UNDERSTANDING THE GENDER-BASED PRODUCTIVITY GAP IN MALAWI S AGRICULTURAL SECTOR

Transcription

1 UNDERSTANDING THE GENDER-BASED PRODUCTIVITY GAP IN MALAWI S AGRICULTURAL SECTOR A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in Public Policy By Jason T. Clark, B.S. Washington, DC April 16, 2013

2 Copyright 2013 by Jason T. Clark All Rights Reserved ii

3 UNDERSTANDING THE GENDER-BASED PRODUCTIVITY GAP IN MALAWI S AGRICULTURAL SECTOR Jason T. Clark, B.S. Thesis Advisor: Alan de Brauw, Ph.D. ABSTRACT Much attention has been given to the development potential of Africa in recent years. One of the most frequently cited areas for investment is agriculture, especially given that more than 70% of people across sub-saharan Africa rely on agriculture for both food and income. While both men and women across the developing world contribute to this sector, the notion that women are less productive than men persists. Recent studies demonstrate that men do, in fact, tend to produce more agricultural output than women. However, that is only the beginning of the story. Using both OLS regression and fixed effects models, this paper finds that much of the productivity gap between men and women in the agricultural sector of Malawi is explained by differences in access to vital agricultural inputs, including high quality land and extension services. Additionally, the presence of plots containing multiple crops negatively impacts agricultural yields and tends to harm productivity more significantly in women than in men. Using these findings as a basis for policy, direct government intervention that expands the scope and availability of extension services, reforms land ownership and inheritance rights, and invests in female empowerment programs should further reduce gender-based productivity differences. If fully implemented, these policies should support the country s rapid economic growth and help lead to a Malawi that is food secure. iii

4 The research and writing of this thesis is dedicated to everyone who helped along the way. Many thanks, Alan de Brauw (especially for your patience and last minute feedback) Adam Thomas Barbara Schone iv

5 TABLE OF CONTENTS Introduction... 1 Background Literature Review Conceptual Framework and Hypothesis Data and Methods Data Source Analysis Plan Descriptive Results Discussion Data Limitations Policy Implications Conclusion Appendix References v

6 TABLE OF FIGURES Figure 1: Conceptual Model Table 1: Variable Definitions. 14 Table 2: Gender Breakdown of Maize Output/Yield. 16 Table 3: Gender Breakdown of Agricultural Inputs.. 17 Figure 2: Distribution of log_maize_yield Variable.. 18 Table 4: Regression estimates of maize yield OLS & fixed effects models Table 5: Regression estimates of maize yield OLS & fixed effects models Table 6: Regression estimates of maize yield Extension Usage Table 7: Regression estimates of maize yield Good Land Quality Table 8: Regression estimates of maize yield Multiple Crops Table 9: Unit Conversions Figure 3: The Relationship Between ln(maize yield) & ln(labor) vi

7 INTRODUCTION In the decades following independence, most countries in sub-saharan Africa suffered from stagnant economic growth rates and an overall lack of development. Economists cite many factors for this occurrence: persistent conflict, statist policies that prevented functioning markets from developing, the effects of climate change, macroeconomic instability, and numerous others. Beginning in the 1980 s, African leaders, along with support from Western countries and multilateral organizations like the World Bank, started moving away from agriculture-based growth strategies and made nominal investments in this sector (Thurow 2010). The outcomes of these actions included the reduced availability of vital extension services, elimination of subsidies on agricultural inputs, and stagnation and often deterioration of markets. This policy change has proven to be devastating for farmers in these countries, who represent around 70% of the population and rely on agriculture to both feed their families and generate income (ibid.). For decades, most African farmers lacked access to improved seeds, fertilizer, extension services, irrigation systems, and other productivity-boosting technologies. Additionally, the weaknesses, or the complete absence in some cases, of banking and credit systems across much of Africa has exacerbated the problem, preventing farmers from purchasing needed inputs or making important capital improvements to their land. The lack of clearly defined and enforceable land rights also provides disincentives to make these investments. Without crop insurance, rural farmers suffer from frequent droughts, which increase malnutrition, disease, and the prevalence of conflict. However, the last decade brought hope to sub-saharan Africa. Agricultural research is expanding, technologies are being utilized to provide market data and extension services throughout rural areas, and governments and private organizations alike are providing access to 1

8 vital banking and credit services (Thurow 2010). Another trend is the changing roles of women in the agricultural sector, who grow food not only for their families but for economic profit as well (Food and Agriculture Organization of the United Nations 2011). Despite these positive changes, many studies acknowledge the large differences in agricultural yields between men and women, indicating the presence of a gender-based productivity gap (Quisimbing 1996). However, these same studies often reveal that this productivity gap is smaller than expected or simply does not exist when properly controlling for access to quality land and vital agricultural inputs such as seeds, fertilizer, and water (ibid.). As the role and standing of women vary across societies, it is important to investigate what accounts for differences in gender productivity. This analysis may provide a path for future research aimed at identifying the prevalence of these productivity gaps and subsequent policies that can help eliminate them when they do exist. Malawi provides an excellent setting to investigate the presence of a productivity gap and its implications. There is near universal participation in agriculture by households throughout the country, with women responsible for a majority of the labor. The presence of gender-based differences in productivity is a concern in this setting, as it has important implications for food security and income generation in the household. In 2011, GDP per capita, PPP (current international dollars) was $893 in Malawi, meaning it is one of the least developed countries in the world (World Bank). If the government can propose and implement policies aimed at reducing the productivity gap between genders, total agricultural productivity within the country stands to increase dramatically. However, the presence of a productivity gap must be established and fully understood before such policies can be proposed. This paper seeks to accomplish these two tasks. 2

9 In this analysis, which uses the World Bank s Third Integrated Household Survey ( ) for Malawi, I will investigate the presence of the aforementioned gender-based productivity gap and determine which factors (quality of land, access to seeds, fertilizer, extension services, etc.) contribute to differences in agricultural output. BACKGROUND In order to better appreciate the subject matter of this paper, a brief contextual analysis of key issues in the Malawian agricultural sector, which accounts for 40% of GDP and 60% of export earnings (Diao 2012), is necessary. This analysis will cover three main areas: past agricultural performance, recent initiatives to improve productivity, and the presence of gender disparities. In the early 2000 s, Malawi was said to have one of the worst performing agricultural sectors in sub-saharan Africa in terms of productivity. During this period, population growth rates exceeded economic growth, which led to decreases in GDP per capita. Malawi suffers from the effects of climate change and lacks resources to overcome extreme weather events such as droughts, which devastated the sector during the maize season. Malawi is also one of the most densely populated countries in sub-saharan Africa, and its high rural density has constrained growth. The average landholder only has 1.13 hectares. The fragile state of the agricultural sector in the early 2000 s is much different from the 1990 s, which saw growth exceeding 7% a year due to structural reforms aimed at improving markets, infrastructure, and access to credit (Diao 2012). Around 2005, the government of Malawi decided that improving the productivity of maize farmers was the key to improving overall wages and reducing poverty (Ministry of 3

10 Agriculture and Food Security 2010). Around 90% of the population is engaged in agriculture in the country, and around 97% of these people grow maize, which also provides around 60% of the daily caloric intake for this group. Despite actions by the World Bank and Western countries to limit or prevent agricultural subsidies in developing countries, Malawi started a program with UN support that provided fertilizer and improved maize seeds at discounted rates. The government issued around 3.4 million coupons that allowed farmers to pay $16.40 for about $44.00 worth of inputs. Seventy-five percent of these coupons were redeemed, providing 132,000 tons of fertilizer and 6,000 tons of seed at a total cost of $58 million. In 2005, the drought caused a 40% decline in production, requiring 4.2 million people to seek food aid to survive. After implementation of this subsidy program, the country went from a food deficit of 43% in 2005 to a food surplus of 53% in The total maize surplus in 2007 was 1.34 million tons, which allowed it to provide food aid to neighboring countries. During this time, maize output increased from 0.76 tons per hectare before the subsidy to around 1.59 tons per hectare afterward (Denning 2009). Another important factor when analyzing gender-based productivity differences is the role and status of women in Malawian society. According to the World Bank s Poverty and Vulnerability Assessment in 2007, the status of women in Malawi is substantially lower than men. Female-headed households represent about 23% of total households. Despite improvements in health, maternal mortality remains at 675 per 100,000 live births in 2010, which is still one of the highest rates in the world. The report states that there is not a significant difference between men and women in terms of size of landholdings, but female heads of the household remain disproportionately poorer than males. Within the typical household, men use high-yield maize seeds while women are often denied access. In households headed by males, 4

11 women have little decision-making power and are often relegated to crops not requiring fertilizer. Other statistics show that 14% of men use extension while only about 8% of women do. Additionally, women are mostly denied access to credit for even moderate amounts, meaning they are limited to microcredit when available (World Bank 2007). LITERATURE REVIEW The literature related to the current participation trends for women in the agricultural sector of developing countries remains relatively mixed and inconclusive. There appears to be a debate over whether women are participating more in this sector or if participation levels are the same but the responsibilities of female farmers are changing. Deere (2005) shows that rural women in Latin America are increasingly becoming the principal farmers on smallholder plots of land and have experienced growth in wages. This has primarily occurred because men are migrating to more densely populated areas to seek better paying jobs. According to de Brauw et al. (2012), this finding is replicated in China, where labor force participation by women in the agricultural sector is increasing relative to men, and women are taking over management duties on farms without losses to efficiency (assuming equal access to vital inputs). However, Mu and van de Walle (2011) found a less conclusive result in China. Similar to Latin America, migration has played a significant factor in labor force participation, as men have increasingly sought work off the farm. While this has led to a growing role for women on the farm in many cases, just as many women have seen no changes in farm participation. This lack of consensus points to the importance of context, as each country, and often regions within countries, experience varying trends of female agricultural participation. 5

