Formal Land Rights, Plot Management, and Income Diversification In

Formal Land Rights, Plot Management, and Income Diversiﬁcation in Tigray Region, Ethiopia

Dissertation

Presented in Partial Fulﬁllment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Charity Maria Troyer Moore, M.A.

Graduate Program in Agricultural, Environmental, and Development Economics The Ohio State University 2012

Dissertation Committee Joyce Chen, Advisor Dave Kraybill Brian Roe Copyright by Charity Maria Troyer Moore 2012 Abstract

This dissertation examines plot certification that was carried out in Tigray Re- gion, Ethiopia, in the late 1990s. The program was expected to increase smallholders’ tenure security and encourage producers to make key investments on plots that would ultimately increase agricultural productivity. The analysis is based on a retrospective panel dataset with four years of information from the community to the plot level, and analysis is performed at both the plot and household levels. Using variations of fixed effects that account for time- varying heterogeneity at the village and household levels, and latent characteristics at the household and plot levels, the analysis examines the impact of certification on agricultural investments with short and long-term returns, agricultural productivity, and household income generation strategies. Some models also attempt to control for the endogeneity of certification due to unobserved plot shocks. The estimates suggest that certification did not appreciably increase investments with either short or long-term returns, outside of investments in organic inputs. An accompanying theoretical analysis suggests certification should have affected these investments, so intervening factors have limited the program’s effectiveness. Simi- larly, certification appeared to have had no impact on agricultural productivity. The productivity results were tested using several estimation strategies to examine their robustness and investigate why estimates diverge qualitatively from results put forth by others. Estimates suggest that the different results obtained here were a result of methodological, rather than data, differences between the current analysis and that performed by other researchers. They point to the importance of accounting for

ii heterogeneity at the household and plot levels when investigating the impact of land rights formalization. Results examining the impact of certification on households’ income diversification strategies are more optimistic. They suggest that households adjusted their activities and asset holdings as a result of certification. The changes households made were consistent with certification reducing the risk to which households were exposed and allowing them to take on greater risk with its concomitant higher rewards. This result is not unqualified, since only the highest-earning households leveraged certification to increase their participation in the relatively higher-return off-farm and non-agricultural sectors. If anything, the lowest-earning households specialized their income sources due to certification, perhaps as a result of exiting desperation-driven diversification activities. The results from the analysis suggest that certification did not meet its intended goals. Since there is evidence that certification was perceived positively by producers, and certification affected households’ diversification strategies, its lack of impact in the agricultural sector was, most likely, not due to it being an ineffective policy per se. Instead, the impact of certification on agricultural investments and productivity may have been limited by households’ extreme poverty and factor market failures that limited households’ capacity to respond to the policy. Given the recent resurgence of interest in formalizing rights to plots in Sub- Saharan Africa, these results have important policy implications. They point to the potential for land rights formalization to improve outcomes in impoverished households, but they also highlight the need for the development of complementary markets that may otherwise limit the impact of such a policy.

iii Dedication

Dedicated to Ted. Thank you.

iv Acknowledgements

I would like to thank Joyce Chen for her support and helpful critiques throughout my time at Ohio State. I also thank the other members of my committee, Dave Kraybill and Brian Roe, for their support. Thank you to Mario Miranda for his support and ﬂexibility for me throughout my tenure at Ohio State. I am grateful to the International Food Policy Research Institute, International Livestock Research Insti- tute, Mekelle University, and the Amhara National Regional Bureau of Agriculture and National Resources, the Ethiopian Agricultural Research Organization, and the Agricultural University of Norway for the use of the data. All errors are my own.

v Vita

Education B.A., Business Management (Economics, Spanish), Asbury University, Wilmore, Ken- tucky USA, 2003 (Summa cum Laude) M.A., Economics, The Ohio State University, Columbus, Ohio USA, 2008 (Magna cum Laude) Ph.D. (March 2012), Agricultural, Environmental, and Development Economics, The Ohio State University, Columbus, Ohio USA

Research Experience and Employment Consultant, The World Bank April 2009 - present

Graduate Teaching Assistant, Supervisor: Stan Thompson The Ohio State University, Columbus, Ohio USA September 2009 - March 2010

Research Assistant, Supervisor: Joyce Chen The Ohio State University, Columbus, Ohio USA September 2008 - September 2009

Research Associate, International Poverty Centre (United Nations Development Programme) Bras´ılia,Brazil August 2007 - December 2008

Intern, International Poverty Centre (United Nations Development Programme) Bras´ılia,Brazil June 2007 - August 2007

vi Senior Market Research Methodologist and Statistician, The Horace Mann Companies Springﬁeld, Illinois USA September 2004 - June 2006

Intern, Instituto para el Desarrollo Hondure˜no (Institute for Honduran Development) Tegucigalpa, Honduras June 2002 - August 2002

Honors, Scholarships, and Fellowships Bernie Erven Outstanding Graduate Teaching Assistant Award 2010 Dean’s Distinguished University Fellowship, The Ohio State University 2006 and 2010 Mershon Center for International Security Studies Student Grant Recipient 2009 Nominee, Ohio State Graduate Associate Teaching Award 2009 Best Research Manuscript by a Second-Year Ph.D. Student, The Ohio State University 2008 Ohio Agricultural Research and Development Center International Associateship 2006

Publications

Akresh, Richard, Joyce Chen, and Charity Moore. 2012. “Productive Eﬃciency and the Scope for Cooperation in Polygynous Households.” American Journal of Agri- cultural Economics. 94(2):395-401.

Garcia, Marito, and Charity Moore. 2012. The Cash Dividend: The Rise of Cash Transfer Programs in Sub-Saharan Africa. Directions in Development Series. Wash- ington, DC: The World Bank.

vii Moore, Charity. 2010. “The Political Economy of Social Protection in Honduras and Nicaragua.” In Conditional Cash Transfers in Latin America: A Magic Bullet to Re- duce Poverty? Eds. Michelle Adato and John Hoddinott. Baltimore: John Hopkins University Press.

Moore, Charity. 2009. “Nicaragua’s Red de Protecci´onSocial: An Exemplary but Short-Lived Conditional Cash Transfer Programme,” Country Study Number 17, In- ternational Poverty Centre for Inclusive Growth, Bras´ılia, Brazil.

Moore, Charity. 2009. “Impact Is Not Enough: Image and CCT Sustainability in Nicaragua.” One Pager, International Poverty Centre for Inclusive Growth, Bras´ılia, Brazil.

Moore, Charity. 2008. “PRAF: The Unexpected Reality of Honduras’ CCT Pro- gramme,” Country Study Number 15, International Poverty Centre, Bras´ılia,Brazil.

Moore, Charity. 2008. “Why Sources of Funding for CCTs Matter in Honduras and Nicaragua,” Poverty in Focus, Number 15, International Poverty Centre, Bras´ılia, Brazil.

Fields of Study

Major Field: Agricultural, Environmental, and Development Economics Secondary ﬁelds: Land Economics; Agricultural Households; Applied Microeco- nomics; Health, Education, and Welfare; Social Protection Policies

viii Table of Contents

Abstract ...... ii Dedication ...... iv Acknowledgements ...... v Vita ...... vi List of Tables...... x List of Figures ...... xii Chapter 1: Plot Certiﬁcation in Tigray Region, Ethiopia: Setting the Stage ...... 1 Chapter 2: Formal Land Rights and Plot Management in Tigray, Ethiopia ...... 14 Chapter 3: Rethinking Land Rights and Agricultural Productivity ...... 64 Chapter 4: Did Formalizing Land Rights Help Diversify the Ethiopian Economy? 99 Chapter 5: Was Plot Certiﬁcation Worthwhile? ...... 144 References ...... 147 Appendix A: Derivation of Comparative Statics from Chapter 2 ...... 160

ix List of Tables

Table 1.1. Plot Investments...... 52 Table 1.2. Plot Characteristics...... 53 Table 1.3. Household and Village Characteristics ...... 54 Table 1.4. Plot Rights and Certification Status ...... 55 Table 1.5. Plot Characteristics in Households with Plots with Different Certification Status in the Same Year...... 56 Table 1.6. Investment Regressions, Basic Results Using Household-Year and Plot Fixed Effects ...... 57 Table 1.7. Basic Investment Results, Instrumented...... 58 Table 1.8. Investment Regressions, Controlling for Concurrent Investments...... 59 Table 1.9. Investment Regressions, Various Sample Restrictions ...... 60 Table 1.10. Short-term Investment Regressions by Type of Investment ...... 61 Table 1.11. Investment Regressions for Households with All Plots Certified by 1999...... 62 Table 1.12. Household-Level Investment Regressions ...... 63 Table 2.1. Plot-level Descriptive Statistics ...... 90 Table 2.2. Household-level Descriptive Statistics ...... 91 Table 2.3. Plot-level Productivity Regressions ...... 92 Table 2.4. Plot-level Value Regressions ...... 93 Table 2.5. Household-level Productivity Regressions ...... 94 Table 2.6. Household-level Value Regressions ...... 95 Table 2.7. Plot-level Profit and Input Regressions...... 96 Table 2.8. Plot-Level Regressions with Variations in Controls ...... 97 Table 2.9. Replication of HDG Estimation Strategy...... 98 Table 3.1. Household Characteristics ...... 133

x Table 3.2. Household Income Shares and Diversification ...... 134 Table 3.3. Household Income Share Regressions ...... 135 Table 3.4. Herfindahl-Simpson Income Concentration Regressions ...... 136 Table 3.5. Tests of Instruments ...... 137 Table 3.6. Herfindahl-Simpson Income Concentration Index Regressions, Split Sample ...... 138 Table 3.7. Household Livelihood Strategies Regressions, All Households ...... 139 Table 3.8. Household Livelihood Strategies Regressions, Split Sample ...... 140 Table 3.9. Herfindahl-Simpson Asset Concentration Index Regressions ...... 141 Table 3.10. Household Asset Regressions: Count of Oxen ...... 142 Table 3.11. Household Asset Regressions: Share Oxen ...... 143

xi List of Figures

Figure 1.1. Ethiopia and Tigray Region ...... 13 Figure 3.1. Plot Certiﬁcation and Household Risk-Return Levels ...... 131 Figure 3.2. Livelihood Strategies and Household Income ...... 132

xii Chapter 1: Plot Certiﬁcation in Tigray Region, Ethiopia:

Setting the Stage

The pressures for land reform on the African continent are intense, and the topic often receives popular support as a means to redress past injustices and address current inequalities and land scarcity. Early attempts at land reform on the continent had mixed results at best (Atwood 1990, Deininger and Feder 2009), and such reforms waned from development agendas for a period as governments and donors were discouraged at the potential for reforms to affect positive change and spur economic development given contextual challenges. More recently, the topic has enjoyed a resurgence of attention, and many governments throughout the region have newly outlined land policies that take some lessons learned from early land reforms into account. These reforms, for instance, recognize the validity of communal ownership or the importance of traditional land systems rather than blithely supporting indi- vidualization of property rights (Deininger et al. 2008). While the specific land-related issues important in each African country vary, and one of the main lessons derived from early experiences with land regularization on the continent is that policies should be context-specific (Deininger and Feder 2009), economists and policymakers alike are interested in learning whether land reforms - especially titling and registration programs - are effective at addressing core issues for many African economies, where large proportions of populations still rely on subsistence agriculture for their survival. Such programs typically provide legal provision for land rights that are already being exercised by individuals or groups, formalizing previously informal claims on property through delineating parcels and recording the formal rights in state-sponsored administrative systems (Atwood 1990).

1 The newest land reform initiatives have been slow to garner legislative and procedural support (Deininger et al. 2008). However, some African countries have been trying to enforce reforms using new programs and policies. This dissertation examines one of the earliest of the newest generation of land reform programs in the African continent: the registration of agricultural parcels in northern Ethiopia’s Tigray Region. The lessons learned from this program, while drawn from a relatively unique African historical and political experience and not directly transferable to all other countries in Sub-Saharan Africa1, provide insight into the potential of such a program to encourage agricultural growth and economic development. There is an increasing urgency to address these issues as relatively wealthier, land-poor countries lease or buy large tracts of land from developing countries, many located in Africa, for agricultural cultivation. As such, individuals and communities face greater threats to their tenure security than in the recent past. The so-called “land grab” is just one of the most recent factors justifying the need for greater regularization of land rights. This regularization is considered a necessary institutional component to ensure vulnerable groups are protected through such a process (Deininger et al. 2011). Growing environmental degradation, vulnerability to climate change, and population pressure in some parts of the continent also suggest that pressure on agricultural plots will not diminish in the near future. This dissertation examines several aspects of the effectiveness of plot certification that occurred in northern Ethiopia in the late 1990s. It addresses three very important issues related to certification: 1.) Did certification encourage agricultural investments with both immediate and long-term expected returns? 2.) Did certification result in increased agricultural productivity in smallholders with certified plots?

1Ethiopia’s historical experience has been very unique among African countries, as it is the only Sub-Saharan country that was never colonized (with the possible exception of Liberia). Inequities exacerbated in the colonial era often drive land-related debates in other African countries, particularly those countries that had a signiﬁcant experience with European settlers working in agricultural production.

2 and 3.) Did certification help diversify the Ethiopian economy? These issues are all salient and address some of the core challenges to the economy of northern Ethiopia, and Africa in general: land degradation and potential, agricultural productivity, and the off-farm/non-agricultural economy. The conclusions drawn speak directly to many challenging policy issues in Ethiopia and throughout the region. Before examining the impact of plot certification in these crucial areas, it is important to understand contextual details that illustrate some of the relevant dynamics related to land rights in Ethiopia, and especially Tigray Region. Additional information on the certification exercise and agricultural systems in Tigray are also provided to set the stage for the analysis in subsequent chapters.

Land Rights in Ethiopia

Historically, rights to property in Ethiopia can be characterized as limited and uncertain. Until the emperor was overthrown in 1974, land rights in northern Ethiopia, including Tigray, were based on a complex system known as Rist, which was predicated on families’ private ownership of land (Dercon 1999). Rist provided male and female descendants of a recognized original inhabitant/founder a usufruct share of land. Mortgages and bequests were not allowed since land belonged to the descent group. In the area that is now Tigray Region, the Rist system often had a collective component, in which some parcels dedicated to annuals were redistributed. There were also restrictions on sales, and redistribution of productive output was used as a safety net mechanism (Abegaz 2004). Land was also available to the pow- erful through the Gult system, in which property was awarded to individuals by the monarch or other rulers. Those with Gult land beneﬁted from free peasant labor that the population was required to provide as tribute (Adenew and Abdi 2005). Incen- tives for agricultural investment in these systems were suboptimal, and the peasantry regularly hovered near the subsistence threshold (Abegaz 2004).

3 The feudal system was overturned by the Derg, a Communist military junta that reallocated property to address inequities but did not provide permanent rights to land (Dercon 1999). Early in the Derg’s rule, land was transferred to state ownership. Privately owned land was outlawed, and it became illegal to lease, sell, or otherwise mortgage land. Bequests to family members were highly restricted2. Individuals could only retain access to land by maintaining a continual presence in their location, a ruling that eﬀectively precluded labor migration (Adenew and Abdi 2005). Local Peasant Associations were in charge of land-related issues, which led them to maintain a registry with information on local land and its users, collect taxes, resolve disputes, and redistribute land when necessary (Adenew and Abdi 2005)3. Households that wanted land had to register with the Peasant Association and petition them for land for formation or expansion. Maximum landholdings were limited (Dercon 1999). In theory, land was distributed to the peasantry on the basis of household size, but local factors and political issues also aﬀected allocations, and those who distributed land had to make decisions regarding both the quantity and quality of apportioned land (Dercon and Ayalew 2007). There were allegations of favoritism, nepotism, and the like in land distribution. Redistributions occurred regularly to ensure all households that wanted land had access to it (Dercon 1999, Adenew and Abdi 2005). As population pressure increased, worse quality lands had to be allocated to households. Political pressure served to help ease some of the restricted measures by the late 1980s, and the Derg was overthrown in 1991. The Transitional Government of the Ethiopian People’s Revolutionary Democratic Front (EPRDF) replaced the Derg.

2Early land reforms during the Derg era were accompanied by high taxation of agricultural output and restrictions on the transportation and marketing of agricultural products. There were also bans on employment-related migration, wage labor, forced labor, and villigization (Adenew and Abdi 2005). 3Although parts of Tigray were controlled by the Tigray People’s Liberation Front (TPLF), which fought the Derg for 17 years, land was managed similarly in these areas. An institution known as the baito corresponded to the Peasant Associations (Howard and Smith 2006).

4 Land redistributions began again in 1992, with land often given to demobilized sol- diers at the expense of former Derg leaders (Dercon 1999). After the Derg left power, government-led programs that encouraged agricultural investments through extension, input promotion, and credit were used extensively. Markets were liberalized as many Derg policies were reversed (Abegaz 2004). A new constitution was put in effect in 1995. The Constitution declared that Ethiopian land and natural resources remained under state control and ownership (Constitution of Ethiopia, Article 40)4. The new constitution permitted land to be allocated to any farmer without cost, and producers were not to be evicted from their land. It allowed for leases and transfers to family members, although sales were still prohibited. Despite the technical protection provided for usufruct ownership and a general acceptance that the major reforms implemented during the Derg era were over, Dercon (1999) suggested that subsistence producers did not make long-term investments on their plots due to concerns that local authorities may transfer their property to landless households. Beginning in 1994, Ethiopia’s regions theoretically were given control over their own development (Dercon 1999). While central involvement and rule has practically still been very important (Dercon 1999), Tigray Region has a comparatively stronger tradition of local influence in decision-making (Keeley and Scoones 2000)5. This regionalization has allowed for differences in land policies across regions.

4Article 40.3 and 40.4 provide the following information: “3. The right to ownership of rural and urban land, as well as of all natural resources, is exclusively vested in the State and in the peoples of Ethiopia. Land is a common property of the Nations, Nationalities and Peoples of Ethiopia and shall not be subject to sale or to other means of exchange. 4. Ethiopian peasants have [the] right to obtain land without payment and the protection against eviction from their possession. The implementation of this provision shall be speciﬁed by law.” 5Much community-level involvement has been limited to the provision of labor, with central decision making still dominating (Dercon 1999).

5 Tigray’s Plot Certiﬁcation Program

In Tigray Region, located in the north of the country (see figure 1.1), no large- scale land redistributions have occurred since 1989, although small tabia-level (ward- level) redistributions have occurred and can still occur in limited cases (Haile et al. 2005)6. In 1997, Tigray Region’s government declared that land redistributions would no longer be used, and it started a legally supported land registration program for rural cultivated lands (Dercon and Ayalew 2007)7. Prior to the region’s declaration in 1997, clear public statements had been made by the Tigrayan government that future land redistributions were not expected (Haile et al. 2005). After the legislation was passed, formal registration was to be carried out at the tabia level (Haile et al. 2005). Land was registered and certificates issued on most rural agricultural land in Tigray between 1996 and 1997 (Haile et al. 2005)8. The first round of certification concluded in early 2002 (Holden, Deininger, and Ghebru 2009b.). Plot certification did not change the distribution of land or de facto land rights. Although some households were landless, this was not affected by the registration and certification process. Property still could not be sold, and certification did not affect households’ ability to use land as loan collateral. Collecting on land used as collateral for a defaulted loan was deemed unconstitutional and not tolerated in districts where it occurred. Additionally, certificates were not issued on resettled land until rights to plots were clearly outlined and agreed upon (Haile et al. 2005). This feature of Ethiopia’s certification system allows the policy to be examined distinctly from redistributive policies, which are sometimes linked to formalization in land reform

6As late as 2011, Ali, Dercon, and Gautam reported that Ethiopians still felt vulnerable to property expropriation and felt unable to transfer land. 7Other systems are in place to register different land types. Lands of all reported uses were reported as certified in the data used here, so I rely on this variable and control for land use rather than exclude land of specific types. 8Holden, Deininger, and Ghebru (2009b) use baseline data from 1998 for their analysis of Tigrayan certification and suggest that 80 percent of Tigrayan households were certified from 1998 - 1999. This statistic lines up better with my data, as well.

6 programs (Abegaz 2004). Although certification technically did not change rights over land use or exchange, evidence suggests it increased producers’ confidence that they would retain their land. Using village-level data from the 2006 Ethiopian Economic Associa- tion/World Bank Land Certification survey, Deininger et al. (2008) provide descriptive statistics related to certification. The data confirms the impact certification had on peasants’ attitudes and reported sense of security. Households in Tigray said certificates decreased the number of conflicts over inheritance (87 percent), encouraged soil and water conservation activities (85 percent), encouraged tree planting (80 percent), increased their desire to invest in their land (83 percent), enhanced women’s positions (81 percent), and increased confidence for land rentals (87 percent). Never- theless, almost one-quarter of household survey respondents from Tigray expected the size of their landholdings would change over the next five years due to redistribution, although approximately 87 percent believed they would receive compensation if their land was taken. Plot certification was low-cost and relied on community involvement and unsophisticated delineation technologies. Supervision was conducted by Agricultural Bureau offices in the woredas (districts). Relevant forms were printed at the regional headquarters of the Agricultural Bureau and distributed to tabias. Meetings, or- ganized by local councils and directed by tabia-level Development Agents and land registration technicians, were held in tabias to inform peasants of the law and the registration process (Haile et al. 2005). Land was delineated by Agricultural Bureau Development Agents, land registration technicians, and a group of community members. Technicians involved in plot delineation received cursory training that allowed them to fill in the required forms or assist smallholders in doing so. The involved community members usually included elderly males that participated in previous land distributions and a local represen-

7 tative from the tabia leadership. Properties were typically walked with the involved individuals, and boundary information was written into the household’s application form (Haile et al. 2005). Meetings were usually held to review the results of application forms (Haile et al. 2005). A registration form was then completed, which listed the household head’s name along with attributes of the household plots. These included plot size, quality (poor/medium/fertile), name of neighborhood, name of contiguous landholders, and household size. The registration form was placed in a book located in the tabia’s Agricultural Development Agent’s office or in the office of the tabia chairperson. Finally, handwritten certificates were filled in within the tabias and presented to the producers. The cost to producers to register and certify cultivated land was Birr 2 (USD 0.25), which covered the cost of the certificate (Haile et al. 2005). According to Haile et al. (2005), whether households participated in certification was largely a random process. They report that Tigray’s local and state governments were able to deter abuse of the system. Households usually failed to register land when forms were exhausted at the local level or the involved technical personnels’ tenure expired before tabia-level certification was complete. In contrast, Holden, Deininger, and Ghebru (2009a) suggested that possession of a certificate varied based on several factors, only some of which were random. These factors included the administrative failures just mentioned, in addition to temporary out-migration while certification was being carried out, failure to collect certificates, misplacement of certificates, or failure to update certificates when relevant data had changed. I discuss later how non-random variations in plot-level certification within households also occurred, and why both household and plot heterogeneity should be controlled for in empirical estimates of the impact of certification on relevant plot outcomes.

8 Agriculture and the Rural Nonfarm Economy in Tigray

The importance of agricultural production in Tigray cannot easily be overstated. In 1997, the year that certification began, just over 85 percent of Ethiopians lived in rural areas, and almost as many were poor9. Subsistence producers cultivated almost all available farmland, and most plots were strictly rain-fed. In the Northern and Central Highlands, where approximately half of the non-nomadic population resides, cereals are still cultivated using oxen and plows (Dercon 1999). Households often raise other livestock in addition to cultivating crops. Tigray is characterized by unimodal or bimodal seasons, depending on location and elevation. Main rains are from June/July through September (Kiremti), and secondary rains are from March through May/June in some areas (Belg) (Corbeels, Shiferaw, and Haile 2000). Rainfall in the region typically increases with altitude, although it is highly variable and characterized by micro-climates (Atakilte, Gibbon, and Haile 2001)10. Main plantings occur at the beginning of the long rains, and harvesting, threshing, and storing occur from October through December (Atakilte, Gibbon, and Haile 2001, Howard and Smith 2006). Labor requirements are the highest for plots from June through August, and any food shortages usually occur from May through September. Labor has traditionally been gender-specific: plowing and sowing are completed by males, while females tend to complete weeding (Howard and Smith 2006). However, these practices vary at the local level. Major cultivated crops also vary by elevation in Tigray. In the lowlands, maize, pearl millet, and sorghum dominate; in the midlands, wheat, barley, and teff are grown; and barley, potatoes, pulses, lentils, and vegetables, among others, are cultivated in the highlands (Howard and Smith 2006). Given the variable rains in many

9This was the poverty headcount for those living under 2 US dollars per day purchasing power parity for 1995 (World Bank 2011). 10Rainfall averages anywhere from 300 through 800 millimeters per year on average, depending on location within Tigray (USAID/GOE 2009).

9 parts of the region, cropping strategies often are determined in part to diversify risk as well as to increase output (USAID/GOE 2009). Land degradation, including erosion, soil depletion, and stress on moisture levels, are major concerns in Tigray. These issues have been exacerbated in part due to hillside cultivation, low vegetative soil cover, dung and crop residue burning, brief and diminishing fallowing, erratic and variable rains, and suboptimal application of organic and inorganic fertilizers. The region also faces severe population pressure11. To improve soil fertility, producers use fallowing, although pressure on land has been associated with decreased use and length of fallow (Corbeels, Shiferaw, and Haile 2000). Other practices used to maintain or improve plot output include crop rotation, application of crop residues (when they are not needed for other purposes), application of manure (when available), terracing, and reduced tillage. Investment in manure can help maintain crop outputs over the medium-term (3-5 years), and it has a larger impact in deeper soils, whereas inorganic fertilizers are thought to improve plots for a single season (Atakilte, Gibbon, and Haile 2001). Other relatively small measures, including improved management of livestock and increased investment in oﬀ-farm income generating activities, are expected to encourage economic development in the region (Gebremedhin, Tesfay, and Assefa 2003). Despite agricultural reforms completed since the overthrow of the Derg under the EPRDF’s strategy of agricultural development-led industrialization (ADLI)- typically through promotion of productivity-enhancing measures for smallholders, such as provision of fertilizer, agricultural extension support, support for conservation and rain harvesting, resettlement in new lands, and commercialization of targeted products - agricultural yields per hectare and per laborer have not improved signiﬁcantly. The potential for the non-agricultural sector to spur growth has not materialized, as this sector also has had weak growth (Abegaz 2004). Many producers live in

11Annual rural population growth was 2.6 percent in 1997 (World Bank 2011).

10 chronic food insecurity and have been susceptible to adverse idiosyncratic and covariate shocks, with 62 percent of the population considered undernourished in 1997 (World Bank 2011), and parts of the population dependent on recurrent food aid and other safety net schemes. Poor and variable agricultural outcomes imply that the rural nonfarm economy, while weak, remains vital for the survival of some agricultural households. Generally speaking, the Ethiopian rural nonfarm sector suﬀers from low returns due in part to factor market failures and poor market integration. Its higher use by females and during low agricultural periods suggests it absorbs excess labor and entrance into the sector is not always driven by the expected return it will generate, but instead often by a lack of on-farm opportunities (Loening et al. 2008).

Conclusions

The following three chapters discuss the role of plot certification in agricultural investments, productivity, and household income diversification. Understanding the causal impact of Tigray’s plot certification program in these crucial areas will help policymakers understand whether similar programs may be useful in an environment with insecure land tenure. However, the previous description of Ethiopian land tenure issues and agriculture was made to highlight the importance of these contextual factors in the certification program and the current analysis. While the conclusions drawn here can provide useful insights into the possibilities of similar programs, the results should not be directly transferred to other environments. The forthcoming analysis of plot certification relies heavily on econometric techniques that allow me to isolate the impact of certification from household and plot- level heterogeneity that may be correlated with a plot’s certification status. Given this feature, the analysis is a marked improvement on other analyses of tenure security or land rights formalization that are unable to control for these latent characteristics.

11 The analysis in chapter 3 of the impact of certification on productivity strongly suggests that controlling for such heterogeneity may be an important determinant of the conclusions drawn from such studies. The analysis in chapter 4 of the impact of certification on households’ participation in the off-farm and non-agricultural sectors examines an issue important to agricultural development and rural economies, but one that is often not directly examined in investigations related to land rights formalization. The results suggest that the relationship of similar programs to the nonfarm economy may be of direct importance to economic development. Before discussing the role of plot certification in agricultural productivity and the diversification of household income sources, an analysis is first provided of the outcomes considered of most direct relevance to land rights formalization- namely, the impact of certification on investments that are critical for maintaining and restoring soil fertility and, potentially, agricultural productivity. It is this issue that is examined in depth in chapter 2.

12 Figure 1.1. Ethiopia and Tigray Region

13 Chapter 2: Formal Land Rights and Plot Management in

Tigray, Ethiopia

There have been heated arguments regarding the merits of formal land registration and titling for many years. These issues are especially pressing in Sub-Saharan Africa, a region where the rural poor typically rely on subsistence agriculture as an important source of their livelihoods. Households must make crucial agricultural investments to increase land productivity within an environment with weak formal property rights. The formalization of rights through activities such as land titling or registration promises to provide multiple benefits. These include the potential of formal land rights to increase land-related investments and productivity, relax credit constraints through increasing land collateralizability, and encourage efficient market transactions (Besley and Ghatak 2010). Despite these benefits, challenges inherent in registering or titling property in Sub-Saharan Africa, such as its cost or potential to foment conflict, as well as institutional and governance-related challenges, must also be considered (Deininger and Feder 2009). Early attempts to formalize and individualize plot rights in the developing world often were expensive, and they relied on sophisticated technologies. The highly visible failures of several attempts at formalization caused the donor community and domes- tic counterparts to reassess their efforts, and they created a growing sensitivity to the potential pitfalls of property rights formalization and land reforms (Atwood 1990, Chimhowu and Woodhouse 2006, Deininger and Feder 2009, Pinckney and Kimuyu 1989). Some more recent efforts to formalize property rights in Sub-Saharan Africa have reflected these concerns by maintaining low overall costs and using relatively unsophisticated technologies for parcel delineation.

14 The earliest major example of low-cost land registration in Sub-Saharan Africa occurred in Ethiopia, a country in which land distribution, usage rights, and ownership have been a contentious issue for many years. Previous analyses of certification in Ethiopia find that it increased the use of plot investments with significant long- term expected returns (Deininger et al. 2008, Holden, Deininger, and Ghebru 2009a), and it facilitated rental market transactions (Holden, Deininger, and Ghebru 2009b). The impact of plot certification on land management strategies with short-term expected returns has received less attention, perhaps in part because it is assumed that certification will not affect these investments. In this chapter, I address whether plot certification in Tigray Region, Ethiopia, impacted agricultural investments with short and long-term expected returns. I motivate the investigation using a model that illustrates why it is assumed that plot certification should affect investments with long-term returns more than investments with short-term returns. This result has been suggested before, but the assumptions involved have not been examined within an analytical framework. I then examine the issue empirically using a unique plot-level panel dataset that allows me to control for various sources of plot-level heterogeneity including unobserved plot quality, plot disputability, and shocks to plots in a given year. This strategy ensures that estimates are not biased by potential omitted variables that may be correlated with plot certification status and investment decisions. In this sense, the specifications presented are superior to methodologies commonly used to examine land rights, which are often plagued with lingering concerns over bias due to unobservable plot characteristics. I find that, in general, investments with short expected return horizons were not affected by certification. However, use of organic inputs responds positively to certification. The results for long-term investments suggest a potential positive relationship between certification and these investments, but estimates are not clear enough to say that certification definitively increased long-term investments. In ad-

15 dition to examining the effect of certification, I control for usage rights to plots in the regressions and find that they do not affect long-term investment decisions, while specific usage rights affect short-term investments. The conclusions are important, particularly since they stand in contrast to those found by others examining the same topic.

Tenure Security and Investments

For many years, economists have argued that establishing more clearly outlined rights to property can improve agricultural investments and productivity in developing countries. Property rights can increase investments through several channels. Most recently, Besley and Ghatak (2010) highlight that property rights decrease the risk of expropriation of property and/or output and reduce the need to protect property using private resources. Clearly outlined property rights can also facili- tate market transactions by creating collateral for credit markets and encouraging efficiency-enhancing land exchanges. Whether these theoretical possibilities translate to reality has been a contentious topic in the literature. There is an abundance of empirical research examining whether rights to plots affect investments, with mixed results. Bandiera (2007) finds that the length of tenancy contract is positively associated with tree planting in Nicaragua, and owner- operators are more likely to cultivate trees than tenants. Tenancy reform, which increased tenure security, increased agricultural productivity in West Bengal, India (Banerjee, Gertler, and Ghatak 2002). Within Africa, Deininger and Ali (2008) find tenure security, but not plot registration, is important for land investments in trees, manure use, and soil conservation activities in Uganda. Goldstein and Udry (2008) find that insecure land tenure in Ghana is associated with less fallowing time and lower agricultural productivity, and Besley (1995) finds that Ghanaians with more secure plot rights are more likely to grow trees.

16 Others, including Place and Hazel (1993) and Place (2009), suggest there is only a weak relationship between land rights and agricultural outcomes. This relationship often varies by investment and translates into relatively minor overall impacts. For instance, although Jacoby, Li, and Rozelle (2002) find that expropriation risk decreases investments in organic fertilizer in China, it does not affect inorganic fertilizer investments or other plot maintenance or cropping strategies. They also find the social cost of expropriation risk to be rather low. Within Ethiopia, there is evidence that investments with medium to long-term returns are related to tenure security. Deininger and Jin (2006) find that insecure tenure is associated with increased investment in trees, a visible investment thought to increase security, but it does not encourage investment in terracing, an activity undertaken strictly for productive purposes. Similarly, Gebremedhin and Swinton (2003) use double hurdle statistical analysis on 250 farming households in Tigray and find that terracing occurs with secure tenure and bunding with insecure tenure. Ali, Dercon, and Gautam (2011) use household-level panel data from 1997 through 1999 and suggest that vulnerability to expropriation curbs the potential benefits of improved transfer rights in Ethiopia, which are important determinants of investment in coffee, chat, and eucalyptus. According to producers’ own accounts, tenure security is key to their investments: Gebremedhin, Pender, and Ehui (2003) report that Ethiopian farmers say that tenure security affects their plot investments, particularly terraces, bunds, and trees. Several studies in Ethiopia have examined whether investment in activities with short-term expected returns is affected by tenure security. These find no relationship between the two. Dercon and Ayalew (2007) find that improvements in land rights in southern Ethiopia increase allocations of land to perennial crops with medium to long-term returns, but improved tenure security is not associated with changes in input investments with short-term expected returns. Holden and Yohannes (2002)

17 find little relationship between tenure security and perennial planting in southern Ethiopia, but they also conclude that perceived tenure insecurity does not affect the amount of purchased inputs used on agricultural land. Similarly, Pender and Fafchamps (2006) find that agricultural inputs and productivity do not vary across plots cultivated under different arrangements, and thus different levels of security, in Oromia Region, Ethiopia.

