Groupe de recherche en économie appliquée et théorique. aéâ{éâå gütéü

Transcription

1 Les Journées du savoir Groupe de recherche en économie appliquée et théorique aéâ{éâå gütéü Doctorate, Hunter College, Department of economics, City University of New York, 695 Park AVE, New York, NY 1001 (USA) . Bamako, février 01 BP. E155 Bamako (Mali) Tel. (+3) Web.

2 Table of contents Abstract Introduction Literature Review Research Design and Data Model Effective Labor Endogeneity and Simultaneity Instrumental Variables Data Empirical Results Conclusion References Appendix... 18

3 Abstract Using household panel data from the northern region of Mali, I test for the effect of nutritional status - proxied by caloric intake and weig-for-heig - on farmers productivity. The estimation method takes into consideration the potential bias that could arise due to the endogeneity of nutritional status and individual unobserved heterogeneity. While caloric intake is found to have a strong and statistically-significant effect on productivity, output is not responsive to weig-for-heig. The output elasticity is found to be substantially responsive to changes in level of caloric intake with a stronger effect at a low level of intake. Contrary to most previous empirical results, it is found that the effect of caloric intake on farm productivity is strictly increasing, although at a decreasing rate, since the level of intake at which extra calories will decrease productivity is not reachable. 1. Introduction The idea that a person s capacity to work is related to his nutritional status is not a recent one. For a long time, scientists, nutritionists, and economists have been exploring possible manifestations of this idea. In their 1950 book, The Biology of Human Starvation, Keys et al. 1 write: It is common knowledge that the capacity of an organism to accomplish physical work decreases with continued caloric undernutrition. Weakness is one of the cardinal symptoms of starvation; in extreme cases the individual does not possess sufficient strength and endurance to walk or even to stand, but remains prostrate, passively waiting for death. In spite of the general appreciation of the qualitative aspects of the physical deterioration produced by famine conditions, no quantitative analysis of the progressive deteriorative processes has been made until now. Physical work capacity may be limited, not only by lack of fuel, but also by the performance of the circulatory, respiratory, and neuromuscular systems. Alterations and limitations of function in any or all of these systems will, of course, be reflected in the capacity to perform work. (Page. 714) It is unfortunate that the idea that gave birth to the efficiency wage hypothesis and whose implications concern the well-being of millions of people throughout the developing world is still not well developed and tested. If productivity is an increasing function of nutritional status as much of the previous literature has suggested, then workers with lower caloric 1 Using the Minnesota Experience, in which 3 men in residency from November 19, 1944 through October 0, 1945 were subject to a clinical study at the Laboratory of Physiological Hygiene, Keys and his colleagues proved a correlation between caloric intake and productivity. The average daily calories intake of the 3 men was reduced from 349 calories to 1570 calories during the 4 weeks period of semi-starvation and then increased to a daily average of 449 calories over the rehabilitation period of 1 weeks. As a result, during the semistarvation period, the subjects lost 4% of their control body weig and their productivity significantly decreased. Wolgemuth et al. 198, Strauss (1986), Deolalikar (1988), and Strauss and Thomas (1997), among others, find a significant and positive correlation between nutritional status indicators and productivity. Wolgemuth et al.198 test the effect of current nutrition status of Kenyan road workers on their productivity and also compare the consequence of increased energy and a low level of energy dietary supplement on worker s nutritional status and productivity. The main finding was that the better nourished workers were more productive than the less well-nourished ones. They also found that with successful supplementation, workers increased their productivity. 1

