrett et al. 2007). Ethiopia is one of the African countries that have recently started paying particular attention to climate change as a national as well as a global problem. Ethiopia is active in climate change negotiations representing Africa. There are also a number of activities undertaken over the past decade to address climate change. These include submission of initial national communications on GHG emissions to UNFCCC, submission of National Adaptation Program of Action (NAPA) as well as the Nationally Appropriate Mitigation Actions (NAMA) to UNFCCC, preparation of a REDD readiness plan, and ongoing preparation of a national level adaptation program as well as a green growth strategy as part of a climate resilient green economy (CRGE) strategy. Such attempts should be supported by rigorous empirical studies on various aspects of climate change including its impacts which are very limited. 5 Ethiopia has experienced at least five major national droughts since 1980, along with literally dozens of localized ones (World Bank 2008b). These cycles of drought create poverty traps for many households, constantly consuming any build up of assets and increase in income. Evidence shows that about half of all rural households in the country experienced at least one major drought during the five years preceding 2004 (Dercon 2009). The evidence also suggests that these shocks are a major cause of transient poverty. That is, had Ethiopian households been able to smooth consumption, then poverty in 2004 would have been at least 14 percent lower, which translates into 11 million fewer people falling below the poverty line. 8 3. The data Our data was collected through a rural household survey conducted by the Ethiopian Development Research Institute and Addis Ababa University in collaboration with Gothenburg University, and through financial support from the Swedish International Development Agency (Sida). The survey sites include households in two Zones (South Wollo and East Amhara) of the Amhara National Regional State, a region that encompasses part of the Northern and Central Highlands of Ethiopia and was conducted in the years 2000, 2002, 2005 and 2007 cropping seasons on the same households. The rainfall and temperature data obtained from the Ethiopian Meteorology Authority includes monthly observations from the years 1976 to 2006, collected in stations close to the study villages (kebeles). The farming system is a mixed crop-livestock system, with a household having several field plots for crop cultivation, and livestock grazing mainly on communal fields. The crop production system consists of cereals, pulse and legume crops, oil seeds and others. The major cereal crops include teff, wheat, barley, sorghum and maize. Pulses cover several kinds of beans and peas as well as lentils and vetch. Perennials include coffee, fruit trees (orange, mango, papaya, banana, avocado, guava, and pineapple) and spices. It is evident that cereal crops account for the majority of the crops grown in the study area. This followed by a considerable number of plots covered by pulse crops and legumes. Oil crops also account for a small share of the crops grown, followed by vegetable crops, spices, and perennials. While these are the major crops, several different crops are grown by households albeit in much smaller quantities. 9 Table 2 presents description of the variables along with their descriptive statistics. It should be noted that since the regressions are based on household and not plot level observations, all the variables are household level or averaged at a household level. 