Formal Plot Rights and Agricultural Investments

Outside of Sub-Saharan Africa, formalizing land rights has been shown to affect agricultural investments and outcomes. Formal titling and/or registration has been important for increasing land prices and improvements in Thailand (Chalam- wong and Feder 1988; Feder, Onchan, and Chalamwong 1988); off-farm labor and perennial cultivation in Vietnam (Do and Iyer 2008); land investments in Brazil (Al- ston, Libecap, and Schneider 1996); plot investments and land values in Nicaragua (Deininger and Chamorro 2004); and land-attached investments in Paraguay (Carter and Olinto 2003). The role of formal land rights on investments in Sub-Saharan Africa has been shown to be much less important. Carter, Wiebe, and Blarel (1994) find that once farm size and means of access to plots is controlled for in Kenya, the impact of titling on investments is no longer significant. Similarly, Jacoby and Minten (2002) use household fixed effects to examine titling in Madagascar. They find that titles do not increase plot investments, and effects on productivity and plot values are minimal. Since Ethiopia’s experience with certification is very different from that of the African titling programs just mentioned, it would not be surprising to find different effects of certification from those found in Kenya or Madagascar. Examining the impact of country-wide plot certification in Ethiopia, Deininger et al. (2008) find that plots in kebeles (wards) in which certification was completed 12 months or longer

18 prior to the survey were 5 percent more likely than non-certified kebeles to have new investments, primarily in terracing and bunding. This investment was 4.4 percent higher than that in non-certified kebele areas. Provided kebele-level certification was exogenous to household investment decisions and households did not act differently in anticipation of certification, this estimate demonstrates that the country-wide program had important impacts on long-term agricultural investments. In the paper most similar to this chapter, Holden, Deininger, and Ghebru (2009a) examine plot certification in Tigray Region. Using panel data from 1998, 2001, and 2006, they base their results on a sample of certified and uncertified plots planted to cereals that they match based on plot characteristics. Their investment regressions control for predictable and random components of plot certification, which were constructed using household-level fixed effects and controls for plot characteristics. In this way, they are able to isolate the impact of certification from household- level heterogeneity and observable plot characteristics that are correlated with certification status and investment choices. They conclude that certification increased maintenance and improvements in existing soil conservation investments and plantings of eucalyptus and seedlings. New investments in bunds are unrelated to certification, and new investments in terraces show weak evidence of being stimulated by plot certification. They also suggest that productivity grew by about 45 percent due to certification. In this paper, I make two advancements to the study just mentioned. First, because of the structure of the dataset utilized here, I am able to control for unobservable plot characteristics that may influence both certification and plot investments. Due to the structure of the available data, the previous study is able to control for household, but not plot-level, heterogeneity. Therefore, the coefficient for certification will be biased if variations in certification status of plots within households are driven by unobservable plot heterogeneity, something for which I find evidence. The empiri-

19 cal strategy used in this paper controls for household-year and plot heterogeneity, and it instruments for time-varying plot shocks. Using this identification strategy, I find that certification marginally increased long-term investments, while it did not have a significant positive impact on short-term investments outside of organic inputs. Second, most of the previous analyses, particularly those within Ethiopia, have focused on agricultural investments with returns that accrue to producers over multiple seasons. The evidence related specifically to certification in Ethiopia has focused entirely on these longer-term outcomes. Here I examine whether plot certification affected short-term investments critical for agricultural productivity and soil maintenance. Although certification did not affect most short-term investments, usage rights influenced some short-term land management strategies. Somewhat surprisingly, usage rights appear unimportant in decisions about major plot investments with long-term expected returns.

A Model of Certiﬁcation and Plot Investments

To motivate the empirical analysis, I present a simple model involving plot certification and short and long-term agricultural investment decisions. Typically, land rights are thought to influence the threat of expropriation, access to credit, and the potential to exchange land. Since property sales are illegal in Ethiopia, this model does not consider land exchanges outside of rental agreements. A model of certification that characterizes Tigray also need not consider the traditional credit effect of tenure security. Although property can theoretically be used as collateral for loans, banks in Tigray that have tried to collect on collateralized land in defaulted loans were forced to return the land (Haile et al. 2005). The survey data used here support this evidence: land was used as collateral for less than 1 percent of loans received, and this did not vary based on whether households had certified plots. Therefore, any observed certification effect is unrelated to the collateralizability of

20 land. For purposes of illustration, I assume that a single agricultural household exists with an endowment of one plot that can be cultivated over T discrete time periods, t = {1, 2,...,T } . The producer of the household maximizes the current period’s proﬁt by selecting optimal levels of short and long-term investments. Short-term

investments, its, increase production (ft) in the current period only, while long-term

investments, itl, provide a return in both the current and subsequent period. The producer’s investment choice can be thought of as being drawn from a composite variable of household resources, including labor, time, and capital needed for the investment. They come from a time-constant endowment of ¯i, such that they can run from zero through the size of the endowment, with the total of both investments

not exceeding the endowment constraint: (its, itl) ∈ [0,¯i], its + itl ≤ ¯i. The long-term investments do not depreciate in the second period, and investments are additively separable across time. I assume there are no credit or labor markets. Although these are simplifying assumptions, the survey data suggest access to these markets is unrelated to certiﬁcation status and does not drive the main results12. The producer faces an exogenous, time-varying threat of expropriation equal to

(1 − τt), τt ∈ [0, 1], which reduces expected production each period. Tenure security

2 evolves according to the following process: τt+1 = τt +t, where t ∼ N(0, στ ). Per the discussion of plot certiﬁcation in chapter 1, I assume that obtaining a certiﬁcate of ownership does not change plot usage rights. Its primary function is to decrease the

12Participation in labor markets occurs primarily through labor exchanges. Some households have access to short-term credit for seasonal inputs; the access to such credit increased greatly over the survey timeframe, during which time the government expanded programs to support the use of agricultural inputs, such as fertilizer. Estimates of the probability of having access to credit for short-term inputs (formal credit) using kushet-year and household fixed effects, household and summary plot controls, and instrumenting for households’ percentage of certified plots using the percentage of surveyed households in the same community, excluding the current household, with any certified plot and the local mean of the maximum number of years any plot had been certified in these households suggest that households’ certification status is unrelated to their reported credit access. However, uninstrumented models suggest households with a higher percentage of certified plots are slightly less likely to have access to this formal credit. Even if credit access differed by households’ certification status, the household-year fixed effects absorb this in the estimated models.

21 13 probability that the plot will be expropriated (i.e., dτt < 0) . Since certification did not require significant temporal or financial expenditures to purchase or maintain, I abstract from costs related to it. The expected output of the producer is a function of the selected investments, tenure security, and a normally distributed stochastic output shock that depends on

2 household (h) and plot (p) characteristics, At (ht, pt) ∼ N (A, σA ) ,A ≥ 0. Expected production each time period equals Ft = Et [τtft (its, it−1,l, itl,At)] . The production function is well-behaved in each time period: it is real-valued, twice continuously diﬀerentiable, monotonically increasing, and concave ( ∂ft ≥ 0, ∂ft ≥ 0, ∂ft ≥ 0, ∂its ∂it−1,l ∂itl 2 2 2 2 ∂ ft ∂ ft ∂ ft ∂ ft 2 ≤ 0, 2 ≤ 0, 2 ≤ 0, ≤ 0). The short and long-term investments ∂its ∂it−1,l ∂itl ∂it−1,l∂itl may be substitutes or complements. In each time period, the consumer side of this agricultural household maximizes a twice continuously diﬀerentiable, time separable utility function, which represents well-behaved preferences on consumption and leisure: Ut (ct, lt). Utility is locally non- satiated and maintains the usual assumptions on its curvature: ∂Ut ≥ 0, ∂Ut ≥ 0, ∂ct ∂lt 2 2 ∂ Ut ∂ Ut 0 2 ≤ 0, 2 ≤ 0, as well as the Inada condition that limc →0 U (ct) = ∞ and ∂ct ∂lt t 0 limlt→0 U (lt) = ∞. Since there are no markets for trade outside of a land rental option, consumption equals expected production for a producer who operates his or her own plot (ct = Ft), and it equals the return to sharecropping, the primary form of land exchange, on rented out plots. Leisure is equal to the remaining endowment

(lt = ¯i − its − itl), and it is valued as the disutility of endowment use. The producer has an outside option that allows the plot to be sharecropped out, which acts as a participation constraint on plot cultivation. If the household sharecrops out its plot, consumption equals the agreed upon return to sharecropping: ct = rFt, where r ∈ [0, 1] is the proportion of the output that the household receives

13Theoretically, peasants could remain as well oﬀ even if their land or output were dispossessed (provided the act in itself is not utility-decreasing), as long as they receive compensation equal to the discounted value of the expected future return from the land. In this sense, the model also incorporates the probability that a producer will be compensated for an expropriated plot.

22 from the sharecropping arrangement. The sharecropper, rather than the household, invests in the plot when it is rented out, and the producer has an expectation of

the sharecropper’s plot investments (Et(˜its),Et(˜itl), and Et(˜it−1,l)), which he takes as given. The previous period’s investment is taken as given regardless of who made it.

The current period’s value to the producer (Vt) depends on whether s/he cul- tivates or sharecrops out, and on the selected investment levels:

  ¯ maxits,itl Ut [τtft (its, it−1,l, itl,At) , lt] + λt (i − its − itl)     if Et(Ut(cultivatet)) ≥ Et(Ut(sct)) Vt = (1)  ˜ ˜ ˜ ¯ ¯ Ut rτtft(its, it−1,l, itl,At), i + λt(i)     if Et(Ut(cultivatet)) < Et(Ut(sct))

where the multiplier λt is the utility value of relaxing the endowment constraint, and sc stands for sharecrop. The decision to sharecrop out or cultivate the plot depends on expected utility realizations of each scenario, which in turn depend on the expected investment choices of the sharecropper and producer, the division of produced output, and the relative utility of consumption and leisure for the agricultural household14. A more realistic model would also consider agency issues, which are abstracted from here. This framework does not imply that certified plots that are sharecropped out would be of higher or lower quality than non-certified sharecropped plots, suggesting that estimates that include or exclude sharecropped plots should not change due to plot quality selection15, which may affect use of investments as well. Instead, dif-

14Although it is frequently assumed that certification should increase the propensity for a plot to be exchanged, I do not find strong evidence for this once household-year and plot heterogeneity are controlled for. These results may vary if the sample were restricted to specific types of households, such as female-headed households, although I do not look into this issue (Holden, Deininger, and Ghebru 2009b). 15The bigger concern is that plot shocks that affect certification and sharecropping decisions would affect estimates of the impact of certification depending on whether sharecropped plots are excluded or included in the sample. Plot shocks, rather than simply constant plot quality, are the more

23 ferences across estimates that include or exclude sharecropped plots should reflect incentives for investment given certification status depending on the relationship of plot operator to plot owner. Other evidence related to certification and renting suggests that the decision to rent out plots is more related to household composition than plot quality (Holden, Deininger, and Ghebru 2009b). The effect of certification on a sharecropper’s investments is also unclear: it may increase investments since overall tenure security has increased, or it may decrease incentives for investment because the sharecropping renter has a lower probability of being able to obtain the plot in a future redistribution. Since incentives for investment due to certification are somewhat ambiguous for sharecropped plots, and they are not the focus of the paper, the following analysis considers an owner-operated plot only16. Including the non-negativity constraints for short and long-term investments

(ψs, ψl), the agricultural household seeks to maximize the following:

T X t−1 max Et β [Ut(ct, lt) + λt(¯i − its − itl) + ψsits + ψlitl] (2) {i ,i }T ts tl t=1 t=1

subject to lt = ¯i − its − itl and ct(its, it−1,l, itl, τt,At) = τtft(its, it−1,l, itl,At).

Assuming the endowment is non-trivial, the limits implied by the Inada condition ensure the producer will select non-negative levels of consumption and leisure. Under these assumptions, the endowment constraint does not bind and its period- specific multiplier equals zero by the Karush-Kuhn-Tucker condition. Remaining agnostic regarding whether any non-negativity constraint will bind, the first order conditions are as follows: relevant concern in uninstrumented models that control for household-year and plot fixed effects. 16A future avenue for investigation is to include the possibility of sharecropping in a plot using part of the producer’s endowment, and to explicitly consider the differences involved in decisions about investments on each type of plot (owner-operated, sharecropped out, and sharecropped in) separately.

24 ∂Ut ∂ft ∂Ut [its]: τt − + ψs = 0 (3) ∂ct ∂its ∂lt

∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂Ut [itl]: τt + Etβτt+1 − + ψl = 0 (4) ∂ct ∂itl ∂ct+1 ∂itl ∂lt

Combining the two ﬁrst order conditions yields the following description of an optimal decision:

∂Ut ∂ft ∂Ut ∂ft ∂Ut+1 ∂ft+1 τt = τt + Etβτt+1 + ψl if its > 0 and itl = 0 ∂ct ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂Ut ∂ft ∂Ut ∂ft ∂Ut+1 ∂ft+1 τt + ψs = τt + Etβτt+1 if its = 0 and itl > 0, ∂ct ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂Ut ∂ft ∂Ut ∂ft ∂Ut+1 ∂ft+1 τt = τt + Etβτt+1 if its > 0 and itl > 0, and (5) ∂ct ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂Ut ∂ft ∂Ut ∂ft ∂Ut+1 ∂ft+1 τt + ψs = τt + Etβτt+1 + ψl if its = 0 and itl = 0. ∂ct ∂its ∂ct ∂itl ∂ct+1 ∂itl

At the optimum, the producer equates the marginal utilities of production across the two investment types while incorporating the shadow prices of the two investments, which are negative if the investment level is zero and zero if the given investment is positive. When one non-negativity constraint binds, the producer is encouraged to use more of the other investment type. These expressions also equal the marginal disutility of the leisure lost from making the given investment. I focus here on the case where non-zero amounts of each investment are optimal. This scenario is very plausible given the absence of major ﬁxed costs associated with the long-term investments investigated and the signiﬁcant returns expected to accrue due to use of each type of investment. The discounted expected marginal utility

∂Ut+1 ∂ft+1 provided by the long-term investment in the future time period, Etβτt+1 , ∂ct+1 ∂itl is positive. If the short and long-term investments contributed equally to current production and tenure security were held constant, the current marginal utility of production of the long-term investment would be lower than that of the short-term

25 ∗ ∗ investment. In this case, the producer would use more of the long-term investment than the short-term investment (its < itl). When, more realistically, the marginal productivities of the two types of investments are unrestricted, the optimal investment choices will depend on each investment’s relative contribution to production. This problem is an extension of the most basic version of Besley and Ghatak’s (2010) analysis of property rights. As expected, the threat of expropriation serves as a tax on output. The key results of the model relate to the reactions of short and long-term investments to an improvement in tenure security through plot certiﬁcation. When both long-term and short-term investments are positive, the relevant comparative statics are as follows17:

2 2 2 ∂U ∂ft ∂U ∂ ft ∂Ut+1 ∂ ft+1 ∂U ∂ft ∂Ut+1 ∂ft+1 ∂U ∂ ft

26 t t t t (− )(τt 2 + Etβτt+1 2 ) + ( + Etβ )(τt ) dits ∂ct ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂its∂itl = 2 2 2 2 (6) ∂Ut ∂ ft ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂Ut ∂ ft 2 dτt (τt 2 )(τt 2 + Etβτt+1 2 ) − (τt ) ∂ct ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂its∂itl

2 2 ∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂Ut ∂ ft ∂Ut ∂ft ∂Ut ∂ ft (− − Etβ )(τt 2 ) + ( )(τt ) ditl ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂its ∂ct ∂its ∂ct ∂itl∂its = 2 2 2 2 (7) ∂Ut ∂ ft ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂Ut ∂ ft 2 dτt (τt 2 )(τt 2 + Etβτt+1 2 ) − (τt ) ∂ct ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂its∂itl

The responses of short and long-term investments to plot certiﬁcation are determined by the level of tenure security, the return to increasing each investment, the relative decline in return to each investment due to the concavity of the production function, and the degree of complementarity between short and long-term investments. The production function is concave, so

17See Appendix A for the derivation. the first two terms in the denominators of the comparative statics are both negative. The second term in the denominators is obviously positive. By assumption, utility is a well-behaved maximum, and utility increases with production, which implies that the determinant of the Hessian, which is the denominator of the derivatives, satisfies the final second-order condition that it be positive. This implies that the comparative statics have the same sign as their numerators. If one of the investments is optimally zero, the comparative statics will contain additional components accounting for the marginal disutility cost of using that investment rather than the unconstrained investment. These expressions reflect how changes in investment levels and tenure security affect the shadow price of using the investment that would optimally be zero, and they increase the response of the other investment to an increase in tenure security. (See Appendix A for the exact comparative statics for these cases.) It is unlikely that both investments are zero, unless the household has a very high utility of leisure and the investments contribute very little to consumption. It is more likely that the household would only choose to make non-zero investments of one type of investment, or it would select positive amounts of both types of investments. The producer is more likely to choose to use only one investment type if the investments have significantly different marginal productivities. If the marginal productivity of the two investments is similar, or the producer discounts the future enough to make the marginal utility return of the two investments similar (in the case that the long-term investment has a significantly higher marginal productivity than the short-term investment), it is unlikely that either of the investments will be nonpositive.

Result 1: If investments are complements or contribute independently to production, both short and long-term investments increase with tenure

27 security.

Result 2: If investments are substitutes, an increase in tenure security has an ambiguous eﬀect on investments.

When the goods contribute independently or in a complementary manner to

2 production, ∂ ft ≥ 0, and the numerator of both partial derivatives is the sum of ∂its∂itl two non-negative expressions. This ensures that the derivatives of interest are posi-

2 tive. If the goods are substitutes, ∂ ft < 0, and the signs of the numerators depend ∂its∂itl on the relative size of the return of the given investment and the curvature of the production function with respect to the other good, and on the degree of substitutability and return to the other investment, all in terms of their eﬀect on utility. Provided the degree of substitutability between the goods is not too strong, the utility returns of the investments are somewhat similar, and the concavity of the production function is not too extreme, an increase in tenure security is likely to increase both short and long-term investments. Given the additional return to long-term investments in the subsequent period, long-term investments are more likely to increase with tenure security, all else held equal.

Result 3: If the net marginal return to the long-term investment is greater than the net marginal return to the short-term investment, an increase in tenure security will increase the long-term investment more than it will increase the short-term investment.

This result is obvious. The relative response of short and long-term investments to a change in tenure security depends on 1.) the relative return of each investment in terms of utility, which takes the concavity of the production functions into account;

28 and 2.) the degree of substitution or complementarity in production between the

2 current period’s short and long-term investments ( ∂Ut ∂ ft ). For long-term invest- ∂ct ∂its∂itl ments to respond more than short-term investments to an increase in tenure security through plot certiﬁcation, the following must hold:

∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂U ∂ft + Etβ t ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂its 2 2 2 > 2 2 (8) ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂Ut ∂ ft ∂Ut ∂ ft ∂Ut ∂ ft 2 + Etβ 2 + 2 + ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂itl∂its ∂ct ∂its ∂ct ∂itl∂its

That is, the marginal return to long-term investments, taking the concavity of the production function and the productive relationship between the two investments into account, must be greater than the net marginal return to the short-term investments. If the long-term investment has a higher marginal productivity than the short-term investment in the current time period, long-term investments will always be more responsive than short-term investments to an increase in tenure security. If the short-term investment has a higher marginal productivity in the current time period, the relative changes in investments depend on the future expected marginal return from the long-term investment. Even if the long-term investment’s current period marginal productivity is lower than that of the short-term investment, long-term investments may still be more responsive to an increase in tenure security due to the increased production and utility they provide in the subsequent period. Valuing the future more will increase the relative response of long-term investments to an increase in tenure security. Although this simple framework assumes that long-term investments aﬀect production for only two time periods, the analysis could be generalized so that long-term investments aﬀect additional time periods, perhaps with depreciation. All else held equal, the relative response of the short-term investment to an increase in tenure security compared to that of the long-term investment will be smaller the more time periods in which the long-term investment contributes to production and utility. In

29 the Ethiopian context, major investments such as terracing or bunding can increase plot production for many years. Therefore, it seems likely that long-term investments will respond more strongly to increased tenure security through plot certiﬁcation than short-term investments.

Survey Data and Descriptive Statistics

The survey data used is the Policies for Sustainable Land Management in the Ethiopian Highlands Dataset, whose main purpose was to determine how to improve productivity and encourage appropriate land management in Tigray and Amhara Regions18. A cross section of community, village, household, and plot-level data was collected. Retrospective information was collected such that a panel can be constructed that includes data from 1991, 1997, and 1998, in addition to the data collected in 1999. The survey collected information from a stratified random sample of 50 tabias. Within each tabia, two randomly selected villages were surveyed, within which five randomly selected households were surveyed per village19. Unless otherwise specified, I limit the sample to owner-operated plots in Tigray Region. Within each surveyed household, information on all plots owned and operated since 1991 was collected. The data is structured such that plot-level data can be tied together over time. The classification of plot activities into investments with short and long-term expected returns is based on the survey itself, which distinguishes between major land investments and land management practices/use of inputs. The short-term in-

18Support for data collection was provided by the International Food Policy Research Institute (IFPRI), along with the International Livestock Research Institute (ILRI), Mekelle University (MU), the Amhara National Regional Bureau of Agriculture and National Resources (ANRBANR), the Ethiopian Agricultural Research Organization (EARO), and the Agricultural University of Norway. 19In Tigray, sampling was stratiﬁed based on whether an irrigation project was used in the Peasant Association or whether the location was within 10 kilometers of the woreda town. This sampling process allowed 54 Peasant Associations to be selected for surveying, 4 of which could not be surveyed due to the Ethiopian-Eritrean war (Pender et al. 2001). Woredas mainly located lower than 1,500 meters above sea level were not surveyed.

30 vestment variable is an indicator for whether any of the following inputs or land management strategies were used on the selected plot in the previous year: organic inputs, which included manuring, composting, mulching, and growing green manure; inorganic inputs of DAP, urea, herbicides, or pesticides; cropping strategies, which included strip cropping, intercropping, mixed cropping, relay cropping, and alley cropping; plowing techniques, including contour plowing, plowing in crop residues, and reduced tillage/no till; and improved seeds. Table 1.1 provides basic information on the use of these practices in the sample. Major plot investments with long-term expected returns are also reported in table 1.1. In Tigray, these major investments are often undertaken using public initiatives including mass mobilization and public works programs, so the investment indicator is limited to include only private investments. The sample of long-term investments is only available for 1997, 1998, and 1999, because data from 1991 does not distinguish between public and private investments. Approximately 90 percent of plots benefited from some type of short-term investment. Between 10 and 15 percent of plots were cultivated using a cropping strategy, particularly mixed cropping, and just under three in ten plots benefited from an organic input to a plot (primarily manure). Use of inorganic inputs, particularly DAP and urea, increased greatly after the first year reported, and between 27 and 33 percent of plots were treated with these inputs for the final 3 years. The vast majority of fields were cultivated using contour plowing. Reduced tillage was also used to increase fertility or manage soil quality on approximately 13 percent of plots. Unsurprisingly, major private investments were rarer, occurring in just over six percent of plots in years with available data. Investments made most frequently were terracing (3.33 percent overall), clearing stones (1.40 percent), and building irrigation canals (1.21 percent). Tables 1.2 and 1.3 provide general descriptive statistics of plot and household

31 characteristics relevant to the analysis. The mean plot size was very small at approximately .30 hectares, expressed in local units (tsimad 20). Most plots were located on flat land or low slopes at high altitudes averaging over 2,100 meters21. Plots had relatively low stone cover, and 41 percent had shallow soil. Three in ten plots had sandy soil, and a little over one-third had loamy soils. Less than 13 percent of plots were classified as highly fertile, and almost 40 percent suffered from mild or severe erosion. Although private investments in terracing and bunding were rather infre- quent, plots had over 50 meters of terraces on average. Bunding was less extensive. The most popular crops grown were barley, teff, maize, and wheat, and fallowing was still used in the sample. Households plots were located an average of 20 minutes from the homestead. Almost all plots were allocated to households by the tabia, with plots having been operated a little over 7 years on average. As expected, the households in the sample were extremely poor, with less than 20 percent owning a piece of durable farm equipment. The average household operated just under 3.5 plots, owned just over one oxen, over two cattle, and a little over five goats and sheep. Almost 80 percent of households reported the village had a formal source of credit, and just under 87 percent reported access to informal credit. On average, households were located over two and one-half hours walking distance from the nearest market town. In 1999, households were composed of over 5 members on average, one-quarter of households were headed by females, and the average age of the household head was a little over 47 years. Tabias had experienced an average of almost 3.6 land redistributions since the Derg came to power, highlighting the general tenure insecurity in the region. Data on certification status and plot rights is located in table 1.4. In 1991, the first year with available data, no plots were certified. By 1999, 91.7 percent of owner- operated plots were certified, with most plots being certified in 1998 or 1999. Out

20A tsimad is approximately one-fourth of a hectare. 21The elevation reﬂects the sampling strategy.

32 of 18 possible land rights, plots enjoyed 14.7 of these rights on average, with rights remaining stable over the years with available information. The total number of rights, and most individual rights, did not vary with the plot’s certification status. Most plots could be leased out, sharecropped out, loaned, or even exchanged, although bequests to family members and gifts of the plot to others were more restricted. Similarly, the vast majority of producers reported they could pledge harvest as collateral on a given plot, and almost 80 percent of all plots could be pledged as loan collateral, although this right was rarely exercised in practice. Interestingly, 84.1 percent of uncertified plots could be used as loan collateral, which was significantly higher than the 76.6 percent of certified plots that had this right. Producers could choose crops to plant on over 96 percent of the full sample, although uncertified plots were less likely to have this right than certified plots. Producers could plant fruit trees on 57 percent of all plots, and agroforestry trees could be planted on 84 percent of plots. Eucalyptus trees could be planted on less than half of plots22. Producers could cut trees on only one in three plots, and uncertified plots were more likely to have this right. Most producers could prevent grazing immediately after harvest. Producers could prevent other animals from grazing after their own animals had done so on 71 percent of plots, with certified plots less likely to have this right. If anything, these statistics suggest that uncertified plots had fewer restrictions on their use than certified plots.

Empirical Strategy

The equations to be estimated are as follows:

E(Ift) = β0 + fftβ1 + cftβ2 + kkwt + hhwt + ff wt + θt + εft, t = {1, ....T } (9)

where Ift is a dichotomous variable indicating whether an investment was undertaken

22This practice was regulated by authorities (Jagger and Pender 2003).

33 in the survey year. fft is a vector of plot-level characteristics important to invest-

ments, discussed below, and cft is the plot’s certiﬁcation status. kk is a village-speciﬁc

effect, hh a household effect, and ff a plot-specific effect. wt is a vector containing (1, t), indicating that there are village, household, and plot-level effects contain time- constant and time-varying components. The vector t contains dummies for each time period, which allow the time-varying effects to vary in an arbitrary manner. The error term contains idiosyncratic shocks. The estimating equation can be rewritten as

E(Ift) = β0 + fftβ1 + cftβ2 + kk + lkt + hh + jht + ff + gft + θt + εft, t = {1, ....T } ,

where lkt, jht, and gft represent the time-varying components of the village, household, and plot effects, respectively. A major concern when analyzing most plot titling or certification programs is whether formal plot status is an endogenous regressor. For example, title status may be influenced by household selection into titling. Theoretically, these concerns should not influence the results for the Tigrayan certification program, because the program was not voluntary: all households were supposed to participate in the certification process. Households could not affect the general timing of their plots’ certification, which was based on local roll-out schedules, and all plots within a household were to be certified at once. Nevertheless, actual program implementation may have been affected by characteristics, such as household location, that were correlated with investment decisions due to geographical differences. Given this concern, I include household-year fixed effects in the models. By controlling for household-year fixed effects, the equation becomes the following:

¨ ¨ ¨ E(Ift) = fftβ1 +c ¨ftβ2 + ff +g ¨ft +ε ¨ft, t = {1, ....T } ,

34 where the umlaut represents the demeaned change in the variable from the mean change within a household-year. There also may be a plot selection effect into titling. Generally speaking, models that fail to control for plot-level unobservables will overestimate the effect of certification if these unobservables are both positively or negatively correlated with certification and plot investments, and it will underestimate the impact of certification if the sign of these two relationships differ. The potential for plot selection clearly arises in the data, where approximately 10 percent of households reported that all plots were certified in 1997 but not in 1998, and 15 percent of households reported that all plots were certified in 1998 but not in 1999. In many cases, this apparent discrepancy arose because the household acquired an uncertified plot after their original plots were certified. If this newly acquired plot had characteristics not controlled for in the models that were different from characteristics of other plots (and these characteristics were related to both certification status and investment decisions), the selection effect will bias the certificate coefficient. To account for this possibility, I use both household-year and plot fixed effects, which results in the following equation:

¨ ¨ E(Ift) = fftβ1 +c ¨ftβ2 +g ¨ft +ε ¨ft, t = {1, ....T } , (10) where the umlaut now represents the deviation of the variable from the mean level within a household-year and across the same plot. This specification controls for many characteristics that might otherwise bias the certificate coefficient. The influence of local effects, such as prices or location in relation to conflict areas, is eliminated. Household-specific heterogeneity, including households’ wealth; access to resources for investments, including formal and informal credit; level of entrepreneurial spirit or ability; average understanding of land issues; family member who is in charge of the plot; and more, are removed. A general secular trend is also removed. Additional static plot traits, such as altitude, slope,

35 toposequence, and more, disappear (and are therefore controlled for) in the plot fixed effects. The key identification condition in this model is as follows:

E(εft|fft, cft, θt, kk, lkt, hh, jht, ff ) = 0, t = {1, ...T } , which states that conditional upon past, contemporaneous, and future realizations of the included covariates, a secular trend, time-varying and constant village and household effects, and plot fixed effects, the error term equals zero. Estimates will still be consistent if unobservable plot, household, or village heterogeneity are correlated with both certification and investment decisions, and if time-varying household and village effects affect certification and investments. This identification condition is more likely to hold than those used in models examining plot investments that only control for a limited set of observable household and plot characteristics, or even those that control for higher-level latent variables, such as household-level fixed effects.

Nevertheless, single-season plot shocks (gft) are still subsumed in the error term. If these shocks affect both certification and investment, coefficients will be biased. Related to this, another source of variation in plots’ certification status, and a potential selection effect, was driven by whether a plot could be demarcated in the field. I call this unobserved trait plot disputability. There is evidence of non-random variation in plot certification, which appears to be driven by disputability, within households. Although all household plots were supposed to be certified at once, there are numerous households in the data that have some certified and uncertified plots within the same year, and identification rests upon these plots. Some differences in certification status within household years is driven by the acquisition of new plots, but the remainder of these plots probably remained uncertified due to boundary disputes that could not be resolved at the time of parcel demarcation. Table 1.5

36 compares characteristics of certified and uncertified plots in households in which some, but not all, plots were certified. The first two columns report the statistics across all years, while the last two columns report the statistics for the actual survey year (1999), in which plots were most likely to have been certified. The table provides suggestive evidence that the uncertified plots in these households were a bit different from the household’s certified plots. Plots that remained uncertified in households with other certified plots were more likely to have been given to the producer, rather than allocated by authorities. They were more likely to have relatively low stone cover and deeper soil. Their slopes did not differ from certified plots, but they were less likely to be located on sloped plots in general. They were less likely to suffer from erosion, although they had more problems with water logging, probably because they were more likely to have clay soil. The higher quality of these uncertified plots and the different acquisition method suggest that these plots were more highly desired, and less securely held, than households’ certified plots. This points to the potential importance of disputability in determining plot certification status. Disputability clearly could also affect households’ plot investment decisions. Therefore, models that do not control for this characteristic will produce biased estimates of the impact of plot certification. Plot disputability is most likely an evolving process determined by a combina- tion of constant and slowly evolving factors. It would also be affected by some single season shocks. Each plot would have a specific base level of disputability, which would evolve according to local pressure on the land and its productivity path, as well as plot-specific shocks to disputability. For instance, the propensity to be disputed could vary from year to year based on changes in plot quality, population fluctuations, and environmental changes occurring in the immediate plot vicinity. If a plot-specific shock that affected disputability occurred in the time period in which certification occurred, it is possible that the plot failed to be certified. For instance, a landless

37 household may try to encroach on the boundaries of one household plot. This act may affect the ability of the household to certify the plot and also impact its willingness to invest on the plot. Most other factors that would normally necessitate the use of an instrument for certification status are not a major source of concern. For instance, it is not likely that households would try (or be able) to certify some plots but not others due to a direct plot-level shock such as pests or a flood on a particular plot, since certification was supposed to occur on all possible household plots at once. If necessary, parcel delineation could occur without walking the plot boundaries. Recall error of the timing of certification is also unlikely since plots had only been certified for a maximum of three major cropping seasons by the time the survey was conducted. Nor is there reason to believe that households had more difficulty recalling whether a plot was certified or not depending on the type of investment the household used. Nevertheless, since plot disputability, and thus the coefficient for certification, may be affected by single season shocks, I instrument for certification status. To isolate the impact of certification from the role of plot disputability, I present models that instrument for certification status using variables that describe the degree to which local certification had been completed. These instruments are the percentage of local surveyed plots that were certified in a given year, excluding the status of the given plot, and the mean years since all other surveyed village plots had been certified. These variables are clearly correlated with whether a plot was certified23. Since certification was rolled out at the local level and this process was determined primarily by administrative concerns, these instruments should be uncorrelated with plot-specific disputability shocks. It is possible that local roll-out of certification was correlated with characteristics that may play into plot disputability, such as the num-

23A concern with the instruments, particularly the percentage variable, is that net of the ﬁxed eﬀects, they are driven by the plot’s own status. This problem will be addressed in future iterations of this analysis.