4 intakes, like a large number of farmers and farm workers in developing countries, will be less productive than their counterparts with higher caloric intake. Therefore they have to accept lower wages in the labor market, which then implies that they get less food to eat, cannot afford their medical bills or school. These workers will be further disadvantaged since they have less money to save and less food to eat, they will accumulate fewer assets and their health status will decline over time, further decreasing their productivity. Hence, consuming at a low level of caloric intake could prevent millions of farm workers throughout developing countries from escaping severe poverty. 3 It mig be of interest to know why those poor workers cannot escape from the vicious circle of poverty trap in a laissez faire economy. One of the possible explanations is that job candidates consuming at a low level of caloric intake do not supply the type of labor needed in a market economy. In a competitive market where asymmetric information exists, employers have incomplete information on the work capacity of each job candidate. Since every worker is paid by his/her marginal product of labor, malnourished workers will get a lower wage in the market. With their low wages, those people will not be able to meet the financial need of their family; therefore, they will use the labor of their children as a source of extra income. Those children will most likely be malnourished since they are consuming at low levels of caloric intake along with their parents. Further they will be illiterate since they cannot attend school because they are working. When raised under those conditions, those children will be even further disadvantaged when they officially enter the labor market since they are not only malnourished but also unskilled, and as a consequence they will be constrained to accept even lower wages. This process goes on from generation to generation. For further reading on poverty traps and its implications see Dasgupta (1997). Gaining a better understanding of the nature and the prevalence of such a relationship, and then implementing policy interventions to break such nutrition-productivity traps potentially offers the opportunity to improve the lives of millions of people throughout the world. During the last two decades, a literature on the topic has been developed which makes clear that better nutritional status leads to higher productivity. However, there is not yet enough empirical evidence on the issue to persuade world policy-makers and government officers in developing countries that giving more food assistance to poor households is not just a transfer but an investment. Food assistance can make individuals more productive, may lead to higher wages in the labor market, increase investment in health and total output, increasing the quality of life. What is not clear from the previous literature, however, is when and where such effects are likely and why, at a certain level of intake, extra calories decrease productivity. Previous literature is also plagued with endogeneity and heterogeneity issues partly due to the type of data and the econometric models used to estimate the relationship between productivity and nutritional status. One important study that tried to clarify some of the above concerns is Strauss (1986). Strauss uses an instrumental variable estimation method to investigate the link between nutritional status and productivity of farm workers in Sierra Leone. One of his main variables is effective labor which he creates by taking the product of family labor hours and the efficiency function which is a non linear function of average calorie intake per consumer equivalent in the 3 According to an estimation of IMF, over 1 billion people live in extreme poverty (defined as less $1 a day), some 800 million go hungry each day, and 8 million people die each year because they are too poor to remain alive.

5 household. 4 Using a Cobb-Douglas production function and a set of instrumental variables, 5 Strauss finds that effective labor is a strong, positive, and significant input in the production function. It increases significantly at a decreasing rate with caloric intake. He estimates the output elasticity to be 0.34 at the sample means of daily average calorie intake. This elasticity increases to 0.49 at an average daily intake of 1,500 kilo-calories and decreases to 0.1 at a daily calorie intake of 4,400 kilo-calories. After reaching a daily calorie intake of 5,00 kilocalories, extra calories decrease productivity. Although Strauss uses a well defined functional form to estimate the relationship and attempts to control for unobserved heterogeneity and endogeneity issues by using instrumental variables, his findings remain subject to significant problems. It is almost impossible to appropriately control for unobserved individual and household fixed effects when using cross-sectional data. Using an instrumental variable method to control for such effects will only work if those unobserved fixed effects do not influence both productivity and caloric intake. The most difficult challenge a researcher faces when trying to estimate the effect of nutrient intake on farm productivity is endogeneity and unobserved heterogeneity issues, which can both lead to misleading results. For instance, both productivity and nutrient intakes are positively correlated to income; if a careful econometric approach and suitable dataset are not used, the estimated effect of caloric intake on productivity can just be a simple representation of the effect of income on caloric intake. Also failing to control for household fixed characteristics such as entrepreneurial and managerial skills will induce an upward bias in the estimated coefficient of nutrient intakes. Thus, controlling for endogeneity and heterogeneity is a critical step in estimating the effect of nutrient intakes on productivity. This paper extends the approach used by Strauss (1986) to estimate the link between caloric intake and farm productivity. It addresses the problem of endogeneity more explicitly by eliminating most of the shortcomings found in Strauss (1986). First, I relax the functional relationship used by Strauss. While Strauss (1986) defined effective labor as the product of efficiency and family labor hours, here it is defined as the product of family labor and the exponential of the efficiency function. 6 Second, in this paper panel data are used compared to cross-sectional data by Strauss (1986) and the data were collected from Mali, which is an appropriate country to test such a relationship. Mali is a landlocked country in West Africa with a population of approximately 1 million people living in an area of about twice the size of Texas or 1. million square kilometers. The country is among the 10 poorest countries in the world and these data were collected from one of the poorest regions of Mali. The survey sample consists of 1,086 individuals. The economy in the survey area is mainly based on subsistence agriculture. Farm products are used by household as the main source of consumption and income. Farm activity is mainly practiced using intensive labor; therefore, it 4 i Strauss effective labor function is: L = h( X ) L i i * where L i represents family or hired labor and ( ) i c h is the efficiency which is a non linear function of caloric intake X. 5 The list of instruments used by Strauss (1986)is: output price, rice price, root and other cereals price, oils and fat price, fish and animal product price, miscellaneous foods price, nonfoods price, male adult wage, wage squared, hired labor calorie consumption squared, capital stock, upland, land, capital x upland, land x upland, household size, and number of adults. 6 I borrowed the efficiency function from Strauss (1986) where it is defined as a non-linear function of caloric intake: h( X ) X 1+ α 1 X X 1 + α X = 1 from his approach by applying the exponential form: * L = e X c. However I defined differently the effective labor h ( X ) L f. 3

