Ali, Daniel Ayalew

Person:
Ali, Daniel Ayalew

Development Research Group, World Bank

Fields of Specialization

Agriculture and Rural Development, Land Economics

ORCID

0000-0002-3253-4897

External Links

Google Scholar

Departments

Development Research Group, World Bank

Last updated: January 2, 2025

Biography

Daniel Ayalew Ali is a Senior Economist in the Sustainability and Infrastructure Team of the Development Research Group. His main research area is applied microeconomics with a focus on Africa and rural issues: property rights, contracts in land and investment incentives, impact evaluation, risk coping and management. He has done extensive research on the impact evaluation of land related projects in Ethiopia, Rwanda, Tanzania, Nigeria and Ghana. He received his PhD in Economics from the Catholic University of Leuven, Belgium.

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Publication Search Results

Now showing 1 - 10 of 44

Reforming Land Valuation and Taxation in Ukraine: A Path towards greater Sustainability Fairness, and Transparency
(Washington, DC: World Bank, 2024-12-13) Deininger, Klaus; Ali, Daniel Ayalew; Bukin, Eduard; Martyn, Andrii
The shift from administrative to market-based property valuation is critical for effective decentralization and local revenue collection in Ukraine. To demonstrate the viability of such a shift, this paper uses prices for nearly 200,000 agricultural land sale transactions in 2021–24 together with parcel attributes from public data to estimate a hedonic model and predict prices for all of Ukraine’s 7.5 million commercial agricultural land parcels. Despite the war, mean predicted prices are significantly above current ‘normative monetary’ valuations (NMVs). Inter-regional differences are pronounced. Steps to extend mass appraisal to residential urban properties and legislative changes needed to replace NMVs with a market-based approach are discussed, noting that a shift to market-based valuation will have far-reaching implications for the volume of public and private investment that can be attracted to support reconstruction; the likely effectiveness of such investment; and local governments’ ability to benefit from any land value appreciation that may result from it.
Land Price Effects of Informality, Farm Size, and Land Reform: Evidence from More Than One Million Transactions in Ukraine
(Washington, DC: World Bank, 2025-01-02) Deininger, Klaus; Ali, Daniel Ayalew
This paper uses a rich set of geo-coded administrative and remotely sensed data on more than 1 million agricultural land transactions in Ukraine to explore how informality, size, and recent land reforms affect land prices. