Calorie demand and labour productivity in Peru: evidence from the National Household Survey

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1 Calorie demand and labour productivity in Peru: evidence from the National Household Survey Pablo Lavado Jose Gallegos 1 The World Bank plavado@worldbank.org Syracuse University jvgalleg@maxwell.syr.edu Abstract Is the extreme poverty line a good measure of food insecurity and nutritional status? It is said that an individual who is above the extreme poverty line has enough resources to achieve a basic food basket. In Peru, extreme poverty has increased 7.5% between 1998 and 2002; however, daily calorie demand per capita have decreased 17.8% and the proportion of people who are not able to cover their caloric needs has increased from 22.3% to 36.3%. How could this happen? The reason for this is that families assign their budget in an irrational way substituting food for other goods or services. This has consequences not only in the nutritional status of individuals but also in their productivity. According to human capital theory [Becker (1962)], health is an input for productivity, so a poor nutritional status will transpire in a low productivity and finally in low wages. Between 1998 and 2002, wages per hour have decreased more than 30% in Lima Metropolitana only. In this context, a question rises: is there any relationship between household s calorie availability (demand) and labour productivity in Peru, after controlling by income turn down (in other words, the raise of extreme poverty)? Although this topic has been treated widely by economic literature [Bliss y Stern (1978a y 1978b), Strauss (1986), among others], there is still no consensus about a systematic correlation between these variables. In order to attempt to answer this scenario in Peru, using the 2002 National Household Survey (ENAHO), we first estimate an extended Mincer equation using the two-step Heckman procedure to control for selection bias. We previously estimate the demand for calories in the individual s household (using calorie availability as a proxy) with the aim to deal with the simultaneity relationship between this variable and wages. Using instrumental variables we also estimate the access to feeding assistance programs, such as community kitchens, in order to take into account their effect on daily calorie demand per capita. In order to analyze in depth household s demand for calories and its effect on productivity we also apply quantile regressions. Finally, using a pool-data sample, we explore the period effect from 1998 until 2002 in order to take into account the economic turn down. Our main findings suggest that calorie demand has a positive and significant effect on wages, which means that for higher levels of calorie consumption correspond higher levels of productivity. Furthermore, we show that the education level of the household chief has also an important roll on the daily calorie demand determination, as well as household s demographic characteristics. Regarding the access to feeding assistance programs, it is shown that programs with a higher level of focalization have a positive and significant impact on calorie demand, which represents an important finding as in terms of policy analysis. JEL CODE: I12, I32, J24 1 We are very thankful to Alejandro Ortiz de Zevallos for the assistance on the development of this document. This research is possible thanked to the grant provided by the Economic and Social Research Consortium from Peru. We also thank all the comments received at the Annual Meeting of the Network on Inequality and Poverty held on Puebla, Mexico, on July

2 1 THE DEMAND FOR CALORIES AND PRODUCTIVITY: WHAT DOES THE INTERNATIONAL LITERATURE SAY? Currently, poverty reduction is one of the great subjects of discussion dealt by economic literature. On one hand, there are some social researchers who argue that economic growth is the most effective way to eliminate poverty, since it generates greater income for population and thus, increases life standards. On the other hand, there are some who argue that economic growth is not sufficient, since the distribution of income generated by economic growth does not necessarily favor the most needed ones, and it could even increase existing inequality. For that reason they maintain that it is necessary to design effective social programs in order to improve income distribution so the most needed ones would have a secure access to the benefits generated by economic growth. This debate is related to the main theoretical thoughts which approach the existing relation between the individuals health level and productivity. In particular, the main topic of the debate is focused on discussing in what direction is the causality between calorie intake and individuals labour productivity. At the moment, literature related to this subject is divided in two theoretical positions: The first one focuses on how a family s income level affects calorie demand or calorie intake capacity. And the second one focuses on the effect of caloric consumption level on individuals capacity to generate income. Although it is a common subject on international economic literature, until now, there is still no consensus about the relation between these variables, and even less between these theoretical currents. On one hand, the first current is supported by traditional knowledge: low caloric consumption levels are a consequence of low income levels. The defenders of this current, among them the World Bank (1980 2, ), suggest that the determinants of nutrient consumption are: prices, income, donations and transferences. Even some authors as Dawson (2002) demonstrate that the existing relation is unidirectional; that is to say, changes on income take to changes on calorie consumption levels, but not the opposite. In light of these facts, authors from the first current maintain that hunger and undernourishment would disappear as a result of economic growth. This argument is reinforced by studies that include economic growth as one of the 2 Banco Mundial (1980), World Development Report, 1980 Oxford: Oxford University Press for the World Bank. 3 Banco Mundial (1981), World Development Report, 1981 Oxford: Oxford University Press for the World Bank. 2