38 ber of landless households in the tabia, but these influences are controlled for in the household-year fixed effects. Therefore, the instrumented certification coefficient will identify the impact of the random component of plot-level certification on investment decisions. Additional plot controls are included in the models to help control for time- varying plot traits. Community investments in conservation activities on plots were based on households’ locations in a given watershed. These public investments are controlled for in case plot investments are sequential or accumulate, or if unobserved plot characteristics are correlated with both public and private investments. For the same reason, the initial capital stock of stone terraces and soil bunds, as well as the count of other existing investments on plots, are included in the regressions. Since plot location is also important to production decisions (Atakilte, Gibbon, and Haile 2001), a control for the location of the plot in relation to the household’s residence, which changed in some cases, is included in the models. Land management strategies and inputs will also vary by the crops cultivated on a plot, so indicator variables are used to control for whether barley, corn, wheat, sorghum, fingermillet, teff, fababeans, or other crops were grown on the plot. A control is also included for whether the plot was fallowing. Since input availability in relation to plot size may be important when households decide how to invest on a given plot, particularly when these choices are made jointly with other household plots, controls for the households’ ratios of oxen, number of males, and number of adults to the specific plot’s area are also included. A control is also included for usage rights to each plot. Table 1.4 highlights that there is no obvious positive relationship between certification and plot rights. However, several means of individual plot rights differ by plot certification status, even though the differences in rights are not practically very large. To ensure that the certificate variable is not confounded by changes in plot rights, and in case usage rights

39 are correlated with an omitted variable that is also related to both plot certification status and land improvements, plot usage rights are controlled for in the regressions. Various methods of representing land rights in empirical analyses have been suggested (for instance, see Besley 1995, Braselle et al. 2002). For most regressions, I simply use a variable controlling for the total number of rights available on a given plot. The potential simultaneity of land rights and investments, shown to be important in several areas of Africa (Besley 1995, Braselle et al. 2002, Ali, Dercon, and Gautam 2011) is at least partially corrected for using the household-year and plot fixed effects. Variation in rights to plots are probably primarily driven by household characteristics, such as local influence, or static plot characteristics, such as location. Although some concern remains that period-specific plot shocks may bias the coefficient on land rights, a suitable instrument for land rights could not be found. This instrument would need to vary within plots over time, a requirement which eliminates most of the sensible candidates for instruments for land rights, such as method of plot acquisition. In the household-year and plot fixed effects models, interactions of plot and time-varying characteristics, such as the method of acquisition and number of local landless households, are too weakly correlated with land rights to be used as instruments. However, even if land rights are affected by single-season plot shocks, the instrument for the certificate variable ensures the certification coefficient is not confounded by this issue, provided the instruments are not related to shocks that affect specific plot rights. Assuming local certification status variables are unrelated to plot-specific shocks that affect usage rights seems relatively unrestrictive, particularly given the rather weak relationship between certificate status and aggregate usage rights. Linear probability fixed effects models are presented in the next section. Un- fortunately, nonlinear limited dependent variable fixed effects models using a small

40 number of time periods suffer from the incidental parameters problem, even with large cross section samples (Neyman and Scott 1948). The extent of the inconsis- tency in limited dependent variable fixed effects models is still being investigated24. Bias corrections for these models are available, but they require more than the four survey periods available here. Limited dependent variable random effects models can be used, but the unobserved heterogeneity must be correctly specified for estimates to be consistent. In most cases, linear probability models provide useful information on the sign of partial effects averaged across the population of interest (Wooldridge 2002). Since this is the effect of interest in the current analysis, this method is considered the most appropriate estimation strategy.

The Impact of Plot Certiﬁcation

Table 1.6 reports results from linear probability regressions of the investment variables on plot certification and other plot controls using household-year and plot fixed effects. Columns 1 and 4 report results from parsimonious models that only explicitly control for the certificate variables and the year. These models suggest that certification was associated with no significant increase in short-term or long- term investments, although the coefficient for the long-term investment regression is positive. When land rights are added (columns 2 and 5), the coefficients for the certificate variable decrease very slightly for both types of investments. The land rights coefficient is relatively large and significant in the short-term investment models. However, since the uninstrumented models do not control for plot-specific shocks, the estimates are biased. Adding the additional plot controls increases the coefficient on certification, although the estimate is not significant in either model. The land rights coefficient in the short-term model decreases and no longer is significant. This result

24For example, see Greene (2004).

41 may be surprising at first glance, but it simply shows that once household and plot characteristics that drive land rights are controlled for, the impact of usage rights themselves do not appear to be very important to investment decisions. This result will be examined at greater length shortly. Most of the other controls in these regressions are also insignificant. Short-term investments are more likely to be made when plots are growing certain crops, such as sorghum, and less likely to be made on a fallowing plot. Greater male labor availability also increases the probability that a plot will benefit from a short-term investment. Investment accumulation appears to be one of the most important factors affecting long-term investments. An additional initial plot investment besides a terrace or bund makes the plot over 20 percent less likely to benefit from a long-term investment, and a public investment makes the plot 9 percent less likely to benefit from a long-term investment in that year, suggesting that crowding out occurs. Using an expanded set of controls for time-varying soil characteristics, distance to market towns, and more, does not change conclusions drawn about the impact of certification25. Table 1.7 shows results from the models controlling for plot-related variables and land rights and instrumenting for the certification variables. The coefficients in the first stage are extremely large and reflect that once household-year and plot heterogeneity are controlled for, local progress in certification is actually negatively related to a plot’s certification status. Their size also reflect that intercepts for household-years were estimated in addition to the plot fixed effects. Nevertheless, the F-tests of the first stage excluded instruments are both over 10. For the second stage regressions in the instrumented models, certification still appears not to affect short-term investments: the coefficient is negative but very close to zero, with a standard error twice the size of the coefficient. In these regressions, the identification statistics are easily passed. Interestingly, the endogeneity of certification

25Results available upon request.

42 can be rejected in these estimates. However, the certification coefficient in the long- term investment regression is much larger than the coefficient in the uninstrumented regressions. While the standard error is relatively much smaller in the long-term investment equation, it is still not significant at conventional levels once the standard errors are heteroskedasticity-robust and clustered at the plot level (p=0.151). The weak identification statistics for the instruments do not provide additional confidence in the standard errors, suggesting that the null hypothesis that the coefficient on certification equals zero may be rejected a bit too frequently. An additional concern is that the overidentification statistic in the long-term equation is rejected, suggesting the instruments are still correlated with the estimate residuals, and the increased instrumented coefficient is not clearly identifying the impact of certification. Provided the instruments in the long-term investment equation do not bias the coefficients in the wrong direction (i.e., they move them closer to what the true value is, despite their failures), the difference between the uninstrumented and instrumented models is consistent with a plot-level shock being negatively correlated with certification, unrelated to short-term investments, and positively correlated with long-term investments. These results can easily be explained if time-varying plot disputability is the key unobservable plot characteristic driving the results in uninstrumented models. Single-period shocks that made plots more subject to dispute during the period in which certification occurred would delay certification, but the plot dispute would not be resolved immediately. The dispute should not affect short-term investments since the producer would expect to retain the plot for the duration of the cropping season regardless of the plot’s disputed status: disputability was not a factor that resulted in immediate expropriation of the plot. However, the producer may try to make a major long-term investment on the plot to place a stake in plot ownership and improve his or her chances of retaining the plot when the dispute was considered by a court.

43 Including the short-term investments in the long-term investment models and vice-versa shows that concurrent short and long-term investments are nearly independent (see table 1.8). The coefficient for the long-term investment in the short-term regression is -0.006, and the coefficient of the short-term investment variable in the long-term model is -0.012 in the instrumented models (column 4), which is very similar to the results in the uninstrumented regressions (column 3). The investment coefficients all have relatively large standard errors. If investments truly are independent, the model previously presented indicates that the response of long-term investments to certification should be larger than that of short-term investments, but both types of investments should increase with an improvement in tenure security through certification. In these regressions, the coefficients on the certificate variables in the long-term investment models remain virtually unchanged, but they increase in the short-term models. The land rights coefficient in the long-term investment regression does not change appreciably from the result in table 1.7, but this coefficient decreases in the short-term investment regressions. This result suggests there may be an underlying relationship between long-term investments and land rights that is not controlled for in previous estimates of the short-term investment equations. The basic instrumented results are not qualitatively sensitive to the sample specification (see table 1.9). Additional samples examined are all plots, all plots excluding private pasture, and owner-operated plots excluding private pasture. In- cluding rented26 plots in the sample may result in smaller estimates of the impact of certification if renters have less incentive to invest on plots, particularly for long-term investments whose return may not be realized by a tenant. Also, only cultivated plots were supposed to take part in the certification program, but some pasture lands report being certified, and for this reason were included in the original sample. Excluding them from the sample of plots should result in a larger certification coefficient.

26Once again, these plots are usually exchanged via sharecropping. I use the term “rent” because some plots are exchanged through another arrangement.

44 The certificate coefficient in the short-term investment models is always negative and has a large standard error. In the samples of all plots, and all plots excluding private pasture, the land rights variable in the short-term models is larger and significant again. However, when the sample is limited to owner-operated plots that exclude pasture, the effect of plot rights is similar to that of the original sample shown in the first column. The estimated effect of certification on long-term investments is smaller than the original estimate when rented plots are included in the sample. As expected, the impact of (instrumented) certification on long-term investments is slightly larger in the sample of rented and owner-operated plots excluding pasture lands than in the sample using all plots. In the plots with the strongest incentive to respond to certification, owner-operated plots excluding pasture lands, certification has the largest apparent impact on long-term investments seen yet in the instrumented models. For this sample, the instrumented estimates suggest that certification makes a plot 15 percent more likely to benefit from a major long-term investment, although the robust, clustered standard errors still do not meet conventional significance levels. Once again, the validity of the instrumented long-term investment estimates are called into question. The uninstrumented long-term investment estimates for owner-operated, non-pasture plots are similar to those for the owner-operated sample. Finally, the short-term investments variable is composed of several very different land management strategies that may be affected differently by certification27. While the survey classifies them all into a similar category, some of these strategies are considered more helpful than others to plot productivity, and some affect plot productivity for longer than one season. (This notwithstanding, their benefits are not expected to last as long as those of the long-term investments.) To test whether

27I split these investments partially to ensure that contour plowing, which was used extremely frequently, is not the sole driver of the results for the short-term estimates. While it would also be helpful to examine estimates for speciﬁc long-term investments, the low frequency of such investments may limit conclusions that can be drawn from these models.

45 certification had an impact on any of these specific short-term investment strategies, I break the short-term variable into indicators for cropping strategies, plowing strategies, use of improved seeds, use of inorganic inputs, and use of organic inputs. Results from these models, shown in table 1.10, show that certification significantly increased the probability that organic inputs would be used on a plot. The instrumented estimates (bottom panel) for organic inputs appear to have some of the problems that the long-term investment models have, and these results should be taken with cau- tion. However, the estimate is similar and significant in the uninstrumented models (top panel). Of all the short-term investments examined, organic inputs have one of the longest-lasting effects on plot productivity, with the benefits of applying manure enjoyed for several years after it is applied. Because of this, organic inputs should be more likely than the other short-term investments to be positively impacted by certification. Although certification did not significantly increase the probability that a plot would receive other specific short-term investments, it decreased the use of improved seeds. This result was only significant in the instrumented regressions, although it was negative in both uninstrumented and instrumented models. This could suggest that producers do not consider improved seeds to be a superior product, a finding that appears to be supported by the significant negative land rights coefficient in these models. The table also shows that land rights positively impact the use of plowing strategies, such as contour plowing.

Additional Robustness Checks

Since there is remaining concern over the validity of the estimates for the long- term investment regressions, I re-estimate the models using a restricted sample limited to households in which all plots had the same certiﬁcation status in 1999, the latest year of available data. By restricting the sample to these households, I ensure that

46 households in which plots remained uncertified due to plot disputability (or perhaps another similar unobservable trait) are no longer included in the sample. This restriction also eliminates households that obtained an additional uncertified plot after their original plots were certified. The households that remain in the sample should be those whose variation in certification status was due to the supposedly random roll-out of certification at the local level. Given this rationale, uninstrumented models are justified. I present instrumented models to be complete. These estimates are presented in table 1.11. The coefficients on the certificate variable are consistent within the short-term investment equations and long-term investment equations. In the short-term models, certification has a negative, but still insignificant, coefficient. The coefficients for land rights are somewhat larger again and have relatively small standard errors, although they are not significant at conventional levels. The coefficient on the certificate variable in the long-term regressions (0.040) is larger in the uninstrumented model than it was in the comparable model using the original sample specification (0.026). Instrument- ing the certificate variable in the long-term investment model does not provide much additional information beyond that found in the original sample, as the endogeneity of the certificate variable still cannot be rejected, and the overidentification tests fail again. This final result suggests that there may be additional unobservable plot shocks that are related to certification and long-term investment, beyond those that would be eliminated by restricting the sample to that found in table 1.11. Although not extremely informative, the estimates in table 1.11 confirm that when the households whose plots’ certification may have been influenced by certain known, yet unobservable, traits are not included in the analysis, the main conclusions drawn do not differ qualitatively from those obtained from the original sample. There is not a clear, significant impact of certification on either type of investment. Finally, table 1.12 presents estimates of the impact of certification at the house-

47 hold level. Both uninstrumented and instrumented results are presented (in columns 1 and 3, and 2 and 4, respectively). To control for heterogeneity at the local and household level, I use kushet-year and household fixed effects. The certificate variable of interest is the percentage of household plots that are certified, and other controls average the variables over owner-operated household plots. The certificate variable is instrumented in a first stage using kushet-level variables similar to those already described for the original regressions. The instruments used in the household-level estimates are more arguably exogenous than those used at the plot level. The years since certified variable is the village average of the maximum years any plot in each local surveyed household had been certified, excluding the current household. The other certificate instrument is the percentage of local surveyed households that had any plot certified, excluding the current household. Excluding the entire household’s information from the instrument provides more rationale, both logically and empirically, for the exclusion restrictions. Unsurprisingly, the overidentification statistics clearly pass for both short and long- term investments in these models. While the household-level certificate variable is more likely to be endogenous in these models (and the E-statistic confirms this is true), the instruments more clearly perform well, and they are stronger in the household- level models. Results from these estimates suggest that neither short nor long-term investments were affected by certification. Mean plot rights, however, have a significant positive impact on short-term investments, a result which holds across uninstrumented and instrumented models. Note that the models do not attempt to control for time-varying factors that may influence plot rights, so this result should be taken cautiously. However, its similarity to plot-level estimates is encouraging. While these estimates seem to be more trustworthy than those at the plot level, particularly for the long-term investment models, it is important to remember what

48 they estimate. These regressions suggest that the percentage of a household’s plots that are certified does not impact the overall probability that any household plot will receive a short or long-term investment. They do not rule out the possibility that households manipulate a limited number of long-term plot investments in favor of certified plots, something that the plot-level models suggested. If it were the case that both the plot-level and household-level estimates were a reflection of the causal impact of certification, the results would suggest that households feel greater security on certified plots, and they (very weakly) choose to make more long-term investments on these plots. Nevertheless, households’ overall short and long-term investments do not increase with certification. Such a result could easily reflect factor market limitations that preclude households from increasing agricultural investments, provided households want to invest more in their plots.

Conclusions

Analysis of tenure security and formal property rights has often focused on investments with expected returns that accrue over many years. Within Ethiopia, studies of tenure security and use of inputs with short-term expected returns typically have found the two to be unrelated (Dercon and Ayalew 2007, Holden and Yohannes 2002, Pender and Fafchamps 2006). This paper examines the previously uninvestigated topic of the impact of plot certification in Tigray, Ethiopia, on land management strategies with short-term expected returns, as well as the impact on investments with long-term expected returns. The results suggest that certification did not increase the propensity to use short-term investments on plots, with the exception of organic inputs, which had a longer return horizon than most investments classified as short-term. While the certificate coefficient for long-term investments was much larger, it did not meet standard significance levels, and the validity of plot-level estimates is doubtful given remaining concerns over endogeneity. These results hold while

49 controlling for household-year and plot-level heterogeneity, as well as instrumenting for single season plot shocks. Estimates at the household level suggest a similar story: certification appeared to have no overall impact on households’ propensity to invest in their plots. The empirical results suggest that concurrent short and long-term investments are nearly independent. The model presented here shows that if these investments are independent, the improved tenure security afforded through plot certification should increase both short and long-term investments. The fact that certification did not significantly affect short-term investments besides organic inputs, and its impact on long-term investments could not be definitively established, suggests that additional constraints may be limiting producers’ investment decisions. These could include missing or imperfect credit or labor markets that would allow households to access important inputs for investments. Although households in the data had access to short-term credit for seasonal inputs and participated in labor market exchanges, these activities may still be constrained below their optimal levels. Households in the data had less access to credit for major long-term investments than they did for short-term investments, suggesting that the response of long-term investments may be even more sensitive than that of short-term investments to credit market limitations. The fact that organic inputs were the only investment that appeared to increase is unsurprising if factor market failures are limiting household responses, since most households using organic inputs applied manure available from their own livestock. Another possibility that should not be excluded is that certification may not have appreciably increased producers’ sense of tenure security. The Ethiopian peasantry have experienced years living under regimes of intimidation, and their confidence in the ability or desire of the state to protect their parcels from (government-led) expropriation may not be greatly affected by certification. In other words, citizens may sense an incentive compatibility failure when the same institution that expro-

50 priated their land now claims to provide protection from future expropriations. The relative role of this institutional failure, as compared to factor market failures, is an important avenue for future investigation. The results in chapter 4 shed a bit more light on this issue and suggest that this explanation is probably not a driving force of the results. Results in this chapter do not provide much reason to believe that certification would have a significant impact on plot or household productivity outcomes. However, others have studied plot certification in Tigray and concluded that the program had a strong, positive impact on agricultural productivity. The next chapter investigates these issues, attempting to disentangle why these other researchers have concluded that certification had such strong productivity effects, given the relatively weak evidence for it presented here.

51 1991 1997 1998 1999 All Years Percentage of plots beneﬁting from 90.40% 91.27% 89.85% 89.88% 90.35% short-term land management activity/input Cropping 11.73% 13.27% 13.80% 13.40% 13.23% Strip cropping 0.81% 0.91% 0.98% 1.33% 1.01% Inter cropping 1.91% 2.17% 2.38% 2.30% 2.19% Mixed cropping 8.59% 9.29% 10.43% 10.89% 9.81% Relay cropping 0.44% 0.42% 0.63% 0.70% 0.55% Alley cropping 1.17% 1.19% 1.47% 1.47% 1.33% Organic inputs 27.86% 28.56% 27.87% 28.05% 28.09% Manuring 26.76% 26.96% 26.26% 26.52% 26.62% Composting 2.64% 3.70% 3.64% 3.84% 3.46% Mulching 0.15% 0.14% 0.07% 0.14% 0.12% Green manure 0.29% 0.56% 0.56% 0.63% 0.51% Inorganic inputs 3.08% 30.10% 32.91% 34.75% 25.47% Diammonium phosphate (DAP) 2.71% 28.42% 31.72% 33.36% 24.31% Urea 1.98% 27.23% 30.88% 33.22% 23.58% Herbicides 0.00% 0.14% 0.21% 0.35% 0.18% Pesticides 0.37% 2.86% 3.36% 4.47% 2.79% Improved seeds 0.37% 2.86% 3.36% 4.47% 2.79% Plowing 89.52% 89.73% 88.17% 88.00% 88.85% Contour plowing 89.22% 89.32% 87.46% 87.58% 88.39% Plowing in crop residues 1.39% 1.33% 1.19% 1.12% 1.26% Reduced tillage/no till 12.43% 13.27% 13.80% 13.40% 13.23% Sample size (plots) 1,364 1,432 1,428 1,433 5,657 Percentage of plots beneﬁting from NA 6.21% 6.23% 6.27% 6.24% major (long-term) private land investment Check dam NA 0.21% 0.21% 0.14% 0.19% Drainage ditch NA 0.07% 0.07% 0.07% 0.07% Stones cleared NA 1.33% 1.26% 1.60% 1.40% Stone terrace NA 3.14% 3.36% 3.48% 3.33% Live fences or barriers NA 0.56% 0.35% 0.35% 0.42% Fence constructed NA 0.49% 0.70% 0.56% 0.58% Soil bund NA 0.77% 0.63% 0.35% 0.58% Irrigation canal NA 1.12% 1.12% 1.39% 1.21% Trees planted NA 0.77% 0.63% 0.28% 0.56% Sample size (plots) NA 1,433 1,429 1,436 4,298 Sample limited to owner-operated plots.

Table 1.1. Plot investments

52 Mean Standard Sample deviation size Plot area (tsimad) 1.186 0.994 5,674 Plot altitude (meters) 2,145 370.9 5,408 Flat slope 0.622 0.485 5,674 Gentle slope 0.294 0.456 5,674 Slope position = Top 0.124 0.329 5,674 Slope position = Middle 0.199 0.399 5,674 Slope position = Bottom 0.245 0.430 5,674 5-15% stone cover (medium) 0.128 0.334 5,674 15-40% stone cover (high) 0.065 0.246 5,674 >40% stone cover (very high) 0.027 0.163 5,674 Shallow soil depth 0.407 0.491 5,674 Medium soil depth 0.369 0.482 5,674 Black soil 0.233 0.423 5,674 Brown soil 0.137 0.344 5,674 Grey soil 0.241 0.428 5,674 Red soil 0.380 0.485 5,674 Clay soil 0.230 0.421 5,674 Loamy soil 0.345 0.475 5,674 Sandy soil 0.296 0.456 5,674 Salinity is a problem 0.022 0.145 5,674 Waterlogging is a problem 0.107 0.309 5,674 Moderately fertile 0.612 0.487 5,674 Highly fertile 0.127 0.333 5,674 No erosion problem 0.616 0.486 5,671 Mild erosion problem 0.296 0.456 5,671 Oxen/land area 2.697 5.776 5,674 Males/plot area 2.656 6.459 5,674 Adults/plot area 5.695 10.990 5,674 Number of public investments on plot-year 0.051 0.258 5,674 Stone terracing at beginning of year (meters) 51.2 119.6 5,674 Soil bunding at beginning of year (meters) 8.31 45.6 5,674 Number of investments in addition to terraces and bunds 0.210 0.548 5,674 Barley grown on plot 0.235 0.424 5,664 Corn grown on plot 0.129 0.335 5,664 Wheat grown on plot 0.120 0.324 5,664 Sorghum grown on plot 0.077 0.267 5,664 Fingermillet grown on plot 0.082 0.274 5,664 Teﬀ grown on plot 0.227 0.419 5,664 Fababean grown on plot 0.064 0.245 5,664 Other crop grown on plot 0.126 0.332 5,664 Homestead land 0.257 0.437 5,674 Fallow 0.092 0.289 5,664 Walking time from plot to residence (minutes) 20.3 23.9 5,664 Plot allocated by tabia 0.975 0.156 5,674 Time since plot was acquired (years) 7.052 4.170 5,643 Sample is limited to owner-operated plots and reported at the plot-year level. When units are not given, the number indicates the percentage of plots that share the characteristic. Table 1.2. Plot characteristics

53 Mean Standard Sample deviation size Number of owned and operated plots (household- 3.463 1.645 1,903 year) Household owns a piece of durable farm equipment 0.176 0.381 1,902 Household owns at least one ox 0.706 0.456 1,903 Number of oxen owned 1.16 1.00 1,940 Number of pack animals owned 0.72 1.12 1,940 Number of cattle owned 2.47 3.05 1,939 Number of goats/sheep owned 5.25 10.79 1,940 Household has formal credit source 0.799 0.401 1,944 Household has informal credit source 0.869 0.337 1,940 Household member belonged to organization 0.780 0.414 1,903 Household member held an oﬃce 0.068 0.252 1,903 Number of residential rooms used 2.054 0.904 1,891 Walking distance to nearest market town (minutes) 166.13 125.3 1,946 Head of household born in village 0.806 0.395 1,903 Person days spent in mass mobilization 36.578 26.845 1,799 Female head, 1999 0.23 0.42 500 Age of head, 1999 (years) 47.2 13.9 500 Household size, 1999 (persons) 5.33 2.23 500 Number of tabia land distributions since 1974 3.580 1.126 50 Sample is at the household-year level. When units are not given, the number indicates the percentage of households that share the characteristic.

Table 1.3. Household and village characteristics

54 All Years 1991 1997 1998 1999 0.415 0.000 0.018 0.706 0.917 Plot is certified (0.493) (0.000) (0.134) (0.456) (0.276) 5,674 1,376 1,433 1,429 1,436 14.653 14.712 14.652 14.658 14.594 Sum of land rights (2.322) (2.437) (2.241) (2.246) (2.363) 5,668 1,373 1,432 1,428 1,435 All plots Plot not certified Plot certified 0.987 0.988 0.987 Lease out 0.112 (0.111) (0.112) 4,280 1,939 2,341 0.990 0.991 0.989 Sharecrop out (0.101) (0.096) (0.105) 4,295 1,945 2,350 0.990 0.992 0.989 Loan to someone (0.097) (0.088) (0.105) 4,295 1,945 2,350 0.966 0.970 0.963 Exchange (0.181) 0.172 (0.188) 4,295 1,945 2,350 0.614 0.609 0.617 Bequeath to family members (0.487) (0.488) (0.486) 4,295 1,945 2,350 0.541 0.565*** 0.520 Give away to someone (0.498) (0.496) (0.500) 4,292 1,944 2,348 0.976 0.969** 0.981 Pledge harvest as collateral (0.154) (0.173) (0.137) 4,295 1,945 2,350 0.800 0.841*** 0.766 Pledge land as collateral (0.400) 0.366 (0.424) 4,295 1,945 2,350 0.968 0.949*** 0.984 Choose crops to plant (0.176) (0.221) (0.126) 4,295 1,945 2,350 0.574 0.563 0.583 Plant fruit trees (0.494) (0.496) (0.493) 4,295 1,945 2,350 0.837 0.822** 0.850 Plant agroforestry trees (0.369) (0.383) (0.357) 4,295 1,945 2,350 0.436 0.442 0.430 Plant eucalyptus trees (0.496) (0.497) (0.495) 4,295 1,945 2,350 0.326 0.358*** 0.300 Cut trees (0.469) (0.479) (0.458) 4,292 1,944 2,348 Prevent grazing after own 0.714 0.768*** 0.669 (0.452) (0.422) (0.471) animals graze 4,295 1,945 2,350 0.975 0.970* 0.978 Prevent grazing right after harvest (0.157) (0.170) (0.146) 4,295 1,945 2,350 Percentage of plots that have the given characteristic, standard deviation, and sample size reported for each variable at the plot-year level. Sample limited to owner-operated plots. In bottom panel, data are from 1997, 1998, and 1999 only (after certification was initiated). Stars reflect significant differences across certified and uncertified plots. * p≤0.10,** p≤0.05, *** p≤0.01. Graze animals, construct a fence, and construct a surface water catchment not shown to save space; no differences across these. Table 1.4. Plot Rights and Certification Status

55 All Years 1999 Only Plot Plot Plot Plot Uncertified Certified Uncertified Certified Plot allocated by tabia 94.82 94.95 93.94 95.21 Plot inherited 2.59 3.47 1.52 3.59 Plot received as gift 2.59*** 0.00 4.55*** 0.00 Plot received from a matrimonial agreement 0.00* 1.58 0.00 1.20 0-5% (low) stone cover 80.83*** 65.62 78.79** 64.07 5-15% (medium) stone cover 3.63*** 12.30 3.03*** 15.57 15-40% (high) stone cover 9.33 9.46 9.09 7.78 >40% (very high) stone cover 2.07 3.79 3.03 2.99 Deep soil 30.05*** 14.51 40.91*** 14.37 Medium soil 35.23 36.59 27.27 35.93 Shallow soil 33.68*** 48.90 30.30*** 49.70 Highly fertile soil 13.47* 8.83 15.15 8.98 Moderately fertile soil 63.73 62.15 62.12 61.68 Infertile soil 21.76 28.08* 21.21 28.74 Clay soil 34.72*** 13.25 36.36*** 11.98 Loamy soil 35.23** 46.37 42.42 44.91 Sandy soil 23.73** 29.02 15.15*** 31.73 Silty soil 8.29 11.36 4.55 11.38 Flat slope 65.28 60.88 77.27* 64.67 Gentle slope 27.46 32.18 19.70 28.74 Steep slope 7.25 6.94 3.03 6.59 Slope position = top 8.29 11.36 4.55 11.38 Slope position = middle 21.76 21.77 13.64 20.36 Slope position = bottom 21.76** 29.97 18.18 27.54 Not on slope 48.19** 36.91 63.64*** 40.72 No erosion 65.80*** 52.40 75.80*** 54.50 Mild erosion 24.90*** 37.20 19.70** 35.30 Salinity is mild or severe problem 2.07 1.26 3.03 1.80 Waterlogging is mild or severe problem 12.44** 6.94 15.15* 7.78 Sample size 193 317 66 167 Plot altitude (mean meters) 2272.48 2169.83 2324.77 2176.05 Standard deviation (396.76) (409.25) (397.86) (398.51) Sample size 189 310 66 166 Walking time from plot to residence (mean min- 20.29 18.79 19.17 17.16 utes) Standard deviation (24.26) (22.61) (28.36) (21.20) Sample size 193 317 66 167 Sample is at plot-year level and is limited to owner-operated plots. Unless indicated otherwise, numbers reflect the percentage of plots in the given category that share the given characteristic. Stars reflect significant differences across certified and uncertified plots. * p≤0.10, ** p≤0.05, *** p≤0.01. Table 1.5. Plot Characteristics in Households with Plots with Different Certification Status in the Same Year

56 Short-term investment Long-term investment

Certificate of ownership 0.000 -0.002 0.004 0.023 0.022 0.026 (0.023) (0.023) (0.019) (0.029) (0.029) (0.028) Sum of plot rights 0.032*** 0.011 0.003 0.001 (0.009) (0.014) (0.011) (0.009) Initial meters of terracing 0.000 -0.000 (0.000) (0.000) Initial meters of bunding -0.000 -0.001*** (0.000) (0.000) Number of other initial 0.003 -0.207*** investments (0.011) (0.067) Number of public investments -0.013 -0.093*** made on plot-year (0.013) (0.019) Walking time from plot -0.000 0.004 to residence (minutes) (0.002) (0.014) Barley grown 0.022* 0.031* (0.013) (0.018) Corn grown 0.003 0.076*** (0.016) (0.028) Wheat grown 0.016 0.031* (0.014) (0.018) Sorghum grown 0.051** 0.051** (0.020) (0.023) Fingermillet grown 0.016 0.021 (0.015) (0.022) Teff grown 0.023* 0.015 (0.014) (0.017) Fababeans grown 0.008 0.024 (0.011) (0.025) Plot fallowing -0.349*** 0.035 (0.032) (0.023) Other crop grown 0.024* 0.046** (0.014) (0.020) Oxen/plot area -0.003 0.001 (0.002) (0.002) Adults/plot area -0.006*** 0.006 (0.002) (0.010) Male adults/plot area 0.010*** -0.007 (0.003) (0.011) Sample size 5,657 5,654 5,634 4,298 4,295 4,280 Number of groups 1,536 1,535 1,535 1,481 1,480 1,480 R-squared (overall) 0.0244 0.0883 0.0693 0.0689 0.0747 0.0003 Corr(ui,Xb) -0.3294 -0.2294 -0.5478 -0.3461 -0.3380 -0.9060 Rho (plot) 0.7530 0.7280 0.8431 0.6217 0.6106 0.8982 F-test 3.30 7.89e+07 5.41e+08 1.04 9140.98 2.71e+08 Models control for household-year and plot fixed effects. Sample limited to owner-operated plots. Robust standard errors clustered at the plot level reported in parentheses. Year controls and constant not shown.* p≤0.10, ** p≤0.05, *** p≤0.01. Table 1.6. Investment Regressions, Basic Results Using Household-Year and Plot Fixed Effects

57 First Stage Certificate Certificate Percentage of certified plots in community -1.121*** -1.176*** excluding the given plot (0.217) (0.248) Mean years since certified for plots in community -3.640 -3.710 excluding the given plot (2.923) (2.824) Sample size 5,544 4,223 Number of groups 1,448 1,425 F-test 1067.47 2463642.141 F-test of excluded instruments 23.45 11.61 Short-term Long-term Second stage investment investment -0.009 0.133 Certificate of ownership (0.018) (0.093) 0.011 -0.001 Sum of plot rights (0.012) 0.008 Sample size 5,544 4,223 Number of groups 1,448 1,425 F-test 7.41 2.02 Centered R-squared 0.2810 0.0442 Identification test (K-P rk LM statistic) 99.603 92.148 p-value 0.000 0.000 Weak identification test (K-P rk Wald F statistic) 23.450 11.606 Hansen J statistic 1.050 6.00 p-value 0.306 0.0143 E-stat p; Ho: Certificate endogenous 0.0541 0.2101 Sample limited to owner-operated plots. Household-year and plot fixed effects used. Controls for initial meters of stone terracing, initial meters of soil bunding, initial count of other plot investments, count of public mass activities for plot improvements, walking distance from plot to residence in minutes, fallow status, crop grown, oxen/plot area ratio, adult males/plot area ratio, adults/plot area ratio, year controls, and constant not shown. Robust standard errors clustered at the plot level reported in parentheses. * p≤ 0.10, ** p ≤ .05, *** p≤ .01. Weak, underidentification, Hansen J, and E-test reported after partialling out all controls except land rights, certificate variable, and year dummies. F-test calculated after partialling out household-year dummies. 10 percent maximal IV size critical value equals 19.93 for weak identification test. Table 1.7. Basic Investment Results, Instrumented

58 Short-term investment Long-term investment -0.006 -0.006 Long-term investment (0.015) (0.014) -0.010 -0.012 Short-term investment (0.026) (0.023) 0.017 0.008 0.026 0.133 Certificate of ownership (0.019) (0.017) (0.028) (0.093) -0.009 -0.008 0.000 -0.002 Sum of plot rights (0.013) (0.012) (0.009) (0.008) Instrumented No Yes No Yes Sample size 4,275 4,219 4,275 4,219 Number of groups 1,478 1,424 1,478 1,424 R-squared (overall) 0.1171 0.000 Centered R-squared 0.2616 0.0443 F-test 1775.26 5.27 2.72e+07 1.94 K-P rk LM statistic 92.535 92.051 p-value 0.000 0.000 Weak identification test (K-P 11.726 11.615 rk Wald F statistic) Hansen J stat 0.383 - p-value 0.5363 - All models control for household-year and plot fixed effects. Sample limited to owner-operated plots. Controls for initial meters of stone terracing, initial meters of soil bunding, initial count of other plot investments, count of public mass activities for plot, walking distance from plot to residence in minutes, fallow status, crop grown, oxen/plot area ratio, adult males/plot area ratio, adults/plot area ratio, year dummies, and constant not shown. Robust standard errors clustered at the plot level reported in parentheses. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. Hansen J statistic could not be computed for long-term models. 15 percent maximal IV size equals 11.59 for weak identification test. Table 1.8. Investment Regressions, Controlling for Concurrent Invest- ments

59 Uninstrumented Models Short-term investment Long-term investment 0.004 -0.012 -0.020 -0.005 0.026 0.008 0.002 0.023 Certificate of ownership (0.023) (0.019 (0.019) (0.023) (0.028) (0.023) (0.027) (0.033) 0.011 0.026*** 0.026*** 0.011 0.001 0.001 0.001 -0.000 Sum of plot rights (0.014) (0.009) (0.009) (0.014) (0.009) (0.007) (0.007) (0.009) Sample size 5,634 6,451 6,255 5,466 4,280 5,014 4,854 4,151 Number of groups 1,535 1,701 1,649 1,489 1,480 1,707 1,653 1,435 F-test 5.41e+08 - - - 2.71e+08 - - - Instrumented Models Short-term investment Long-term investment -0.009 -0.020 -0.023 -0.019 0.133 0.090 0.102 0.150 Certificate of ownership (0.018) (0.019 (0.022) (0.022) (0.093) (0.072) (0.087) (0.111) 0.011 0.026*** 0.026*** 0.011 -0.001 0.001 -0.000 -0.004 Sum of plot rights (0.012) (0.008) (0.008) (0.012) (0.008) (0.006) (0.006) (0.008) Owner- All Non- Owner- Owner- All Non- Owner- Sample (plot-level) operated plots private operated operated plots private operated plots pasture non-pasture plots pasture non-pasture Sample size 5,544 6,399 6,205 5,378 4,223 4,976 4,817 4,097 60 Number of groups 1,448 1,652 1,602 1,404 1,425 1,671 1,618 1,383 F-test 7.41 8.45 8.45 3.83 2.02 2.18 2.17 6.17 K-P rk LM statistic 99.603 144.588 104.557 70.695 92.148 141.889 103.097 66.482 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Weak identification test 23.450 25.386 17.811 18.035 11.606 25.450 16.020 7.523 (K-P rk Wald F) Hansen J statistic 1.050 2.152 1.667 0.991 6.00 3.984 3.420 - p-value 0.306 0.1424 0.1966 0.3195 0.0143 0.0459 0.0644 - E-stat p; Ho: Certificate endogenous 0.0541 0.3778 0.4649 0.0791 0.4649 0.3349 - All models control for household-year and plot fixed effects. Sample limited to owner-operated plots. Controls for initial meters of stone terracing, initial meters of soil bunding, initial count of other plot investments, count of public mass activities on plot, distance from plot to residence in minutes, fallow status, crop grown, oxen/plot area, ratio, adult males/plot area ratio, adults/plot area ratio, years, and constant not shown. Robust standard errors clustered at the plot-level reported in parentheses. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. F-test and identification statistics are computed after partialling out household-year dummies. Statistics not reported when they could not be calculated. 10 percent maximal IV size critical value equals 19.93, 15 percent maximal IV size equals 11.59, and 25 percent maximal IV size equals 7.25.