6 requires more physical work. This set of conditions makes these data useful for testing the effect of nutritional status on productivity. Third, I was able to control for past calori intakes by including the weig-for-heig measure in my regressions. This anthropometric measure is believed by nutritionists and scientists to be a good indicator of longer-term nutritional status. Finally, along with the use of panel data and an instrumental variable method, I went even further in controlling for heterogeneity and endogeneity bias by controlling for idiosyncratic shocks such as financial shocks, 7 droug, lost of productive time due to illness, lack of labor and death or lost of livestock. Idiosyncratic shocks are believed to be important source of heterogeneity in such a rural context. Given that it is hard to collect data on individual workers, I use household total output as a proxy for productivity and I use daily average calorie intake per consumer equivalent and weig-for-heig as indicators of nutritional status. The use of average calorie intake per consumer equivalent can lead to biased results since the variable is constructed with the assumption that individual food consumption within the household is roughly proportional to a calorie requirement that varies by age and sex. If intrahousehold allocation of food varies at the same time with some of the instrumental variables used like prices, the estimate of the calorie effect on productivity is likely to be biased (Deolikar 1988). The severe requirement of the data needed for this exercise makes it harder to solve the endogeneity and heterogeneity issue. Although I approached these problems with a more rigorous and sophisticated econometric model than previous literature did, I am not still totally convinced that my estimations are free from all the endogeneity and heterogeneity issues. I believe one way to fully tackle unobserved heterogeneity and endogeneity bias would be to do a randomized experiments where we can truly create a well-balanced control and treatment groups and easily estimate the effect of nutrient intake on productivity by making simple differences rather than using complex econometric model to estimate the relationship. This approach can be costly. However, I believe it is worth doing since it helps to clarify a relationship that concerns the well-being of millions of poor people throughout the developing world. I follow the existing literature in using food prices, fertilizers, farm assets, land area, land quality, labor, and household characteristics as instrumental variables. 8 Using this set of instrumental variables and the Mali panel data, I test the effect of nutritional status on productivity by estimating a Cobb-Douglas agricultural production function via random and fixed effect regressions. The results suggest a significant and positive correlation between workers caloric intake and their productivity even after controlling for simultaneity and endogeneity. The magnitude of the effect is non-linear and greatest for those workers whose intake is at the lowest level. The marginal product is an increasing function of caloric intake but at a decreasing rate. The level of intake at which any extra calorie will decrease productivity is unreachable; thus, these findings support the fact that farm productivity is a strictly increasing function of caloric intake, albeit at a decreasing rate. The results are robust and important for at least four reasons. First, the relationship between nutritional status and productivity and its causality are tested using a well-defined production function. Secondly, endogeneity and simultaneity issues were handled by using a set of 7 Financial shock occurs when household can not use of their exploitable land because they do not have enough money to do so. 8 I first used the same set of instrumental variables used by Strauss (1986). I later use different set of instrumental variables by adding idiosyncratic shocks variables and the outcome was similar. 4

7 instrumental variables and by estimating the relationship through a non-linear regression. Thirdly, panel data are used, and therefore fixed unobserved household characteristics that mig have contributed to both nutritional status and productivity are controlled. Finally, the caloric intake variable is constructed from weekly food consumption data instead of a 4-hour recall food measure 9 from which it is less likely to offer an accurate measure of current calorie intake; thus, the chances are good that the intake variable used in this exercise is an accurate representation of the daily caloric intake of workers. The next section is a review of the literature. Section-3 deals with research design and is divided into two subsections: Methodology and Data Analysis. Section-4 discusses the results and is followed by a concluding section.. Literature Review Finding an explanation of how the nutritional status of a working man affects his productivity has been a subject of research for economists and nutritionists for a long time. Earlier empirical work on the topic has developed the notion of a nutritional efficiency wage hypothesis, introduced by Leibenstein (1957) and Mazumdar (1959) 10 and later formalized and extended by Mirrlees (1975), Rodgers (1975), Stiglitz (1976, 198), Gersovitz (1983), and Dasgupta (1993), among others. Early surveys include Bliss and Stern (1978a, 1978b) and Binswanger and Rosenzweig (1984). The efficiency wage hypothesis has been used to explain many economic phenomena, among them: (1) how the marginal productivity of labor can be zero with a positive wage; () the coexistence of unemployment with a very small positive wage in developing countries; (3) savings, food distribution, and other economic decisions within households; (4) why low-wage employees are less productive; and (5) the disparity between wages in the industrial and agricultural sectors. In answering some of the above economic questions, many arguments have been put forth to explain the relationship between efficiency and wages; however, the main concern of the efficiency wage hypothesis is still a possible explanation of the relationship between efficiency and nutrient intake (Strauss 1986). Despite the existence of some compelling papers 11 on the subject and the importance of its implication for developing countries, the link between nutritional status and productivity is still a relatively underdeveloped area of research. More than half a century ago, two studies attempted to clarify the relationship between caloric intake and productivity. Using The Minnesota Semi-Starvation Experience in the 1940s, (Keys et al. 1950) found a considerable decrease in human productivity due to substantial decrease in his/her calorie intake. Their 9 Strauss and Thomas (1998) argued that while 4-hour food recalls measures may suffer less from bias caused by income related waste and transfers, it may suffer more from random measurement error since it can not capture day to day food variation. 10 In his paper The Marginal Productivity Theory of Wages and Disguised Unemployment, Mazumdar explained how the productivity of labor in a backward economy is zero and why the wage falls below the flow level. Through his analysis he explains why the wage-efficiency mechanism which keeps the wage at the flow level is undetermined in backward economies. One of his two main arguments of the non-application of the efficiency-wage theory in those economies is that employed workers who are getting high wages are living with their unemployed relatives therefore the higher wage for employed workers will not lead to higher consumptionlevel and the consequence is the break in the relation between wages and efficiency. His second argument is the existence of multiple employers and the fact that agricultural employment is mostly casual, thus the efficiency wage relationship does not work and consequently there is no wage flow. 11 For a more extended list of review, see Thomas and Strauss (1998) and Lipton (001). 5