3 main reasons for poverty reduction since , as well as by other studies that maintain that income elasticity of household s calorie demand is near to one 5. However, economic literature has also questioned the validity of these arguments. In this sense, some authors indicate that the relation between calorie intake and income level would not even exist. According to these authors [Wolfe and Behrman (1983), Behrman and Deolalikar (1987), Behrman, Deolalikar and Wolfe (1988), and Bouis and Hadad (1992), among others], even in the most humble households, an increase on income level would only lead to an increase on food purchases that would not necessarily transpire to a better diet of family members. The main argument for this is that family members do not necessarily base their decisions about food consumption on a true knowledge about an appropriate diet. Regarding this, Behrman and Deolalikar (1989) maintain that a good way to increase caloric consumption levels would be to provide population of suitable nutritional information 6. With regard to the second current, it is possible to identify that literature has taken into account two particular aspects. The first one is the direct effect of calorie intake on labour productivity, which is the main topic of this research. The second one is related to the indirect effect: it states that better feeding during the first years of life assures an adequate health level that facilitates individuals learning processes and development, which favours human capital accumulation Strauss and Thomas (1998) 7 - and, as a consequence, greater productivity levels during adulthood (Becker[1964] 8 ). In light of this current and in order to demonstrate the positive effect of an individual s health level on income level, several proxies have been used. Among the most common are the individual s height which correlation with income level was observed first by Villermé 9 since 1829, and in the last years by Komlos and Merman (2004)- and the Z-score of weight-for-height of family members (Deolalikar, 1988). Likewise, Thomas and Frankenberg (2002) used the Body Mass Index (weight/height 2 ), among others. While this index depends on energy intake which means that it is not constant through life-, anthropometric measures as height or the Z-score of height-for-age incorporate the effects of feeding conditions during the first years of life. With respect to these indicators, authors as Behrman and Deolalikar 10 or Strauss and Thomas (1997) 11 suggest that it is advisable to use them with 4 Knowsman (2004), World Bank Finds Global Poverty Down by Half Since 1981, U.N. Wire. 5 Among them, the reseach papers of Strauss (1984) y Pitt (1983) are highlited 6 Behrman and Deolalikar (1989), Is Variety the Spice of Life? Implications for Calorie Intake. The Review of Economics and Statistics. Vol. 71, No. 4 Pp Strauss and Thomas (1998), Health, Nutrition and Economic Development, Journal of Economic Literature, Vol. XXXVI 8 Becker, G.S. (1964), Human Capital: a theoretical and empirical analysis with special reference to education, Cambridge University Press, Cambridge. 9 Villermé, L.R. (1829). Mémoire sur la Taille de L`Homme en France. Annales D Hygiène Publique et de Médecine Légale, 1: Behrman and Deolalikar. Agricultural Wages in India: The Role of Health, Nutrition and Seasonality In: Seasonal Variability in Third World Agriculture: The Consequences for Food Security. Pp

4 nutrient intake measures and with indicators that reflect changes on an individual s health level. In that sense, these authors maintain that it is important to include those two kinds of variables on the wage equation since nutrient consumption reflects, in the short term, changes on energy expenditure, but not on an individual s health level. For that reason, when including them both as explanatory variables, calorie consumption has an additional effect on productivity and therefore, on wage. Also, it should be noticed that there is a great theoretical discussion about the source of data to measure the existing relation between income level (or productivity) and calorie consumption. The wide range of results among different research studies is mostly explained by the fact that some use calorie consumption, whereas others use calorie availability. The difference between these variables is clear, because when using household s available calorie level it is assumed that its distribution is equitable among household s members. Nevertheless, if the information about individuals calorie intake is available, there is no need to assume any kind of distribution 12. It is also important to notice that the second current has also been taken into account by a great number of research studies and still the relation between these two variables has not been defined. Studies like the ones developed by Bliss and Stern (1978a and 1978b), Strauss (1986), Behrman and Deolalikar (1988), Behrman (1993) and Strauss (1993) provide an idea about the wide variety of empirical edges that can be found on the estimation of the effect of an individual s health level on wages and productivity. Bliss and Stern (1978a) develop a theoretical analysis of the effect of bad feeding practices on an individual s performance on labour market. Later, Bliss and Stern (1978b) discuss different ways to measure calories required by an individual in order to perform an average activity level. These authors find a significant and positive effect of nutritional status on an individual s productivity through the relation between wage and calorie consumption level. Likewise, Strauss (1986) uses information about Sierra Leona to prove that greater calorie consumption levels increase the productivity of families on field work. The author estimates a production function using instrumental variables in order to take into account the possible simultaneity between calorie intake and productivity (the instruments used were food prices, household s demographic characteristics and assets of the family, among other variables). His results suggest a highly significant effect of calorie consumption on labour productivity. Nevertheless, some research studies show a nonexistent effect of calorie consumption on productivity. Based on a panel data for the countryside of India, Deolalikar (1988) estimates individuals wage and production functions taking 11 Strauss and Thomas (1997), Health and Wages: Evidence on Men and Women in Urban Brazil. Journal of Econometrics. 12 Strauss and Thomas, Human Resources: Empirical Modeling of Household and Family Decisions. Pp The authors state that families with low expenditure level, food availability is lower than consumption level, while the opposite happens in families at the top of the expenditure distribution. They also indicate that food consumption level among older individuals is higher than children s and men s more than women s (Pitt-1990). 4

5 into account calorie consumption and worker s nutritional status 13. The author finds that both wages and familiar production are not affected by changes on calorie consumption, but he does find a high elasticity with respect to the undernourishment indicator (weight-for-height). Therefore, he concludes that whereas human body may adapt to inadequate caloric levels in the short term, it does the same in a very slow way to a possible chronic problem that would eventually result in weight loss in relation to the individual s height 14. It should also be noticed that Deolalikar does not take into account the simultaneity problem between undernourishment, calorie intake level and wage and production levels. Other authors as Sahn and Alderman (1988), explore the effects of human capital on labour supply 15. Using a three-stage model, worked hours are estimated. Calorie consumption level is included on wage determination (second stage) in order to prove the relation between a better nutritional status and an improvement on individuals labour productivity. As well as Strauss (1986), Sahn and Alderman also take into account the simultaneity problem between these variables. Therefore, calorie demand is estimated using the instrumental variables procedure (using home demographic variables and prices as instruments). It is important to realize that Sahn and Alderman identify significant differences on calories demand according to residence geographic location, as well as significant differences on wages estimates when differences by gender are taken into account. One of the most interesting research papers from the second current is developed by Thomas and Strauss (1997), which is based on a Brazilian household survey. Given low wages, investment levels on human capital are extremely low and most of the population shows a low health status. The authors estimate the effect of four health indicators (height, body mass index, calorie intake and protein intake per capita) on workers wages level in urban areas. It should be noticed that they also take into account the simultaneity problem between this indicators and wages by using instrumental variables. Their results suggest that health, measured through these indicators, have a great effect on individuals wages. Likewise, they also take into account the economic sector, and workers labour condition (wage-earner or independent). Thomas and Strauss maintain that labour performance requires different calorie levels according to the economic sector in which workers participate. As it can be appreciated, international literature has reviewed these subjects in an extensive way and through different points of view. Although some studies have been developed in Peru, most of them have a different approach from those presented lines above. Most of the Peruvian economic literature on this 13 Deolalikar (1998), Nutrition and Labor Productivity in Agriculture: Estimates for Rural South India. The Review of Economics and Statistics, Vol. 70, No Regarding this document, it is not possible to introduce an indicator of chronic undernourishment since the National Household Survey does not provide anthropometric information 15 Sahn y Alderman (1988), The Effects of Human Capital on Wages, and the Determinants of labor Supply in a Developing Country. 5

6 issue has focused on the analysis of social programs that were designed in order to help to overcome population s undernourishment status. Suárez Bustamante (2003) 16 estimated children undernourishment. He proposed that chronic and acute undernourishment is explained by several factors as household s chief health practices, morbidity, accessibility to health services, feeding, in addition to other individual and socioeconomic factors. His results suggest that socioeconomic factors as much as household s chief characteristics have the greatest effect on individuals nutritional level. On the other hand, Gajate and Inurritegui (2002) analyzed the effect of feeding programs on children nutritional status 17. Using propensity score matching technique they explored the relation between Vaso de Leche program and the level of chronic undernourishment. Their results show that this program has a very small effect on children s nutritional status. Likewise, with respect to this program, Francke (2002) suggested alternatives to improve its focalization and thus, to increase its effect on children s nutritional levels 18. Another research study developed by Herrera (2001) 19 shows the main methodological innovations on poverty estimates for Peru, taking into account international caloric norms. It should be noticed that this document provides the methodology to calculate an individual s calorie requirements in Peru. Likewise, the relation between individuals income level and health status has been analyzed by Murrugarra and Valdivia (1998) and Cortez (1999). The first document used the number of days an adult reported sick at work as a proxy of an individual s health status. The estimated model followed the two-stage Heckman procedure and its results showed that healthier individuals received higher wages, even after controlling by education level. On the other hand, Cortez (1999) measured the effect of health on hourly wages for adults in urban and rural areas in Peru. His results show that this variable has a positive and significant effect on an individual s labour productivity. Given these results, he argues that public health investments must be considered as a mechanism to increase income level. In light of the research studies developed on Peruvian literature, and taking into account international literature, it can be concluded that there are still many approaches to analyze on this subject. This document expects to be a start point. The wide Peruvian cultural and geographic panorama may enrich this analysis and help to clear the relation between calorie demand and labour productivity. On the following section of this document, calorie availability as well as other household s characteristics will be analyzed. 16 Suárez Bustamante (2003). Caracterización del Programa del vaso de Leche. General Direction of Economic and Social Affairs - Ministry of Economics. 17 Gajate and Inurritegui (2002), El Impacto de los Programas Alimentarios sobre el Nivel de Nutrición Infantil: Una Aproximación a partir de la Metodología del Propensity Score Matching Research Project Social and Economic Research Consortium 18 Francke (2002), Análisis de los Criterios de Asignación de los Recursos Públicos que son Transferidos desde el Gobierno Central a los Gobiernos Subnacionales. Ministry of Economics. 19 Herrera (2001), Requerimientos y Déficit Alimentarios en el Perú,

7 2 DESCRIPTIVE ANALYSIS: CALORIE AVAILABILITY ON PERUVUAN HOUSEHOLD S AND LABOUR PRODUCTIVITY 2.1 Calorie availability and hourly wages: evolution A first step to approach the effect of household s calorie availability on an individual s labour productivity is to analyze the characteristics of those who are not able to satisfy their caloric requirements, known as caloric poor. Based on the information provided by the National Household Survey (ENAHO) developed by the National Institute of Statistics (INEI), caloric poverty in Peru is estimated comparing household s daily available calorie level and the amount of calories required by its members. Therefore, if the amount of available calories is not sufficient to satisfy each individual s caloric requirements, household members are considered as caloric poor. It should be noticed that ENAHO does not provide information about an individual s calorie intake. In that sense, the first presumption in this document is that calories are distributed on equal shares among household members. Table No. 2.1 presents the main characteristics of individuals who are not able to satisfy their calorie requirements. It is shown that caloric poverty has increased significantly between 1998 and In the same way, daily calorie availability level per capita shows a decrease by 17%, while hourly wages fell on 38% in the same period. On the other hand, calorie availability fell 12.9% among those who fulfill their calorie requirements, while real hourly wages fell 32.5%. In the case of those who do not cover their caloric needs, calorie availability presents a slight increase of 3.3%, whereas average hourly wages fell 45.8%. Taking into account gender differences, caloric poverty increased for males as much as for females (from 23.2% to 37.1% and from 21.5 to 35.6%, respectively), whereas real hourly wages fell 38.3% and 37%, respectively. Also, caloric poverty has increased on every age interval. Table No. 2.1: Individuals General Characteristics Caloric poverty - individuals (%) Average available calorie level per capita Real hourly wages per capita Men Women Caloric poverty by gender (%) Men Women Total Other important characteristics are shown on Appendix 7.1 A. 21 Real remunerations are expressed in Nuevos Soles at 2002 prices. 7

8 Caloric poverty by age intervals (%) From 0 to From 6 to From 16 to From 26 to More than Total Average calorie availability level per capita Not caloric poor Caloric poor Real hourly wages Not caloric poor Caloric poor Source: National Household Survey (ENAHO) 1998, 1999, 2000, 2001 and 2002, National Institute of Statistics Elaboration: own 2.2 Caloric poverty: seriousness of the problem and inequality How far are caloric poor from fulfilling their calorie requirements? Has there been any improvement between 1998 and 2002? In order to answer these questions, the average poverty gap is calculated based on individuals caloric requirements and household s available calorie level per capita 22. Assuming Z and Kcal as the calorie level required by each household i and the calorie availability level in each caloric poor household i, respectively, the poverty gap for household i is defined as follows: BK i Kcal Z = Z i The index is finally calculated by averaging gaps for each caloric poor household. Information provided by this index is very useful because it allows to approach the seriousness of caloric poverty. This is, on average, how far caloric poor households are from satisfying their caloric requirements are. As it is observed on Table No. 2.2, seriousness of caloric poverty has decreased: whereas in 1998 the average caloric poverty gap was 25.7%, towards 2002 it fell to 23.8%. Nevertheless, taking into account the distribution by quintile of household total expenditure in the same period, caloric poverty has worsened among those included in the lowest quintiles (I and II). Table No. 2.2: FGT 2, national caloric poverty, National Distribution by quintiles of household expenditures I II III IV V Source: National Household Survey (ENAHO) 1998, 1999, 2000, 2001 and 2002, National Institute of Statistics. Elaboration: own 22 This index is well known in economic literature as FGT 2. Usually it is calculated on the basis of individual s expenditure level and the monetary value of the poverty line. 8

9 Taking into account differences by area of residence, on 2002, those who lived in urban areas would be closer to fulfill their caloric requirements than they were on Only the poorest families living in this area (those included on the first quintile of household total expenditure) would have worsened their caloric status. With respect to those families living in rural areas, figures are not as favourable as for those in the urban areas. Evidence supports that, between 1998 and 2002, caloric poor households would not have shown a significant improvement in terms of fulfilling their calorie requirements. 23 Another important issue in this analysis is the inequality on calorie available levels among households through different total expenditure levels. In order to approach this matter, two indicators are analyzed: the poor-rich ratio and the Gini coefficient. The poor-rich ratio allows to calculate the magnitude of inequality associated to caloric poverty between individuals included in the richest and the poorest quintiles of the distribution of total household expenditure. Valdivia and Mesinas (2002) indicate that the advantage of this indicator is based on its capacity to show the seriousness of these differences. Therefore, regarding caloric poverty, it is possible to calculate that the proportion of individuals who are not able to fulfill their caloric requirements from the lowest quintile is x times the same proportion in the case of those included in the highest quintile of total household expenditure. Nevertheless, they also indicate that this indicator only takes into account the extreme groups, ignoring the magnitude of the problem in the middle levels. Table No. 2.3 shows the poor-rich ratio and the incidence of caloric poverty on each quintile of total household expenditure for the period This indicator shows a slight fall as a result of a greater relative increase on incidence in the highest quintile of the distribution respect to one showed in the first quintile. It should be noticed that, with regard to the middle quintiles of the distribution, the increase has been much more drastic than in the extreme quintiles. Table No. 2.3: Poor-rich ratio and caloric poverty incidence, Poor-rich ratio Quintile I Quintile II Quintile III Quintile IV Quintile V Total Source: National Household Survey (ENAHO) 1998, 1999, 2000, 2001 and 2002, National Institute of Statistics (INEI). Elaboration: own Taking into account the individual s area of residence, incidence increased considerably on every expenditure quintile for those who live in urban area as well as for those who live in the rural one. Nevertheless, it is in the urban area where the middle quintiles were harmed the most between 1998 and 2002, 23 See Table B and C on Appendix

10 while in the rural area were the richest quintiles. Regarding the poor-rich ratio, it did not register changes in the urban area, whereas in the rural it decreased significantly: from 20.8 in 1998 to 8.8 in Unlike the poor-rich ratio, the Gini coefficient allows to incorporate the existent differences between every level of the distribution, in this case, between available calorie levels. Table No. 2.4 shows the Gini coefficient for the distribution of calories. According to these results, inequality has decreased between 1998 and 2002, at the national level, as well as in the urban and rural level. Table No. 2.4: Gini coefficient, daily available calorie level per capita Peru Urban Rural Source: National Household Survey (ENAHO) 1998, 1999, 2000, 2001 and 2002, National Institute of Statistics. Elaboration: own It should be noticed that although inequality in the distribution of available calories have decreased in the same magnitude for the urban area as well as for the rural one, there are important differences among the distribution by quintiles of households total expenditure. In that sense, decrease on inequality in the urban area would respond to a greater reduction of calorie availability in the middle quintiles with respect to the extremes, as shown on Graphic No In the case of the rural area, the picture is quite different due to the greater contraction on calorie availability levels of the richest families in the expenditure distribution. These results are supported by those shown on Table No. 2.2 and Table No Nevertheless, it is necessary to mention that the Gini coefficient does not contribute in terms of calculating the magnitude of inequality between the rich and the poor as the poor-rich ratio, which maintains that in caloric poverty level among the poorest ones is 7.4 times the incidence for the richest ones. 10

11 Graphic No. 2.1: percentual variation of daily available calories per capita 24, by percentiles of total household expenditure Variación porcentual Percetiles del gasto del hogar Nacional Urbano Rural Source: National Household Survey (ENAHO) 1998, 1999, 2000, 2001 and 2002, National Institute of Statistics. Elaboration: own All this analysis allows clarifying the relation between calorie availability and individuals labour productivity. Nevertheless, until this point, the relation among available calorie levels, caloric requirements and measurement of extreme poverty in Peru have not been analyzed in depth. The following section will take into account this issue. 2.3 Caloric poverty and monetary poverty in Peru: substitutes or complementary? Another important issue is the incidence of caloric poverty according to the individual s monetary poverty status. On the year 2002, 7 of each 10 extreme poor individuals were not able to fulfill their daily caloric requirements (See Table No. 2.5). Likewise, it should be noticed that almost half of the extreme poor population do not satisfy their calorie needs: 54.8% in 1998 and 48.9% in On regards of daily available calorie level per capita of the extremely poor, it fell about 2.5% between 1998 and 2002, whereas real hourly wages decreased by 46%. Nevertheless, the incidence of caloric poverty has increased dramatically among the non-extremely poor and non-poor individuals between 1998 and 2002: from 24.9 to 39.7%, and from 6.7 to 14.9%, respectively. Likewise, the proportion of caloric poor who live under non-extreme poor conditions increased from 27.8% in 1998 to 31.9% in On regard of daily available calorie level per capita, its level decreased by 8.7% in the case of the non-extremely poor and by 16.8% for the non-poor. In this context, the reduction in the real hourly wages was 34% and 28%, respectively (see Table No. 2.5). 24 Quadratic trend line. 11