Table 1.9. Investment Regressions, Various Sample Restrictions Uninstrumented Models Cropping Plowing Improved Seeds Inorganic Inputs Organic Inputs 0.025 -0.009 -0.021 0.005 0.053** Certiﬁcate of ownership (0.030) (0.019) (0.019) (0.039) (0.021) -0.005 0.031*** -0.016 0.001 -0.004 Sum of plot rights (0.005) (0.013) (0.010) (0.013) (0.006) Sample size 5,634 5,634 5,634 5,634 5,634 Number of groups 1,535 1,535 1,535 1,535 1,535 R-squared (overall) 0.0890 0.0874 0.0604 0.1069 0.0010 F-test 1.10e+08 314.12 - - - Instrumented Models Cropping Plowing Improved Seeds Inorganic Inputs Organic Inputs 0.030 -0.022 -0.055** 0.019 0.054*** Certiﬁcate of ownership (0.039) (0.018) (0.025) (0.038) (0.019) -0.005 0.031*** -0.015* 0.001 -0.004 Sum of plot rights (0.005) (0.012) (0.008) (0.011) (0.006) Sample size 5,544 5,544 5,544 5,544 5,544

61 Number of groups 1,448 1,448 1,448 1,448 1,448 F-test 31.56 7.66 2.21 5.58 2.38 K-P rk LM statistic 99.603 99.603 99.603 99.603 99.603 p-value 0.000 0.000 0.000 0.000 0.000 Weak identification test (K-P rk Wald F statistic) 23.450 23.450 23.450 23.450 23.450 Hansen J statistic 0.163 1.291 - 1.384 2.821 p-value 0.6862 0.2559 - 0.2395 0.0930 E-stat p; Ho: Certificate endogenous 0.7461 0.1280 - 0.3757 0.4271 All models control for household-year and plot fixed effects. Sample limited to owner-operated plots. Controls for initial meters of stone terracing, initial meters of soil bunding, initial count of other plot investments, count of public mass activities on plot, walking distance from plot to residence in minutes, fallow status, crop grown, oxen/plot area ratio, adult males/plot area ratio, adults/plot area ratio, years, and constant not shown. Robust standard errors clustered at the plot level reported in parentheses. Weak identification and Hansen statistics reported after partialling out all controls except land rights and certificate variable; could not be calculated in all cases. 10 percent maximal IV size critical value is 19.93 for weak identification test. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01.

Table 1.10. Short-term Investment Regressions by Type of Investment Short-term investment Long-term investment -0.031 -0.048 0.040 0.035 Certificate (0.044) (0.040) (0.047) (0.046) 0.021 0.021 0.005 0.010 Land rights (0.018) (0.016) (0.012) (0.008) Instrumented No Yes No Yes Sample size 4,729 4,649 3,576 3,531 Number of groups 1,206 1,206 1,234 1,191 Centered R-squared 0.2920 0.0511 R-squared (overall) 0.1929 0.0090 F-test 5.70e+09 3383.10 1.31e+09 1.71 K-P rk LM statistic 46.808 44.883 p-value 0.000 0.000 Weak identification test (K-P rk Wald F) 222.377 215.534 Hansen J-statistic . 0.200 . 3.429 p-value 0.654 0.0641 E-stat p; Ho: Certificate endogenous 0.0367 0.3753 All models control for household-year and plot fixed effects. Sample limited to owner-operated plots whose plots all had the same certification status in 1999. Controls for initial meters of stone terracing, initial meters of soil bunding, initial count of other plot investments, count of public mass activities for plot, walking distance from plot to residence in minutes, fallow status, crop grown, oxen/plot area ratio, adult males/plot area ratio, adults/plot area ratio, years, and constant not shown. Robust standard errors clustered at the plot level reported in parentheses. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. Table 1.11. Investment Regressions for Households with All Plots Certified by 1999

62 First stage Percentage of household plots certified 2.351*** 2.356*** Years since certified (kushet) (0.666) (0.668) -11.020*** -11.017*** Percentage certified (kushet) (0.666) (0.832) Sample size 1,682 1,682 Number of groups 443 443 F-test 29.412 522.209 Second stage Short-term investment Long-term investment 0.007 -0.003 -0.008 0.002 Percentage of certified plots (0.016) (0.019) (0.022) (0.022) 0.027** 0.031*** -0.002 0.001 Mean plot land rights (0.013) (0.010) (0.010) (0.009) Instrumented No Yes No Yes Sample size 1,745 1,682 1,745 1,682 Number of groups 458 443 458 443 R-squared (overall) 0.0315 0.0620 R-squared (centered) 0.4604 0.0285 F-test . 43304.94 . 1.26 K-P rk LM statistic 127.007 128.007 p-value 0.000 0.000 Weak identification test (K-P rk Wald F) 103.216 103.216 Hansen J statistic 0.015 0.312 p-value 0.9014 0.5768 Ho: Certificate variable endogenous 0.7666 0.7160 Variables describe owner-operated plots only. Kushet-year and household fixed effects used in all models. Robust standard errors clustered at the household level reported in parentheses. Controls for household-year mean of initial meters of stone terracing, initial meters of bunding, count of other initial investments, count of public mass activities for plot, mean walking distance from plots to residence in minutes, fallow status, crop grown, oxen/plot area ratio, adult males/plot area ratio, adults/plot area ratio, year dummies, and constant not shown. First stage dependent variable is percentage of owner-operated household plots that are certified. Second stage dependent variable is percentage of owner-operated plots benefiting from the given investment. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. Table 1.12. Household-Level Investment Regressions

63 Chapter 3: Rethinking Land Rights and Agricultural

Productivity

Increasing agricultural productivity in Sub-Saharan Africa is an on-going challenge to many countries on the continent. Improving agricultural productivity is a means of stimulating growth and improving the welfare of rural households (Timmer 1988), and its importance in driving aggregate economic growth in the region is often underappreciated (de Janvry and Sadoulet 2010). Many factors that can increase agricultural productivity have been identified, ranging from implementing macroe- conomic reforms (Block 1995); encouraging agricultural export growth (Frisvold and Ingram 1995); supporting a peaceful environment with political and civil freedoms that promote market development (Fulginiti, Perrin, and Yu 2004); improving infrastructure (Jayne et al. 1997); supporting agricultural research (Block 1995); promoting the use of drought-resistant seed varieties and fertilizer (Rosegrant et al. 2001, Craw- ford et al. 2003); addressing the misallocation of resources due to dynamics in plots controlled by females (Udry 1996); supporting local agricultural organizations (Jayne et al. 1997); improving caloric intake of producers (Frisvold and Ingram 1995); increasing human capital accumulation (Fulginiti 2010); and improving credit markets (Dong, Lu, and Featherstone 2010). The idea that rights to land can improve agricultural productivity has also been raised, particularly in policy circles, and it has been confirmed in some cases. This chapter examines whether formalizing land rights through plot certification improved agricultural productivity in Tigray Region, Ethiopia. Tigray Region is characterized by environmental degradation, extreme population pressure, and limited non-agricultural income generating opportunities. The region suffers from low

64 agricultural returns that are sensitive to annual rainfall variations. It was at the epicenter of the infamous Ethiopian famine of the 1980s, when political factors exacerbated poor crop production. The low-cost plot certification exercise undertaken in the region in the 1990s was hoped to help producers increase agricultural productivity. Other studies have determined that certification increased maintenance of agricultural investments (Holden, Deininger, and Ghebru 2009a), although the evidence presented in the previous chapter casts a bit of doubt on these estimates. The certification program has also been found to encourage the development of rental markets in some cases (Holden, Deininger, and Ghebru 2009b). The study by Holden, Deininger, and Ghebru (2009a) also examines whether certification increased agricultural productivity. The authors conclude it increased productivity by approximately 45 percent. This chapter re- examines the issue and comes to a less optimistic conclusion, an unsurprising result given that the estimates in chapter 2 could not definitively conclude that certification increased many important plot investments thought to increase productivity. Using strategies that allow me to control for heterogeneity within household- years and plots, and controlling for the potential endogeneity of certification related to plot-level shocks, I find that certification did not have any significant effect on agricultural productivity. These results are confirmed at the household level using estimates that control for unobservable kushet-year and household heterogeneity. Further investigation suggests that certified plots do not enjoy greater input intensity, receive greater short-term investments (as already found in chapter 2), or benefit from different cropping strategies than uncertified plots. Taken together, the results suggest that certified plots were treated virtually identically to uncertified plots. My results stand in stark contrast with those of Holden, Deininger, and Ghe- bru28 (2009a), who find strong impacts of certification on productivity. The difference

28Speciﬁed throughout the rest of the chapter as HDG.

65 in our results may be due to the later timing of the data used by HDG, who used data in which plots had been certified for a longer period of time. If productivity effects due to certification work primarily through long-term investments, which are undertaken relatively infrequently compared to short-term land management strategies, data collected longer after certification occurred should capture larger policy impacts. To examine the robustness of my results and understand what drives the difference between the results presented here and that found by HDG (2009a), I replicate the estimation strategy used by these authors. Using their exact strategy, I find the same results they did, which suggests that the differences in our results are not driven by the later timing of their survey data or other data-based factors. Instead, the results appear to be driven by methodological differences. When I use their estimation strategy but also control for household-year and plot heterogeneity, I confirm my original results: certification did not have an impact on productivity. The result is not a critique of the authors but a commentary on the importance of collecting data that allows the researcher to control for heterogeneity down to the lowest unit possible- ideally, the plot. In the next several sections, I provide contextual information on agricultural productivity and land tenure issues, discuss the basic estimation strategies used, and provide empirical results. I then test the robustness of results by estimating results at the household level and examining additional productivity-related variables. This follows with estimation via HDG’s (2009a) strategy, along with discussion of the results and implications.

Land Rights and Agricultural Productivity

Research on the relationship between land rights and productivity has produced varied results, owing in part to methodologies selected, environments examined, and

66 nature of the land rights investigated. The connection between tenure security and productivity is more tenuous than the relationships sometimes found between tenure security and investments, not a surprise considering the additional intervening factors that may affect productivity. Most of the studies mentioned here focus on land rights formalization through titling or official programs, rather than simply tenure security. Outside of Africa, a program increasing tenure security for sharecroppers improved productivity for Indian sharecroppers compared to sharecroppers in neigh- boring Bangladesh who did not benefit from the program (Banerjee, Gertler, and Ghatak 2002). In Vietnam, Do and Iyer (2003) found that titled households increased investments closely related to agricultural productivity (irrigation and labor inputs). In Central America, Lopez (1996) found plots that benefited from a donor- funded titling program had higher productivity than non-titled plots in one out of two years studied in Honduras, and Broegaard et al. (2002) found that possessing formal land documents increased productivity in Nicaragua by encouraging producers to cultivate perennials. Within Africa, the impact of land rights, and formal rights through titling, has also been mixed. Place and Hazell (1993) examined cross-sectional data from Ghana, Kenya, and Rwanda and determined that land rights in indigenous tenure systems are not a constraint on agricultural productivity. Transfer rights, instrumented using titles, were found to increase agricultural productivity in Ghana (Besley 1995). In Kenya, registration did not clearly increase agricultural productivity (Migot-Adholla et al. 1991, Place and Migot-Adholla 1998). Similarly, title was not associated with a significant increase in land output in Uganda (Pender et al. 2004). While other studies in Sub-Saharan Africa have been less optimistic about the relationship between formal land rights and agricultural productivity, several have found a positive (or potentially positive) relationship. Examining data from Zambia,

67 Smith (2004) determined that land held under titles29 increased fixed investments that ultimately were important in improving agricultural output per available labor, although titles did not have a clear impact on output per land area. As already mentioned, HDG (2009a) used panel data from 1998, 2001, and 2006, and determined that productivity grew by approximately 45 percent due to certification in Tigray Region. While the results in the previous chapter suggest the productivity effects of certification merit a second look, it is not necessarily surprising that the Tigrayan certification program would have a greater productivity effect than that found in other African countries. Tigray’s plot certification program was rolled out to all households, and households theoretically could not select into (or out of) certification. Producers say the program increased their tenure security (Deininger et al. 2008), although it is not entirely clear that they would be entirely forthcoming if this were not the case. Just as certification was expected to increase producers’ confidence that they could freely maintain and continuously cultivate their plots, it was hoped that the program would ultimately increase agricultural productivity. Factor market failures, combined with environmental degradation and extreme sensitivity to climatic shocks, often leads to extremely low yields. Yields for cereal crops in Tigray’s highlands were less than one ton per hectare in the late 1980s (Pender and Gebremedhin 2006)30, and the productivity outcomes calculated from the data used here do not suggest this situation had changed much by the mid-1990s. Since peasants depend on their own production, many households live in chronic food insecurity and are susceptible to adverse idiosyncratic and covariate shocks. Multiple factors have been identified as drivers of productivity in Tigray Re- gion, suggesting the issue is very complex. Within the Ethiopian highlands, Holden,

29The titles were actually 99 year leases. 30Pender and Gebremedhin (2006) cite Hunri (1988), although this statistic is not apparent in Hunri’s paper.

68 Shiferaw, and Pender (2001) determined that labor and land market imperfections decreased land productivity. Land degradation also clearly diminishes agricultural productivity. Problems include erosion, soil depletion, and stress on moisture levels, issues which are exacerbated by hillside cultivation, low vegetative soil cover, dung and crop residue burning, brief and diminishing fallowing, erratic and variable rains, and suboptimal application of organic and inorganic fertilizers. Productivity is expected to improve through relatively small measures including applying manure, reducing tilling and burning, terracing, and improving livestock management (Gebremedhin, Tesfay, and Assefa 2003). Other practices used to maintain or improve plots include crop rotation, application of crop residues, and fallowing (Corbeels, Shiferaw, and Haile 2000).

Data and Descriptive Statistics

The data used in this chapter is the same dataset described in chapter 2. Unless otherwise speciﬁed, I limit the sample to owner-operated cultivated plots in Tigray Region, per the discussion in chapter 2 on the relationship between investments, certiﬁcation, and tenure status on plots. The analysis of productivity is performed at both the plot and household level; descriptive statistics for both levels of observation are presented in tables 2.1 and 2.2. Table 2.1 provides data on plot characteristics. Plot productivity, measured as kilograms of harvested grain31 per planted tsimad, translates into approximately 1.25 tons for 1991, while the production decreased annually until production in 1999 was just under one ton32. However, the larger standard deviations for yield statistics in earlier years suggest some relatively high measurements for the early years; some of this greater yield variability may be partially due to the longer recollection period

31Harvested grain is contrasted with harvested straw; it is not limited to only cereals, which is part of the reason the estimates of kilograms per hectare are so low overall. Estimates limited to harvested cereals would presumably be higher. 32This uses the conversion of 907 kilograms = 1 short ton.

69 involved. Similarly, the value in Birr of harvested grain was lower in 1998 than in 1991, and the standard deviation of the estimates is also smaller in 1998 than 1991. The plot characteristics listed in table 2.1 are those included in the empirical models. They paint a picture of producers that have relative freedom over plot usage (with the major exception of being able to sell plots, which is illegal), with plots having over 14 out of 18 possible land use rights on average33. No plots were certified in 1991, while almost 90 percent had been certified by 1999. On average, plots are located just over 20 minutes from the household’s residence, although the inclusion of homestead plots in this number suggests non-homestead plots are significantly farther from residences. The plots have a variety of soil colors (most frequently red) and local classifications, and loam and sandy soils are the most common types. Plots have more access to female labor than male labor. Many plots have some terracing, although most report being on a low grade or nearly no grade. Just over 40 percent of plots were not on slopes, while the others were on another portion of a slope. Household-level descriptive statistics are presented in table 2.2. Overall productivity is lower at the household level, although the standard deviation of estimates is much smaller and estimates less volatile across the four years, suggesting the estimates are a bit more precise. These estimates confirm that households consistently produce under one ton of non-straw output per hectare planted. The value of household output increased slightly over the two years with available data for the household-level data, in contrast to the plot-level value which decreased over the two available years. Given the smaller standard errors, more consistent estimates, and level of aggregation across the household-level productivity estimates, these are probably more accurate than the plot-level data. However, the fixed effects used should control for any se-

33Possible land rights are the following: lease out, sharecrop out, loan out, exchange, bequeath to family, give away to someone, pledge land as collateral, pledge harvest as collateral, choose crops to plant, graze animals, prevent grazing after harvest, prevent grazing after own animals graze, plant eucalyptus trees, plant agroforestry trees, plant fruit trees, cut down trees, construct fences, and construct surface water catchments.

70 lection effect of reported output, provided these reports do not vary systematically depending on the household’s certification status. The household-level certificate variable represents the percentage of all of the households’ plots that are certified. Certification increases to nearly 78 percent of all household plots by the final year with available data. (The difference between the household certificate variable and plot certificate variable reflects the sample specification used in each table.) Overall, households hold just under 14 rights on all their owned plots. Households are relatively young, with a mean age of under 23. They have 2.4 adults on average, 2.6 children, and just over 1 elderly member, suggesting many households cope with a relatively high dependency ratio. Households hold just over one ox on average, and over two additional cattle. They typically hold small ruminants, and some participate in beekeeping. Contact with extension workers was relatively rare. Over 85 percent of households have access to informal credit, while under 80 percent reported access to formal credit (although this increased greatly over the survey timeframe). Finally, households belonged to approximately two organizations, with 8 percent of households holding a leadership position in a given year. Overall, the data paint a picture of subsistence producers who used a mixed cropping/livestock system. Although most households claim to have access to credit, and labor markets do function in rural Tigray, it is not difficult to understand why these markets would be incomplete for extremely impoverished households. Markets for other important productive factors, such as oxen, are thin in times of high demand. Many households are able to plow only using their own oxen, or they have to rent oxen at less than ideal times. These market imperfections imply that separation of production and consumption decisions generally does not hold, and controlling for household access to labor, credit, and other factors of production (such as oxen) may be important in the upcoming analysis.

71 The Impact of Certiﬁcation on Plot Productivity

To measure productivity differences due to certification at the plot level, I examine the log of plot-level productivity using household-year and plot fixed effects. This method ensures that constant unobservable plot characteristics are not driving results, particularly if these characteristics are related to a plot’s certification status. The household-year fixed effects control for household characteristics that may affect productivity and certification, even if these traits vary over time or are affected by single-season shocks. More specifically, the models are as follows:

Ln(Pft) = β0 + fftβ1 + cftβ2 + hh + jht + ff + gft + +εft, t = {1, ....T } ,

where Ln(Pft) is the natural log of the productivity measures used. fft is a vector of plot characteristics important to productivity, discussed below, and cft is the plot’s certification status (i.e., an indicator for whether the plot was listed on the household’s certificate). hh is a constant household effect, and ff a constant plot-specific effect. jht and gft are the time-varying components of the household and plot heterogeneity, respectively. The error term contains idiosyncratic shocks. Accounting for the household-year and plot fixed effects, the equation becomes the following:

¨ ¨ Ln(Pft) = fftβ1 +c ¨ftβ2 +g ¨ft +ε ¨ft, t = {1, ....T } . (11)

The remaining endogeneity concern in such models is that the plot-level time- varying error term, gft, is correlated with both certification and productivity. For instance, a plot may receive a positive rainfall shock in the year that certification occurred, leading the household to attempt to certify this plot while other plots remain uncertified for other (potentially endogenous) reasons. However, given the structure of the certification program, in which all household plots were supposed

72 to be certified at one time, it seems unlikely that shocks related to productivity would have much influence on certification decisions. Another concern is that plots may have endogenous levels of disputability that varied according to unobserved plot shocks, which were correlated with plot certification status and ultimately related to agricultural productivity. This concern was discussed at greater length in chapter 2, where additional evidence for this possibility was presented. To address this issue, I instrument for a plot’s certification status using the same instruments used in the plot-level investment regressions in chapter 2: the percentage of local surveyed plots excluding the current plot that were certified, and the mean number of years that these plots had been certified. These indicators of the progress of the certification exercise, which was carried out at the local level, will be related to whether a plot was certified or not. Since certification was rolled out at the local level and this process was determined primarily by administrative concerns, these instruments should be unrelated to plot-specific shocks that affected the probability that a plot was certified. A challenge to the identification requirement in these models is that households may negotiate plot investments depending on which plots are certified, which should have an effect on plot productivity. The proportion of household plots that are certified is partially absorbed into the plot-level instrumental variable, an obvious concern. However, the instrument for the plot-level regressions must vary across plots within the same household each year, so other methods must be used to test the robustness of the plot-level instrumented regressions. Fortunately, results are generally consistent across uninstrumented and instrumented models, and household- level estimates, which are not as vulnerable to this identification failure, corroborate the results at the plot level. Therefore, this concern does not qualitatively affect the conclusions drawn. Productivity was calculated as the natural log of total harvested grain in kilo-

73 grams per tsimad planted, the traditional unit of plot measurement in Tigray. Various definitions of productivity were examined, including the log of kilograms of total grain harvested per area planted; and the log of kilograms of total grain and straw output per area planted and per area harvested within a given plot-year or household-year observation. Productivity measures were very similar across these measures, and it was deemed unnecessary to present results using multiple versions of the output variable. Since households may have produced crops of lower volume but higher returns, the natural log of the total value of grain (i.e., non-straw) output per tsimad planted was also calculated and examined in the estimates. The value of crop output was imputed using the average tabia-year prices for the cultivated crops34. Data on crop values is limited to 1991 and 1998, the only years in which local price information is available from the survey. Controls included in the estimates include meters of stone terracing, meters of bunding, count of other plot improvements, whether the plot benefited from a public investment, distance of the plot to the household’s residence, soil color indicators, soil texture indicators, local soil classification indicators, degree of slope indicators, and toposequence indicators. Controls that provide information on input availability, including oxen per tsimad, male labor per tsimad, and female labor per tsimad are also used since these are not expected to be affected by certification. Since certification may affect intervening factors relevant to agricultural productivity, I do not control for concurrent input decisions on plots, such as variable inputs or land management practices. Estimates that include controls for land management strategies and crops planted are examined later in the paper to see what indications these controls provide on the mechanisms driving the coefficient on certification. A certificate will clearly not affect productivity if it does not affect some

34Tabias, or wards, contain approximately ﬁve villages each.

74 of these intervening decisions about plots. If the coefficient on certification were still significant after controlling for plot inputs, land management strategies, and crop cultivation decisions, this would indicate the models may have failed to control for other inputs driven by certification that affect productivity, or the coefficient on the certificate variable did not properly identify the true impact of certification. A positive, significant coefficient on certification could simply reflect that certified plots benefited from positive plot-level shocks. Finally, results may be biased if endogenous land rights are not addressed. Besley (1995, in Ghana) and Braselle et al. (2002, in Burkina Faso) have found evidence of the simultaneity of land rights and investments, which ultimately affect productivity. Use of the household fixed effects will control for endogenous land rights to the extent that these rights are determined by household status or networks. In the plot-level regressions, the household-year fixed effects control for the endogeneity of land rights even if these household factors that are related to land rights, and possibly certification and productivity, change over time or are responsive to household-level shocks.

Certiﬁcation and Plot Productivity: Empirical Results

Estimates of the impact of certification on productivity at the plot level are reported in table 2.3 for yields and table 2.4 for values. In table 2.3, the coefficients vary depending on the controls included in addition to the household-year and plot fixed effects (land rights and additional plot controls), but the story that estimates suggest is consistent across all the models: certified plots did not achieve significantly higher yields than uncertified plots. All the estimates are positive, but they also have large standard errors. When the weak instrument and overidentification statistics can be reported, they suggest the model is specified appropriately. Even though the models in chapter 2 using the same instruments did not always pass these tests, it is

75 not particularly surprising that the instruments fare better in the productivity regressions. Productivity outcomes are probably more subject to shocks than investment decisions are. Estimates of the value of harvested grain on plots (table 2.4) do not contra- dict these results. Estimates are consistently positive but have very large standard errors. The instrumental variables are particularly weak for this outcome, and they do not hold much weight given the overidentification tests do not pass in these value regressions. These estimates are restricted to owner-operated, non-fallowing plots, so they should be interpreted with this sample specification in mind. The estimates do not rule out the possibility that certification affected fallowing decisions, which would ultimate impact plot productivity. However, even if there were a selection effect into certification based on plot quality (i.e., more productive plots were more or less likely to be certified), the plot fixed effects should control for this, and the instrumented models should account for potential productivity differences across plots that are correlated with certification status, even if these varied over time. While certified plots that had a greater potential for productivity improvements may have been more or less likely to be owner-operated rather than sharecropped out, the estimates control for unobserved plot quality and should account for these differences in the fixed effects. However, if certification differentially affected productivity in plots that may be sharecropped out versus those that were owner-operated, the coefficient on the certificate in this sample would be higher than that for sharecropped plots if owner-operated plots had a higher potential productivity response to certification, and vice-versa. This limitation should be kept in mind.

76 Household Productivity Responses to Certiﬁcation

Although it seems clear that plot productivity did not increase as a result of certification, concluding that certification did not affect productivity at all may be premature and fail to capture the richness of household responses to certification. If certification caused households to adjust their cultivation portfolio, it is also important to examine the impact of certification on household-level productivity. This idea is very sensible since household cultivation decisions are negotiated across multiple plots (provided decisions are not entirely separable by plot), and these decisions may be based on aggregate plot potential. The calculus involved in determining plot potential- and thus inputs and ultimately, productivity - will involve plot characteristics, climatic conditions and expectations, and tenure security on plots. The plot-level regressions, and particularly instrumented models, are expected to control for plot conditions that affect productivity and may vary systematically with certification, but household-level models are an additional robustness check on the plot-level assumptions that must hold for proper identification. They are all the more important given some of the diagnostic failures of the instrumented variables in the plot-level value regressions. To examine this issue, I estimate the log of household grain output per tsimad of cultivated land while controlling for household-level demographics and summary plot characteristics. The estimated models are as follows:

Ln(Pht) = β0 + hhtβ1 + chtβ2 + kkt + hh + εht

Using kushet-year and household ﬁxed eﬀects, the model becomes the following:

¨ ¨ ¨ Ln(Pht) = hhtβ1 + chtβ2 +ε ¨ht (12)

77 The certification variable of interest in the household-level estimates is the percentage of households’ owner-operated plots that are certified in a given household- year observation. Kushet-year fixed effects control for unobserved local, time-varying factors that affect household-level productivity, and household fixed effects difference away constant unobservables such as households’ productive ability, preferences, and so on. For identification to hold in these models, variations in certification status within the same household over multiple years must not be related to components of the error term that also affect overall household productivity, net of seasonal local shocks and household heterogeneity. In these models, I control for key access issues that may affect household and plot productivity, including within-household labor access (percentage of female members, mean household member age, number of adults, children, and elderly members), access to on-farm equipment (count of durable farm equipment, number of oxen), other animals that may contribute to land productivity (number of cattle, number of pack animals), other animals that may contribute to other income generating opportunities (count of goats and sheep, participation in beekeeping activities), number of contacts with an extension worker in the previous year, whether the household had access to formal and informal credit, and proxies for social networks (the number of organizations to which the household belonged and the number of organizations in which a household member held a leadership position). I also control for the household-year mean of the plot characteristics controlled for in the plot-level productivity estimates. In the household-level models, there are remaining concerns that more productive households may have tried to certify their plots earlier than less productive households, or vice-versa so they could rent out plots more easily. Since the exogeneity of the household’s certificate variable is questionable, I also estimate models that instrument for households’ certification status. The instruments used for the

78 household-level models, the same as those used in the household-level estimates in chapter 2, are the percentage of surveyed households in the village with at least one certified plot, excluding the current household’s certification status, and the mean of the maximum number of years since other surveyed local households had at least one plot certified, excluding the given household’s data. These variables clearly will be related to the propensity of the household to have its plots certified, while they should not be directly related to households’ productivity in ways that will not be controlled for in the fixed effects35. The instruments are free of concerns over specific plot disputability that affect the households’ percentage of certified plots and are potentially problematic in the plot-level instrumented models. There are several remaining challenges to identification in the household-level models, particularly if there exist economies of scale in production. In this case, if village-level certification instruments capture the ability of households to rent plots, the instruments may proxy for household productivity because they will be related to households’ propensity to exchange plots. If the most productive households are the most likely to rent in plots (as is often suggested), the instruments could still bias certificate coefficients upward in the productivity regressions. Given the failure of separation of production and consumption decisions, however, it may be that household decisions to exchange plots are driven more by initial endowments and access to inputs than they are by productivity potential. The results presented in the next section suggest that these concerns would be more relevant if estimates were expected to be biased downward. 35There is the possibility, for instance, that certification was rolled out in the most productive localities, or least productive localities, first. However, this base level of productivity is controlled for in the fixed effects.

79 Certiﬁcation and Household-Level Productivity: Empirical Results

Results from kushet-year and household fixed effects regressions of the log of household yield on certification and other controls are found in table 2.5. Unin- strumented models reveal that the percentage of household certified plots is actually negatively related to household output, and this result holds even when controlling for concurrent land rights, time-varying household characteristics, and summary plot characteristics. It could be argued that this result occurs because the percentage of certified plots is negatively related to an unobserved aspect of the household or of plot quality that is positively correlated with certification. For instance, the least productive plots could be most likely to be certified. However, when the certificate variable is instrumented using the variables previously described, the estimates be- come even more negative, although they lose a bit of precision when not all controls are included. In no case can the exogeneity of the land rights variable, endogeneity of the certificate variable, or appropriateness of the instruments, be rejected, suggesting these variables are correctly specified. While yield may not be positively related to households’ certification status, it is plausible that the value of household output increases- or at least does not decrease- as a result of certification. Table 2.6 examines this possibility. It suggests that the relationship between certification and the value of household output is not as dismal as the results found in the previous table; the coefficients on certification are negative but only marginally significant in the uninstrumented model with the most robust set of controls. In the instrumented models, the coefficients once again decrease in absolute value. The decline in the certificate coefficient in the instrumented models suggests that households with factors driving lower productivity (both in yields and values) are less likely to have their plots certified. While the direction of the bias at the plot level runs in the opposite direction, this result makes sense if there were a positive selection effect into certification by those households best poised for relatively high agricultural

80 production. For instance, these households may have better networks that both allow them to be certified earlier or more completely than other households, the likes of which also help provide them with important information that plays an important role in overall productivity. These networks may not be captured fully by the network variables included in the models. One caveat to the instrumented results in column 6 is that the exogeneity of households’ certificate status is nearly rejected at conventional levels once household and plot controls are added, suggesting that the results in column 3 should also be taken into account. The estimate in column 3 is less than half that of column 6, and it is significant at the 10 percent level. This result tempers the idea that the value of household output decreased as a result of certification.