12 The area of gender-based productivity differences has received increased attention in recent years, seeking to confirm the presence of these differences and attempting to identify their causes. Abdulquadri and Mohammed (2012) identify agricultural productivity as a means to address the food crisis in Nigeria but state that women are limited to specific farming tasks and have unequal access to inputs. This is similar to a study by Song et al. (2009) in China, which notes that gender inequalities are deepening, in terms of resource and opportunity access. The study identified access to extension and credit as two significant limitations between genders. Another well-known paper that addresses access to inputs is Udry (1996), which discusses the Pareto efficient allocation of inputs across multi-plot households. Ideally, each plot should receive the amount of inputs that maximizes factor productivity across plots. In reality, he notes that plots controlled by women are farmed much less intensively than similar plots within the household controlled by men, leading to a 6% decrease in output relative to the level deemed Pareto efficient. Akresh (2005) disputed these claims, stating that Udry s results were localized and that inputs are generally distributed in a Pareto efficient manner. Quisumbing (1996) agreed with Udry s findings, noting that evidence of allocative inefficiency within households exists despite her overall finding that women are as efficient as men after controlling for inputs and human capital. Her study utilized data from developing countries across Asia and Africa, including Kenya, Thailand, Nigeria, Burkina Faso, and several other countries. Kinkingninhoun-Medagbe et al. (2010) provides additional support for Quisumbing s findings, writing that women in Benin were as technically efficient as men after accounting for discrimination regarding access to land and machinery. As access to inputs such as quality land, improved seeds, fertilizer, pesticides, water, extension services, and many others varies greatly by economic status, gender, and location, it is 6

13 necessary to investigate the effects of these inputs on agricultural output. Nosiru and Rahji (2012) studied the availability of these resources in Nigeria and concluded that female-headed households suffered less access to credit facilities and farmland. These women were actually more productive and had higher incomes than men when all the necessary production inputs are made available to them at least at an equal level with the male farmers (ibid.). A similar study (Peterman et al. 2010) revealed that in terms of inorganic fertilizer, seed varieties, extension, and group membership, women have less utilization of these inputs than men. Goldstein and Udry (2008) point to the importance of land rights, which make it feasible to invest in capital improvements on farmland. These investments increase productivity and improve land fertility. Additionally, they found that women tend to leave land fallow much less than men, which decreases output. This is relevant in Malawi were the government is attempting reforms aimed at improving land rights for women (Holden et al. 2006). While women may obtain approval to farm the land that was previously used by their mothers, these women often lack any enforceable rights to this land. This hinders attempts to improve agricultural productivity. In the past decade, several studies have investigated the positive economic and social growth that stems from gender-based interventions in the agricultural sector. Ogunlela and Mukhtar (2009) provide the example of Nigeria where women make up 60-80% of the agricultural workforce but have minimal or no decision-making power. Through the provision of extension services made directly to women, nongovernmental organizations (NGOs) have greatly improved access to inputs and have succeeded in establishing lines of credit for female farmers. Quisumbing and Pandolfelli (2010) provide a summary of interventions that occurred throughout the developing world over the last two decades that sought to improve the well-being of female farmers. Examples of interventions include the 1998 Uganda Land Act that 7

14 strengthened tenure security and legal protection of customary owners and women, Nigeria s Unified Extension System designed to provide better extension services to women, and the Malawian voucher program as discussed above (Quisumbing and Pandolfelli 2010). They believe that the primary focus of development efforts has been human development, much to the detriment of improving access to needed inputs. They note: Future interventions need to consider interactions among inputs rather than treat each input in isolation, adapt interventions to clients needs, and pay attention to the design of alternative delivery mechanisms, the trade-offs between practical and strategic gender needs, and the culture and context specificity of gender roles (Quisumbing & Pandolfelli 2010). These concepts will provide guidance when offering policy recommendations aimed at improving the productivity of women relative to men in Malawi. CONCEPTUAL FRAMEWORK AND HYPOTHESIS This paper investigates the presence of a productivity gap between men and women in the agricultural sector of Malawi. Numerous studies from across the developing world have shown that in terms of pure agricultural output women produce less than men. However, common knowledge of agriculture reveals that the location and quality of farmland have a large effect on the level of output. Additionally, improved seed varieties, fertilizer, water, pesticides, extension services, and other technologies strongly affect agricultural output. To truly measure the effect of being a woman on output, these factors must be accounted for in the analysis. Figure 1 presents a graphical representation of these inputs. 8