12 Table No. 2.5: Caloric poverty, calorie availability and monetary poverty Caloric poor by poverty levels (%) Extremely poor Non-extremely poor Non-poor Total Caloric poverty, distribution by monetary poverty levels (vertical %) Extremely poor Non-extremely poor Non-poor Total Average available calorie level per capita, by monetary poverty level Extremely poor Non-extremely poor Non-poor Real hourly wages, by monetary poverty levels Extremely poor Non-extremely poor Non-poor Source: National Household Survey (ENAHO) 1998, 1999, 2000, 2001 and 2002, National Institute of Statistics. Elaboration: own These results raise a new issue: while extreme poverty has remained relatively stable, caloric poverty has increased significantly. In the first place, it is important to remember the traditional definition of monetary poverty used in Peru: if household s total expenditure level is below the total poverty line, household is labeled as poor. Likewise, if household s total expenditure is below the food poverty line 25, it is labeled as extreme poor. That is to say, this methodology takes into account household s expenditure level, but not its distribution among certain items. In order to clarify this issue, it would be useful to define extreme poverty based on the share of food in the total household expenditure. Thus, as shown on Table No. 2.6, based on this alternative definition, the proportion of extreme poor individuals from total population has increased dramatically, whereas caloric poverty has also increased, and extreme poverty in the last years based on the traditional definition- has remained almost stable. If it is taken into account that the food basket used to calculate the extreme poverty population is based on a minimum calorie level to fulfill an individual s caloric requirement, this apparent contradiction would imply that the share of food in the households total expenditure is not assigned to purchase the food items included in the assumed food basket, despite households may have the economic capacity to purchase them. 25 It is necessary to understand the way in which the food poverty line is built. According to the INEI, extreme poverty or food poverty line is calculated on the basis of a food basket that contains the necessary calories for an individual to subsist. The food poverty lines are valued with seven different price levels (the medians), each corresponding to a geographic area: Urban and Rural Costa, Urban and Rural Sierra, Urban and Rural Selva, and Lima Metropolitana. Prices for each food item are obtained by dividing the total expenditure by the quantity purchased by the reference population. Finally, the value of the aliment line is calculated by adding the values of each basket. 12

13 Table No. 2.6: Alternative extreme poverty, caloric poverty and traditional extreme poverty Extreme poverty (alternative definition) Caloric poverty Extreme poverty (traditional definition) Source: National Household Survey (ENAHO) 1998, 1999, 2000, 2001 and 2002, National Institute of Statistics Elaboration: own It also would be interesting to identify the proportion of individuals whose share of food in the household expenditure per capita is above the extreme poverty line from those individuals who are considered as non-extremely poor according to the traditional definition. Results show (Table No. 2.7) that, for example, 82.6% of the population in 1998 was labeled as non-extreme poor. Nevertheless, 33.8% out of them assigned an insufficient share of their budget to purchase food, figure that increases to 48% until Therefore, these results may be an evidence to argue that extreme poverty has been underestimated. Table No. 2.7: Proportion of non-extremely poor individuals whose food share in the household expenditure (FE) is higher than the extreme poverty line FE < aliment line FE >= aliment line Total Source: National Household Survey (ENAHO) 1998, 1999, 2000, 2001 and 2002, National Institute of Statistics Elaboration: own This analysis could also be developed in terms of the acquired calories. For that reason, four different scenarios are proposed, taking into account if the share of food in a household s total expenditure is above the extreme poverty line or not 26 : On one hand, the share of food in the household expenditure is below the extreme poverty line, two scenarios are identified: Scenario I: Household does not fulfill its caloric requirements. If the extreme poverty line is based on a food basket that includes the minimum calorie level for an average individual to subsist, it is expected that every household with an expenditure level per capita below that of the extreme poverty line was not able to satisfy its caloric requirements. Scenario II: Household fulfills its caloric requirements. This scenario is quite interesting, since it seems to be contradictory that households assigning an insufficient share of their budget to purchase a basic food basket were able to fulfill its members caloric needs. On the other hand, two more scenarios are identified if household s expenditure share on food is greater or equal to the extreme poverty line: 26 See Appendix