3.2. Variables used in the analysis Dependent variable: Farm level crop revenue The dependent variable is measured as the total revenue from the different crops grown on a given farm. Weather and climate change measures6 The climate change and weather measures are constructed from the respective monthly from the stations. Average annual and seasonal rainfall and temperature measures corresponding to the survey years are used as weather indicators. For climate change measures we used two measures: moving average and overall average measures. For the year 2007, for instance, the rainfall and temperature moving average climate change measures are calculated as the average of the monthly observations for the years 1982 to 2006. Similarly moving average climate measures for the years 1980 to 2004, 1978 to 2002 and 1976 to 2000 represent the climate variables for the years 2005, 2002 and 2000 respectively. Similarly, the long term annual average measures are calculated as the average of the observations between the years 1976 and 2006 for the respective villages. We chose the spring (the Belg) and the summer (the Kiremt) months in the seasonal weather and climate measures as they correspond to the minor and major rainy seasons 6 The rainfall and temperature data are obtained from eight meteorological stations close to the twelve study villages. In consultation with the Meteorology Authority, the rainfall and temperature values assigned to the villages were based on proximity. Hence, the rainfall data we have is village level i.e. households in two villages sharing the same rainfall values, in some instances. 10 respectively7. Accordingly, the moving average climate measure for the kiremt season includes the mean rainfall and temperature values for the 26 years in the kiremt season. Similarly, the Belg (spring) long term moving average measure is calculated as the mean rainfall and temperature values for the spring months. Accordingly, the long term Belg and Kiremt seasonal moving average measures are computed as the seasonal means for the years 1976 to 2000, 1978 to 2002, 1980 to 2004, and 1982 to 2006 for the years 2000, 2002, 2005 and 2007 respectively. The mean seasonal rainfall and temperature observations corresponding to the survey years are used as seasonal weather measures. Similarly, the annual weather measure is computed as the mean rainfall and temperature observations corresponding to the survey years . Other independent variables Female headed households make up around 19% of the respondents. An average percentage of the respondents that are able to write is 39%. The average age of the respondents is 47. On average male and female adult members within the household are 2 and 1.9 respectively. This is not surprising considering that there are limited off-farm opportunities and limited mobility out of agriculture in the study area and in rural Ethiopia in general. The average livestock holding was 5.182 units (tropical livestock units) and oxen ownership is around 1.87, for the average household. The average land holding per household in the area is below 1.18 ha. The proportion of fertile plots in 0.42. flat sloped plots represent 0.67 share of the plots, compared with red plots at 0.5. 7 Meher season (approximately June-September) crops harvested in September-December make up the bulk of food production (90-95%), the belg is the short rainy season, which extends from February to May and Belg production typically accounts for only 5-10% of total annual production (CSA, 2001). 11 4. Estimation Procedure This section sets up a framework for analyzing the link between the farm level and weateher and climate variables. We frame our analysis within the standard Ricardian analysis, which is represented by equation 1: rht xht vht h ht , (1) where ht is farm household h in period t. Farm household-level revenue at time t is denoted by Rht ,8 X ht represents the socioeconomic and farm-level characteristics, and Vht stands for weather and climate variables at time t. , , and represent the respective vector of parameter estimates, and it represents the error term. The composite error term it i uit is composed of a normally distributed random error term u ij ~ n(0, u2 ) and an unobserved household specific effect, h . Under the assumption that h is orthogonal to the observable covariates, a random effects estimator can be employed as an effective estimator of equation (2) (Baltagi 2001; Wooldrige 2002). However, allowing arbitrary correlation between h and the regressors/observed covariates requires a fixed effect, as it takes h to be a group-specific constant term and uses a transformation to remove this effect prior to estimation (Wooldrige 2002). To remedy the major drawback of removing the household specific effects of the fixed effects estimator, Mundlak (1978) and Chamberlain (1982; 1984)9 suggest replacing the Rit takes two distinct measures in our analysis: the beta coefficient and the risk ranking measure. 