15 Figure 1: Conceptual Model Farms Need Quality Land Improved Seed Fertilizer Extension Services Other Inputs Agricultural Yields As previously discussed, almost all Malawians involved in agriculture grow at least one variety of maize. Using plot level data on maize yields in kilograms per acre and gender of the head of the household, we can expect yields for female-run farms to be lower, based on the facts noted above. However, the key question is whether a woman growing maize on a piece of land with a specific amount of inputs will obtain a level of output equal to that of man growing on the same piece of land with identical levels of inputs. To put it another way, is there a fundamental difference between men and women that leads to differing levels of output, holding land and availability of inputs constant? It is possible to argue that men and women differ physically, and these differences explain why men tend to have higher yields. However, the planting and maintenance of crops does not typically require high levels of strength. Harvesting can be physically demanding, but even plots managed by women usually have access to male laborers. Therefore, physical differences do not appear to adequately explain the productivity gap. Another argument involves the breakdown of household chores between men and women. As historical traditions persist, a woman with comparable agricultural knowledge and access to inputs to a man is still likely to spend a significant portion of her day maintaining the household, preparing meals, and taking 9

16 care of children. Yet, this can only explain a portion of the difference, especially in Malawi where most food consumed by the household is grown by that household. This suggests that a significant portion of a woman s daily chores involves farming. A more likely explanation for the productivity gap are differences in access and availability of land and other important agricultural inputs. It must be true that women are relegated to lower quality land, forced to use low-productivity seeds, denied access to quality extension services, prevented from purchasing fertilizer, or some combination of these situations. These would all contribute to lower levels of output. By explaining the productivity gap through differences in access to land and inputs, the remaining effect of the gender of the household head on output represents the true productivity gap, if it exists at all. If that gap does not exist, the policy focus can then move toward identifying policies that increase access to inputs and provide opportunities for economic equality between men and women. My hypothesis is that this analysis will demonstrate that men and women are equally as productive in the agricultural sector of Malawi. However, agricultural output will differ by gender for a variety of factors that can be controlled for in the regression model. After controlling for these factors, the productivity differences will disappear. I believe the largest determinant of productivity will be quality of land. As women are more likely to be relegated to land of poor quality, productivity will tend to suffer. Additionally, contrary to findings in other developing countries, such as China where women have better access to credit for purchasing important agricultural inputs, I anticipate that access to inputs such as fertilizer and improved seeds will have a significant effect on productivity, as access to credit for purchasing these inputs continues to be limited for African women. 10

17 DATA AND METHODS DATA SOURCE This analysis uses the Third Integrated Household Survey (IHS3) for the country of Malawi. The Government of Malawi initiated this data collection project in order to monitor the country s progress toward the Millennium Development Goals. The National Statistical Office (NSO) collects data every five years in order to monitor this progress. The IHS3 covers the years of Additionally, the World Bank provides technical assistance to the NSO as part of its Living Standards Measurement Survey Integrated Surveys on Agriculture (LSMS- ISA). The sampling design for this survey used information from the 2008 Malawi Population and Housing Census, which divides Malawi into three regions (North, Center, and South). Furthermore, these regions are separated into rural and urban classifications. The urban regions consist of Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. The remaining areas are separated into 27 distinct rural districts, meaning the data set contains 31 districts. The households in the survey were randomly selected and visited once to obtain the survey data. A sub-sample from each district was selected based on proportional size of each district, and these households received two visits during the IHS3 data collection and will receive an additional follow-up in The data set contains information from 12,271 households, which includes 10,038 households located in rural districts. In the original sample, 698 of the selected households were replaced, as data could not be attained for a variety of reasons. The sample used in this analysis represents a sub-set of households from the full data set. There is data covering 13,268 plots from 9,758 different households in this final sample. The 11