8 results were incomplete, however, since we do not know from this evidence whether over time people can adapt to the exposure of low caloric intake and keep productivity high. Another ambiguity from the study is the functional relationship between caloric intake and productivity. Another line of empirical work was carried out in 1946 by Kraut and Mueller 1 who reported that the productivity of railroad workers, coal miners, and steel workers is correlated with their food consumption. When their calorie intake was decreased, their productivity decreased substantially. The findings of this investigation are not so reliable because they were not able to control for endogeneity and simultaneity issues. During the 1970s and early 1980s many new studies were conducted to test the effect of nutritional status on productivity. However, many of them were plagued with endogeneity and simultaneity problems due to the failure to control for households unobservable characteristics and to the type of model employed to estimate the relationship. For instance, using a less careful methodology (Immink et al. 198) 13 finds that increased daily energy intake does not have an effect on daily productivity among Guatemalan sugarcane cutters. Over the last 0 years, further empirical work has tested the relationship through more sophisticated methods and found significant and positive correlations between caloric intake and productivity. Among the best known studies are Strauss (1986), Deolikar (1988), and Strauss and Thomas (1997). Strauss (1986) used an instrumental-variable estimation method to estimate the effect of nutritional status on productivity. Using a Cobb-Douglas agricultural production function, he models farm household production function in Sierra Leone. The effect of nutritional status, measured by caloric intake, is tested by using a non-linear production function. Even after using a different set of instruments and controlling for endogeneity and some family characteristics such as the age and years of education of the head of household, Strauss finds that nutritional status has a significant and positive increasing effect on productivity, albeit at a decreasing rate. Up to a 500 kilo-calories daily calorie intake has positive effect on productivity; however after that level of nutrient intakes additional intake has a negative effect on productivity. Using a Brazilian household budget survey for a single year (August 1974 to August 1975), Strauss and Thomas (1997) investigate the effect of four indicators of health: heig, body mass index, per capita calorie intake, and per capita protein intake on wages. After controlling for endogeneity issues, education, and other dimension of health, their principal findings conclude that all four indicators have a significant and positive effect on wages. The effect of nutritional variables, per capita calorie intake and per capita protein intake, on wages was only significant and positive at low intake levels. On the other hand, using random and fixed effects regressions on ICRISAT data collected in South India in mid-1975, Deolikar (1988) finds that farm productivity is not affected by current calorie intake even after controlling for endogeneity and other household 1 Through different investigations in 194, 1943, and 1944 Kraut and Muller reports that the productivity of German rail road workers, coal miners, and steel workers in increases significantly when they exogenously increase workers calories intake. 13 Immink et al. (198) use four different indicators of lifetime energy, stature and upper-arm muscle area, body weig (standardized for heig), and daily energy intake to explore the effect of childhood and adolescence energy intake and daily energy intake on productivity. The principal findings show that increased energy intake during childhood and adolescence has a positive effect on sugarcane workers productivity once adulthood is reached. Surprisingly there is a weak relationship between daily productivity and increased energy intake. The results find here are not so reliable because a simple regression cannot account for the endogeneity of energy intake. 6