8 Note that 9 Also note that the strict exogeneity assumption on the observed covariates conditional on h is maintained, although the arbitrary correlation between the two is allowed in this case. This implies that the observed covariates only contain time-varying explanatory variables. 12 unobserved effect, with its linear projection onto the explanatory variables, in all time periods plus the projection error. Allowing for correlation between h and xh , and assuming a conditional normal distribution with linear expectation and constant variance, implies that h can be approximated by the linear function in equation (3): _ h x h eh _ eh | x h ~ N (0, 2 ) , (2) _ where x h is the average of the time varying variables in xht , and 𝜎𝑒2 is the variance of eh in equation (3). Substituting the expression in equation (3) for h in equation (2) gives: _ rht xht vht x h ht , ht ~ N (0, 2 ) . (3) This approach of adding the means of time-varying observed covariates as controls for the unobserved heterogeneity without the data transformation in the fixed effects estimator is commonly known as pseudo fixed effects or the Mundlak-Chamberlain random effects model (Wooldridge 2002). 5. Discussion of results and conclusion Table 2 presents the estimation results of the productivity analysis, controlling for the impacts weather variables. This is followed up in Table 3 and Table 4 by the estimation results controlling for the impacts of climate variables and both weather and climate variables respecitively. 13 The objective of this analysis presented in Table 2 is to see the differential impacts of weather and climate variables on farm level revenue. The coefficient corresponding to the seasonal rainfall and temperature variables is insignificant. However, the climate measures are significant determinants of productivity. Overall, these results confirm that climate related variables have significant impact on productivity than weather related variables. Education has a positive and significant impact on productivity. Farm size is negative and significant, conforming to many previous findings that relate to the inverse farm-size productivity relationship. The number of male adults per ha is positive and significant across all the regressions, while its square is negative and significant. This indicates that male labour availability has a strong positive, albeit non linear, impact on productivity. As would be expected, chemical fertilizer and manure contribute positively to productivity; the magnitudes are small, however. Oxen has a negative and significant impact on productivity. Of the soil characteristics, soil slope appears to be the most significant determinant of productivity, while soil colour and fertility are insignificant. In a heavily rainfed agriculture like that of Ethiopia, agricultural incomes may understandably be constrained by weather uncertainty. To the extent that weather variability is stable over time, it may also mimic climate change, making the impact of climate change on agricultural productivity akin to that of weather variability. However, if yearly weather variabtion is by itself irratic, climate change needs to be measured separately and its imact needs to be analysed on its own right. This article assesses the importance of rainfall patterns, and long term weather averages measuring climate change, on farm level crop productivity. The analysis employs plot level panel data from Ethiopia,combined with a 30 year meteorolgical data corresponding to the survey villages, used to construct seasonal and yearly rainfall variability as well as a measure of long term averages. Results using both random and pseudo fixed 14 effects analyses showed that annual rainfall variability is a significant and positive determinant of productivity, although the effect of seasonal rainfall variability and climate change appears to be stronger. This implies that households income is highly conditioned on weather variability. Similarly, climate change variables are significant determinants of productivity while their effect is not uniform for the seasonal measures. Rural development policies should take into account the farmers weather risk management in the face of weather uncertainty. The results highlight that the differential impacts of weather and climate change on productivity. This further points to the need to carefully distinguish between climate change adapation and weather change mitigation/adaptation measures. 