18 only criteria for dropping observations from the full data set involved the mix of crops grown on each plot. To simplify the analysis and allow for easy comparisons between households, only plots where maize was the primary crop were included in the sample. Maize is the most widely grown crop across Malawi, making it an excellent choice for this analysis. It should be noted that this choice does not preclude analysis on the effect of multiple crops grown on the same plot. In fact, the effect of multiple crops will be an important topic later in the analysis. Rather, dropping plots where maize is not the primary crop ensures that agricultural outputs and yields are strictly measured in terms of maize. The inclusion of non-maize output would only muddle the analysis. The data set for the final sample contains plot-level information on household characteristics, agricultural input usage, and maize output. No additional data sets were used. ANALYSIS PLAN The agricultural questionnaire for the IHS3 includes hundreds of variables, covering several agricultural sub-sectors and growing seasons. This analysis will use data collected for the rainy season. The length of the survey and the level of detail provided are extensive, increasing the likelihood of missing data for certain variables. Before the elimination of observations, all irrelevant variables were dropped, including those related to all post-harvest activities, such as crop storage, transportation, and market interactions. Missing data remains a concern, although focusing on household characteristics and agricultural input usage significantly reduces the amount of missing data. The model used in this analysis includes maize yield (in kilograms per acre) as the dependent variable. The data collected for maize output uses various units of measurement, making it necessary to convert to a common unit when calculating yield. Based on the availability of harvesting technologies in Malawi and the lack of conversion table with the data, 12

19 the conversions noted in Table 9 in the Appendix were used to translate maize output into kilograms. The resulting output was then divided by the number of acres for each plot in order to calculate yield. The independent variables included in the regression model are the gender, age, and education level of the head of the household, the number of household members in specific age brackets (under age 15, between 15 and 34, and over 34), the type of seed used, the quantity of organic and inorganic fertilizer used, extension usage, land quality, labor used throughout the growing season, and whether multiple crops were grown on the plot. Therefore, the regression model is as follows: = β 0 + β 1 (male) + β 2 (age of household head) + β 3 (education level of household head) + β 4 (number of household members in specific age brackets) + β 5 (seed used) + β 6 (organic and inorganic fertilizer usage) + β 7 (extension usage) + β 8 (land quality) + β 9 (labor used) + β 10 (multiple crops) + β 11 δ + ε Table 1 provides further information about the variables used in this analysis. 13

20 Table 1: Variable Definitions Variable Name log_maize_yield gender_householdhead age_householdhead na15, na1534, na34 no_education, some_primary, primary_school, junior_school, msce_school, university maize_local, maize_hybrid, maize_hybrid_recycled inorganic_fert, organic_fert extension good_land_quality labor multiple_crop Definition Maize yield is the dependent variable in this analysis. It is measured in tons of maize per acre. The natural log of the yield is used in the regression models. Gender of the head of the household is the main independent variable of interest. It is a dummy variable with a value of 1 if the head of the household is male. Age of the head of the household is a continuous control variable measured in years. These variables indicate the number of household members that under age 15, ages 15-34, and over age 34, respectively. These control variables measure the level of education obtained by the head of the household. These control variables indicate the type of maize seed used on the plot of land. There are three varieties of maize seed used, each containing varying levels of expected yields. These control variables measure the amount of inorganic and organic fertilizer used on the plot. They are measured in kilograms. This control variable indicates whether the head of the household used extension services during the growing season. The services must have focused specifically on growing maize, not storage, transportation, or marketing. This control variable measures the quality of land on the plot. This control variable measures the number of days of labor spent growing maize on the plot. This include the entire farming process from planting seeds to harvesting. It also includes labor from both household members and outside laborers (regardless if they were paid or unpaid). This control variable indicates whether multiple crops were grown on the plot of the land. This analysis uses a series of regressions to investigate the effects of household characteristics and access to agricultural inputs on maize yields. Of course, the main focus is whether the gender coefficient is significant, indicating the presence of a productivity gap between men and women. Regression outputs, located in the Discussion section of this paper, vary based on which of the variables noted above were included in the model. For each different grouping of independent variables, three regressions were run. The first uses ordinary least squares regression to demonstrate the effects of the gender of the head of the household on agricultural output. The second regression adds village fixed effects to the model while the third 14

21 includes regional fixed effects. The latter two regressions attempt to account for village or regional differences that may explain the presence of a productivity gap. These regressions not only help identify the presence of these gender-based differences but also provide important insights into key variables that drive the differences. These insights will inform the Policy Implications section of this paper. DESCRIPTIVE RESULTS There is a significant discrepancy in maize output and yield between male- and femaleheaded households (Table 2). Around 29% of the sample households were headed by women. Female-headed households had an average output of kg of maize per plot, with yields averaging kg per acre. By contrast, plots managed by male-headed households averaged kg of maize, with yields of kg per acre. The gap in maize yield indicates that without controlling for any other factors, male household heads can produce 52.3% more kilograms of maize on an acre of land than female household heads. These statistics are consistent with findings in other developing countries in Africa and Asia, as well as the first claim in the hypothesis stated above. However, the result is no less shocking and reinforces the need to explain this discrepancy. While a significant percentage of the productivity gap between men and women is likely explained by outliers (males are more likely to manage farms in the top end of the distribution), much of this gap remains unexplained. Building on the trends of increased participation of women in agriculture and the overwhelming rates of households engaged in agriculture, the 52.3% productivity gap presents a serious challenge for the Malawian government, as it attempts to become food secure and improve economic outcomes for its people. 15