14 Scenario III: Household does not fulfill its caloric requirements. This is the most interesting scenario, because although the share of food in the household expenditure surpasses the extreme poverty line, individuals are not able to satisfy its caloric requirements. Scenario IV: Household fulfills its caloric requirements. In this case, the share of food in the household total expenditure is greater than the extreme poverty line and it allows household to fulfill its members caloric requirements. Table No. 2.8: Scenario I - IV Share of food in the household expenditure >= extreme poverty line Caloric poor (Scenario I) Not caloric poor (Scenario II) Total Share of food in the household expenditure < extreme poverty line Caloric poor (Scenario I) Not caloric poor (Scenario II) Total Source: National Household Survey (ENAHO) 1998, 1999, 2000, 2001 and 2002, National Institute of Statistics Elaboration: own Again, results (Table No. 2.8) show contradictions: on the first scenario, the proportion of caloric poor individuals was only 46% from those whose share on food in the household expenditure per capita is above the extreme poverty line. Towards 2002, this proportion raised to 55.3%. Results on the last two scenarios are closer to what may be expected from an extreme poverty line based on a minimum food (calorie) basket. Although there is no perfect adjustment in the fourth scenario (every individual whose share on food in the per capita household expenditure is greater than the extreme poverty line should satisfy its caloric needs), this proportion has remained over 90% between 1998 and Likewise, figures exposed on the third scenario respond to the possibility that, although the share on food in the household expenditure per capita is greater than the extreme poverty line, individuals may purchase foods that would not supply enough calories as to fulfill their caloric requirements. Nevertheless, results on the first and second scenarios are the most controversial. The most probable reason is that the extreme poverty line is not measured properly. It should be noticed that quantities included in the food basket for the costa, sierra and selva geographic areas are previously determined according to calorie requirements. However, food baskets are valued on the basis of the median of the prices of the foods purchased by households. In that sense, the value of the line used to measure food poverty (extreme poverty) on each geographical area would be hardly approximating the prices households usually face when purchasing the basic or food basket. Therefore, some households would be able to satisfy its members calorie requirements spending less than the value of the extreme poverty line, whereas 14

15 other households, even though their expenditures levels are above the poverty line, would be considered as caloric poor. 3 ECONOMETRIC STRATEGY AND DATA The estimation of the effect of calorie consumption on labour productivity is complex because the relation between these variables is not limited to one way of causality. Two effects can be clearly identified: an income effect and a substitution effect. The first effect shows that greater calorie consumption levels would lead to higher individuals labour productivity, which finally means an increase on household s economic capacity to purchase calories due to higher wages. The second effect shows that higher productivity translated into higher wages (W ) would allow the individual to make certain labour decisions as reducing its effort at work (or worked hours 27 ), and thus, decrease the share of food (calories) in the household budget. Literature starts with the notion of endowment to solve this problem. The unobservable characteristics of individuals and families that affect their calorie consumption and productivity are known as endowment. These are predetermined characteristics (like body contexture) that are exogenous and random. Normally it is assumed that these endowments ( m ) are part of the error term of the equations of the variables affected by them. However, in estimation strategy raised in this document the simultaneity between the two variables does not only appear when one of them affects the other variable explicitly, but also the correlation on their error terms distorts them. Thus, if we define ε W as the disturbance of the wage equation and ε * as the one of the K household calorie demand, the simultaneity problem would arise if Cov( ε W, ε ) K * was different from zero. If we define φ W and φ * as the real error terms, these could be written as K ε W = mw + φw and ε * = m + * φ *. Consequently, Cov ( ε, * ) (, * ) K K K W ε = Cov m K W m, K no matter that φ W and φ * were independently distributed. In the first place, the K relation between m W and m * could be explained by a similar characteristic or K endowment that allows an increase on productivity and at the same time makes possible an increase on the individual s household calorie demand. In the second place, this relation could be supported on the individual s behavior: higher endowments in favour of productivity ( m W ) could generate incentives to reinforce it through non-observable variables included in m * (incentives to K increase household s calorie demand). In the light of these facts, the wage equation is estimated under Mincer s (1974) scheme. This specification includes individuals characteristics (age, gender), human capital variables (education, experience), labour market variables (economic sector, labour condition, among others), and household s daily 27 Yamada (2004) 15

16 calorie availability level per capita. This estimation should include a first stage where a labour participation model would allow us to correct for selection bias, according to the two-step Heckman procedure. In order to solve the simultaneity problem explained, household s available calorie level should be estimated using instrumental variables. The most important instruments are the education level of household s chief, household demographic composition, foods price index, as well as access to feeding assistance programs. Thus, the calorie demand equation to be estimated is: * K = β 0 + β1x K + β 2F K + ε K (1) With regard to the access of family members to feeding assistance programs administered by the government, a simultaneity problem between this variable and household calorie demand is again found. On one hand, a higher probability of access to those programs would mean a higher household calorie demand. On the other hand, households with greater levels of available calories would not need to access those programs. Therefore, in order to take into account the real effect of these assistance programs on households calorie demand, we previously estimate the probability of access using instrumental variables: F K = + γ 1X F γ 0 + ε (2) F * Once the access to feeding assistance programs ( F ) is estimated and included on the estimation of household calorie availability level or demand * ( K ), the wage equation is defined as: ( ) * ln W α0 α1xw αkk εw = (3) where X W is a set of relevant variables for the determination of wages (educative level, age, experience in the labour market, among other observable * characteristics), K is the daily available calories level at the individual s household (previously estimated), and ε W is the error term. As we have already explained, the estimation of this equation corresponds to the two-step Heckman procedure. Regarding the data, the National Household Surveys (ENAHO) 1998, 1999, 2000, 2001 and 2002 are used. These surveys provide information about individuals characteristics, human capital, labour and earnings, as well as households calorie availability level included in the sample. 4 RESULTS As described in the previous section, our econometric strategy goes through different steps. First, we estimate the access to feeding assistance programs and include the predicted probability in the estimation of households calorie demand. Finally, using the Heckman procedure, we estimate a wage equation in order to accomplish the main goal of this document: to find the effect of calorie availability on the individual s productivity level. 16