9 characteristics using a fixed-effect regression. He finds, however, that the measure of weigfor-heig is highly positively correlated to productivity. He concludes that the weig-forheig measure is a better indicator of nutritional status because it has less measurement error than the daily average calorie intake. He also states that the measure of weig-for-heig is correlated to productivity, whereas daily calorie intake is not. This is due to the fact that, while the human body can adapt to under-nutrition in the short run, it cannot adapt to it in the long run. 3. Research Design and Data 3.1. Model To estimate household agricultural production function, I employ a Cobb-Douglas functional form in which an optimal combination of effective labor, fixed capital, and other inputs is used by households to maximize their farm production. The design that I use was first used by Strauss (1986) to estimate the effect of caloric intake on productivity. However, I follow a different step in determining the relationship between family labor and effective labor. While Strauss (1986) defined effective labor as the product of efficiency and family labor, here it is defined as the product of family labor and the exponential of efficiency. The farm production function to be estimated is: * (1) lnq = β 1 ln L + β ln K + β 3 ln I + µ h + ε where Q - is total output * L - is family effective labor input K - is the fixed factor of production, here the size of land cultivated I - represents the set of other inputs including fertilizer, and farm assets µ - is household specific unobserved error term ε - is an independently identically distributed error term h - is household index t - is time index Effective Labor Effective labor is a function of family labor input and efficiency per hour work function, h ( ) as represented by equation (). The efficiency function is believed to have two portions increasing at different rates. The first portion is increasing at an increasing rate and that is followed by the second portion increasing at a decreasing rate. In Equation (), effective labor is defined by the product of family labor hours and the exponential of the efficiency function 14 () L * = e h ( X ) L f 14 Here the exponential of the efficiency function is used for functional purposes. If it happens that nutritional status do not influence efficiency, the value of the efficiency function will be zero and effective labor will be equal to family labor supply. In some literature effective labor is the product of the efficiency function, h ( ), and labor supply as did by (Strauss 1986). 7

10 where f L - is family labor input. The efficiency per hour worked function is hypothesized to be a quadratic function of current and past nutritional indicators, represented here by daily calorie intake and weig-for-heig measures. At the sample mean of daily calorie intake and weig-for-heig, labor input in the efficiency unit is equal to family labor input, which implies that the value of the efficiency function is equal to one at the sample mean. If caloric intake and weig-for-heig measure do not influence worker efficiency, the value of the efficiency function should be equal to zero and effective labor will be the same as family labor. 15 Although, the efficiency function specified here allows for either convex or concave portions but not both, it is the most reasonable functional form of the efficiency function: (3) h( X ) X 1+ α 1 X X 1 + α X = 1 Where h - is the efficiency function ( ) X X - represent current and passed nutritional indicators (daily caloric intake, BMI). X - is the sample mean of nutritional indicators. Using equation (1) and (), the farm production function used by household will be: (4) lnq = β 1 [ ln L + h( X )] + β ln K + β 3 ln I + µ h + ε To interpret the effect of nutritional status on productivity, we need to calculate the elasticity of farm output with respect to nutritional input,ε ; that is : (5) dq X dx Q ε = and = Q h ( X ) dq dx From Equation (3), we find that: (6) h ( X ) = 1 β1α X β + 1α 1 X X And finally using Equation (5) and Equation (6) we find: (7) ε X β1α X β1α 1 + X = X In order to calculate the level of intake that maximizes the level of efficiency, Equation (6) is set to equal zero: 15 If h ( ) = 0 f, efficiency labor will be equal to family labor since: L * = e 0 L = L f 8

11 (8) where β 1α 1 = X β α max X 1 β 1α 1 is the coefficient of caloric intake and β 1α is the coefficient of caloric intake squared. For X to be a maximum the second order condition is: ( ) < 0 max h X Endogeneity and Simultaneity Estimating the effect of an endogenous variable such as nutritional status on productivity can easily lead to biased results. Both variables - productivity and nutritional status - may be affected by income. Better nutritional status leads to higher productivity and higher income leads to better nutritional status. Therefore, the positive correlation between nutritional status and productivity can be just the simple representation of the effect of income on nutritional status. Also, the coefficient of nutritional status indicators can be biased because of the unobserved individual-specific endowments like the levels of inherited immunity to diseases and tolerance to infections since such endowments are generally positively correlated with nutrition (Deolikar 1988). And finally both, productivity and nutritional status mig be affected by unobserved variables related to labor supply. The existence of any of the above shortcomings can lead to biased results; therefore, preventing them is necessary in estimating the effect of nutritional status on productivity Instrumental Variables The method of instrumental variables is used to obtain consistent estimates when the causing variable, x, is endogenous, that is E ( u / x) 0 where the causality is determined by the population equation (9): (9) y β + x + u = 0 β1 A good instrumental variable must be exogenous, that is, uncorrelated to the error term of the structural equation and must be partially correlated with the endogenous explanatory variable (Wooldridge 006). In other word, a valid instrument should satisfy two conditions: 1. Exogeneity: cov( u, z) = 0, where z is the instrumental variable. Relevance: Cov ( z, x) 0. In this exercise, the set of instruments used is motivated by Strauss (1986). 16 It includes output prices, food prices, and household characteristics. The use of output and food prices as instruments in such an exercise is a common practice because they are considered to be correlated to household consumption and not related to farm output. In rural areas, farm 16 The basic set of instruments used by Strauss (1986) is: output price, rice price, root crop and other cereal price, oils and fat price, fish and animal product price, miscellaneous foods price, nonfood price, male adult wage, wage squared, hired labor calorie consumption, hired labor calorie consumption squared, capital stock, upland, land, capital x upland, land x upland, household size, and number of adults. 9