22 Table 2: Gender Breakdown of Maize Output/Yield Output (kg) Yield (kg/acre) Male ,212.1 Female The breakdown presented in Table 3 begins to unravel the causes for this large productivity gap. For a variety of reasons, which will be further investigated in the remaining analysis, women typically have less access to vital agricultural inputs and services known to increase productivity. It stands to reason that improving access may decrease the gap between men and women. Table 3 breaks down the average values for the variables used in this analysis by gender. While male headed households have larger averages for every variable, some discrepancies are more likely to explain the productivity gap than others. For example, the use of hybrid seeds and inorganic fertilizer are higher among male headed households, and males also have better access to extension services and labor. Additionally, whether or not multiple crops are grown on the plot likely affects yields. Finally, male household heads tend to be more educated than female household heads, and they use more organic fertilizer. However, though being promoted rather heavily by NGOs in Malawi working on agricultural projects, organic fertilizer is not widely used by its farmers, so this variable may not be of much importance. As mentioned above, gender discrepancies among several notable variables from Table 3 are likely at least partially responsible for the productivity gap. For instance, whereas 49% of male household heads used hybrid seeds, which are known to have higher yields, only 41% of women have access to improved seeds, which increase yields substantially when used with fertilizer. Similarly, 47% of male household heads had access to valuable extension services while only 39% of female household heads had similar access. Similar proportions of male and female farmers reported their plots were of good quality (48% vs. 44%). It is also possible that 16

23 growing more than one crop on a plot reduces the overall output. As Table 3 reveals, multiple crops were grown on plots managed by women 53% of the time. This is consistent with traditional gender roles that dictate women grow crops to feed the family, which often entails growing several crops on the same plot. Men are more likely to grow a single crop that can then be sold at the market. Not surprisingly, men grew multiple crops on their plots around 45% of the time. On the surface, these differences do not appear to be alarmingly different. However, given that over 90% of the population in Malawi participates in agriculture, these differences impact nearly all households, as compared to more developed countries, where only a small subset of households participate in agriculture. Additionally, the productivity gap that persists in the regression models discussed in the next section is not insignificant. It is possible that these differences contribute to the remaining productivity gap. Table 3: Gender Breakdown of Agricultural Inputs Variables Male Female acres age_householdhead some_primary primary_school junior_school msce_school university na na na maize_hybrid maize_hybrid_recycled inorganic_fert organic_fert extension good_land_quality labor multiple crop

24 DISCUSSION Tables 4 and 5 present a series of 15 regression outputs aimed at determining whether the productivity gap between male and female headed households exists by using a multivariate framework that controls for relevant household characteristics and access to agricultural inputs. In these regressions, the natural log of the maize yield serves as the dependent variable for several reasons. Men tend to manage larger plots than women in the data set, meaning output must be standardized to provide a means for comparison. Production per unit of land, measured in kilograms per acre, gives the most useful regression coefficients. Additionally, the distribution of yields is skewed. The yield for a majority of the households in the sample is within a narrow band, but the remainder is skewed to the right, as a small group of households have large landholdings and greater access to technologies that increase productivity. By using the natural log of maize yield, the distribution is closer to normally distributed, minimizing the effects of outliers. Figure 2 reveals the distribution for the natural log of maize yield. Figure 2: Distribution of log_maize_yield Variable The regressions presented in Tables 4 and 5 represent a series of 5 different models focused on measuring the effects of gender on maize yields. Each individual model includes 3 18

25 distinct regressions, which vary by the inclusion of fixed effects. The first includes no measurement of fixed effects while the second and third include village and regional fixed effects, respectively. Regression 1 from Table 4 excludes any additional explanatory variables, other than gender. In this model, the coefficient on gender indicates the raw difference between male and female household heads in terms of agricultural yields, which appears to be 24.5%. The gender coefficient from Regression 2 appears to indicate that 5.7 percentage points of the gender gap can explained by accounting for fixed effects at the village level. However, the difference in productivity between male and female headed households remains large. Regression 4 introduces household and agricultural input variables into the model. With these variables, the gender productivity gap decreases to 10.4%, holding all other variables constant. Combined with Regressions 5 and 6, which add village and regional fixed effects, these models present evidence that both educational levels and the number of household members in the three age brackets (<15, 15-34, >34) do not have significant effects on maize yields. The same is true for both inorganic and organic fertilizer. As mentioned above, these regressions reveal the significant effects of extension, good land quality, and multiple crop plots on yields. It appears that use of extension service can increase productivity by as much as 10%, and self-reported high quality plots are correlated with an improvement of 7-12% in yields. Regressions 7-9 eliminate household variables from the model, in an effort to determine which agricultural inputs have the greatest effects on closing the productivity gap. In these models, extension and land quality have similar effects as those seen in the previous set of regressions. The effect of multiple crops is more pronounced in these regressions, ranging from a decrease of % in yields. However, the productivity gap increases to over 15% using this group of variables. 19