Additional Examination of the Results

Although information on gross crop revenue (i.e., the plot and household value estimates) can capture information on the decisions households make due to certification, information on profits would be more helpful. Data on the costs and use of inputs on plots is available for 1998 only. Unfortunately, sporadic data availability on many important labor inputs applied to plots make it impossible to calculate a precise measure of net profit for each plot. Even if this measure could be constructed, it would not be accurate for each household, which faces endogenous costs of its own labor and inputs due to the failure of separation of consumption and production decisions. It is possible to subtract the value of the seed, fertilizer, and pesticide inputs applied to plots from plot revenue to have a better idea of what household profit may have been. Data on the cost of oxen usage was also available, but it was sporadic and inconsistent across locations. For oxen costs, a simple average of birr per day of oxen usage was calculated overall and used to impute the value of oxen usage per plot.

81 (Plots had information on how many oxen days were used for plowing and threshing that could be used for this purpose36.) Human labor costs also did not link well to actual plot labor usage and would have resulted in very little usable data. Since this appeared to be a data integrity issue, I excluded data on labor usage from profit calculations. Given these limitations, the profit calculation is simply gross revenue per tsimad planted less imputed costs for animal power and seed, fertilizer, and pesticide inputs applied to each plot. I also examined the results excluding the oxen costs since including these costs sometimes made profit a negative number, presumably due to the variations in the cost of acquiring oxen in different localities. This profit valuation excluding the oxen costs is the second pseudo-profit measure listed in the regressions (columns 3 and 4 of table 2.7). These measures will not be particularly informative if certified plots benefited from differential human labor than uncertified plots due to their certification status, but it will provide some information on this profit measure if input and draft animal usage differed significantly across certified and uncertified plots. Table 2.7 provides results of single-year household fixed effects regressions of the profit measures on the controls used in the previous household-level models37. Unin- strumented and instrumented models are presented. Ideally, the instrumented models are the preferred estimates since the fixed effects cannot eliminate unobserved plot- level heterogeneity that may drive certification and productivity. (This is confirmed by the E-statistics, which do not reject the endogeneity of the certificate variable in

36The cost of using an ox each day was estimated to be 32 Birr, which was the average cost per day per oxen for cash leases both inside and outside the village. This data was imputed using village- level information. Three percent of plots were plowed using animals besides oxen, and sometimes in addition to oxen, and just under 19 percent of plots recorded threshing grain from the plot using animals other than oxen. Since there is not information on the value of the activities provided by other animals, and most of these animals were bulls, cows, heifers, or donkeys, I applied the oxen valuation to these activities as well. 37I do not display results for the natural log of proﬁt since the proﬁt has some zero and negative values given the nature of its construction.

82 all cases.) Unfortunately, the instruments are weak in these models given the lower variability in the instruments, so the results in this table are purely suggestive. If anything, they suggest a plot’s certification status did not have a clear effect on either of the profit measures utilized, a result that is consistent with the productivity results already presented. In columns 5 through 8, I present similar estimates of the value of seed, fertilizer, and similar inputs used per tsimad planted, and the number of oxen days used per tsimad planted on the given plot. These estimates suggest, once again, that certification did not affect households’ treatment of plots with respect to these inputs. Again, these results are purely suggestive as they may still suffer from omitted variable bias, and a selection effect could drive results for the specific year. Their purpose is purely to illustrate that there does not appear to be a clear positive relationship between certification and profit, and certification and input use. This result is consistent with earlier estimates. A final way to examine how certification may have affected, or not affected, plot productivity is to control for some of the means through which certification may have affected plot productivity. This can be completed by examining how the coefficient on certification changes when various sets of control variables are introduced into the estimates. Table 2.8 provides results from these models, which are variations from the estimates presented in tables 2.3 and 2.4. These estimates control for the certificate variable, land rights, the basic controls used in initial models, and either crops grown or land management strategies used. When both the crops grown and land management strategies are included (separately) in the regressions, the coefficient on certification decreases. This suggests that, if anything, certified plots are more likely to grow crops and benefit from land management strategies that enhance plot productivity. However, since the coefficient on certification does not change significantly in any of these cases, any improved strategies were not substantially productivity-

83 enhancing. The validity of the instrumented models is also a bit questionable given the diagnostic statistics reported. All in all, certification does not appear to have significantly impacted strategies that affect immediate plot productivity.

Why Do These Results Diﬀer from Those Found by Others?

If the results presented here can be believed, they suggest that plot certification had no effect on productivity in agricultural households in Tigray. These results are a bit surprising, given the only other investigation of Tigrayan certification and productivity find that certification had a robust, significant, positive impact on productivity. The estimation strategy of HDG (2009a) makes a careful attempt to identify the causal impact of certification on productivity, just as this paper does. Therefore, an understanding of why the two sets of estimates differ is important to determining whether the results presented here are a reflection of reality. The preferred model of productivity in the analysis of HDG (2009a) controls for two components of certification, which they call predictable and random components. The predicted component is the model prediction for the probability that a plot will be certified, constructed using a household fixed effects model and controlling for plot- level covariates. The random certification component is the residual term from the predicted certificate variable models. Household fixed effects GLS estimates of the log of household output per hectare controlling for both these certificate variables finds the certification error term (the random component of certification) to be positive and significant at the 10 percent level. There is reason to believe that HDG’s estimation strategy is unable to account for important heterogeneity. If certification was driven by unobservable plot-level characteristics, as the data here suggest, the certification error term from a household fixed effects regression will still contain plot-level heterogeneity. When these unobservable plot characteristics are correlated with both certification and mainte-

84 nance decisions that ultimately affect productivity, estimates of a certification effect using the error component of certification will be biased by these plot-level characteristics. For instance, if an important latent plot characteristic is plot disputability, and this was negatively correlated with certification and productivity-enhancing plot investments, a certificate estimate that contained this omitted variable would be biased upward38. Since disputability may change over time, it is important to control for changes in this latent variable whenever possible. Models that control for this characteristic would result in a lower estimated impact of certification on productivity. Similar arguments could be made related to household-level shocks. To understand whether time-varying household and plot heterogeneity are driving the results found here, I examine household productivity using as close a specification to that used by HDG (2009a) as possible. Following their preferred specification, I first apply propensity score matching based on plot characteristics to limit the sample to those satisfying a common support of non rented-in plots that cultivated a cereal crop39. Per their specification, I matched the plots based on the non-investment characteristics available in their data: an indicator for whether the plot was a homestead plot, plot size, indicators for soil depth (shallow, medium, deep); indicators for slope position (flat, gently sloped, or steep); soil type (Cambisol/Bae’khel, Vertisol/Walka, Reogsol/Husta, Luvisol/Mekayho), walking distance from the plot to the residence; sex, age, and education of the household head; log of adult females in the household per tsimdi; log of adult males in the household per tsimdi; log of oxen per tsimdi; log of standardized livestock units per tsimdi; and size of the farm in tsimdi. The common support requirement, which drops observations of plots with certificates whose propensity score is outside of the range of the matched uncertified plots, leaves the sample with only 2,553 observations out of 5,667 plot-year observa-

38While this is a possibility, the estimates in chapter 2 do not suggest this relationship exists. This example is used purely for illustration. 39Plots growing barley, corn, ﬁngermillet, sorghum, teﬀ, and wheat were included in the sample.

85 tions that cultivated cereal and were not rented in. Examining the log of the value of plot-level output on the matched samples, I am unable to reject the equality of the distributions using a Kolmogorov-Smirnov test (p-value= 0.581); their corresponding p-value is 0.004. Following HDG’s preferred estimation strategy, which they call “Strong Instru- mentation”, I estimate linear probability models for whether a plot was listed on a household’s certificate of registered plots. The model controls for the years since the household was certified, homestead plots, area of the plot, soil depth, slope, soil type, distance from the residence, and household fixed effects. From this, I estimate the idiosyncratic error term and the predicted certificate variable. According to their specification, I then regress the log of the value of output per unit of area (theirs is hectares; mine is tsimad) on the same plot characteristics listed above, as well as an indicator for whether the plot benefited from a public investment in the given year, and the predicted and residual components from the previously estimated fixed effects models. Results from these regressions, with several variations shown, are found in table 2.9. The certificate variable listed in column 1 is the plot’s actual certification status, which is positive but insignificant. The second column presents results following the preferred specification of HDG (2009a). Surprisingly, results in column 2 suggest that certification had a positive impact on plot productivity. The predicted certificate term acts as a control for the endogenous nature of certification, while the certificate error term is supposedly a measure of the causal impact of the program. The result is highly significant and suggests a similar impact as that found in their paper, with a slightly higher 50 percent increase in plot productivity as a result of holding a certificate on a plot. The main challenge to this result is to test whether it is robust to controlling for changes in household fixed effects and constant unobservable plot quality. I do

86 this by following the methodology of HDG, but controlling for household-year and plot fixed effects rather than just household fixed effects, to estimate both the certificate variables and the second-stage productivity estimates. The coefficient for the actual certificate variable using these fixed effects and the matched sample is positive but insignificant (column 3). The results from models using the predicted and error components of the certificate variable and controlling for the lower-level fixed effects are presented in column 4 of the same table. They suggest that the random component of certification (the certificate residual) is not an important predictor of plot productivity. Rather, the predicted component of certification is negative and significant. Note that this predicted certification variable is not the same as an instrumental variable estimate of certification since no source of exogenous variation is used to identify plot certification status. The residual term is the only variable that is independent of household-year and plot heterogeneity. This predicted component serves as a pseudo control function for household-year and plot heterogeneity related to certification. The increase in the predicted components between columns 2 and 4 suggests that plots with lower inherent productivity (and/or adverse productivity shocks at the household-year level) were more likely to be certified than higher productivity plots (and/or household-years receiving positive productivity shocks). This result aligns with that found in chapter 2, which suggests that plots that remained uncertified in households with certificates were more likely to be disputed. These plots tended to be of slightly higher observable quality and were more likely to have been received as a gift. Although this explanation fits well with existing evidence, these claims are still somewhat speculative since the relative role of household-year heterogeneity and constant plot heterogeneity is not examined in depth here. The result pertaining to the residual component of certification is robust to examining the log of output per hectare, including or excluding the indicator for whether

87 the plot received a public investment in the given year, and using a more complete set of time-varying plot controls. (Results not presented here.) Therefore, when the methodology used by HDG (2009a) also controls for household-year and plot heterogeneity, the estimates conﬁrm instead what was found in this paper: certiﬁcation did not clearly increase productivity.

Conclusion

The results presented in chapter 2 suggest that plot certification in Tigray did not increase most agricultural investments with short-term expected returns, and evidence of its impact on investments with long-term expected returns is weak and questionable at best. This chapter found no evidence that certification increased agricultural productivity, not a surprising result given the previous analysis. Nev- ertheless, it is important to consider what other factors could be driving the results found. The data used in both chapters 2 and 3 examine certification within a relatively short timeframe after certification occurred. It is possible that the data has not captured long-term changes induced by certification, particularly given households’ limited access to factor markets important for their investment decisions. Responses to certification may evolve slowly as households build up asset bases and are able to make key productivity-enhancing investments. This limitation in the data is more important for the analysis of major investments that could have larger up-front labor or capital costs40. Although the data here cannot test whether certification had a larger impact on agricultural productivity after more time had passed, it is possible that models employing the preferred methodologies used in this chapter would find a positive, significant effect of certification on agricultural productivity several years into the

40Note that most of the long-term investments do not have major ﬁxed costs, although households may prefer to undertake major investments at once rather than build them incrementally.

88 future. The fact that I can replicate HDG’s (2009a) result using my data from shortly after certification occurred does not say with certainty that applying the methods used here to a more recent dataset would yield the same results as that found in this exercise. The very different estimates of productivity distributions by certification status, as reported using the Kolmogorov-Smirnov tests for both datasets, provide support for this possibility. Nevertheless, my replication of HDG’s results does rule out the possibility that the difference in results is entirely data-driven. Instead, it suggests that empirical methodologies may be the culprit. The results presented here are not a critique of the authors of the paper just re- examined. The researchers took major steps to address the potential bias generated by unobserved heterogeneity. Rather, the results are illustrative of the potential problems encountered when investigations of land rights issues cannot control for household-level time-varying heterogeneity and plot-level heterogeneity. Some of the controversy in the land rights literature may be driven by these data limitations and could more properly be resolved using data that allows researchers to utilize methods robust to plot and temporal variability.

89 Variable 1991 1997 1998 1999 All 288.347 270.949 216.364 206.282 243.589 Weight (kilograms) harvested grain per tsimad (2125.491) (1707.845) (511.981) (703.722) (1392.099) planted 1,217 1,463 1,473 1,458 5,611 444.435 - 382.442 - 410.489 Value (Ethiopian Birr) harvested grain per tsi- (3166.967) - (1007.285) - (2256.528) mad planted 1,217 - 1,473 - 2,690 0 0.019 0.690 0.896 0.412 Certificate (0.000) (0.135) (0.463) (0.305) (0.492) 1,546 1,660 1,704 1,704 6,591 All Years 14.256 24.009 Land rights (3.094) Initial meters of stone terracing (87.058) 6,591 6,591 2.737 0.149 Initial meters of soil bunding (20.704) Initial count of other investments (0.726) 6,571 6,591 0.048 22.004 Number of public investment on plot-year (0.254) Walking distance from plot (25.510) 6,591 to residence (minutes) 6,589 2.414 0.378 Oxen/plot area (5.030) Females/plot area (0.795) 5,616 5,616 0.270 0.371 Males/plot area (0.741) Soil color = Red (0.483) 5,616 6,591 0.253 0.129 Soil color = Black 0.435 Soil color = Brown (0.336) 6.591 6,591 0.233 0.112 Soil color = Grey (0.423) Soil texture = Silt (0.316) 6,591 6,591 0.243 0.335 Soil texture = Clay (0.429) Soil texture = Loam (0.472) 6,591 6591 0.298 0.063 Soil texture = Sandy (0.457) Local soil classification = Mekayho/Luvisol (0.244) 6,591 6,591 0.245 0.302 Local soil classification = Husta/Reogsol (0.430) Local soil classification = Shahsher (0.459) 6,591 6,591 0.197 0.188 Local soil classification = Bae’khel/Cambisol (0.398) Local soil classification = Walka/Vertisol (0.391) 6,591 6,591 0.629 0.287 Flat slope (0.483) Gentle slope (0.452) 6,591 6,587 0.083 0.434 Steep slope (0.277) Slope position = Not on slope (0.496) 6,587 6,591 0.124 0.196 Slope position = Top (0.329) Slope position = Middle (0.397) 6,591 6,591 0.247 Slope position = Bottom (0.431) 6,591 Sample limited to owner-operated plots and reported at the plot-year level. Unless otherwise indicated, statistics reported for each variable are percentage of plots, standard deviation, and sample size, respectively. Table 2.1. Plot-level Descriptive Statistics

90 1991 1997 1998 1999 All 177.432 178.422 184.258 157.331 174.195 Weight (kilograms) harvested grain per tsimad (156.308) (198.386) (213.357) (153.512) (183.548) planted 391 476 482 482 1,831 330.636 - 371.414 - 353.741 Value (Ethiopian Birr) harvested grain per tsi- (428.293) - (302.904) - (365.339) mad 392 - 482 - 880 0.000 0.037 0.581 0.779 0.355 Percentage of plots certiﬁed (0.000 (0.157) (0.438) (0.332) (0.444) 454 478 482 484 1,898 13.889 0.519 Mean land rights, all plots (2.838) Percentage female household members (0.213) 1,832 1,898 22.972 1.051 Mean age in household (age 50 and older) (10.922) Number elderly members (0.751) 1,898 1,898 2.406 2.617 Number adults (age 15-49) 1.189 Number children (14 and younger) (1.711) 1,898 1,898 0.230 5.016 Count of durable farm equipment (0.577) Count of chickens (6.134) 1,898 1,897 1.172 2.519 Count of oxen (0.997) Count of cattle (3.075) 1,898 1,881 0.728 5.369 Count of pack animals (1.133) Count of goats and sheep (10.915) 1,882 1,882 0.503 0.105 Count of beehives (1.481) Contacts with extension workers (count) (0.439) 1,897 1,966 0.793 0.859 Household has formal source of credit (0.405) Household has informal source of credit (0.348) 1,898 1,897 Number of organizations 1.987 Number of organizations 0.080 in which household belongs (1.056) in which member holds (0.285) 1,814 leadership position 1,814 Statistics reported for each variable are mean, standard deviation, and sample size, respectively. When units are not reported, the ﬁrst statistic is the percentage of households that share the listed characteristic. Table 2.2. Household-level Descriptive Statistics

91 First stage Plot is certiﬁed

Years since certiﬁed (kushet) -0.724*** -0.631** -0.720*** (0.260) (0.279) (0.263) Percentage certiﬁed (kushet) -1.987 -2.083 -2.116 (2.243) (2.215) (2.199) Sample size 4,652 4,652 4,652 Number of groups 1,326 1,326 1,326 F-test 18.166 16.302 11.959 Second stage Ln(Kilograms Harvested Grain per Tsimad Planted)

Certificate 0.078 0.078 0.095 0.274 0.281 0.330 (0.081) (0.081) (0.082) (0.236) (0.253) (0.262) Land rights 0.000 0.002 -0.005 -0.004 (0.038) (0.040) (0.038) (0.038) Additional plot-level controls No No Yes No No Yes Instrumented No No No Yes Yes Yes Sample size 4,775 4,775 4,774 4,652 4,652 4,652 Number of groups 1,445 1,445 1,444 1,326 1,326 1,326 F-test - - - 0.995 0.606 2.946 K-P rk LM statistic 44.594 44.240 47.771 p-value 0.000 0.000 0.000 Weak identification test (K-P rk 18.166 8.388 9.718 Wald F statistic) Hansen’s J: overidentification 0.460 0.460 - p-value 0.498 0.498 - E-stat p-value; Ho: Certificate en- 0.730 0.627 - dogenous Sample limited to owner-operated plots. Controls for initial meters of terracing, initial meters of bunding, count of other initial plot investments, number of public investments on plot-year, walking distance from plot to residence in minutes, oxen/plot area, females/plot area, males/plot area, soil color, soil texture, local soil classification, slope, toposequence, years, and constant not shown. Robust standard errors clustered at the plot level reported in parentheses. When statistics are not reported, it is because they could not be calculated. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. 10 percent maximal IV size critical value equals 19.93 and 25 percent maximal IV size equals 7.25 for weak identification tests. Table 2.3 Plot-level Productivity Regressions

92 First stage Plot is certified -0.000 0.000 -0.022 Years since certified (kushet) (0.000) (0.000) (0.052) -1.138 -1.129 -1.498** Percent certified (kushet) (0.711) (0.692) (0.703) Sample size 1,758 1,758 1,758 Number of groups 879 879 879 F-test 0.21 5.8e+06 6908.668 Second stage Ln(Value Harvested Grain per Tsimad Planted) 0.229 0.233 0.237 1.152 1.164 1.043 Certificate (0.208) (0.208) (0.208) (0.941) (0.913) (0.716) -0.011 -0.028 -0.034 -0.045 Land rights (0.080) (0.080) (0.081) (0.080) Additional plot-level controls No No Yes No No Yes Instrumented No No No Yes Yes Yes Sample size 2,231 2,231 2,230 1,758 1,758 1,758 Number of groups 1,351 1,351 1,350 879 879 879 F-test 339.61 - 2.06e+11 1.21 0.67 2.946 K-P rk LM statistic - - 15.027 p-value - - 0.000 Weak identification test (K-P rk Wald F) - - 2.457 Hansen’s J: overidentification 3.152 3.152 3.195 Hansen’s j p 0.0759 0.0759 0.0739 E-stat p; Ho: Certificate endogenous 0.2967 0.2928 0.2042 Sample limited to owner-operated plots. Controls for initial meters of terracing, initial meters of bunding, count of other initial plot investments, number of public investments on plot-year, walking distance from plot to residence in minutes, oxen/plot area, females/plot area, males/plot area, soil color, soil texture, local soil classification, slope, toposequence, years, and constant not shown. Controls not shown because many were dropped. Robust standard errors clustered at the plot level reported in parentheses. When statistics are not reported, it is because they could not be calculated. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. 25 percent maximal IV size critical value is 7.25 for weak identification test. Table 2.4 Plot-level Value Regressions

93 First stage Percentage of certiﬁed plots

Years since certiﬁed (kushet) 1.835* 1.828* 1.974* (1.050) (1.063) (1.070) Percent certiﬁed (kushet) -10.511*** -10.509*** -10.676*** (0.754) (0.753) (0.710) Sample size 1,809 1,809 1,728 Number of groups 482 482 461 F-test 129.737 87.837 13.533 Second stage Ln(Kilograms Harvested Grain per Tsimad Planted)

Percent certified -0.100** -0.105** -0.091** -0.150* -0.149* -0.172** (0.041) (0.041) (0.043) (0.081) (0.082) (0.085) Land rights 0.017* 0.019 0.018* 0.021* (0.010) (0.012) (0.010) (0.011) Percentage female members 0.026 0.029 (0.143) (0.127) Mean age -0.003 -0.003 (0.004) (0.003) Count of adults 0.020 0.018 (0.035) (0.032) Count of children 0.025 0.021 (0.025) (0.022) Count of elderly -0.008 -0.012 (0.058) (0.052) Count of durable equipment -0.046 -0.047 (0.041) (0.037) Count of ox -0.007 -0.007 (0.015) (0.013) Count of other cattle 0.007 0.007 (0.006) (0.005) Count of pack animals 0.049** 0.048** (0.023) (0.020) Count of goats and sheep 0.000 0.000 (0.002) (0.002) Count of chickens 0.001*** 0.001*** (0.000) (0.000) Count of beehives 0.009 0.009 (0.011) (0.010) Number contacts with extension worker -0.007 -0.005 (0.026) (0.023) Formal credit source 0.113 0.108 (0.104) (0.094) Informal credit source 0.092 0.088 (0.131) (0.118) Number of organizations (participate) 0.047** 0.045*** (0.019) (0.017) Number of organizations (leadership) 0.036 0.037 (0.055) (0.050) Plot controls No No Yes No No Yes Instrumented No No No Yes Yes Yes Sample size 1,813 1,813 1,732 1,809 1,809 1,728 Number of groups 485 485 464 482 482 461 F-test . . . 2.840 2.036 1.648 K-P rk LM statistic 102.423 101.825 98.645 p-value 0.000 0.000 0.000 Weak identification test (K-P rk Wald 129.737 130.269 148.395 F) Hansen’s J: overidentification 0.001 0.002 0.137 Hansen’s j p 0.972 0.964 0.712 E-stat p; Ho: Certification variable en- 0.441 0.489 0.271 dogenous Kushet-year and household fixed effects used in all regressions. Dependent variable in first stage is percentage of household’s owner-operated plots that are certified. Household-year means of soil color, soil texture, local soil classification, slope, toposequence, and homestead plot indicators not shown to save space. Robust standard errors clustered at the household level in parentheses. Controls describe households’ owner-operated plots only. * p< 0.10, ** p<0.05, *** p<0.01. 10 percent maximal IV size critical value is 19.93 for weak identification test. Table 2.5. Household-level Productivity Regressions

94 First stage Percentage of certiﬁed plots

Years since certiﬁed (kushet) 11.452*** 12.109*** 7.405*** (3.981) (3.941) (2.547) Percent certiﬁed (kushet) -20.623*** -21.631*** -17.274*** (3.341) (3.285) (2.554) Sample size 754 754 716 Number of groups 377 377 358 F-test 44.298 36.653 16.105 Second stage Ln(Value Harvested Grain per Tsimad Planted)

Percent certified -0.134 -0.144 -0.181* -0.403 -0.370 -0.405** (0.089) (0.090) (0.106) (0.248) (0.232) (0.194) Land rights 0.024 0.016 0.028 0.022 (0.018) (0.023) (0.019) (0.023) Percent females 0.221 0.240 (0.252) (0.229) Mean age -0.016** -0.018*** (0.007) (0.006) Count of adults -0.036 -0.033 (0.063) (0.058) Count of children -0.045 -0.049 (0.037) (0.034) Count of elderly 0.007 0.017 (0.081) (0.075) Count of durable equipment -0.158*** -0.170*** (0.057) (0.054) Count of ox -0.044 -0.046 (0.035) (0.032) Count of other cattle -0.006 -0.005 (0.010) (0.009) Count of pack animals 0.057 0.050 (0.040) (0.037) Count of goats and sheep 0.001 0.002 (0.003) (0.003) Count of chickens 0.001* 0.001* (0.000) (0.000) Count of beehives 0.030 0.030 (0.033) (0.031) Number contacts with extension worker -0.056 -0.050 (0.043) (0.040) Formal credit source 0.056 0.002 (0.160) (0.150) Informal credit source 0.171 0.116 (0.176) (0.164) Number of organizations (participate) -0.025 -0.029 (0.034) (0.032) Number of organizations (lead) 0.143 0.147 (0.103) (0.095) Plot controls No No Yes No No Yes Instrumented No No No Yes Yes Yes Sample size 856 856 816 754 754 716 Number of groups 479 479 458 377 377 358 F-test . . . 2.293 1.345 1.598 K-P rk LM statistic 62.282 64.711 60.522 p-value 0.000 0.000 0.000 Weak identification test (K-P rk Wald 44.298 50.863 57.749 F statistic) Hansen’s J: overidentification 0.197 0.225 0.724 Hansen’s j p 0.657 0.635 0.395 E-stat p; Ho: Certificate endogenous 0.200 0.269 0.096 Kushet-year and household fixed effects used in all regressions. Dependent variable in first stage is percentage of household’s owner-operated plots that are certified. Household-year means of soil color, soil texture, local soil classification, slope, toposequence, and homestead plot indicators not shown to save space. Robust standard errors clustered at the household level in parentheses. * p< 0.10, ** p<0.05, *** p<0.01. 10 percent maximal IV size critical value is 19.93 for weak identification test. Table 2.6. Household-level Value Regressions

95 First stage Plot certified 0.061 0.061 0.061 0.061 Years since certified (kushet) (0.284) (0.284) (0.284) (0.284) -1.803 -1.803 -1.803 -1.803 Percent certified (kushet) (1.324) (1.324) (1.324) (1.324) Sample size 1,182 1,182 1,182 1,182 Number of groups 358 358 358 358 F-test 0.655 0.655 0.655 0.655 Second stage “Profit” measure 1 ”Profit” measure 2 Ln(Input value per tsimad) Oxen days per tsimad 73.343 200.042 51.148 -20.934 0.473 0.269 -0.694 -6.905 Certificate (114.785) (187.943) (53.416) (111.717) (10.323) (18.622) (2.811) (6.962) 23.329 23.606* 26.509** 26.587** -3.803 -3.823 0.099 0.093 Land Rights (14.184) (14.007) (12.021) (11.847) (3.347) (3.305) (0.275) (0.269) Sample size 1,260 1,182 1,260 1,182 1,260 1,182 1,260 1,182

96 Number of groups 435 358 435 358 435 358 435 358 F-test - 1.975 - 2.602 - 0.655 - 0.559 K-P rk LM statistic 22.759 22.759 22.759 22.759 p-value 0.000 0.000 0.000 0.000 Weak identification test (K-P rk Wald F statistic) 0.932 0.932 0.932 0.932 Hansen’s J: overidentification 0.093 1.954 1.804 2.423 Hansen’s j p 0.760 0.162 0.179 0.120 E-stat p; Ho: Certificate endogenous 0.476 0.499 0.868 0.465 Data is from 1998 only. Household fixed effects used in all models. Robust standard errors clustered at the household level presented in parentheses. Controls for initial meters of stone terracing, initial meters of soil bunding, count of other initial investments, indicator for whether plot received public investment in 1998, walking distance from plot to residence in minutes, indicators for soil color, indicators for soil texture, indicators for local soil classification, indicators for slope, indicators for position on slope not shown. ”Profit” measure 1 is the value of produced grain less costs of seed inputs, fertilizer inputs, and estimated draft animal costs per tsimad, in Ethiopian Birr. “Profit” measure 2 is the value of produced grain less the costs of seed, fertilizer, and similar inputs only, per tsimad, in Ethiopian Birr. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. 25 percent maximal IV size critical value is 7.25 for weak identification test. 25 percent maximal IV size critical value is 7.25 for weak identification test. Table 2.7. Plot-level Profit and Input Regressions Second stage Ln(Kilograms of Harvested Grain per Tsimad Planted) 0.094 0.077 0.084 0.329 0.297 0.218 Certificate (0.082) (0.079) (0.079) (0.264) (0.260) (0.197) 0.006 0.013 -0.000 0.001 0.007 -0.003 Land rights (0.037) (0.039) (0.037) (0.037) (0.038) (0.035) Basic controls Yes Yes Yes Yes Yes Yes Land management strategies No Yes No No Yes No Crops grown No No Yes No No Yes Instrumented No No No Yes Yes Yes Sample size 4,774 4,762 4,774 4,652 4,640 4,652 Number of groups 1,444 1,441 1,444 1,326 1,323 1,326 F-test 1.219e+14 8.924e+13 5.415e+14 11.788 3.114 7.601 K-P rk LM statistic 47.689 47.836 48.980 p-value 0.000 0.000 0.000 Weak identification test (K-P rk Wald F) 9.146 8.484 9.226 Hansen’s J: overidentification 0.499 - - Hansen’s j p 0.480 - - E-stat p; Ho: Certificate endogenous 0.576 - - Second stage Ln(Value of Harvested Grain per Tsimad Planted) 0.232 0.185 0.210 1.038 0.934 0.362 Certificate (0.209) (0.159) (0.213) (0.726) (0.725) (0.435) -0.028 -0.022 -0.068 -0.050 -0.045 -0.072 Land rights (0.080) (0.084) (0.068) (0.080) (0.084) (0.063) Basic controls Yes Yes Yes Yes Yes Yes Land management strategies No Yes No No Yes No Crops grown No No Yes No No Yes Instrumented No No No Yes Yes Yes Sample size 2,230 2,227 2,230 1,758 1,754 1,758 Number of groups 1,350 1,349 1,350 879 877 879 F-test 1186.21 29519.844 403.11 6.96 9.382 14.23 K-P rk LM statistic 14.806 14.143 14.634 p-value 0.001 0.001 0.001 Weak identification test (K-P rk Wald F) 2.451 2.404 2.394 Hansen’s J: overidentification 3.195 2.621 3.633 Hansen’s j p 0.074 0.105 0.057 E-stat p; Ho: Certificate endogenous 0.209 0.257 0.660 Sample limited to owner-operated plots. Controls for initial meters of terracing, initial meters of bunding, count of other initial plot investments, number of public investments on plot-year, walking distance from plot to residence in minutes, oxen/plot area, females/plot area, males/plot area, soil color indicators, soil texture indicators, local soil classification indicators, slope indicators, toposequence indicators, years, and constant not shown. Controls not shown because many were dropped. Robust standard errors clustered at the plot level in parentheses. When statistics are not reported, it is because they could not be calculated. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. 25 percent maximal IV size critical value is 7.25 for weak identification test; 15 percent maximal IV size critical value equals 11.59. Table 2.8. Plot-Level Regressions with Variations in Controls

97 Ln(Value Harvested Grain per Tsimad Planted) 0.014 0.132 Certificate (actual) (0.066) (0.187) -0.348*** -0.123*** Predicted certificate (0.101) (0.000) 0.538*** 0.447 Certificate error term (0.167) (0.419) 0.056 0.067 -0.311** -0.311** Number of public investments on plot-year (0.118) (0.123) (0.131) (0.131) 0.230*** 0.237*** Homestead land (0.076) (0.076) -0.026 -0.022 Plot area (tsimad) (0.041) (0.041) 0.115 0.170* Soil depth = deep (0.095) (0.093) 0.077 0.141* Soil depth = medium (0.082) (0.078) -0.202** -0.144 Soil depth = shallow (0.094) (0.091) -0.018 -0.044 Flat slope (0.147) (0.147) -0.152 -0.174 Gentle slope (0.149) (0.150) -0.024 -0.018 Soil classification= Bae’khel/Cambisol (0.104) (0.104) -0.074 -0.070 Soil classification = Walka/Vertisol (0.093) (0.093) 0.019 0.040 Soil classification = Husta/Reogsol (0.093) (0.092) 0.058 0.072 Soil classification = Mekayho/Luvisol (0.112) (0.112) -0.004** -0.004*** Walking distance from residence (0.002) (0.002) 0.012 0.017 Year (0.074) (0.073) Household-year Household-year Fixed effects Household Household and plot and plot Sample size 1,104 1,104 1,104 1,104 Number of groups 247 247 895 895 F-test 130.015 10838.387 - - Robust standard errors clustered at the household level for first two columns and at the plot level for last two columns. Sample limited to plots meeting common support criteria obtained through propensity score matching. Statistics not shown when they could not be calculated. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. Table 2.9. Replication of HDG Estimation Strategy

98 Chapter 4: Did Formalizing Land Rights Help Diversify the

Ethiopian Economy?