12 production is the main activity which implies that farm outputs are used not only for household consumption but also to buy other household goods and services. Thus, household farm output is not necessarily the same as household consumption. Since traditional farming is mainly dependent on rainfall, it is almost impossible for farmers to forecast output prices; therefore output and food prices are only determined after the crop. Following the above arguments, we can assume that while food and output prices are correlated to calorie intake, they are not related to farm output. However, some concerns rise from the use of household characteristics such as household income, health expenditure, household composition, household size, household member age, education, and other household characteristics as instrumental variables. It is reasonable to argue that these variables are correlated to farm output and, therefore, cannot be used as good instruments. The economic intuition of treating these variables as good instruments or at least as good candidates for valid instruments depends on the socio-economic and cultural realities of the Zone Lacuste. Household characteristics such as health expenditure and household composition can be taken as exogenous or at least not determined in household production function. In rural Mali, household farm outputs usually depend mainly on other factors such as rainfall or social network rather than the supply of labor within the household or the use of agricultural capital. This is because the notion of extended family is well-developed in the area. If a household lost one of its productive members because of sickness, death, or immigration, it can get extra laborers from extended families to fill the void. By the same token, an increase in household labor supply will increase the probability of that household transferring some labors to extended families. Thus, the change in household composition would have little effect on their farm production. Although farm outputs are used directly or indirectly to satisfy all financial needs of the household, it is not frequent for households in rural Mali to consider household s expenditure such as health expense within their farm production function. For these reasons, the cost that a household will face when one of its members is sick is not correlated to the farm outputs of that household, but will have an effect on the allocation of food within that household because the unexpected health expense will be covered by selling part of the food stock designed for household consumption. For instance, it is a common practice in Bambara households to give the majority of food to kids and old people when there is a shortage of food in the household and on the other hand, old people have the rig to eat more meat or fish when they are eating with kids. This means that while the change in household composition or health expenses can have a major effect on household food consumption; it mig not have any significant effect on household farm production. In other words, household characteristics such as gender and demographic composition, change in household size, and health expenditure can be used as good instruments because they are less likely to be correlated to household farm production but have an effect on household food consumption. Following the above arguments, output prices, food prices, household farm assets, household characteristics can be taken as exogenous or at least predetermined to the household and they are also correlated with nutritional status, thus they can be considered as good candidates for instrumental variables as well. 17 However, the exogeneity of farm assets and certain 17 The set of instruments used are: Cereals Price Index, Fish Meat and Poultry Price Index, Oils and Fat Price Index, Vegetable Fruit and Product Price Index, Household Size, Average Age of Household Members, Household Members Aged 1 and Up, Change in Household Member Aged 1 and up, Change in Number of Female in the Household, Change in Number of Male in the Household, Household Male Member less than 5 years, Household Female Member less than 5 years, Household Income, Household Health Expenditure, Per 10

13 household characteristics would be in doubt if there exist unobservable household or farm characteristics, such as management skill or land quality that continue over time (Strauss 1986). Also, output prices mig be endogenous since higher expected output price can lead farmers to grow a larger field therefore obtain higher output. For these reasons, I use different sets of instrumental variables and I test the correlation between each set of instrumental variables and residuals using the Sargan overidentification test. The Sargan over identification test is a test of the validity of instrumental variables. The hypothesis being tested is that instrumental variables are exogenous, that is, they are uncorrelated to the error term. If the test statistics fail to reject the null hypothesis that means the set of instruments used are suitable for the estimation; therefore, can be used as valid instruments. The test works as follows: 1. Use the instrumental variable method to estimate the equation and obtain the residuals µˆ. IV i IV µˆ i. Regress the residuals on all exogenous variables, instruments and controls, and obtain R. 3. Finally, the Sargan statistics is computed as: S = nr where n is the number of observations. Under the null hypothesis that all instruments are exogenous S is distributed as χ. The degree of freedom, p, is the difference between the number of p instruments and the number of endogenous variables. 3.. Data I use the zone Lacustre (Mali) household dataset collected by the International Food Policy Research Institute (IFRI) to estimate the effect of nutritional status on productivity. The zone Lacustre data is a four-round panel dataset collected in the northern region of Mali over a thirteen-month period (August 1997 through August 1998). Data was collected on agricultural production, food consumption, expenditure, income, health, and socio-economic characteristics for 75 randomly chosen households in 10 different villages selected according to the geographic, climatic, and resource representation of the area. The data also contain six rounds of anthropometric surveys, a two-day rapid rural appraisal (RRA) in each village, and weekly market surveys in the two main markets. Since the information necessary to construct useful strata was not available, households were selected according to a two-stage sampling procedure. A sample of 10 villages covering the different agricultural and livelihood systems was first selected, and then one-third of the households within each village were chosen. Finally, to account for gender effects, men and women were interviewed separately in each household. 18 During the third round of the survey, the dead period (February-March 1998), there was no agricultural activity; therefore, I use only three rounds out of the four rounds of the survey: the hunger period of 1997 (August-September 1997), the post-harvest period (November- December 1997), and the hunger period 1998 (August 1998). Since data on agricultural Capita Household Health Expenditure, Average per Capita Village Income per Round, Age of Household Head, Sex of Household Head, Education of Household Head, Main Occupation of Household Head, Ethnic group of Household Members, Main Occupation of other Household Member. 18 For more information on the data see Christiaensen (1999) 11