15 References AfDB (African Development Bank) (2011). Federal Democratic Republic of Ethiopia: Country Strategy Paper 2011-15, April. Antle, J. 2010. Adaptation of Agriculture and the Food System to Climate Change: Policy IssuesIssue Brief 10‐03. Washington, DC: Resources for the Future. Barrett, C.B., B.J. Barnett, M.R. Carter, S. Chantarat, J.W. Hansen, A.G. Mude, D.E. Osgood, J.R. Skees, C.G. Turvey, and M.N. Ward. (2007) ‘Poverty Traps and Climate Risk: Limitations and Opportunities of Index-Based Risk Financing’. IRI Technical Report 07- 03. New York: Columbia University. Chavas, J.P., and M.T. Holt (1996): “Economic Behavior Under Uncertainty: A Joint Analysis of Risk Preferences and Technology.” Review of Economics and Statistics 78:329– 35. 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Accessed 23 February 2010. 18 Table 1: Definition of variables used in the regressions and descriptive statistics Variable Description Socio economic characteristics of the household Sex Sex of the household head 0.192 0.394 Age Age of household head 46.763 18.006 Write Head’s formal education (1=read and write ; 0=otherwise) 0.392 0.488 Adult male The number of male working-age family member of the household 1.957 1.235 Adult female The number of female working-age family member of the household 1.876 1.056 Oxen The number of oxen 1.877 1.393 Livestock The number of livestock ( in tropical livestock units) 5.182 4.244 Physical farm characteristics of the household Landsize Total farm size of the household in hectares 1.777 14.841 avg_fertile Proportion of highly fertile plot in the total plots managed by the household 0.420 0.372 avg_red propotion of red soil plots in the total plots managed by the household 0.501 0.370 avg_flat slope proportion of flat slope plot in the total plots managed by the household 0.675 0.335 Time variant variables (averaged over the survey years) mean_female Number of female adults averaged over years 5.769 8.719 mean_male The number of livestock averaged over years 22.496 25.729 mean_ox The number of oxen averaged over years 8.963 10.027 mean_livestock Number of male adults averaged over years 3.969 3.022 Weather and climate change variables Annual mean the mean annual rainfall corresponding to the survey year 0.972 1.117 Summer mean the mean summer rainfall corresponding to the survey year 0.271 0.085 Spring mean the mean spring rainfall corresponding to the survey year 0.496 0.162 Long term annual the long term mean annual rainfall corresponding to mean (by survey the survey year year) Long term summer the long term mean summer rainfall corresponding mean (by survey to the survey year year) Long term spring the long term mean spring rainfall corresponding to mean (by survey the survey year year) 19 Long term annual mean (by kebele) Long term summer mean (by kebele) Long term spring mean (by kebele) Total farm revenue the long term mean annual rainfall corresponding to the survey kebele the long term mean summer rainfall corresponding to the survey kebele the long term mean spring rainfall corresponding to the survey kebele The sum of all revenues from the different crops grown by the household 0.484 0.323 Table 2: Ricardian analysis of the impact of climate variables on farm level revenue farm_output1 age Coef. Std. Err. z P>z [95% Conf. -0.57305 1.312125 -0.44 0.662 sex -73.4218 48.91548 maleadult 156.7915 18.75093 femaleadult 164.9335 write Interval] -3.14477 1.998666 -1.50 0.133 -169.294 22.45078 8.36 0.000 120.0404 193.5427 18.37549 8.98 0.000 128.9182 200.9488 -2.80815 33.33372 -0.08 0.933 -68.1411 62.52475 livestock -0.24937 2.758662 -0.09 0.928 -5.65625 5.15751 