Income diversification is an important component of development. It serves as a means for poor households facing limited credit and insurance markets to smoothe income ex ante (Morduch 1995), allowing them to partially self-insure. On the other hand, diversification activities may not be optimal, as extremely poor and vulnerable households may participate in low-return, low-variance means of income generation rather than specializing in higher-risk, higher-return activities. Diversification decisions, therefore, may have an important effect on households’ well-being, with those least able to access credit and insurance markets and closest to subsistence thresh- olds most sensitive to the potential vulnerabilities resulting from household income portfolio decisions. Although diversification is very important, the effect of state policies on household income diversification strategies is often unclear, and greater understanding of the drivers of diversification and specialization is warranted. One policy that may affect households’ income portfolio decisions is that of land reform and the formalization of property rights. This issue has enjoyed a recent resurgence of interest in the policy and research arenas, particularly through the possibility of implementing low-cost land certification programs in Sub-Saharan Africa. Plot certification that occurred in Tigray Region, Ethiopia, provides an ideal environment for the study of households’ diversification activities. In many parts of the country, households still rely heavily on subsistence production, and large swathes of the population are vulnerable to climatic vagaries affecting production possibilities. The diversification of income sources is an important risk management strategy for

99 households living in environments in which credit and insurance markets are sticky at best. While the main purpose of Tigray’s plot certification program was clearly to increase incentives for agricultural investment, certification may also have shifted the relative returns to households’ income generating activities. These changes could have important welfare implications for Tigrayan households. Non-agricultural and off-farm activities, if available and accessible, could provide agricultural households with important improvements in their well-being. To better understand how plot certification affected income generation decisions, I examine how households’ income portfolios, activities, and assets changed as a result of certification. Using retrospective information that allows me to construct a panel of household-level information over four different years spanning before and after the initiation of certification, I show that certification altered households’ income generation strategies. Rather than simply shifting all households’ strategies in the same manner, I find evidence that diversification strategies were also influenced by households’ general well-being. The worst-off households show evidence of increased specialization, focusing their efforts on farm-based agricultural activities and increasing the strength of their livestock asset base. Households that were better off reacted to certification by diversifying their activities to off-farm and non-agricultural activities. This reaction to certification points out several factors working in the Ethiopian environment. First, Ethiopian producers readjusted their portfolio of income sources, activities, and assets as a result of certification, suggesting the certification exercise may have affected their perceived sensitivity to income-related risk and decreased the costs associated with land exchanges. Second, the reaction of the worst-off households to certification suggests that diversification in these households had been a negative coping strategy that certification ameliorated. Finally, only the best-off households responded to certification by diversifying into off-farm and non-agricultural activities.

100 This result suggests that, similar to the existing commentary on diversification possibilities throughout Sub-Saharan Africa (Barrett, Reardon, and Webb 2001), Africans face barriers to entry to the highest-return diversification strategies. Nevertheless, plot certification had an important effect on household portfolio decisions. The next portion of the paper discusses economists’ understanding of the role of income diversification in economic development in Sub-Saharan Africa, the role of income diversification in risk management, and how plot certification in Tigray Region relates to these concepts. Next, information on the empirical strategy used is provided, along with estimation results. Finally, I discuss the implications of the findings.

Income Diversiﬁcation, Risk, and Household Outcomes

Nonfarm income is an important correlate of households’ well-being in rural Africa. Nonfarm, or non-agricultural, income in Africa is estimated to be 40-45 percent of households’ average income, and its role in the region is growing (Barrett, Reardon, and Webb 2001, Reardon 1997, and Little et al. 2001)41. Nonfarm income in rural Africa is important to a degree beyond that seen in other parts of the world (Reardon 1997; Reardon, Matlon, and Delgado 1998). In general, rural African households’ proportion of nonfarm income is increasing with total household income (Reardon 1997), and there is a positive correlation between nonfarm activities and livestock and landholdings in rural areas of Africa. Barrett, Reardon, and Webb (2001), and Block and Webb (2001) ﬁnd that income from nonfarm activities also increases consumption and earnings growth. Nonfarm income has also been shown to help households deal with shocks. Households with greater nonfarm income could

41To avoid confusion and align as closely as possible with well-accepted deﬁnitions, I follow Barrett, Reardon, and Webb (2001) and Barrett and Reardon (2000) in classifying farm activities as those including any activities that produce agricultural output from natural resources, including cultivating (which includes cropping, aguaculture, raising livestock, and reaping from woodlots) or gathering activities (such as hunting or ﬁshing) i.e., the micro-level correspondence for primary level activities (versus secondary/manufacturing and tertiary/services).

101 cope better with the Ethiopian and Burkina famines in the 1980s, and better ex- ploit the devaluation of the FCFA in the 1990s (Reardon, Delgado, and Matlon 1992; Reardon and Taylor 1996; Ellis 1998). Also within Ethiopia, panel data from households recently affected by the famines of the 1980s shows that the households that fared the best through the crisis (i.e., those that came out with above average income and food consumption) had better diversified income and significant livestock assets. Households believed that earning income from non-cropping activities helped reduce their risk, and estimates confirmed this belief. Female-headed households were even more likely than other households to believe that off-farm income could reduce their vulnerability to famine, but they were less likely to have diversified income sources (Block and Webb 2001). Although a diversified income base, and especially nonfarm earnings, are important predictors of households’ income and well-being, the relationship between nonfarm earnings and household well-being is more nuanced than a first glance suggests. The literature also shows that households diversify to reduce their risk rather than simply to increase their returns, and the worst-off households often diversify into multiple low-return activities. Examining drivers of migration and nonfarm activities in Ethiopia, Kenya, and Uganda, Matsumoto, Kijima, and Yamano (2006) find that members of households located in low-potential areas for agriculture have a higher probability of diversifying into nonfarm activities, suggesting push factors drive some individuals to these activities42. These ideas may be consistent with the finding that many Africans tend to diversify their livelihoods in suboptimal ways (Barrett et al. 2001). Rural African households choose sub-par diversification strategies due to endowment/asset limitations, difficulties in market access, and trouble accessing credit and savings markets, all of which help determine the riskiness of specific income portfolios to their welfare (Dercon and Krishnan 1996, Dercon 1998, and Barrett et al.

42Despite being driven to diversiﬁcation by low agricultural returns, the authors suggest nonfarm activities (and migration) may decrease poverty in these areas.

102 2001). These studies within Africa are consistent with evidence found throughout the continent and around the world suggesting that poor households are very sensitive to risk. In a classic study using ICRISAT data from India, Rosenzweig and Binswanger (1993) find evidence that wealthier producers benefit from higher agricultural returns while tolerating higher levels of risk than do poorer producers. The poor often trade off higher-risk activities with higher returns for lower-risk activities with lower mean returns. Although they may take significant steps to reduce or manage risks, impoverished households are typically only partially able to smooth consumption through informal means of savings and sharing risk (Morduch 1995, Townsend 1995, Dercon and Christiansen 2011), leaving ex ante income smoothing activities (income diversification) as a means to deal with remaining risk. Therefore, the reliance of agricultural peasant households on multiple diverse income sources is a reflection of the lack of, or imperfections in, credit and insurance markets (Alderman and Paxson 1992). The sensitivity of the poor to this risk may have important implications on income diversification decisions. Other evidence of risk-avoiding behaviors by households with incomplete consumption smoothing possibilities include Tanzanian agricultural producers’ production of lower return sweet potatoes rather than higher-risk, higher-return crops (Der- con 1996); Indian producers’ use of fertilizer, which is dependent on the depth of the nonfarm labor market (Lamb 2003); and Ethiopian producers’ lower fertilizer use in the presence of greater consumption risk (Dercon and Christiansen 2011). Dercon (1998) also shows that households with lower incomes are more likely to enter markets with relatively lower risks and lower returns in Western Tanzania; credit constraints and the indivisibility of cattle also affect the result. Additional factors that drive the poor to diversify - so called “push factors” - include diminishing or varying marginal returns to factors, reaction to crises, and

103 transaction costs that discourage households from making market purchases and encourage self-production. Those fortunate enough to leverage high-return diversification activities are lured into these activities through “pull factors” which include specialization due to comparative advantage, and complementarities across several activities (Barrett, Reardon, and Webb 2001). The nature of the observed relationship between nonfarm income (and activities) and household wealth suggests the poor may face barriers to entry and fixed costs precluding them from entering high-return sectors of the nonfarm economy (Barrett, Reardon, and Webb 2001). These barriers impeding entry into high-return non-agricultural activities are thought to increase land and asset inequality in rural areas of Africa (Francis and Hoddinott (1993) for Western Kenya, and Andréand Platteau (1998) for Rwanda). Since it is not clear the poor can take advantage of nonfarm income opportunities, and the diversification activities of the very poor may not result in greater income or significantly decrease risk exposure (if risk covariance across low-return sectors is high), policies aimed to encourage diversification will not necessarily directly help the rural poor (Barrett, Reardon, and Webb 2001). Overall, the role of policies in rural households’ diversification strategies is less clear. Most policies are not aimed at affecting these strategies, but they may play important indirect roles in household diversification. For example, evidence from Kenya suggests that the security provided by local food-for-work projects expanded poor households’ possibilities for adopting higher-return livelihood strategies (Barrett, Bezuneh, and Aboud 2001). There are specific instances in Africa in which the worst-off households could not take advantage of policies that affected their optimal diversification strategies. Exploiting the devaluation of the FCFA in the 1990s and the subsequent increased returns to specific farm and nonfarm activities, Barrett, Bezuneh, and Aboud (2001) examine panel data from Côted’Ivoire and find that relatively poorer households

104 were unable to take advantage of this positive shock through investing more in higher return or lower risk activities. At an aggregate level, the policy change increased specialization as many households switched from nonfarm production to farm production. However, households with ex ante less land and income tended to remain in agricultural wage labor, a low-return strategy. Although not based on policy changes, at least one study within Ethiopia also confirms that poor households are kept from entering the highest return nonfarm sectors. Using data from 201 households from 1996 and 1997 in two woredas in Tigray Region, Woldenhanna and Oskam (2001) find that agricultural households expand their income into off-farm wage employment due to their low agricultural income and excess household labor supply, but self-employment away from home is undertaken for the relatively higher return it generates. Entry barriers keep poorer agricultural households from participating in semi-skilled non-agricultural activities with higher returns, such as masonry or petty trade. Multinomial logit models show that higher farm income, greater transport animal ownership, and better harvests allow households to enter self-employment over wage employment. They conclude that wage employment is a employer of last resort that absorbs excess labor supply. Outside of the possibility of desperation-driven diversification43, and given the positive correlation between nonfarm income and wealth and the potential of nonfarm income to increase earnings and consumption, a logical question becomes how to increase access of poor rural Africans to the nonfarm sector, given their limited access and barriers to entry (Barrett, Reardon, and Webb 2001). This chapter seeks to examine this question in Tigray Region, Ethiopia, where plot certification occurred in the late 1990s. Certification was expected to relax constraints faced by rural agricultural households, particularly by increasing the security with which producers could operate their plots. Certification also may have increased households’ willingness to

43I borrow this term from Barrett and Reardon (2000).

105 participate in land exchange markets44. If plot certification was also able to encourage rural households to participate in relatively higher-return nonfarm activities, it may provide the economy with additional benefits not yet fully appreciated. Given the multitude of factors driving households’ income portfolios, assets, and activities, the data requirements for identifying specific channels through which certification affects household portfolios are steep. I do not claim to quantify the importance of these various channels or the exact means by which household portfolios are affected through certification. Rather, this chapter attempts to isolate the causal impact of certification on these outcomes and to suggest possible pathways through which certification may have worked to affect diversification decisions. The literature just outlined suggests that household characteristics will influence responses to certification, so I also examine these outcomes in samples of households that may be more or less sensitive to risk, and more or less capable of overcoming barriers to entry to high-return diversification activities.

Plot Certiﬁcation and Diversiﬁcation

There are two major channels through which plot certification is expected to affect households’ diversification strategies. First, certification may have increased households’ confidence that they could retain their land even if they did not cultivate it, thereby increasing land rental markets and exchanges (as shown by Holden, Deininger, and Ghebru 2009b). Although land exchanges were used before certification occurred, certification also may have decreased transaction costs associated with exchanges, to the same end. Households that made use of land markets could have specialized in another activity in which they had a comparative advantage, or they may have specialized more in agricultural production through sharecropping or otherwise exchanging in additional plots. Second, if certification was credible as a

44This does not include land sales, which were illegal.

106 government policy aimed at increasing tenure security and reducing the threat of land expropriation, it reduced the level of risk that households took on in their agricultural endeavors. If households were willing to tolerate a threshold level of risk, reducing risk exposure through certification would allow them to tolerate additional risk, potentially in another sector. This could induce households to adjust their portfolios to take on activities with a higher level of risk and its concomitant higher potential return. One area in which certification was not expected to play a prominent role is in the potential for households to use the certificate as collateral for loans that could finance fixed entry costs into higher income activities. Land was very rarely used as collateral as a result of certification. In a rare case when a bank tried to collect land from a defaulted, collateralized loan, the government required the bank to return the dispossesed land (Haile et al. 2005). I illustrate the potential for the increase in tenure security through certification to affect households’ portfolio decisions by examining basic ideas of efficient portfolios as outlined by Markowitz (1952). In this setting, households are sensitive to the mean

and variance of returns to multiple investments. Portfolio return (R) and risk (σρ) share a direct relationship: dR > 0, implying that higher-return activities also bear dσρ greater risk, which is quantified through the standard deviation of the portfolio return. To sketch a very general framework of household income portfolio responses to plot certification, assume that an agricultural household with a single decision maker has a fixed amount of resources,x ¯, normalized to 1, that is continuously divisible and can be devoted to income generation in a single time period. This endowment can be used to earn income in any of i sectors, i = {1, 2, ...N}, with the endowment used

in sector i equal to xi. The endowment does not provide any return if not devoted to an income generating sector. For the moment, ignore the possibility of barriers to entry into speciﬁc sectors.

107 The objective of the household is to maximize the return to its investments in the various income generating activities, with return valued as the utility of total household consumption provided through the activity, whether through own production, exchange, or purchase. Utility increases monotonically in returns, and it retains the usual properties45. I do not make any assumptions about the covariance of returns across diﬀerent income generating activities, but I assume that returns do not vary according to the proportion of the endowment used in a given activity, which rules out economies of scale. This is intended to be a simplifying assumption. Although it is probably not accurate, it does not aﬀect the main result suggested here. The household chooses to maximize the return to its portfolio of income generating activities by selecting the proportion of the endowment to use on each activity

N X max xiE(Ri), (13) N P xi3 xi≤1 i=1 i=1

where E(Ri) is the expected return to the given activity. The household expresses a form of risk aversion, with risk measured as the expected variance of the return to its activities. The household’s risk aversion is expressed through a minimal variance constraint: N 2 X σρ ≤ υ(E[ xiE(Ri) − s¯]), (14) i=1

2 where σρ denotes the variance of the household’s income generating portfolio, ands ¯ represents the household’s subsistence constraint. This constraint ultimately aﬀects the household’s utility since it inﬂuences household returns through the risk-return relationship. The amount of variance (risk) the household tolerates is increasing in

45The monotonic relationship between utility and returns allows me to focus on the return to investments rather than household utility in much of the forthcoming explanation. I do this to keep a clear focus on this component of the analysis.

108 the expected diﬀerence between its expected return and the subsistence threshold:

dυ ≥ 0. (15) N P dE[ xiE(Ri) − s¯]] i=1

This constraint incorporates the common observation that extremely poor households often pursue low-return activities with relatively low risk, while better-oﬀ households choose to pursue higher-return activities with more volatile returns. I assume that households know the expected return and risk to agricultural activities on their own plots, as well as the expected returns, risks, and risk covariance of all other possible income-generating activities. The return to agricultural activities depends in part on the tenure security of each plot: Rag = f(z, τ), where z are other factors that determine agricultural returns and τ, tenure security, runs from 0 to 1.

τ is distributed normally (τ ∼ N(τ, στ )), and an increase in tenure security increases

∂Rag the expected return to agriculture: ∂τ ≥ 0. When a plot is certified, smallholders have a greater probability of retaining their plot’s return regardless of how they choose to use the plot (i.e., cultivate or exchange out). This implies that (dτ > 0), which increases the return to agricultural activities. A second crucial assumption is that certification decreases the risk associated with agricultural activities. Certification provides additional security to households that the government will not violate its commitment not to expropri- ate plots through local redistributions. Therefore, when a plot is certified, the risk associated with agricultural returns declines (dσag < 0). These ideas are represented graphically in figure 3.1. The line denoted by τ represents the household’s overall risk/return portfolio possibilities before plot certification occurs; the line denoted by τ 0 represents the same portfolio after the household’s plots have been certified. Household utility before certification is represented by U, and utility after certification is U 00. As mentioned, utility is a direct func-

109 dU tion of returns (U = f(R), dR > 0). The household’s responses to a change in the risk/return portfolio depend on many factors not addressed here, including the exact features of the risk-return trade-off, the curvature of the household’s utility function, and so on. The important component to the analysis is simply the increased return and decreased variance of return brought about by plot certification. While the increased return obtained through certification pushes the household to a higher utility level (U 0) since it can obtain a higher return to agricultural activities while bearing less risk, the increased return also ensures that the household’s N P return over the subsistence level is increased (dE[ xiE(Ri) − s¯] > 0). This change i=1 implies that the household is willing to bear additional risk, which is borne out in the movement of the utility curve to U 00, where the household bears additional risk (and is rewarded with additional return) in the second τ 0 portfolio46. The increased return provided by certification induces the household to take on additional risk, but the riskiness of agricultural production also has decreased as a result of certification. Therefore, the household may shift its portfolio of activities to pursue non-agricultural activities with higher returns and higher volatility. Depending on the type of relationship between the household’s willingness to bear volatility and their subsistence constraint (i.e., the exact form of υ), there could be a non-linear relationship between the willingness to bear risk and the subsistence constraint. While this framework makes multiple simplifying assumptions, including the household’s rationality, the independence of returns from the investment size and no barriers to entry, it cleanly illustrates the potential general impact of plot certification on a household’s income generating activities. Since certification increases the return to agriculture, reduces the risk of agricultural activities, and increases the household’s willingness to bear risky returns, it will induce a readjustment in the household portfolio that is driven by several factors, including preferences and risk

46The utility curve would shift to the right even if risk tolerance did not increase since certiﬁcation decreases the existing level of risk the household bears.

110 aversion, the returns to agricultural and other activities, and the risk involved in each type of activity. When there are barriers to entry to higher-return sectors, portfolio adjustment will only fully reflect the effect of certification if the household has adequate resources to enter higher-return sectors. A household without adequate resources may not shift its portfolio significantly, but it may focus on asset accumulation to allow it to surmount barrier costs in the future that will, in turn, allow it to diversify into higher-return activities. However, a household whose returns are initially very close to its subsistence level may diversify out of the lowest return activities that previously helped them diversify their risk. Now that the household is able to bear additional risk since certification has decreased its overall portfolio volatility, it may specialize in fewer, relatively higher-return activities. For a household that previously diversified into several low-return, relatively low-risk activities in an effort to reduce its risk exposure, this specialization is a form of bearing additional risk.

Measuring Diversiﬁcation

Before moving on to the empirical strategies used and models estimated, it is important to clarify a few definitions. A non-agricultural activity is one that does not include agricultural/livestock/aquacultural activities or hunting/fishing/gathering of primary materials. Classification as a non-agricultural activity does not depend on where an activity takes place or how income is generated, such as through a salary, wages, or contract employment. Therefore, non-agricultural activities could be completed while living or working on a farm. Off-farm income, in contrast, is defined as its name suggests: it is income generated off the farm. It can include agricultural activities, whether through self-employment or wage labor. Barrett, Reardon, and Webb (2001) highlight various methods of measuring diversification, which include means of measuring income sources, household asset

111 ownership, and income-generating activities. There are strengths and drawbacks to each measure of diversification. Income provides a measure of the welfare of the unit of observation, but it has a partially stochastic component. The stochastic realization of income is particularly problematic in the data used here, where income information is only available for a single year and may be affected by idiosyncratic shocks. Assets are useful to examine because they provide a store of wealth and can generate income for households. The weakness inherent in measuring assets is that they are difficult to value, since their worth may change over time or be dependent on how the household uses them. Activities are useful measures of households’ choices of how to utilize assets to create income, and they are not plagued by productivity and income shocks. Conversely, it is difficult to discern the exact value of a particular activity to a given household, and activities cannot reflect potential income generated from non-productive assets like jewelry. Since no one diversification measurement is clearly superior to the others, I examine all three measures to determine whether they point to general trends in diversification in response to certification. Several methods of measuring diversification are proposed by Barrett and Rear- don (2000), of which I use several in this chapter. I measure the proportions of total income by source, a measure that typically serves best in fairly aggregated levels. I also provide an estimate of the Herfindahl index for income concentration, which provides a general estimate of income diversification and is not dependent on having data from all possible income sources. This index is calculated very simply as

N X 2 H = si i=1 where si represents income share, and N represents the number of potential income sources. The index runs from 0 to 1, with lower numbers representing more diver- siﬁcation of income sources, and an index of 1 meaning all income comes from one

112 category. Herfindahl indices allow for simple measures of household diversification that capture data on multiple diversification possibilities. I also examine households’ propensity to participate in important livelihood strategies, including livestock-related activities, off-farm activities, and non-agricultural activities. Finally, I provide measures of households’ Herfindahl index for livestock diversification, an important component of income generation and risk management, and I examine the importance of oxen in household asset ownership.

Descriptive Statistics

The survey data used here is the same dataset described in chapter 2, although all information is estimated at the household level, and actual income data is only available for 1998. Descriptive data on households is available in table 3.1, with data presented for 1998 only, all other years, and all years. Since all income data is only available for 1998, it is important to understand whether variables were very different in this year from other years. The percent of certified plots, both at the household and village level, is different across the two sets of years, although this is a function of the progressive roll-out of the certification program. Most other means do not differ across the two sets of years. Access to markets via all-weather roads improved in later years, although households still had to walk a mean of over two hours to reach such a road, and walking time to the nearest market center was around 80 minutes in all years. Households had slightly fewer adults in 1998. The household dependency ratio and percentage of female members, at approximately 1.78 and 52 percent, respectively, did not differ over the years. Four in five household heads were born in the village in which they lived, with a mean age in the head in their forties depending on the survey date. Just under one-fourth of households were headed by females, and household heads’ education level was equivalent to just over the first

113 cycle of primary school on average47. Households owned approximately ﬁve units of livestock in livestock equiva- lents48, with one head of cattle equal to one unit. The mean plot altitude is over 2,100 meters above sea level, partially due to the sampling strategy. Approximately 12 percent of plots are on the top of hills, 18 percent on mid-slope, and 24 percent on the bottom of slopes, with the remaining plots not on any slope. On average, household plots are located a mean of 20 minutes from the residence, and total household plot area is just under 4 tsimad, or approximately one hectare. Table 3.2 presents information on the share of household income by source. Al- though income data is limited to 1998 only, it can still provide information on general trends in household income sources. Since some products are retained for household production, income includes the imputed values of retained income using local price information available from the survey. Within the entire household sample, 53 percent of income was kept as retained agricultural production in both crops and livestock form. Sold agricultural income comprised just under 23 percent of household income. The next most frequent source of income was from aid programs. The generally small quantities of aid distributed highlight the vulnerability and extreme poverty of households in the dataset. Non-agricultural labor income equalled approximately 7 percent of households’ income, and other non-agricultural income equalled 3.5 percent of household income. These statistics had relatively large standard deviations, and results were expected to vary by household characteristics. For these reasons, I

47Household heads’ education was measured as an ordinal variable of the head’s highest education level with one equal to no education through grade two for secular education, completion of a beginner literacy campaign, or a beginner religious course; two represents the completion of anywhere from grades three through six, completion of an advanced literacy course, or completion of an intermediate religious course; and three represents the completion of grade seven or above or the completion of an advanced religious training course. 48I convert livestock to their per unit equivalent using international equivalent data for livestock in Africa south of the Sahara and then normalize the animal values so that cattle = 1 in order to provide a more realistic reference for the data (Chilonda and Otte 2006). The values for livestock are as follows: cattle=1, sheep/goats=.2, pigs=.4, donkeys=.6, horses=1, mules=1.2, camels=1.4, and chickens=.02.

114 also examine this information by the sex of the household head and the household’s income quartile, with this summary data located in the remaining columns. Male and female-headed households clearly relied on different income diversification strategies, with female-headed households retaining less agricultural production and relying more on non-agricultural income and aid income than male-headed households. Female-headed households also had more diverse income sources, indicated by their lower average Herfindahl-Simpson income concentration index. This diversification strategy on the part of female-headed households may be used as a coping mechanism, and it is an example of the aforementioned desperation-driven diversification. Dividing along income quartiles, there are less obvious differences, although the poorest and wealthiest households tend to have the most specialized income sources. Given the extreme poverty of households in the sample, greater specialization of the poorest households’ incomes could reflect these households’ inability to surmount barriers to entry into higher-return non-agricultural activities. Households in the third quartile have the most diversified income portfolios, while the increased specialization within the wealthiest group probably reflects the use of specialization as an income- generating strategy once a sufficient asset and income base is secured. Information about households’ income generating activities, also termed livelihood strategies, is available in figure 3.2, which displays the cumulative distributions of the natural log of household income in 1998 according to whether the household’s top three income generating activities in that year came from livestock-related activities, off-farm activities, and/or non-agricultural activities. While these are clearly na¨ıve estimates that do not provide any information on causality, they illustrate that incomes in households that earned money from livestock, off-farm activities, and non- agricultural activities tend to dominate the incomes of those not earning income from these sectors. The only case where this does not hold as clearly is for the highest earn-

115 ing households in the agricultural sector, which tend to earn nearly as much as those participating in the non-agricultural sector or those participating in both sectors. This result is similar to the finding of Barrett, Bezuneh, and Aboud (2001) in Côte d’Ivoire, where the wealthiest households split across two groups: those specializing entirely in agriculture, and others working in high-return non-agricultural activities. Kolmogorov-Smirnov tests easily reject the equality of distributions across households participating and not participating in each of the activities or sectors. This illustrates that movement from households into these sectors or activities (from a base of not participating in them) will probably be associated with increased household income. Returning to table 3.2, the middle panel presents evidence related to asset diversification and households’ participation in off-farm activities as a main (top three) source of their income. In this sample, male-headed households were more likely to participate in off-farm activities, in contrast to the finding on the share of females’ incomes from non-agricultural activities. (Note, however, that non-agricultural and off-farm activities do not overlap entirely.) The poorest quartile of households also were less likely to participate in off-farm, non-agricultural, and livestock-related activities than the wealthier quartiles, as expected49. Information from households’ Herfindahl-Simpson livestock diversification index suggests that male-headed households have more diversified livestock bases than female-headed households, but no significant differences are apparent across income levels. It is important to keep in mind that although household incomes exhibited a relatively substantial range within the dataset, with the wealthiest quartile earning almost six times more than that of the poorest quartile on average (see the bottom panel), even the wealthiest quartile of households earned very little, with their mean income equivalent to just under 375 US dollars for the entire household, although

49The analysis by income quartiles are based on income data from 1998 only and are not immune to a single-season shock, as already explained. However, they are expected to provide general information on household well-being.

116 their purchasing power was probably higher than this50.

Certiﬁcation and Income Diversiﬁcation

For single-year income decisions, the following equation is estimated:

Sh = β0 + hhtβ1 + chtβ2 + kk + εkht (16)

where Sh is the share of household income from retained agricultural products (imputed value), sold agricultural products, or non-agricultural activities. The vector hht contains variables relevant to household income generation decisions. These include households’ access to markets (distance from all-weather roads and market center), access to labor resources (number of household members in adult equivalents, dependency ratio, percentage of female members), and potential for agricultural production (sum of area planted, livestock in livestock equivalent units, mean plot altitude, percentage of plots on top, middle, and bottom slopes, and mean plot distance from residence). I include kushet (village) fixed effects to control for local prices and wages, infrastructure availability, market access, rental markets, economic structure and traditional use of various sectors (agriculture, non-agriculture, etc.). I also cautiously include several other variables important to household income share, including formal and informal credit access, durable farm equipment owned, and mean number of rights on household plots51. The final set of variables clearly could be decided jointly with income share, which is an obvious concern. Unfortu- nately, there are not plausible instruments for these variables, particularly given the use of the fixed effects. Controlling for these variables is important if they are also important factors in determining household certification status. Fortunately, the coefficient on certification is consistent across models that include and do not include 50The average exchange rate in 1998 was 1 Ethiopian Birr = 0.1433 USD. 51The land rights variable is the household-level mean for the number of rights out of 18 possible land rights on each household plot, as described in previous chapters.

117 these variables, as well as models that attempt to control for the endogeneity of land rights (available upon request).

The variable capturing household certification status is cht, the percentage of the households’ owned plots that are certified. The percentage of certified plots may be correlated with remaining components in the error term, such as unobserved household ability, comparative advantage, and preferences for a specific sector, or shocks to the household in the given year that affected both income shares and certification status. To address this issue, I instrument for the household certification variable using the instruments introduced in previous chapters. These variables capture information on the progress of certification at the village level. The first instrument is the percentage of surveyed village households that have at least one certified plot excluding the given household, and the second is the mean years since households’ earliest certified plot was certified across all surveyed households in the village excluding the given household. These variables are clearly correlated with households’ percentage of certified plots, although the correlation is negative once other controls and village effects are included. The main identifying assumption in these models is that the instruments are related to household-level variations in certification status from their local averages, but progress in local certification does not directly affect household income shares and diversification. Several challenges to the exclusion restriction of this instrument will be addressed, through both traditional overidentification, and additional empirical, tests. One potential problem with the instruments arises if local progress in certification is affected by deviations in single-season household and plot-level shocks from local shocks controlled for in the fixed effects. For instance, a group of households could live in an area which is flooded at the time of certification, precluding certification from continuing and affecting households’ income shares as well. A factor that diminishes this possibility is that certification was probably not easily affected

118 by the types of shocks that drove income share outcomes. Even if plots or households were flooded or affected by another shock that precluded surveyors from arriving at the plot for parcel delineation, evidence suggests that in some cases surveyors simply recorded the relevant landmarks of the plot boundaries as they were told them by household and community members (Deininger et al. 2008). Delineators could avoid walking plot boundaries to complete the certification process in some cases given the unsophisticated technology used for delineation. Therefore, it is doubtful that such a shock affected the certification status of multiple households within the same village. An additional remaining threat to identification is that the instruments, particularly the percentage of surveyed households with a certified plot, capture changes in rental markets that affected some surveyed households’ access to rental markets in ways not controlled for by the fixed effects. This threat would be more likely if the survey data on village certification status contained information on plots located within a relatively close proximity to other household plots52. The random sampling used in data collection decreases this possibility. Since households were sampled randomly, surveyed households should not have systematically better or worse rental market access than the average level of local surveyed households53. This concern is also alleviated by using the percentage of surveyed households that have at least one certified plot rather than the percentage of all surveyed plots that were certified in each village. Results from regressions of income shares on kushet fixed effects and household- level controls, including controls for average plot characteristics, are located in table 3.3. The table provides estimates from uninstrumented linear probability fixed effects models (column 1), instrumented linear probability fixed effects models (col-

52Other scenarios are possible. A similar argument could be made using social distance and rental markets. 53This concern was not as important in previous chapters because regressions controlled for household-year ﬁxed eﬀects - and thus household rental market access, and variations in this access - in most cases.

119 umn 2), uninstrumented fixed effects tobits with their accompanying marginal effects (columns 3 through 5), and instrumented fixed effects tobits with their marginal effects (columns 6 through 8) for income shares that are censored54. The instrumented fixed effects models present results from overidentification tests, which also test the validity of treating mean household land rights as exogenous. These tests lend support to the identification strategy chosen. It is noteworthy that for retained farm income, the endogeneity of the certificate variable is rejected, so the uninstrumented models are the preferred estimates. Tobit models are presented since some households do not obtain any income from specific sources. Greene (2004) shows that the bias of coefficients in tobits using fixed effects is negligible, although the bias in the standard errors and marginal effects is not. However, if censoring occurs in close to 50 percent of observations, the bias in the marginal effects is very small. The bias in the marginal effects increases as the degree of censoring departs from 50 percent55. Therefore, these models should be taken with the understanding that the marginal effects have a bias dependent on the proportion of censored observations, and the standard errors of the estimated coefficients have a downward bias. The most obvious result from the income share models is the apparent lack of impact of household certification on income shares. In all instances, using instrumented and uninstrumented models of OLS fixed effects and tobit fixed effects alike, income share decisions in 1998 are unrelated to certification status. The coefficient for sold agricultural products is the most consistent, with a positive sign in each case. Looking at these shares in a slightly different way suggests households may have negotiated a different overall mix of income shares as a result of certification, although

54Only one household had zero percent of income from retained farm income, so tobit results are not reported for this category. 55The percentage of households with left-censored income shares from the speciﬁc categories varies greatly, with less than 1 percent for retained farm income, 8 percent for all sold agricultural products, and 61 percent for non-agricultural income (excluding aid). The size of the bias in tobit models should be the lowest for non-agricultural income and higher for the sold agricultural category.

120 households’ reactions to certification were not easy to predict. Table 3.4 contains results from regressions of households’ Herfindahl-Simpson income concentration index using kushet fixed effects and household-level controls. The shares for the index are calculated using the categories of retained agricultural income, sold crops, sold livestock, off-farm agricultural labor, non-agricultural labor, other (non-aid) agricultural income, and aid income. Lower numbers represent more diversification of income sources, and an index of one means all income comes from one category56. Similar to the previous table, the first column presents results from uninstrumented models, and the second column presents results using models that are instrumented using the previously discussed variables for kushet-level certification. In the second stage, it is clear that the income concentration index is not affected by households’ plot certification status. The results are qualitatively stable across the instrumented and uninstrumented models, and the instrumented model passes the relevant diagnostic tests. Although the overidentifying tests suggest that the instruments for the percentage of household certified plots satisfy the usual diagnostics, there is a remaining concern over the validity of the instruments. Although overidentification tests ensure the instruments are not confounded by average levels of household or plot heterogeneity, the percentage of local households that have any certified plots could be related to higher order distributional differences in household and plot-level heterogeneity from local averages. This would occur, for instance, if certification were more likely to be undertaken, or progress in certification were greater, in areas that have a wider variance in household and plot characteristics, such as those with wider dispersion of household wealth or education levels, household income generation strategies or preferences, or soil types. In essence, this would result in a selection effect that is

56Splitting the income shares just examined into all of these categories did not provide additional useful information, which is why income shares are presented at a more aggregate level in the previous table.