26 Table 4: Regression estimates of maize yield OLS & fixed effects models ln (maize yield) Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) gender_householdhead (0.027) (0.022) (0.031) (0.028) (0.025) (0.026) (0.026) (0.023) (0.030) age_householdhead (0.001) (0.001) (0.001) some_primary (0.032) (0.029) (0.033) primary_school (0.047) (0.045) (0.053) junior_school (0.051) (0.046) (0.058) msce_school (0.062) (0.058) (0.044) university (0.088) (0.099) (0.086) na (0.008) (0.007) (0.007) na (0.012) (0.010) (0.015) na (0.017) (0.017) (0.016) maize_hybrid (0.024) (0.021) (0.030) (0.026) (0.022) (0.031) maize_hybrid_recycled (0.061) (0.047) (0.053) (0.062) (0.050) (0.056) inorganic_fert (0.001) (0.000) (0.000) (0.001) (0.001) (0.000) organic_fert (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) extension (0.025) (0.024) (0.028) (0.025) (0.023) (0.028) good_land_quality (0.025) (0.023) (0.023) (0.025) (0.022) (0.024) labor (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) multiple_crop (0.031) (0.029) (0.034) (0.032) (0.027) (0.035) Fixed Effects? No Village Region No Village Region No Village Region Observations 12,925 12,925 12,925 12,925 12,925 12,925 12,925 12,925 12,925 R-Squared Note: Clustered standard errors in parentheses. Models include: (1-3) Gender Only, (4-6) All Variables, and (7-9) Only Agricultural Inputs. Significance level: 5%. 20

27 Table 5 presents the final two sets of regressions, which test for non-linearities in the relationship of inputs to maize yields. Household variables are initially added back into the model along with squared terms for organic and inorganic fertilizer as well as labor. Regression 11 shows that the estimated productivity gap between male headed and female headed households decreases to 8.9% when squared terms are included. Additionally, the effect of extension, land quality, and multiple crops is similar to those values previously reported, providing further evidence that policies addressing these factors may contribute to minimizing the discrepancies between men and women in the agricultural sector. The final set of regressions uses the natural log of labor and eliminates the squared terms from the previous models. Under this analysis, the productivity gap increases by 1 percentage point. Figure 3 in the Appendix shows the relationship between the logs for yield and labor. It provides support for the notion that the relationship between the two is quadratic. Like the previous models, these regressions demonstrate the strong relationship between extension usage, good land quality, and the lack of multiple crops on improved agricultural yields. Comparing Regressions 1 and 14, I find that 15.3 percentage points of the original 24.5% productivity gap are explained by the inclusion of household and agricultural input variables along with the natural log of labor. Therefore, the data provide evidence of a persistent gender productivity gap between male and female headed households for maize production in Malawi that cannot be explained by controlling for household characteristics and inputs. The findings contradict the stated hypothesis of this analysis and are somewhat surprising given cases in both Africa and Asia where nearly the entire gap was explained by the variables included in this analysis. For instance, de Brauw et al. (2012) found that the female gender variable was insignificant in regressions analyzing the factors that affect agricultural yields in 21

28 China. Similarly, Nosiru and Rahji (2012) found that women were not burdened by the presence of a productivity gap in Nigeria. While the persistence of these gender-based differences in Malawi warrants additional analysis and research, especially in the face of contradictory analyses, the above results do provide insights into potential policies that may further reduce the magnitude of these differences. The policy implications of this analysis are found below. Table 5: Regression estimates of maize yield OLS & fixed effects models ln (maize yield) Variables (10) (11) (12) (13) (14) (15) gender_householdhead (0.028) (0.024) (0.026) (0.028) (0.024) (0.026) age_householdhead (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) some_primary (0.031) (0.028) (0.032) (0.032) (0.028) (0.033) primary_school (0.045) (0.043) (0.050) (0.047) (0.044) (0.052) junior_school (0.048) (0.043) (0.053) (0.051) (0.045) (0.058) msce_school (0.059) (0.053) (0.041) (0.063) (0.057) (0.044) university (0.082) (0.085) (0.085) (0.090) (0.091) (0.084) na (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) na (0.011) (0.010) (0.015) (0.012) (0.011) (0.015) na (0.016) (0.016) (0.015) (0.017) (0.017) (0.015) maize_hybrid (0.023) (0.020) (0.026) (0.025) (0.021) (0.029) maize_hybrid_recycled (0.060) (0.049) (0.056) (0.061) (0.050) (0.056) inorganic_fert (0.000) (0.000) (0.000) (0.001) (0.001) (0.000) inorganic_fert_squared (0.000) (0.000) (0.000) organic_fert (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) organic_fert_squared (0.000) (0.000) (0.000) extension (0.024) (0.023) (0.029) (0.025) (0.023) (0.028) good_land_quality (0.025) (0.022) (0.024) (0.025) (0.023) (0.024) 22