14 production output are available only for the above rounds, the third round is completely omitted from the data set. Information on household consumption from market-purchased and self- production of 74 different foods, collected on a weekly basis, is available in all rounds of the survey. The food consumption data were converted into caloric intake using Food Composition Table 004 for Mali (TACAM -Table de Composition d Aliments du Mali). Anthropometric data of each person interviewed and parents in each household are also available for each round. Using these measurements, the weig-for-heig or Body Mass Index (BMI) variable is constructed by dividing the person s weig in kilograms by his/her heig in meters squared. Finally, output and price indexes are created using weekly market prices. I first created four commodity groups: Cereals, Fish Meat and Poultry, Oils and Fats, and Vegetables Fruits and Products. 19 For each commodity group, a price index is calculated by summing the weiged price of each food in the commodity group. The output index is calculated by summing the weiged quantity of the five main crops produced by households: Millet, Sorghum, Rice, Corn, and Beans. 0 While data on individual caloric intake would have been preferable, that is practically impossible to gather. However, household average caloric intake and household average weig-for-heig measure can be used as excellent indicators of nutrients intake. While current nutritional status can be represented by current daily calorie intake, the measure of weig-for-heig is used to reflect past nutrient intake, body endurance and strength, or risks of multiple health-related problems Deolalikar (1988). Since household characteristics can have significant effect on farm output, a set of information such as household size, changes in household population, number of kids in households, number of female in household, number of adults in household, and household s income information are used to control for family composition. It is evident that households with higher income will plausibly have higher output and also since extended family is important in Mali, well off household are more likely to attract more people therefore have more labor available for farm production and more people to feed. Another issue to control for is household shocks. Since household s idiosyncratic shocks have a major impact on household production output, they are controlled for by using a set of information on different idiosyncratic shocks such as financial shock, droug, lost of productive time due to illness, lack of labor, and death or lost of livestock. Despite some minor defects, the IFPRI zone Lacustre data is a good dataset to estimate farm household production function, since it contains thorough information on household farm output, farm assets, farm inputs, land use, land quality, household food consumption, food prices, household characteristics, and idiosyncratic shocks. 4. Empirical Results Estimates of the household farm production function (Eq.4 in the Model section) are shown in Table 1. The first column (OLS) contains the results of the linear estimation of the Cobb- Douglas production function. Since a linear estimation of such an endogenous and simultaneous variable as caloric intake can easily lead to biased results even using good instruments, the OLS regression will not be part of the analysis. 1 However, it is worthwhile 19 For information on the components of each commodity group see Table 4. 0 To see the formula used to create those indexes, see Appendix. 1 (Strauss and Thomas 1997) in estimating the effect of nutritional status on wages wrote: OLS estimates of the effect of health on wages are likely to be contaminated by both simultaneity and unobserved heterogeneity bias. 1