121 based on the second and higher moments, rather than the mean values. To test for this possibility, I run Kolmogorov-Smirnov tests of the equality of the distribution of the residuals when comparing all possible combinations of households with no certified plots, some certified plots, and all certified plots. The tests are unable to reject the equality of the residuals’ distribution for all tested models (see table 3.5). This evidence points to the validity of the instrumental variables and the estimated coefficients on the household certificate variable. Given the basic premise that households will select different diversification strategies based on their ability to shoulder risk and surmount barriers to entry, I run regressions for the Herfindahl-Simposon income concentration index separately for households by their income quartile. The necessary identifying requirements to isolate the causal impact by income quartile (and allowing for valid comparisons across quartiles) is that there are not components in the error term that affect these groups and their reaction to certification differentially. Provided the instrumented results isolate exogenous variation in certification levels irrespective of the household’s income level, the instrumented models will correct for potential bias across the split sample models. I run the models separately rather than simply interacting these characteristics with certification levels since regressions that are fully interacted with these covariates reveal that the included covariates tend to have differential effects on household income diversification decisions depending on households’ income level (i.e., Chow tests suggest the models should be split.) By splitting the sample, I allow the models to exhibit the greatest flexibility possible without having to deal with potential weaknesses introduced by interacting instruments, particularly since income quartiles are endogenous. Table 3.6 shows that the uninstrumented semi-elasticity of the Herfindahl- Simpson income concentration index equals 0.102 for households whose income falls

122 within the lowest income quartile57, suggesting these households specialize their income sources more due to certification. Conversely, households in the second lowest income quartile respond to plot certification by diversifying their income sources, with an instrumented semi-elasticity of -0.172. The impact on households in the top two quartiles is negligible and insignificant. These results are qualitatively consistent across uninstrumented and instrumented models, and they suggest that income portfolios were responsive to certification in ways that were conditional upon households’ level of well-being. This result is consistent with the literature and analytical framework presented earlier. Regressions of specific income shares divided by income levels do not suggest these shares responded to certification in a systematic way corresponding to household income quartiles, so they are not presented here58. In summary, while specific income shares do not appear to be significantly affected by households’ plot certification status, the overall mix of shares suggests that the worst-off households (in terms of income levels) decreased the diversity of their income sources, while the next income quartile diversified more. This response on the part of the lowest earning households may reflect the security certification provided, which allowed them to better cope with vulnerability, something they were previously doing by diversifying their income sources into multiple low-return activities.

Evidence from Households’ Livelihood Strategies

While interesting, these results should also be corroborated by data that is available for multiple years since the estimates just presented could reflect a single- season selection effect that cannot be controlled for in the models. This is especially important if the instruments were also affected by a single season shock. To examine whether these results hold over multiple years, I make use of an expanded data sample

57I highlight the uninstrumented result since the endogeneity of certiﬁcation is rejected in the instrumented models. 58Results available upon request.

123 that includes information from 1991, 1997, and 1999, in addition to 1998. Although income information is not available for these years, data is available on households’ top three income-generating activities and livestock ownership. Household activities and asset holdings are less likely to be affected by single season shocks than are specific income shares, and multi-year models are also more robust to shocks. The difficulty with comparing estimation results from income-generating activities across those with income share information lies in the classification of activities and income shares, which is not entirely consistent. However, examining households’ livelihood strategy decisions can shed light on whether the results suggested by the Herfindahl-Simpson income concentration index, in particular, bear out in household activities. If they do, one would expect to see the worst-off households increasing their specialization (perhaps by increasing on-farm activities), while better-off households would diversify their income-generating activities to take advantage of the returns from diversification. At the aggregate level, one would expect not to see an aggregate change in livelihood strategies, similar to the insignificant change in income shares and portfolios. Fortunately, since multiple years of data are available, estimates examining the impact of certification on these activities are able to control for unobserved household preferences, comparative advantage, and rental market access. To estimate whether a household participated in income-generating activities that included livestock, off- farm work, and/or non-agricultural work, denoted Aht, the model is estimated as follows:

Aht = hhtβ1 + chtβ2 + kkt + hh + εht and I use kushet-year and household ﬁxed eﬀects so that the model becomes as follows:

¨ ¨ ¨ Aht = hhtβ1 + chtβ2 +ε ¨ht (17)

124 In this case, kushet-year fixed effects control for unobserved factors affecting the dependent variables at the village level within each year, and household fixed effects difference away constant unobservables such as households’ comparative advantage or ability with respect to a specific type of production or asset, or household preferences for a certain level of diversification. Coefficients are then interpreted as the deviation from the household mean net of kushet-year averages. Similar models for households’ livestock diversification, in which the dependent variable is the Herfindahl-Simpson concentration index for annual livestock holdings, are also run in order to understand how certification may have affected this crucial type of asset ownership. Models are also estimated examining oxen holdings. The previous models examining income shares required the deviation of household certification from local levels to be unrelated to unobserved household and plot heterogeneity and shocks. For the models employing multiple years of data, the identification requirement is that deviations in certification status within the same household over multiple years not be related to components of the error term that also affect livelihood strategies or livestock ownership, net of seasonal local shocks. (Incidentally, these fixed effects also control for institutional factors affecting local progress in certification). Since plot disputability decreases the expected marginal return to agricultural activities, households facing such a shock may be more likely to turn their efforts toward the off-farm or non-agricultural sector. The effect on livestock activities, which may have complementarities with agricultural activities, is more ambiguous. Models that fail to control for plot disputability would exhibit a negative bias in the off-farm and non-agricultural models. However, additional time-varying household shocks, such as the death or migration of a household member, as well as changes in the portfolio of plot quality, may also affect estimates. This leaves the potential bias of uninstrumented models difficult to sign.

125 To attenuate concerns over the effect of time-varying household and plot-specific shocks that may affect households’ percentage of certified plots and be related to livelihood and asset outcomes, I present results from models that instrument for households’ certification status using the same instruments utilized in the income share estimates. The identifying assumption in these models is that the variation in progress in local certification, net of local and household heterogeneity and annual village-level shocks, does not directly affect livelihood strategies or livestock diversification. Given the fixed effects, the instruments indicating local progress in certification are unlikely to be correlated to a household or plot-specific shock affecting the certification variable and outcome of interest. Barring something like the death of a household member who was also a crucial part of the local certification process and whose death slowed local certification roll-out, the instrument should be independent of time-varying household shocks. All in all, the demands of identification in these models are more likely to hold than those in the income share estimates. Table 3.7 presents results of models examining the role of plot certification on the probability of whether a household earned income from livestock-related activities, non-agricultural activities, or off-farm activities. The estimates suggest that, overall, households did not have a significantly higher probability of generating income through these activities as a result of plot certification. These results are consistent thus far with the initial income results. Table 3.8 divides the results along the demographic lines created for the income share models. The income quartiles, unfortunately only available for 1998, are at best a proxy for household well-being in earlier and later years. The results in table 3.8 suggest that the worst-off households increased livestock-related activities, while the best-off households appeared to (insignificantly59) decrease livestock-related activities. The best-off households were more likely to participate in non-agricultural

59In the uninstrumented case.

126 and off-farm activities as a result of certification. The instrumented results suggest the same story even more strongly: the best-off households decreased livestock activities and increased off-farm and non-agricultural activities, while the worst-off households increased livestock-related activities, and generally decreased off-farm and non-agricultural activities (with the exception of the marginally significant positive results for non-agricultural activities for the worst-off households). Unfortunately, the lowest income quartile instrumented results for the livestock model fails the overidentification test. Overall, these results are consistent with the worst-off households focusing greater energy on livestock activities as a result of certification. To better understand the dynamics of livestock diversification, an important coping strategy for many Ethiopian households, I also present results from regressions of households’ Herfindahl-Simpson livestock concentration index. Ownership of these various livestock are affected by, and affect, household income opportunities. Oxen are important for cultivating higher-return cereals, whereas households without oxen tend to cultivate more lower-return pulses (Gryseels 1988). Smaller livestock like sheep and goats are used as wealth stores and help households manage risk. Table 3.9 presents results from regressions of households’ Herfindahl-Simpson livestock asset concentration indices on kushet-year and household fixed effects, as well as additional controls for household and summary household plot characteristics that vary over time. The first two columns provide the results for the entire sample, while the following eight columns present estimates for households by income quartile beginning with the lowest-earning households. The aggregate results from the estimates are consistent across instrumented and uninstrumented models, and they suggest that households diversified their livestock holdings as more plots were certified. The results across income quartiles are smaller in instrumented models for the second and higher quartiles, although the instrumented coefficient increases in the worst-off households. The estimates for the

127 lowest two income quartiles are a bit volatile, changing signs, and they do not fully pass diagnostic tests, so these should be taken cautiously. The significant negative coefficient in the aggregate results and the strong results for the best-off households suggest that, on the whole, households diversified their livestock base in response to certification, with the best-off households most responsive to certification. It is important to note that these estimates control for livestock ownership and do not reflect asset accumulation or divestment. Estimates of livestock ownership (run separately for households’ number of cattle, goats and sheep, and pack animals) suggest certification had no impact on the total number of each animal group held in the aggregate sample. If certification allowed households to better cope with ex ante risk, then one might expect certification to be associated with greater relative holdings of animals directly related to higher production levels, such as oxen60, since households have less need to maintain smaller livestock as a wealth store to manage risk. To test this hypothesis, I estimate models of the number of oxen held by households, and I also examine uninstrumented and instrumented fixed effects models of the percentage of household livestock that are oxen. Tables 3.10 and 3.11 provide results from these models, which suggest that the best-off households did indeed respond to certification by increasing their overall and relative holdings of oxen. Given credit and savings constraints, it is not surprising that there appears to be no relationship between the two for the worst-off households.

Conclusions

The diversiﬁcation of income sources is a crucial means by which the poor manage risk in settings with incomplete capital markets, although this diversiﬁca- tion sometimes is made into sub-optimal low-return activities with low relative risk

60This result would be more likely to be true in households that did not overstock the animals due to oxen market failures.

128 exposure. Specialization at the highest ends of the income spectrum can leverage economies of scale and provide enhanced income growth to those able to take advantage of these opportunities. The pathways determining households’ portfolios of income generating activities are complex and dynamic, and it is often difficult to discern how specific policies have affected these portfolio-based decisions. This chapter attempted to disentangle how the issuance of land certificates to rural producers in Tigray Region, Ethiopia, affected households’ income portfolio decisions. The results suggest that impacts vary depending on households’ ability to cap- italize on income-generating opportunities. Existing literature suggests that higher- return income diversification opportunities in Sub-Saharan Africa are only available to those able to surmount entry costs. The results here bear this idea out: the best-off households increased their non-agricultural and off-farm activities, while they focused less on livestock activities. The worst-off households were not more likely to enter these higher-return sectors. Results are also consistent with the notion that certification relaxed binding coping constraints in the lowest income households, allowing them to decrease lower- return off-farm activities, such as wage labor, that they may have previously used to cope with risk. There is evidence that these households increased their likelihood of relying on livestock-related activities to generate income. The behavior of the worst-off households is consistent with households that initially had diversified into multiple low-return activities as a means of dealing with risk, and then responded to certification by bearing additional risk through specializing in preferred activities. If the estimates indicating the worst-off households increased participation in livestock activities can be believed, they suggest that households’ increased focus on livestock activities may have been an important stepping stone by which households accumulated assets that could eventually be liquidated to surmount barriers to entry into higher-return off-farm or non-agricultural activities. In general, households also

129 appeared to diversify their livestock asset base in response to certification. This asset diversification may be an important means by which households retain wealth and cope with risk possibilities. The relative accumulation of some of the most productive livestock, oxen, was limited to the best-off households, once again highlighting potential limitations on the productive decisions of the worst-off households. Evidence within Ethiopia confirms this description of household asset accumulation. Examining diversification and household well-being in Ethiopia and Tanzania, Dercon and Krishnan (1996) suggest that income from livestock is associated with increased income and consumption, but off-farm wage labor (with no major barriers to entry) are not associated with higher consumption or asset holdings. They suggest that livestock holdings eventually drive income diversification. The lumpiness of the most valuable livestock may keep the poorest households from purchasing oxen or cattle frequently, although they may incrementally build flocks of smaller, less expensive livestock, such as sheep and goats, to eventually build up their cattle herds. Since households did not appear to adjust their share of agricultural income, which was primarily obtained through crop production, relative to other income, the results found here cannot be clearly attributed to the incentives certification provided for increased plot exchanges. Instead, results most clearly reflect the increased security certification brought to households. By decreasing the risk associated with agricultural activities and earnings, households adjusted their income portfolios, activities, and livestock holdings in ways that allowed them to bear additional risk and reap higher returns. While not the main focus of such a program, plot certification may eventually have important long-term equilibrium effects on Tigray’s rural economy, which would benefit greatly from increased income generation opportunities.

130 Notes: X-axis denotes risk of portfolio return as measured by the standard deviation of all returns. Y-axis illustrates the overall portfolio return, which is the sum of the return to investments in each sector. The line depicted by τ illustrates the risk-return portfolio before plot certification; τ 0 represents the risk-return portfolio after plots have been certified. The household’s original utility level is U; it shifts to U 00 by way of U 0 after certification occurs. Graph is simply meant to illustrate general trend; it does not depict a specific situation.

Figure 3.1. Plot Certiﬁcation and Household Risk-Return Levels

131 Notes: Graphs depict whether top three income generating activities for the household include livestock-related activities, oﬀ-farm activities, and/or non-agricultural activities respectively. N = 483 households. Activity information and income information is from 1998, the only year with all available data.

Figure 3.2. Livelihood Strategies and Household Income

132 1998 only ’91,’97,’99 All years 0.629*** 0.294 0.379 Percent certified owner-operated plots (0.463) (0.447) (0.474) 483 1420 1903 0.732*** 0.338 0.438 Percent kushet certified plots (0.310) (0.456) (0.457) 481 1413 1894 14.882 14.869 14.873 Household mean plot rights (1.886) (2.026) (1.991) 442 1307 1749 127.172*** 168.526 158.03 Walking time to nearest all-weather road (minutes) (229.1968) (307.916) (290.468) 483 1420 1903 81.311 80.742 80.887 Walking time to nearest market center (minutes) (51.225) (51.336) (51.295) 483 1408 1891 3.620*** 4.173 4.243 Number of members (adult equivalents) (1.468) (1.521) (1.531) 483 1420 1903 1.825 1.760 1.777 Dependency ratio (1.254) (1.192) (1.208) 466 1364 1903 0.525 0.519 0.520 Percentage of female members (0.203) (0.216) (0.213) 483 1420 1903 0.805 0.806 0.806 Household head born in village (0.396) (0.395) (0.395) 483 1420 1903 1951.418 1951.18 1951.241 Year household head born (13.689) (13.596) (13.617) 483 1420 1903 0.232 0.234 0.233 Female-headed household (0.422) (0.423) (0.423) 483 1420 1903 1.389 1.399 1.397 Household head education level (ordinal) (1.037) (1.035) (1.036) 483 1420 1903 4.943 5.115 5.071 Livestock units (livestock equivalent) (4.501) (5.140) (4.984) 482 1401 1883 2136.212 2135.383 2135.594 Mean plot altitude (meters) (361.027) (358.2259) (358.839) 456 1401 1883 0.123 0.122 0.122 Plots on top slope (0.238) (0.237) (0.237) 483 1420 1903 0.185 0.184 0.185 Plots on mid-slope (0.279) (0.278) (0.278) 483 1420 1903 0.236 0.237 0.237 Plots on bottom slope (0.328) (0.329) (0.237) 483 1420 1903 19.599 19.497 19.523 Walking time from plots to residence (minutes) (17.314) (17.264) (17.272) 483 1416 1899 3.991 3.969 3.974 Total household plot area (tsimad) (2.427) (2.401) (2.407) 483 1420 1903 Stars denote statistically significant differences in means from 1998 versus all other years. Mean, standard deviation, and sample sizes reported for each variable. Variables without units indicate the percentage of households that share the given characteristic. * p≤ 0.10; ** p≤ 0.05; p≤ 0.01. Household heads’ education was measured as an ordinal variable of the head’s highest education level with one equal to no education through grade two for secular education, completion of a beginner literacy campaign, or a beginner religious course; two represents the completion of anywhere from grades three through six, completion of an advanced literacy course, or completion of an intermediate religious course; and three represents the completion of grade seven or above or the completion of an advanced religious training course.

Table 3.1. Household Characteristics

133 Income Shares (1998) Retained agricultural income (imputed value) 0.530 0.562*** 0.423 0.539 0.524 0.502 0.555 (0.237) (0.227) (0.237) (0.245) (0.231) (0.221) (0.250) Sold agricultural income 0.227 0.239** 0.189 0.210* 0.235 0.261 0.204 (0.193) (0.189) (0.200) (0.202) (0.187) (0.185) (0.194) Non-agricultural labor income 0.066 0.061 0.081 0.051 0.060 0.083 0.071 (0.148) (0.145) (0.157) (0.133) (0.136) (0.160) (0.160) Non-agricultural other income, excluding aid 0.035 0.018*** 0.091 0.030 0.036 0.033 0.041 (0.118) (0.086) (0.177) (0.103) (0.133) (0.099) (0.132) Aid income 0.142 0.119*** 0.216 0.171 0.144 0.122 0.129 (0.176) (0.155) (0.219) (0.203) (0.168) (0.159) (0.168) Herﬁndahl-Simpson Income Concentration Index 0.493 0.503** 0.459 0.518*** 0.484 0.449 0.521 (0.174) (0.173) (0.173) (0.183) (0.175) (0.158) (0.170) Sample size 459 353 106 115 115 115 114

134 Activities and Assets (All Years) Household earned income through livestock-related activities 0.425 0.504*** 0.167*** 0.297 0.473 0.459 0.474 Household earned income through off-farm activities 0.217 0.234*** 0.163 0.130*** 0.210 0.277 0.253 Household earned income through non-agricultural activities 0.239 0.232 0.262 0.122*** 0.240 0.336 0.260 Sample size 1,887 1,444 443 476 476 470 462 Herfindahl-Simpson asset diversification index 0.682 0.665*** 0.758 0.686 0.671 0.690 0.680 Standard deviation (0.226) (0.221) (0.233) (0.223) (0.232) (0.227) (0.222) Sample size 1,675 1,372 303 401 431 417 424 Household Income (1998) Mean household income, 1998 Ethiopian Birr 2,615.32 2,972.72 1,425.12 935.181 1684.701 2462.788 5402.855 Standard deviation (4,070.71) (4,472.28) (1,835.30) (518.848) (665.894) (943.786) (7343.665) Sample size 459 353 106 115 115 115 114 Sample All Male-headed Female-headed Income=Q1 Income=Q2 Income=Q3 Income=Q4 Stars denote statistically significant differences in means across males and females or across at least two of the income divisions. Sample size by income quartile for livestock activities is 478, 476, 475, and 471 respectively. * p≤ 0.10; ** p≤ 0.05; p≤ 0.01. Table 3.2. Household Income Shares and Diversification Retained farm income Percent certified -0.031 -0.001 (0.031) (0.031) Sample size 410 404 Number of groups 101 96 F-test 4.840 4.461 K-P rk LM statistic 42.448 p-value 0.000 Weak identification test (K-P rk Wald F statistic) 514.115 Hansen’s J (overidentification) 0.931 p-value 0.335 E-stat p; Ho: Certificate endogenous 0.0285 Sold agricultural income Percent certified 0.022 0.027 0.026 0.028 0.019 0.032 0.035 0.023 (0.026) (0.027) (0.026) (0.032) (0.018) (0.028) (0.035) (0.020) Sample size 410 404 410 410 410 408 408 408 Number of groups 101 96 101 101 101 100 100 100 Number left censored 30 30 30 30 30 30 30 30 F-test 2.906 3.101 - - - K-P rk LM statistic 42.448 p-value 0.000 Weak identification test (K-P rk Wald F statistic) 213.853 Hansen’s J (overidentification) 0.814 p-value 0.367 E-stat p; Ho: Certificate endogenous 0.897

135 Wald Chi-Sq 6.95e+09 6.95e+09 6.95e+09 Log pseudolikelihood 160.989 160.989 160.989 399.422 399.422 399.422 All non-agricultural income (excluding aid) Percent certified -0.009 -0.025 0.015 0.016 0.005 -0.027 -0.028 -0.008 (0.025) (0.025) (0.071) (0.075) (0.022) (0.075) (0.080) (0.024) Sample size 410 404 410 410 410 408 408 408 Number of groups 101 96 101 101 101 100 100 100 Number left censored 253 253 253 252 252 252 F-test 1.97 2.07 - - - K-P rk LM statistic 42.448 p-value 0.000 Weak identification test (K-P rk Wald F statistic) 514.115 Hansen’s J (overidentification) 1.492 p-value 0.222 E-stat p; Ho: Certificate endogenous 0.2219 Wald Chi-Sq 1.23e+09 1.23e+09 1.23e+09 Log pseudolikelihood -117.789 -117.789 -117.789 121.791 121.791 121.791 Instrumented No Yes No No No Yes Yes Yes Method OLS FE OLS FE Tobit FE Tobit FE Tobit FE Tobit FE Tobit FE Tobit FE Marginal Effect p >0 E(y)|y > 0 p > 0 E(y)|y > 0 Kushet fixed effects used in all models. Robust standard errors are clustered at the kushet level. First stage controls for walking time to all-weather road, walking time to market center, number of household members in adult equivalent, dependency ratio, percentage of female household members, location of head of household’s birth, year head of household was born, head of household sex, head of household’s education level (ordinal), livestock in livestock equivalent units, mean plot altitude, percentage of plots on slope positions, percentage of plots with given soil types, mean plot distance from household, sum of household planted area, and sum of household plot area not shown. * p≤ 0.10, ** p≤ 0.05, *** p≤ 0.01. 10 percent maximal IV size critical value is 19.93 for weak identification test. Table 3.3 Household Income Share Regressions First stage Percent certified Percent certified (kushet) -2.879*** (0.271) Years since certified (kushet) -0.370* (0.189) Sample size 404 Number of groups 96 F-test 66.472 Second stage Herfindahl-Simpson Income Concentration Index Percent certified -0.007 0.011 (0.023) (0.022) Method Uninstrumented Instrumented Sample size 410 404 Number of groups 101 96 F-test 2.274 2.296 K-P rk LM statistic 42.448 p-value 0.000 Weak identification test (K-P rk 213.853 Wald F statistic) Hansen’s J (overidentification) 1.141 p-value 0.286 E-stat p; Ho: Certificate endogenous 0.147 Kushet fixed effects used in all models. Robust standard errors are clustered at the kushet level. First stage controls for walking time to all-weather road, walking time to market center, number of household members in adult equivalent, dependency ratio, percentage of female household members, location of head of household’s birth, year head of household born, head of household sex, head of household’s education level (ordinal), livestock in livestock equivalent units, mean plot altitude, percentage of plots on slope positions, percentage of plots with given soil types, mean plot distance from household, sum of household planted area, and sum of household plot area not shown. * p≤ 0.10, ** p≤ 0.05, *** p≤ 0.01. 10 percent maximal IV size critical value is 19.93 for weak identification test.

Table 3.4. Herﬁndahl-Simpson Income Concentration Regressions

136 Retained All agricultural All H-S Income farm income income non-agricultural income Concentration Index Kolmogorov-Smirnov test for equality of distribution functions across household types (p-values) 137 All plots certified vs. those with some certified 0.767 0.419 0.767 0.790 All plots certified vs. none certified 0.972 0.647 0.169 0.796 Some plots certified vs. none certified 0.952 0.700 0.258 0.918 Table 3.5. Tests of instruments Herfindahl-Simpson Income Concentration Index Percent certified -0.007 0.011 0.102** 0.147*** -0.122*** -0.172*** -0.042 -0.018 -0.027 -0.055 (0.023) (0.022) (0.049) (0.045) (0.042) (0.038) (0.090) (0.092) (0.055) (0.062) Sample All Income=Q1 Income=Q2 Income=Q3 Income=Q4 Sample size 410 404 102 66 104 66 106 74 98 66 Number of groups 101 96 64 28 67 29 64 32 58 26 F-test 2.274 2.300 51.749 32.810 87.061 52.66 7.611 10.21 41.786 31.83 K-P rk LM statistic 42.448 7.996 8.291 13.596 12.050 p-value 0.000 0.018 0.016 0.001 0.002 Weak identification test (K-P rk Wald F 213.853 302.201 36.734 101.233 39.748

138 statistic) Hansen’s J: overidentification 1.141 1.342 2.452 0.020 1.243 Hansen’s j p 0.286 0.247 0.117 0.886 0.265 E-stat p; Ho: Certificate endogenous 0.147 0.031 0.506 0.347 0.461 Kushet fixed effects used in all models. Certification variable instrumented using kushet certification variables. Controls for walking time to all-weather road in minutes, walking time to market center in minutes, number of household members (adult equivalent), dependency ratio, percentage of female household members, location of head of household’s birth, year head of household was born, head of household sex, head of household’s education level (ordinal), livestock units in livestock equivalent units, mean plot altitude, percentage of plots on top position, percentage of plots on middle slope position, percentage of plots on bottom slope position, mean plot distance from household, sum of household planted area, and sum of household plot area not shown. Robust standard errors are clustered at the kushet level. * p≤ 0.10, ** p≤ 0.05, *** p≤ 0.01. 10 percent maximal IV size critical value is 19.93 for weak identification test. Table 3.6. Herfindahl-Simpson Income Concentration Index Regressions, Split Sample Livestock activities Percent certified -0.008 -0.015 (0.017) (0.018) Sample size 1,674 1,659 Number of groups 441 431 F-test . 0.8686 K-P rk LM statistic 142.442 p-value 0.000 Weak identification test (K-P rk 463.803 Wald F statistic) Hansen’s J: overidentification 0.290 Hansen’s j p 0.5905 E-stat p; Ho: Certificate endoge- 0.4907 nous Non-agricultural activities Percent certified 0.022 0.032 (0.024) (0.023) Sample size 1,674 1,659 Number of groups 441 431 F-test 0.95 9.83 K-P rk LM statistic 142.442 p-value 0.000 Weak identification test (K-P rk 463.803 Wald F statistic) Hansen’s J: overidentification 1.368 Hansen’s j p 0.242 E-stat p; Ho: Certificate endoge- 0.372 nous Off-farm activities Percent certified -0.005 -0.001 (0.027) (0.027) Sample size 1,674 1,659 Number of groups 441 431 F-test . 80.84 K-P rk LM statistic 142.442 p-value 0.000 Weak identification test (K-P rk 463.803 Wald F statistic) Hansen’s J: overidentification 0.279 Hansen’s j p 0.598 E-stat p; Ho: Certificate endoge- 0.886 nous Instrumented No Yes Kushet-year and household fixed effects used in all models. Certification variable instrumented using kushet certification variables. Controls for walking time to all-weather road in minutes, walking time to market center in minutes, number of household members (adult equivalent), dependency ratio, percentage of female household members, location of head of household’s birth, year head of household was born, head of household sex, head of household’s education level (ordinal), mean plot altitude, percentage of plots on top position, livestock in livestock equivalent units, percentage of plots on middle slope position, percentage of plots on bottom slope position, mean plot distance from household, sum of household planted area and sum of household plot area not shown. Robust standard errors clustered at the household level in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. 10 percent maximal IV size critical value is 19.93 for weak identification test. Table 3.7 Household Livelihood Strategies Regressions, All Households

139 Livestock activities Percent certified -0.008 -0.015 0.080 0.105*** 0.019 0.064*** 0.044 0.017 -0.063 -0.085** (0.017) (0.018) (0.050) (0.039) (0.050) (0.020) (0.068) (0.058) (0.047) (0.037) Sample size 1,674 1,659 410 409 417 415 435 432 410 403 Number of groups 441 431 106 105 109 107 114 113 110 106 F-test . 4.58 . 2.050 . 99.68 . 2264.83 . 71020.68 K-P rk LM statistic 142.442 22.412 14.789 26.225 26.893 p-value 0.000 0.000 0.001 0.000 0.000 Weak identification test (K-P rk Wald F statistic) 463.803 75.938 75.260 194.220 23.841 Hansen’s J: overidentification 0.290 4.157 2.409 0.848 1.734 Hansen’s j p 0.590 0.041 0.121 0.357 0.188 E-stat p; Ho: Certificate endogenous 0.491 0.319 0.197 0.281 0.070 Non-agricultural activities Percent certified 0.022 0.032 0.027 0.065* -0.038 -0.122* -0.112 -0.100 0.179* 0.207*** (0.024) (0.023) (0.030) (0.039) (0.074) (0.071) (0.098) (0.087) (0.099) (0.069) Sample size 1,674 1,659 410 409 417 415 435 432 410 403 Number of groups 441 431 106 105 109 107 114 113 110 106 F-test . 9.82 . 0.830 . 0.02 . 1.78 . 804.33 K-P rk LM statistic 142.442 22.412 14.789 26.225 26.893 p-value 0.000 0.000 0.001 0.000 0.000 Weak identification test (K-P rk Wald F statistic) 463.803 75.938 75.260 194.220 23.841 Hansen’s J: overidentification 1.368 1.900 2.026 0.272 8.926

140 Hansen’s j p 0.242 0.168 0.155 0.602 0.003 E-stat p; Ho: Certificate endogenous 0.543 0.794 0.232 0.187 0.555 Off-farm activities Percent certified -0.005 -0.001 -0.106 -0.106 -0.044 -0.125* -0.051 -0.025 0.170* 0.197*** (0.027) (0.027) (0.098) (0.088) (0.075) (0.072) (0.086) (0.075) (0.097) (0.070) Sample size 1,674 1,659 410 409 417 415 435 432 410 403 Number of groups 441 431 106 105 109 107 114 113 110 106 F-test . 80.84 . 166.96 . 442.05 . 50.03 . 966.46 K-P rk LM statistic 142.442 22.412 14.789 26.225 26.893 p-value 0.000 0.000 0.001 0.000 0.000 Weak identification test (K-P rk Wald F statistic) 463.803 75.938 75.260 194.220 23.841 Hansen’s J: overidentification 0.279 1.351 2.274 0.137 8.862 Hansen’s j p 0.598 0.245 0.132 0.711 0.003 E-stat p; Ho: Certificate endogenous 0.886 0.224 0.310 0.284 0.681 Sample All Income=Q1 Income=Q2 Income=Q3 Income=Q4 Kushet-year and household fixed effects used in all models. Certification variable instrumented using kushet certification variables. Controls for walking time to all-weather road in minutes, walking time to market center in minutes, number of household members (adult equivalent), dependency ratio, percentage of female household members, location of head of household’s birth, year head of household was born, head of household sex, head of household’s education level (ordinal), livestock in livestock, equivalent units, mean plot altitude, percentage of plots on top position, percentage of plots on middle slope position, percentage of plots on bottom slope position, mean plot distance from household, sum of household planted area, and sum of household plot area not shown. Robust standard errors are clustered at the household level. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. 10 percent maximal IV size critical value is 19.93 for weak identification test. Table 3.8. Household Livelihood Strategies Regressions, Split Sample Herfindahl-Simpson Asset Concentration Index Percent certified -0.037* -0.048** -0.033 0.057 0.019 -0.003 -0.038 -0.059 -0.063 -0.092** (0.022) (0.022) (0.072) (0.054) (0.064) (0.068) (0.059) (0.048) (0.060) (0.042) Sample size 1,562 1,542 379 375 391 388 403 399 387 380 Number of groups 425 410 102 98 106 103 109 107 106 102 F-test . 30016.38 . 23.92 . 169.32 . 3.69 . 4454.10 K-P rk LM statistic 130.369 19.579 9.525 25.824 26.808 p-value 0.000 0.000 0.009 0.000 0.000 Weak identification test (K-P rk Wald F 451.134 68.680 200.013 147.838 23.345 statistic) Hansen’s J: overidentification 0.931 . . . 0.304 Hansen’s j p 0.335 0.581

141 Diff in Sargan stat p; Ho: Land rights ex- 0.335 0.932 0.061 0.335 0.581 ogenous E-stat p; Ho: Certificate endogenous 0.384 0.004 0.175 0.441 0.240 Sample All Income=Q1 Income=Q2 Income=Q3 Income=Q4 Instrumented No Yes No Yes No Yes No Yes No Yes Kushet-year and household fixed effects used in all models. Certification variable instrumented using kushet certification variables. Controls for walking time to all-weather road in minutes, walking time to market center in minutes, number of household members (adult equivalent), dependency ratio, percentage of female household members, location of head of household’s birth, year head of household was born, head of household sex, head of household’s education level (ordinal), livestock in livestock, equivalent units, mean plot altitude, percentage of plots on top position, percentage of plots on middle slope position, percentage of plots on bottom slope position, mean plot distance from household, sum of household planted area, and sum of household plot area not shown. Robust standard errors are clustered at the household level. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. 10 percent maximal IV size critical value is 19.93 for weak identification test.

Table 3.9 Herfindahl-Simpson Asset Concentration Index Regressions Count of oxen Percent certified -0.022 0.101 0.095 0.056 -0.853 -0.252 0.093 0.096 0.413* 0.344** (0.078) (0.089) (0.157) (0.133) (0.533) (0.513) (0.113) (0.123) (0.229) (0.163) Sample size 1,674 1,659 410 409 417 415 435 432 410 403 Number of groups 441 431 106 105 109 107 114 113 110 106 F-test . 1557.34 . 48.20 . 130.54 . 175.69 . 36610.80 K-P rk LM statistic 142.442 22.412 14.789 26.225 26.893 p-value 0.000 0.000 0.001 0.000 0.000 Weak identification test (K-P rk Wald F statistic) 463.803 75.938 75.260 194.220 23.841 Hansen’s J: overidentification 2.199 0.480 1.218 1.139 0.018 Hansen’s j p 0.138 0.489 0.270 0.286 0.892 142 E-stat p; Ho: Certificate endogenous 0.322 0.652 0.778 0.704 0.451 Sample All Income=Q1 Income=Q2 Income=Q3 Income=Q4 Instrumented No Yes No Yes No Yes No Yes No Yes Kushet-year and household fixed effects used in all models. Certification variable instrumented using kushet certification variables. Controls for walking time to all-weather road in minutes, walking time to market center in minutes, number of household members (adult equivalent), dependency ratio, percentage of female household members, location of head of household’s birth, year head of household was born, head of household sex, head of household’s education level (ordinal), livestock in livestock, equivalent units, mean plot altitude, percentage of plots on top position, percentage of plots on middle slope position, percentage of plots on bottom slope position, mean plot distance from household, sum of household planted area, and sum of household plot area not shown. Robust standard errors are clustered at the household level. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. 10 percent maximal IV size critical value is 19.93 for weak identification test. Table 3.10 Household Asset Regressions: Count of Oxen Share oxen Percent certified 0.020 0.039 -0.019 -0.038 -0.064 0.085 0.105** 0.095** 0.129** 0.149*** (0.027) (0.029) (0.070) (0.049) (0.097) (0.110) (0.046) (0.043) (0.053) (0.056) Sample size 1,562 1,542 379 375 391 388 403 399 387 380 Number of groups 425 410 102 98 106 103 109 107 106 102 F-test . 1.26 . 1.14 . 0.93 . 1.34 . 1.11 K-P rk LM statistic 130.369 19.579 9.525 25.824 26.808 p-value 0.000 0.000 0.009 0.000 0.000 Weak identification test (K-P rk Wald F statistic) 451.134 68.680 200.013 147.838 23.345 Hansen’s J: overidentification 1.198 0.045 0.520 1.068 1.296 Hansen’s j p 0.274 0.832 0.471 0.301 0.255 143 E-stat p; Ho: Certificate endogenous 0.185 0.328 0.160 0.974 0.843 Sample All Income=Q1 Income=Q2 Income=Q3 Income=Q4 Instrumented No Yes No Yes No Yes No Yes No Yes Kushet-year and household fixed effects used in all models. Certification variable instrumented using kushet certification variables. Controls for walking time to all-weather road in minutes, walking time to market center in minutes, number of household members (adult equivalent), dependency ratio, percentage of female household members, location of head of household’s birth, year head of household was born, head of household sex, head of household’s education level (ordinal), livestock in livestock, equivalent units, mean plot altitude, percentage of plots on top position, percentage of plots on middle slope position, percentage of plots on bottom slope position, mean plot distance from household, sum of household planted area, and sum of household plot area not shown. Robust standard errors are clustered at the household level. * p≤0.10, ** p≤ 0.05, *** p≤ 0.01. 10 percent maximal IV size critical value is 19.93 for weak identification test. Table 3.11 Household Asset Regressions: Share Oxen Chapter 5: Was Plot Certification Worthwhile?