17 However, before presenting the main results of this research it is necessary to clarify some aspects about the different procedures we applied in order to estimate the demand for calories. As it has been mentioned, the National Household Survey (ENAHO) provides the amount of food purchased by the household, thereby, the amount of available calories. In this sense, we assume that these calories are uniformly distributed among household members. In order to relax this assumption, we tried to estimate an individual demand for calories assuming that calories were distributed according to the calorie requirements of each individual. Using the sintaxis provided in Herrera (2001), let us call the amount of calories required by and individual k i. Therefore, the amount of calories required by his household would be represented by: n K j = k i i= 1 j, where i = 1 to n and j refers to a particular household. Based on this information, it is possible to know the proportion of calories an individual demands within each household: j k ki k p ji = = n K k i=1 j i j i j Using this ratio, and assuming that calories are distributed among the household members according to their calorie requirements, we were able to approximate the amount of calories assigned to each individual. An important aspect should be noticed at this point. On the one hand, the estimation of the demand for calories using this approximation as the dependent variable led us to the conclusion that it was not possible to identify the main individual s characteristics that would explain the resultant level of calorie demand based on this distribution. Herrera (2001), assumed an average weight for those individual s residing in the urban areas and a lower weight for those in the rural areas. In fact, the National Household Survey (ENAHO) only provides gender and age, but no anthropometric measure neither other variable that would add useful information to determine this. As a consequence, only household characteristics were available as independent variables. Indeed, these variables were more accurate to estimate a household demand for calories rather than an individual demand for calories. Both positions to estimate an individual s demand for calories and a household s demand for calories- require strong assumptions. However, is it possible to confirm that households distribute their food purchases according to the individual requirements of their members?. This is, we would be assuming that the household s chief or the member who is in charge of preparing meals has enough capacity as to distribute them matching the caloric requirements of each of their members. Unfortunately, there is no evidence supporting this behavior inside Peruvian households. 17

18 At the same time, taking into account that our final goal is to estimate a salary equation, which implies that the sample would include only people above 14 years old (ENAHO only provides information about labour participation for individuals above this age), it would be more conservative to assume that each of them receives, on average, the same ammount of calories within the household, and therefore, estimate the demand for calories at the household level. In this section, the results of these estimations will be presented. We will first focus on the results for the 2002 sample. Once we have presented these estimations, we will analyze in depth the household s calorie demand and its effect on productivity based on the results provided by the quantile regressions procedure. Finally, the estimation of the demand for calories and the wage equation using the pool sample are presented in order to identify the period effect on this variables. 4.1 Calorie availability effect on productivity the Heckman procedure Table No. 4.1 shows the wage equation estimated using the Heckman procedure, for the 2002 sample. Hourly wages are used as proxy of individuals productivity. The first specification does not include households calorie availability, and the second one includes this variable previously estimated in order to solve the simultaneity problem we have already explained 28. Finally, the results for the urban, rural, male and female samples are presented. The estimation for the calorie demand equation will be explained in the next section. International and Peruvian economic literature has already analyzed in depth the determinants of wages. Our results only corroborate what has been found in recent research documents. As shown on Table No. 4.1, for these samples, human capital variables (e.g. experience and education level accomplished by the individual, among others) have a positive and significant effect on wages. Other individuals characteristics as male gender, has also this effect. In this sense, more years of potential experience 29 generate higher salaries. Likewise, higher levels of salaries are related to higher levels of education, as well as to being a male worker. Regarding labour conditions faced by the individual, working as an independent (mostly associated with informal workers) may have a negative and significant effect on wages, as well as working for the government. However, having a secondary job may increase the hourly wage received by the individual. Taking into account the size of the company where the individual works (using the number of coworkers as proxy) it can be observed that workers at bigger companies may receive higher wages. The worker s economic sector is also an important variable on these equations. Including dummy variables for each sector but agriculture, the effect on wages is positive and significant, using both specifications and samples. 28 For simplicity, in this section we will only focus on these specifications. Other specifications are included in the appendices of this document. 29 The individual s experience is calculated based on: Age years of education 6. 18