29 Table 5 cont.: Regression estimates of maize yield OLS & fixed effects models ln (maize yield) Variables (10) (11) (12) (13) (14) (15) labor (0.001) (0.000) (0.001) labor_squared (0.000) (0.000) (0.000) log_labor (0.019) (0.016) (0.020) multiple_crop (0.031) (0.026) (0.034) (0.031) (0.026) (0.034) Fixed Effects? No Village Region No Village Region Observations 12,925 12,925 12,925 12,917 12,917 12,917 R-Squared Note: Clustered standard errors in parentheses. Models include: (10-12) All Variables w/squares and (13-15) All Variables w/logs. Significance level: 5%. This paper relies on the conceptual framework previously established as a basis for interpreting the results contained in Tables 4 and 5. This conceptual framework requires that several important assumptions hold true. Unfortunately, assumptions that are valid conceptually do not always hold true in the real world. Therefore, it is important to address concerns related to these assumptions. First, this analysis assumes that the head of the household makes all agricultural decisions on each plot owned or rented by that household. In most cases, this will be true in Malawian households. If the household is headed by a male, he likely dictates what crop is grown on each plot and how much of each agricultural input owned by the family will be used on a given plot. However, situations do exist where women have the authority to manage plots as they see fit. These women will have limited access to productivity-increasing inputs, as allocation decisions would still reside with the male. If this assumption breaks down, the percentage of women managing plots will likely increase to account for the growing number of women gaining 23

30 autonomy in decision-making. However, the productivity gap is likely to increase as the lack of vital inputs lowers the level of outputs on those plots. The second assumption covers the notion that the models do not suffer from omitted variable bias, meaning the error terms of the regressions are not correlated with the independent variables in the regression. In this case, there appears to be evidence that these models are not affected by this bias. Agriculture in Malawi remains relatively simple when compared with more developed countries. The model includes all relevant agricultural inputs that could ultimately affect yields. Factors such as pesticide and irrigation use are not accounted for in these models, yet are proven to increase yields. However, a review of pesticide and irrigation usage reveals that these factors are almost entirely absent from the agricultural sector of Malawi. At this point, these technologies are simply not widespread in the country. This provides a reasonable level of comfort that omitted variable bias is not a significant problem. A third and final assumption involves the value of extension services for those capable of utilizing this important tool. The models in Tables 4 and 5 assume that the effect of extension services on yield is identical for all of those who use them. Of course, this is relatively unrealistic in the real world. The skill of extension agents, the types of services offered, and the locations where extension is available all vary widely throughout Africa, and Malawi is no different. It is more realistic that some households received higher quality extension than others. This means that the coefficient for extension would vary either by region or type of extension service offered. However, it should be noted that the quality of extension, as a whole, remains very low in Malawi. While some variance in quality is likely, the poor state of extension service means that these differences should be minimal and not statistically different from the effect reported in the regression models. 24

31 DATA LIMITATIONS The IHS3 data set provides an impressive amount of detailed information covering household and agricultural behaviors. The depth of the survey questions and the length of the survey provide a wide variety of variables that researchers may analyze in their efforts to improve agricultural and economic outcomes. However, the extensiveness of the survey also presents problems in terms of missing data and the lack of uniformity in measurement for important variables. First, the level of missing data in the survey remains a challenge despite efforts to minimize the number of variables used in this analysis. The survey collects details on every member of the household as well as every plot that is owned or rented by that household. Variables include breakdowns of labor used by task, financial transactions, storage techniques, use of agricultural inputs, purchasing habits, and many others. Unsurprising, household members were often unable, or possibly unwilling, to disclose this level of detail. As the survey was completed months after harvesting, the data may suffer from recall bias, as interviewees are simply unable to remember the exact detail of transactions or events that occurred in the past. These limitations are common in this area of research. To address the issue of missing data, the analysis focused on a small group of variables, where missing data was less common. Variables measuring gender, age, education, seed type, fertilizer use, and output tend to have less missing data than those measuring the type and extent of labor usage. Fertilizer use, seed type, and output are likely easier to recall since government voucher programs often provide set amounts of seed and fertilizer. Output determines the economic well-being of the family, so this variable is undoubtedly well known by household members. By keeping the focus of the analysis narrow, many of the problems were mitigated. 25