Taken together, results from the previous three chapters suggest that Tigray’s plot certification program was not very effective at increasing important plot investments or agricultural productivity. However, the certification program appears to have caused households to adjust their income generation strategies in ways that reflect their management of risk/return trade-offs and their ability to surmount barriers to entry. The evidence found on certification in these chapters would benefit from additional inquiry into certain key areas, including an examination of whether agriculture- related responses to certification vary depending on household characteristics61; an investigation of whether the results are generalizable to the Tigrayan population in the more sparsely populated lowlands, given the plot altitude selection in the dataset; a search for instrumental variables that do not suffer from some of the weaknesses particularly problematic in the plot-level instrumented models presented in chapters 2 and 3; and the inclusion of robust two-way clustered standard errors that account for both types of fixed effects used, rather than simply the smallest unit of analysis. The results on agricultural investments and productivity point to an important lesson. The conclusions in these chapters, which generally disagree with those found by other authors, were only reached through being able to control for constant and time-varying household and plot heterogeneity. Results from studies of land rights and plot rights formalization often draw very different conclusions, and some of the discrepancy across results may be driven by methodological limitations. The results

61This analysis was not performed in chapters 2 and 3 because diﬀerential household responses to certiﬁcation in agriculture were not taken as a point of departure, in contrast to the analysis in chapter 4.

144 presented here suggest that data allowing for the analysis of property rights and land rights formalization should be collected in such a manner that the researcher can connect plot-level data over time and control for key plot characteristics, particularly latent variables. While the costs associated with such data collection efforts are high, the benefits they will provide may prove worthwhile. It is important to understand why a certification program that supposedly increased households’ tenure security (i.e., Haile et al. 2005, Deininger et al. 2008, and Holden, Deininger, Ghebru 2009a) did not improve key agricultural outcomes. The key to this puzzle may be found in missing factor markets. Although households may have wanted to increase agricultural investments as a result of certification, lack of credit or labor market access may have kept them from reacting optimally to the policy. Nevertheless, households adjusted income generation strategies because of certification. Taken together, these results suggest several possibilities: households were already making nearly optimal agricultural investments prior to certification (and tenure insecurity was not hindering plot investments); households had limited resources to manipulate and factor markets were limited, and they valued the income portfolio adjustments they could make as a result of certification more than the expected return to increased agricultural investments; or factor market failures were a less binding force in households’ portfolio adjustment decisions than in their specific agricultural investment decisions. The conclusion that certification did not positively impact most agricultural investments and productivity is rather dismal, particularly since these results disagree with more optimistic, already-established conclusions on the effectiveness of Tigray Region’s certification program. Given that the idea of the program was to increase agricultural investments and productivity, one might conclude that plot certification was a failure. That assessment is a bit premature. According to accounts of smallholders themselves, plot certification was highly valued due to the increased security

145 and flexibility producers enjoyed with respect to their plots (Deininger et al. 2008)62. This evidence suggests that the program at least partially met its goal of increasing producers’ sense of tenure security. Others have found evidence that certification especially helped some of the worst-off households, such as those headed by females (Holden, Deininger, and Ghe- bru 2009b)63. The results from chapter 5, which showed that certification caused households to adjust their portfolio of income generating opportunities in ways that may increase their asset holdings and decrease food and income insecurity over the long-run by allowing households to take on activities with higher risks, but higher returns, are not trivial. This type of diversification is crucial for the Tigrayan economy. Finally, if supported by the government over the long-run, the program also may increase the validity of the Ethiopian state-citizen contract, still a weak relationship in many regards. Clearly, the program had some merit and may continue to be valuable if the system is maintained and supported by the government.

62Deininger presents evidence related to certification in multiple regions of Ethiopia, but specific evidence from Tigrayan producers in his data also support this statement. 63This study did not focus on the potential for plot certification to increase female-headed households’ well-being, but an important line of future inquiry is to examine outcomes in these households to see whether these conclusions hold using the methodologies employed here.

146 References

Abegaz, Berhanu. 2004. “Escaping Ethiopia’s Poverty Trap: The Case for a Second Agrarian Reform.” The Journal of Modern African Studies. 42(3): 313-342.

Adenew, Berhanu, and Fayera Abdi. 2005. “Land registration in Amhara Region, Ethiopia. Research Report 3.” International Institute for Environment and Develop- ment and SOS Sahel: Addis Ababa, Ethiopia.

Alderman, Harold, and Christina Paxson. 1992. Do the Poor Insure? A Synthesis of the Literature on Risk and Consumption in Developing Countries, World Bank Policy Research Working Paper WPS 1008.

Ali, Daniel Ayalew, Stefan Dercon, and Madhur Gautam. 2011. “Property rights in a very poor country: tenure insecurity and investment in Ethiopia.” Agricultural Economics. 42:75-86.

Alston, Lee, Gary Libecap, and Robert Schneider. 1996. “The determinants and impact of property rights: land titles on the Brazilian frontier.” Journal of Law, Economics and Organization. 12(1): 25-61.

Andr´e,Catherine, and Platteau, Jean-Philippe. 1998. “Land Tenure under Unbear- able Stress: Rwanda Caught in the Malthusian Trap.” Journal of Economic Behavior and Organization. 34(1):1-47.

Atakilte, Beyene, David Gibbon, and Mitiku Haile. 2001. Understanding diversity

147 in farming practices in Tigray, Ethiopia. Managing Africa’s Soils No. 24. Interna- tional Institute for Environment and Development.

Atwood, David A. 1990. “Land registration in Africa: the impact on agricultural production.” World Development. 18(5):659-671.

Bandiera, Oriana. 2007. “Land tenure, investment incentives, and the choice of techniques: evidence from Nicaragua.” The World Bank Economic Review. 21(3):487- 508.

Banerjee, Abhijit V., Paul J. Gertler, and Maitreesh Ghatak. 2002. “Empowerment and eﬃciency: tenancy reform in West Bengal.” The Journal of Political Economy. 110(2): 239-280.

Barrett, Christopher, Mesﬁn Bezuneh, and Abdillahi Aboud. 2001. “Income diver- siﬁcation, poverty traps and policy shocks in Cte dIvoˆıreand Kenya.” Food Policy. 26:367- 384.

Barrett, Christopher B., Mesﬁn Bezuneh, Daniel C. Clay, and Thomas Reardon. 2001. “Heterogenous Constraints, Incentives and Income Diversiﬁcation Strategies in Rural Africa.” Working Paper. Department of Applied Economics and Management, Cornell University. January 2001 version.

Barrett, Christopher B., Thomas Reardon. 2000. “Asset, Activity, and Income Di- versiﬁcation Among African Agriculturalists: Some Practical Issues.” Mimeo.

Barrett, Christopher, Thomas Reardon, and Patrick Webb. 2001. “Nonfarm income diversiﬁcation and household livelihood strategies in rural Africa: concepts, dynamics, and policy implications.” Food Policy. 26:315-331.

148 Besley, Timothy. 1995. “Property rights and investment incentives: theory and evidence from Ghana.” The Journal of Political Economy. 103(5):903-937.

Besley, Timothy, and Maitreesh Ghatak. 2010. “Property rights and economic development.” In Handbook of Development Economics, Volume 5., edited by Dani Rodrik and Mark Rosenzweig, 4525-4595. North Holland, The Netherlands: Elsevier BV.

Block, Steven A. 1995. “The recovery of agricultural productivity in sub-Saharan Africa.” Food Policy. 20(5): 385-405.

Steven Block, and Patrick Webb. 2001. “The dynamics of livelihood diversiﬁcation in post-famine Ethiopia.” Food Policy. 26:333-350.

Brasselle, Anne-Sophie, Fr´ed´ericGaspart, and Jean-Philippe Platteau. 2002. “Land tenure security and investment incentives: puzzling evidence from Burkina Faso.” Journal of Development Economics. 67:373-418.

Broegaard, Rikke J., Rasmus Heltberg, and Nikolaj Machlow-Moller. 2002. Property Rights and Land Tenure Security in Nicaragua. Center for Economic and Business Research, Copenhagen, Denmark.

Carter, Michael, and Pedro Olinto. 2003. “Getting institutions ’right’ for whom? Credit constraints and the impact of property rights on the quantity and composition of investment.” American Journal of Agricultural Economics. 85(1):173-186.

Carter, Michael, Wiebe, Keith, and Benoit Blarel. 1994. “Tenure security for whom? Diﬀerential eﬀects of land policy in Kenya.” In Searching for Land Tenure Security in Africa, edited by John W. Bruce and S.E. Migot-Adholla, 141-168. Dubuque, Iowa: Kendall/Hunt.

149 Chalamwong, Yongyuth, and Gershon Feder. 1988. “The impact of landownership security: theory and evidence from Thailand.” The World Bank Economic Review. 2(2):187-204.

Chimhowu, Admos, and Phil Woodhouse. 2006. “Customary vs private property rights? Dynamics and trajectories of vernacular land markets in Sub-Saharan Africa.” Journal of Agrarian Change. 6(3):346-371.

Constitution of the Federal Democratic Republic of Ethiopia. Enacted 1995.

Corbeels, Marc, Abebe Shiferaw, and Mitiku Haile. 2000. “Farmers’ knowledge of soil fertility and local management strategies in Tigray, Ethiopia.” Managing Africa’s Soils No. 10. International Institute for Environment and Development.

Crawford, Eric, Valerie Kelly, T.S. Jayne, and Julie Howard. 2003. “Input use and market development in Sub-Saharan Africa: an overview.” Food Policy. 28: 277-292.

de Janvry, Alain, and Elisabeth Sadoulet. 2010. “Agriculture for Development in Africa: Business-as-Usual or New Departures?” Journal of African Economies. 19(AERC Supplement 2): ii7-ii39.

Deininger, Klaus, and Daniel Ayalew Ali. 2008. “Do overlapping land rights reduce agricultural investment? Evidence from Uganda.” American Journal of Agricultural Economics. 90(4):869-882.

Deininger, Klaus, and Jin Songqing. 2006. “Tenure security and land-related investment: evidence from Ethiopia,” European Economic Review. 50(5):1245-1277.

Deininger, Klaus, Daniel Ayalew Ali, Stein T. Holden, and Jaap Zevenbergen. 2008. “Rural land certiﬁcation in Ethiopia: process, initial impact, and implications for

150 other African countries.” World Development. 36(10):17861812.

Deininger, Klaus, and Gershon Feder. 2009. “Land registration, governance, and development: evidence and implications for policy.” The World Bank Research Ob- server. 24:233-266.

Deininger, Klaus, Derek Byerlee, with Jonathan Lindsay, Andrew Norton, Harris Selod, and Mercedes Stickler. 2011. Rising Global Interest in Farmland: Can It Yield Sustainable and Equitable Beneﬁts? The World Bank: Washington, DC.

Dercon, Stefan. 1996. “Risk, crop choice, and savings: evidence from Tanzania.” Economic Development and Cultural Change. 44(3): 485-513.

Dercon, Stefan. 1998. “Wealth, risk and activity choice: cattle in Western Tanzania.” Journal of Development Economics. 55: 1-42.

Dercon, Stefan. 1999. Ethiopia: poverty assessment study. Report produced for IFAD. Available at http://www.economics.ox.ac.uk/members/stefan.dercon/IFAD1.pdf. Accessed April 18, 2011.

Dercon, Stefan, and Daniel Ayalew. 2007. “Land Rights, Power and Trees in Rural Ethiopia.” Centre for the Study of African Economies, Working Paper 266.

Dercon, Stefan, and Pramila Krishnan. 1996. “Income Portfolios in Rural Ethiopia and Tanzania: Choices and Constraints.” Journal of Development Studies. 32(6): 850-875.

Dercon, Stefan, and Luc Christiaensen. 2011. “Consumption risk, technology adop- tion and poverty traps: Evidence from Ethiopia.” Journal of Development Eco- nomics. 96: 159-173.

151 Do, Quy-Toan, and Lakshmi Iyer. 2008. “Land titling and rural transition in Viet- nam.” Economic Development and Cultural Change. 56(3): 531-579.

Dong, Fengxia, Jing Lu, and Allen M. Featherstone. 2010. “Eﬀects of Credit Con- straints on Productivity and Rural Household Income in China.” Working Paper 10-WP 516. Center for Agricultural and Rural Development. Iowa State University.

Ellis, Frank. 1998. “Household Strategies and Rural Livelihood Diversiﬁcation.” Journal of Development Studies. 35(1):1-38.

Feder, Gershon, Tongroj Onchan, and Yongyuth Chalamwong. 1988. “Land policies and farm performance in Thailand’s forest reserve areas.” Economic Development and Cultural Change. 36(April):483-502.

Francis, Elizabeth, and John Hoddinott. 1993. “Migration and diﬀerentiation in Western Kenya: a tale of two sublocations,” Journal of Development Studies. 30(1):115- 145.

Frisvold, George, and Kevin Ingram. 1995. “Sources of agricultural productivity growth and stagnation in sub-Saharan Africa.” Agricultural Economics. 13: 51-61.

Fulginiti, Lilyan E., Richard K. Perrin, and Bingxin Yu. 2004. “Institutions and agricultural productivity in Sub-Saharan Africa.” Agricultural Economics. 31: 169-180.

Fulginiti, Lilyan E. 2010. “What comes ﬁrst, agricultural growth or democracy?” Agricultural Economics. 41: 15-24.

Gebremedhin, Berhanu, John Pender, and Simeon Ehui. 2003. “Land use, land tenure and sustainable land management in Tigray.” Summary. Part of Environment and Production Technology Division. Workshop Summary Paper No. 14. Edited by

152 Berhanu Gebremedhin, John Pender, Simeon Ehui, and Mitiku Haile. International Food Policy Research Institute and International Livestock Research Institute and Mekelle University, Mekelle, Ethiopia. March 28-29, 2002.

Gebremedhin, Berhanu, and Scott M. Swinton. 2003. “Investment in soil conservation in northern Ethiopia: the role of land tenure security and public programs.” Agricultural Economics. 29:69-84.

Gebremedhin, Berhanu, Solomon Tesfay, and Dereje Assefa. 2003. Executive Sum- mary. Part of Environment and Production Technology Division. Workshop Sum- mary Paper No. 14. Edited by Berhanu Gebremedhin, John Pender, Simeon Ehui, and Mitiku Haile. Mekelle, Ethiopia. International Food Policy Research Institute and International Livestock Research Institute and Mekelle University. March 28-29, 2002.

Goldstein and Udry. 2008. “The proﬁts of power: land rights and agricultural investment in Ghana.” The Journal of Political Economy. 116(6):981-1022.

Greene, William. 2004. “The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of ﬁxed eﬀects.” Econometrics Journal. 7:98-119.

Gryseels, G. 1988. “The role of livestock in the generation of smallholder farm income in two Vertisol areas of the central Ethiopian highlands.” Highlands Programme In- ternational Livestock Centre for Africa (ILCA). Addis Ababa, Ethiopia.

Haile, Mitiku, Wray Witten, Kinfe Abraha, Sintayo Fissha, Adane Kebede, Getahun Kassa, and Getachew Reda. 2005. Research report 2: land registration in Tigray, Northern Ethiopia. IIED, Mekelle University and Central Research Department, DFID.

153 Holden, Stein T., Klaus Deininger, and Hosaena Ghebru. 2009a. “Impacts of low-cost land certiﬁcation on investment and productivity.” American Journal of Agricultural Economics. 91(2): 359-373.

Holden, Stein T., Klaus Deininger, and Hosaena Ghebru. 2009b. “Gender, low-cost land certiﬁcation, and land rental market participation.” Contributed paper prepared for presentation at the International Association of Agricultural Economists Confer- ence, Beijing, China, August 16-22, 2009.

Holden, Stein T., Bekele Shiferaw, and John Pender. 2001. “Market Imperfections and Land Productivity in the Ethiopian Highlands.” Journal of Agricultural Eco- nomics. 52(3):53-70.

Holden, Stein, and Hailu Yohannes, 2002. “Land redistribution, tenure insecurity, and intensity of production: a study of farm households in Southern Ethiopia.” Land Economics. 78(4):573-590.

Howard, Patricia, and Erin Smith. 2006. “Leaving two thirds out of development: Female headed households and common property resources in the highlands of Tigray, Ethiopia.” Department of Social Sciences. Wageningen University and Research Cen- ter. The Netherlands.

Hunri, Hans. 1988. “Degradation and Conservation of the Resources in the Ethiopian Highlands.” Mountain Research and Development. 8(2/3): 123-130.

Jacoby, Hanan G., Guo Li, and Scott Rozelle. 2002. “Hazards of expropriation: tenure insecurity and investment in rural China.” The American Economic Review. 92(5):1420-1447.

Jacoby, Hanan G., and Bart Minten. 2002. “Is land titling in Sub-Saharan Africa

154 cost-eﬀective? Evidence from Madagascar.” The World Bank Economic Review. 21(3): 461-485.

Jagger, Pamela, and John Pender. 2003. “The role of trees for sustainable management of less-favored lands: The case of eucalyptus in Ethiopia.” Forest Policy and Economics. 3(1):8395.

Jayne, Thomas S., James D. Shaﬀer, John M. Staatz, and Thomas Reardon. 1997. “Improving the Impact of Market Reform on Agricultural Productivity in Africa: How Institutional Design Makes a Diﬀerence.” MSU International Development Working Paper No. 66. Department of Agricultural Economics and Department of Economics, Michigan State University, East Lansing, Michigan.

Keeley, James, and Ian Scoones. 2000. “Knowledge, Power and Politics: The En- vironmental Policy-Making Process in Ethiopia.” The Journal of Modern African Studies. 38(1):89-120.

Lamb, Russell L. 2003. “Fertilizer Use, Risk, and Oﬀ-Farm Labor Markets in the Semi-Arid Tropics of India.” American Journal of Agricultural Economics. 85(2):359- 371.

Little, Peter D., Kevin Smith, Barbara A. Cellarius, D. Layne Coppock, and Christo- pher B. Barrett. 2001. “Avoiding disaster: diversiﬁcation and risk management among East African herders.” Development and Change. 32(3):401-433.

Loening, Josef, Bob Rijkers, and M˚ansS¨oderbom. 2008. “Nonfarm Microenterprise Performance and the Investment Climate: Evidence from Rural Ethiopia.” Africa Region, Agriculture and Rural Development Unit and Development Research Group Policy Research Working Paper 4577, The World Bank: Washington, DC.

155 Lopez, R. 1996. Land Titling and Investment in Honduras. Department of Agricul- tural and Resource Economics, University of Maryland, College Park, MD.

Markowitz, Harry. 1952. “Portfolio Selection.” Journal of Finance. 7(1):77-91.

Matsumoto, Tomoya, Yoko Kijima, and Takashi Yamano. 2006. “The role of local nonfarm activities and migration in reducing poverty: evidence from Ethiopia, Kenya, and Uganda.” Agricultural Economics. 35(supplement): 449-458.

Migot-Adholla, Shem, Peter Hazell, BenoˆıtBlarel, and Frank Place. 1991. “Indige- nous Land Rights Systems in Sub-Saharan Africa: A Constraint on Productivity?” The World Bank Economic Review. 5(1): 155-175.

Morduch, Jonathan. 1995. “Income smoothing and consumption smoothing.” Jour- nal of Economic Perspectives. 9(3):103-114.

Neyman, J., and Elizabeth L. Scott. 1948. “Consistent estimates based on partially consistent observations.” Econometrica. 16(1):1-32.

Pender, John, Berhanu Gebremedhin, Samuel Benin, and Simeon Ehui. 2001. “Strate- gies for Sustainable Agricultural Development in the Ethiopian Highlands.” American Journal of Agricultural Economics. 83(5): 1231-1240.

Pender, John, and Marcel Fafchamps. 2006. “Land lease markets and agricultural eﬃciency in Ethiopia.” Journal of African Economies. 15(2): 251-284.

Pender, John, and Berhanu Gebremedhin. 2006. “Land Management, Crop Pro- duction, and Household Income in the Highlands of Tigray, Northern Ethiopia: An Econometric Analysis.” Chapter 5 in Strategies for Sustainable Land Management in the East African Highlands. eds. John Pender, Frank Place, and Simeon Ehui.

156 International Food Policy Research Institute: Washington, DC.

Pender, John, Ephraim Nkonya, Pamela Jagger, Dick Sserunkuma, and Henry Ssali. 2004. “Strategies to increase agricultural productivity and reduce land degradation: Evidence from Uganda.” Agricultural Economics. 31(23): 181195.

Pinckney, Thomas C., and Peter K. Kimuyu. 1989. “Land tenure reform in East Africa: good, bad or unimportant?” Journal of African Economies. 3(1):1-28.

Place, Frank, and S.E. Migot-Adholla. 1998. “The Economic Eﬀects of Land Reg- istration on Smallholder Farms in Kenya: Evidence from Nyeri and Kakamega Dis- tricts.” Land Economics. 74(3):360-373.

Place, Frank. 2009. “Land tenure and agricultural productivity in Africa: a comparative analysis of the economics literature and recent policy strategies and reforms.” World Development. 37(8):1326-1336.

Place, Frank, and Peter Hazel. 1993. “Productivity eﬀects of indigenous land tenure systems in Sub-Saharan Africa.” American Journal of Agricultural Economics. 75:10- 19.

Reardon, Thomas. 1997. “Using Evidence of Household Income Diversiﬁcation to Inform Study of the Rural Nonfarm Labor Market in Africa.” World Development. 25(5):735-748.

Reardon, Thomas, Christopher Delgado, and Peter Matlon. 1992. “Determinants and eﬀects of income diversiﬁcation amongst farm households in Burkina Faso.” Journal of Development Studies. 28:264-296.

Reardon, Thomas, Peter Matlon, and Christopher Delgado. 1988. “Coping with

157 household-level food insecurity in drought-aﬀected areas of Burkina Faso.” World Development. 16(9):1065-1074.

Reardon, Thomas, and J.Edward Taylor. 1996. “Agroclimatic shocks, income inequality, and poverty: evidence from Burkina Faso.” World Development. 24(4):901- 914.

Rosegrant, Mark W., Michael S. Paisner, and Siet Meijer. 2001. “Global food pro- jections to 2020: emerging trends and alternative futures.” International Food Policy Research Institute (IFPRI): Washington, DC.

Rosenzweig, Mark, and Hans Binswanger. 1993. “Wealth, Weather Risk and the Composition and Proﬁtability of Agricultural Investments.” Economic Journal. 56- 78.

Smith, Robert E. 2004. “Land tenure, ﬁxed investment, and farm productivity: Evi- dence from Zambia’s Southern Province.” World Development. 32(10):16411661.

Timmer, C. Peter. 1988. “The Agricultural Transformation.” Handbook of Devel- opment Economics, Volume I, eds. Hollis Chenery and T.N. Srinivasan. Elsevier Science Publishers B.V.

Townsend, Robert. 1995. “Consumption Insurance: An Evaluation of Risk-Bearing Systems in Low-Income Economies.” Journal of Economic Perspectives. 9(3):83-102.

Udry, Christopher. 1996. “Gender, Agricultural Production, and the Theory of the Household.” Journal of Political Economy. 104(5):1010-1046.

USAID/Government of Ethiopia: Disaster Management and Food Security Sector, MOARD (GOE). 2009. “Tigray Regional Overview and Livelihood Zone Summaries.”

158 Livelihoods Integration Unit, Ministry of Agriculture and Rural Development, Disas- ter Management and Food Security Sector.

Woldenhanna, T., and A. Oskam. 2001. “Income diversiﬁcation and entry barriers: evidence from the Tigray region of northern Ethiopia. Food Policy. 26:351-365.

Wooldridge, Jeﬀrey M. 2002. Econometric Analysis of Cross Section and Panel Data. MIT Press, Cambridge, MA.

World Bank. 2011. Africa Development Indicators, The World Bank. Last update August 2011.

159 Appendix A: Derivation of Comparative Statics

from Chapter 2

The ﬁrst order conditions are the following:

∂Ut ∂ft ∂Ut [its]: τt − + ψs = 0 (1) ∂ct ∂its ∂lt

∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂Ut [itl]: τt + Etβτt+1 − + ψl = 0 (2) ∂ct ∂itl ∂ct+1 ∂itl ∂lt

Totally diﬀerentiate the ﬁrst order conditions:

2 2 ∂Ut ∂ ft ∂ψs ∂Ut ∂ ft ∂ψs (τt 2 + )dits + (τt + )ditl+ ∂ct ∂its ∂its ∂ct ∂its∂itl ∂itl 2 2 (3) ∂Ut ∂ ft ∂Ut ∂ft ∂ψs ∂Ut ∂ ft ∂ψs (τt )dit−1,l + ( + )dτt + (τt + )dAt = 0 ∂ct ∂its∂it−1,l ∂ct ∂its ∂τt ∂ct ∂its∂At ∂At

2 2 2 ∂Ut ∂ ft ∂ψl ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂ψl (τt + )dits + (τt 2 + Etβτt+1 2 + )ditl ∂ct ∂itl∂its ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂itl 2 ∂Ut ∂ ft ∂ψl ∂Ut ∂ft ∂ψl + (τt + )dit−1,l + ( + )dτt ∂ct ∂itl∂it−1,l ∂it−1,l ∂ct ∂itl ∂τt 2 2 ∂Ut ∂ ft ∂ψl ∂Ut+1 ∂ ft+1 ∂ψl + (τt + )dAt + (Etβτt+1 + Et )dit+1,s ∂ct ∂itl∂At ∂At ∂ct+1 ∂itl∂it+1,s ∂it+1,s 2 ∂Ut+1 ∂ ft+1 ∂ψl + (Etβτt+1 + Et )dit+1,l+ ∂ct+1 ∂itl∂it+1,l ∂it+1,l 2 ∂Ut+1 ∂ ft+1 ∂ψl ∂Ut+1 ∂ft+1 ∂ψl (Etβτt+1 + Et )dAt+1 + (Etβ + Et )dτt+1 = 0 ∂ct+1 ∂itl∂At+1 ∂At+1 ∂ct+1 ∂itl ∂τt+1 (4)

Since Etτt+1 = τt, any current change in tenure security results in an equal expected change in the next period’s tenure security (Etdτt+1 = dτt), regardless of

160 whether the realized return changes in the next period. A change in current tenure

∂i security cannot aﬀect the previous period’s investment, so t−1,l = 0 and ∂ψl = 0. ∂τt ∂τt+1 Since changes in tenure status are assumed to be exogenous, the derivatives involving

τt are independent of the other exogenous variables. Therefore, I can abstract from

these changes and focus only on the change in τt. Incorporating these assumptions, the total derivatives are as follows:

2 2 ∂Ut ∂ ft ∂ψs ∂Ut ∂ ft ∂ψs ∂Ut ∂ft ∂ψs (τt 2 + )dits + (τt + )ditl + ( + )dτt = 0 (5) ∂ct ∂its ∂its ∂ct ∂its∂itl ∂itl ∂ct ∂its ∂τt

∂U ∂2f ∂ψ ∂U ∂2f ∂U ∂2f ∂ψ (τ t t + l )di + (τ t t + E βτ t+1 t+1 + l )di + t ∂c ∂i ∂i ∂i ts t ∂c ∂i2 t t+1 ∂c ∂i2 ∂i tl t tl ts ts t tl t+1 tl tl (6) ∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂ψl + ( + Etβ + )dτt = 0 ∂ct ∂itl ∂ct+1 ∂itl ∂τt

These can be placed in matrix form:

2 2  ∂Ut ∂ ft ∂ψs ∂Ut ∂ ft ∂ψs    τt ∂c 2 + ∂i τt ∂c ∂i ∂i + ∂i dits t ∂its ts t ts tl tl =  2 2 2    ∂Ut ∂ ft ∂ψl ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂ψl τt + τt 2 + Etβτt+1 2 + ditl ∂ct ∂itl∂its ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂itl   (7) ∂Ut ∂ft ∂ψs − ∂c ∂i dτt − ∂τ dτt  t ts t  ∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂ψl − dτt − Etβ dτt − dτt ∂ct ∂itl ∂ct+1 ∂itl ∂τt

Cramer’s rule can be used to solve:

2 ∂Ut ∂ft ∂ψs ∂Ut ∂ ft ∂ψs − − τt + ∂ct ∂its ∂τt ∂ct ∂its∂itl ∂itl 2 2 ∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂ψl ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂ψl − − Etβ − τt 2 + Etβτt+1 2 + di ∂ct ∂i ∂ct+1 ∂i ∂τt ∂ct ∂i ∂ct+1 ∂i ∂i ts = tl tl tl tl tl (8) 2 2 dτt ∂Ut ∂ ft ∂ψs ∂Ut ∂ ft ∂ψs τt 2 + τt + ∂ct ∂its ∂its ∂ct ∂its∂itl ∂itl 2 2 2 ∂Ut ∂ ft ∂ψl ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂ψl τt + τt 2 + Etβτt+1 2 + ∂ct ∂itl∂its ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂itl

161 2 ∂Ut ∂ ft ∂ψs ∂Ut ∂ft ∂ψs τt 2 + − − ∂ct ∂its ∂its ∂ct ∂its ∂τt 2 ∂Ut ∂ ft ∂ψl ∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂ψl di τt + − − Etβ − tl = ∂ct ∂itl∂its ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂τt (9) 2 2 dτt ∂Ut ∂ ft ∂ψs ∂Ut ∂ ft ∂ψs τt 2 + τt + ∂ct ∂its ∂its ∂ct ∂its∂itl ∂itl 2 2 2 ∂Ut ∂ ft ∂ψl ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂ψl τt + τt 2 + Etβτt+1 2 + ∂ct ∂itl∂its ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂itl

If short-term investments are positive but long-term investments equal zero, the comparative statics are as follows:

2 2 2 ∂Ut ∂ft ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂ψl ∂Ut ∂ ft ∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂ψl (− )(τt 2 + Etβτt+1 2 + ) − (τt )(− − Etβ − ) di ∂ct ∂its ∂ct ∂i ∂ct+1 ∂i ∂itl ∂ct ∂its∂itl ∂ct ∂itl ∂ct+1 ∂itl ∂τt 162 ts tl tl = 2 2 2 2 2 (10) ∂Ut ∂ ft ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂ψl ∂Ut ∂ ft ∂Ut ∂ ft ∂ψl dτt (τt 2 )(τt 2 + Etβτt+1 2 + ) − (τt )(τt + ) ∂ct ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂itl ∂ct ∂its∂itl ∂ct ∂itl∂its ∂its

2 2 ∂Ut ∂ ft ∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂ψl ∂Ut ∂ft ∂Ut ∂ ft ∂ψl (τt 2 )(− − Etβ − ) − (− )(τt + ) ditl ∂ct ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂τt ∂ct ∂its ∂ct ∂itl∂its ∂its = 2 2 2 2 2 (11) ∂Ut ∂ ft ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂ψl ∂Ut ∂ ft ∂Ut ∂ ft ∂ψl dτt (τt 2 )(τt 2 + Etβτt+1 2 + ) − (τt )(τt + ) ∂ct ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂itl ∂ct ∂its∂itl ∂ct ∂itl∂its ∂its

If long-term investments are positive but short-term investments equal zero, the comparative statics are the following:

2 2 2 ∂Ut ∂ft ∂ψs ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂Ut ∂ ft ∂ψs ∂Ut ∂ft ∂Ut+1 ∂ft+1 (− − )(τt 2 + Etβτt+1 2 ) − (τt + )(− − Etβ ) dits ∂ct ∂its ∂τt ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂its∂itl ∂itl ∂ct ∂itl ∂ct+1 ∂itl = 2 2 2 2 2 (12) ∂Ut ∂ ft ∂ψs ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂Ut ∂ ft ∂ψs ∂Ut ∂ ft dτt (τt 2 + )(τt 2 + Etβτt+1 2 ) − (τt + )(τt ) ∂ct ∂its ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂its∂itl ∂itl ∂ct ∂itl∂its 2 2 ∂Ut ∂ ft ∂ψs ∂Ut ∂ft ∂Ut+1 ∂ft+1 ∂Ut ∂ft ∂ψs ∂Ut ∂ ft (τt 2 + )(− − Etβ ) − (− − )(τt ) ditl ∂ct ∂its ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂its ∂τt ∂ct ∂itl∂its = 2 2 2 2 2 (13) ∂Ut ∂ ft ∂ψs ∂Ut ∂ ft ∂Ut+1 ∂ ft+1 ∂Ut ∂ ft ∂ψs ∂Ut ∂ ft dτt (τt 2 + )(τt 2 + Etβτt+1 2 ) − (τt + )(τt ) ∂ct ∂its ∂its ∂ct ∂itl ∂ct+1 ∂itl ∂ct ∂its∂itl ∂itl ∂ct ∂itl∂its

In the case that neither short nor long-term investments are made, derivatives involving both non-negativity constraints remain in the comparative statics. 163