oxen 0.113002 2.768466 0.04 0.967 -5.31309 5.539095 landarea 0.397093 0.704515 0.56 0.573 -0.98373 1.777916 avg_red 178.7846 44.01943 4.06 0.000 92.50808 265.0611 avg_white -115.338 122.4915 -0.94 0.346 -355.417 124.7407 avg_flatslop 167.5325 72.73298 2.30 0.021 24.97849 310.0865 avg_mslope 200.4785 78.40171 2.56 0.011 46.814 354.1431 avg_fertile 139.632 33.79677 4.13 0.000 73.39159 205.8725 telma -195.439 107.9793 -1.81 0.070 -407.075 16.19631 yamed -426.838 125.7327 -3.39 0.001 -673.27 -180.407 mfemale -162.604 40.65977 -4.00 0.000 -242.296 -82.9126 mmale -18.1014 36.37587 -0.50 0.619 -89.3968 53.19402 mox -109.555 11.03473 -9.93 0.000 -131.182 -87.9268 109.535 10.99509 9.96 0.000 87.98501 131.085 kebele_spr~p 171.1743 24.94281 6.86 0.000 122.2873 220.0613 kebele_sum~p -150.179 22.83675 -6.58 0.000 -194.938 -105.419 kebele_spr~f -2.33866 4.590694 -0.51 0.610 -11.3363 6.65894 kebele_sum~f 11.76492 0.84419 13.94 0.000 10.11034 13.4195 _cons -2189.91 619.7544 -3.53 0.000 -3404.61 -975.213 sigma_u 844.8592 sigma_e 1158.592 rho 0.347152 mlivestock (fraction of variance due to u_i) 20 Table 3: Ricardian analysis of the impact of weather variables on farm level revenue farm_output1 Coef. Std. Err. z P>z [95% Conf. Interval] age -0.57305 1.312125 -0.44 0.662 -3.14477 1.998666 sex -73.4218 48.91548 -1.50 0.133 -169.294 22.45078 maleadult 156.7915 18.75093 8.36 0.000 120.0404 193.5427 femaleadult 164.9335 18.37549 8.98 0.000 128.9182 200.9488 write -2.80815 33.33372 -0.08 0.933 -68.1411 62.52475 livestock -0.24937 2.758662 -0.09 0.928 -5.65625 5.15751 oxen 0.113002 2.768466 0.04 0.967 -5.31309 5.539095 landarea 0.397093 0.704515 0.56 0.573 -0.98373 1.777916 avg_red 178.7846 44.01943 4.06 0.000 92.50808 265.0611 avg_white -115.338 122.4915 -0.94 0.346 -355.417 124.7407 avg_flatslop 167.5325 72.73298 2.30 0.021 24.97849 310.0865 avg_mslope 200.4785 78.40171 2.56 0.011 46.814 354.1431 avg_fertile 139.632 33.79677 4.13 0.000 73.39159 205.8725 telma -48.4462 106.7838 -0.45 0.650 -257.739 160.8463 yamed -427.409 128.7094 -3.32 0.001 -679.674 -175.143 mfemale -162.604 40.65977 -4.00 0.000 -242.296 -82.9126 mmale -18.1014 36.37587 -0.50 0.619 -89.3968 53.19402 mox -109.555 11.03473 -9.93 0.000 -131.182 -87.9268 109.535 10.99509 9.96 0.000 87.98501 131.085 4.011996 0.470906 8.52 0.000 3.089037 4.934954 ksummer_te~2 -3.47323 0.413502 -8.40 0.000 -4.28368 -2.66278 kspring_rf2 0.010397 0.026978 0.39 0.700 -0.04248 0.063273 ksummer_rf2 0.027823 0.001863 14.93 0.000 0.024171 0.031475 _cons -1172.25 374.5671 -3.13 0.002 -1906.39 -438.111 sigma_u 844.8592 sigma_e 1158.592 rho 0.347152 mlivestock kspring_te~2 21 Table 4: Ricardian analysis of the impact of climate and weather variables on farm level revenue arm_output1 Coef. Std. Err. z P>z [95% Conf. Interval] age -0.57305 1.312125 -0.44 0.662 -3.14477 1.998666 sex -73.4218 48.91548 -1.50 0.133 -169.294 22.45078 maleadult 156.7915 18.75093 8.36 0.000 120.0404 193.5427 femaleadult 164.9335 18.37549 8.98 0.000 128.9182 200.9488 write -2.80815 33.33372 -0.08 0.933 -68.1411 62.52475 livestock -0.24937 2.758662 -0.09 0.928 -5.65625 5.15751 oxen 0.113002 2.768466 0.04 0.967 -5.31309 5.539095 landarea 0.397093 0.704515 0.56 0.573 -0.98373 1.777916 avg_red 178.7846 44.01943 4.06 0.000 92.50808 265.0611 avg_white -115.338 122.4915 -0.94 0.346 -355.417 124.7407 avg_flatslop 167.5325 72.73298 2.30 0.021 24.97849 310.0865 avg_mslope 200.4785 78.40171 2.56 0.011 46.814 354.1431 avg_fertile 139.632 33.79677 4.13 0.000 73.39159 205.8725 mfemale -162.604 40.65977 -4.00 0.000 -242.296 -82.9126 mmale -18.1014 36.37587 -0.50 0.619 -89.3968 53.19402 mox -109.555 11.03473 -9.93 0.000 -131.182 -87.9268 109.535 10.99509 9.96 0.000 87.98501 131.085 kebele_spr~p -181.304 76.74917 -2.36 0.018 -331.73 -30.8784 kebele_sum~f 25.13217 12.61348 1.99 0.046 0.410208 49.85413 kspring_te~2 6.728456 2.186762 3.08 0.002 2.442482 11.01443 ksummer_te~2 -1.89459 0.862317 -2.20 0.028 -3.5847 -0.20448 kspring_rf2 -0.01034 0.038895 -0.27 0.790 -0.08657 0.065897 ksummer_rf2 -0.02465 0.028999 -0.85 0.395 -0.08149 0.032187 _cons -1852.12 1310.821 -1.41 0.158 -4421.29 717.0406 sigma_u 844.8592 sigma_e 1158.592 rho 0.347152 mlivestock 22