15 to mention the effect of nutritional status on productivity in the OLS regression. In the OLS regression, while the individual daily caloric intake variable has a positive and significant effect on productivity, the coefficient on weig-for-heig is positive but not significant. Although we get a positive and significant correlation between caloric intake and productivity, the causality of this effect is not clear since we cannot control for simultaneity in a linear regression. The next two columns of Table.1 are successively the random and fixed effect of the Non-Linear Stages Least Squares (NLSLS) estimate of Equation (4) using a set of instrumental variables. In both the random and fixed effect regressions, while caloric intake is a positive and significant determinant of productivity, weig-for-heig is not. By adding weig-for-heig squared in the regression, the sign of the coefficient of weig-forheig turns from negative to positive, but it is still not statistically significant and there is no major change on the coefficient of other variables. In both fixed-effect and random-effect regressions, the magnitude and the significance level of the effect of caloric intake on productivity is exactly the same. In all three regressions - OLS, random-effect, and fixed-effect - the coefficients of caloric intake and caloric intake squared are significant at more than 5% level. With the exception of family labor, the coefficient of other production inputs and their interactions are not significant. This can be explained by the fact that household farm production in the survey area is labor-intensive rather than the combination of both labor and other input. With an average family income of about $ 45, households in the survey area are extremely poor therefore they accumulate less farm asset and cannot afford fertilizers. The only option they have is labor intensive production system using hoes. That can be a reason why other inputs are not significant in determining farm output since they are rarely used. The non-significance of the effect of the fixed capital - land used - can be attributed to the excessive supply of land in the area. The average cultivated household s land is square meters. Since access to land in the area is easy and costless, having more land would make less difference in farm output if the owner does not have adequate means to invest in the land. As discussed above, I test the exogeneity of instrumental variables by using different sets of instrumental variables. This is done by gradually dropping groups of instrumental variables and for each regression the Sargan over-identification test is used to estimate the existence of correlation between the set of instrument used and the residuals. Table. contains the re-estimation of the Random Effect Regression in Table.1 with different set of instruments. I first drop output prices from the set of instruments and the value of the Sargan test statistics decreases from.708 (Pvalue= ) to.608 (P-value= ) which is not a major significant change since the Sargan statistics is still significant at all standard levels. The second set of instruments dropped is household composition variables such as household size, change in household size, number of adult in the household, and number of kids in the household which leads to a decrease in the value of the Sargan test statistics to (P-value= ). Finally, I control for idiosyncratic shocks that households experience during the production period by adding the shock variable in the regression. The Sargan test statistics increases to.64 (P-value= ). In all the three regressions the coefficient on the main variables remains unchanged although the test statistics of the overidentification test decreases a bit in the first, second, and third regression of Table.. The fact that the Sargan test statistic decreases after dropping different set of instruments in column (1), () and (3) of Table. shows that the set of instruments used in the Random Effect Regression are the most suitable To see the list of instrumental variables used, please see the Model section. 13

16 set of instrumental variables. It is also important to mention that the existence of idiosyncratic shocks such as droug, lack of labor, lack of financial means, whether household livestock was stolen or died, and if household member lost some productive time due to illness are significant in determining household production and could be used as good set of instrumental variables. It can be debatable that the significant and positive effect of calorie intake and the nonsignificant effect of weig-for-heig on productivity can be introduced by the collinearity between the two variables. This collinearity can be a result of the fact that an individual s growth (weig and heig) is highly dependent on his/her current calorie intake, since, but not in all cases, current household food consumption can be used as a proxy for households food consumption history. 3 If the two variables are correlated, the strong effect of caloric intake on productivity can merely be the correlation between calorie intake and weig-forheig as explained by Deolalikar (1988) in a different situation. However, estimating the effect of calorie intake and weig-for-heig on productivity separately gave the same effect and significance level as in the regression reported in Table.1. In both random-effect and fixed-effect regressions, while calorie intake has a positive and significant effect on productivity, weig-for-heig does not. In reality, while both nutritional status indicators - caloric intake and weig-for-heig - may have positive and significant effects on productivity, the non-significance of the effect of weig-for heig variable can be attributed to measurement error. The weig-for-heig variable used in this exercise is constructed from the anthropometric data collected on only four people in each household. In the Mali survey, anthropometric data were not collected on all workers in each household but only on the woman and man interviewed and the two heads of households. If the two heads of the household were the one interviewed, there are only two measurements of weig and heig. Thus the weig-for-heig variable constructed may not reflect the household average body mass index, which can introduce the non-significance of the coefficient on weig-for-heig variable. On the other hand, although the collection of food consumption data and the conversion of those data into caloric intake are much more difficult and complex work than collecting anthropometric data, the survey data used here has more information on household food consumption. Food consumption reported in the dataset is weekly food consumed; thus, taking the average will reduce measurement error. Since the caloric intake variable was constructed from much more precise data, it is most likely that it represents a best measure of a household s farm worker daily calorie intake. However, what we learn from this empirical result is that the productivity of a farm worker is affected by his/her nutritional status. The implications of this result are discussed in the next section. Interpretations To interpret the effect of caloric intake on productivity, the estimation of output elasticities and the value of the efficiency function are first considered using Table.3. The estimations in Table.3 are found by using the second column in Table.1 and equation (3) and (4) from the model section. From Table.3, it is clear that caloric intake has a strong and statistically significant increasing effect on productivity. While the output elasticity of calorie intake for 3 According to the consumption smoothing hypothesis, household consumption decision is based on their lifetime income therefore there must not be much variation between a household s current and past calorie intakes which can lead to strong correlation between current caloric intake and weig-for-heig since weigfor-heig represent past nutritional status. 14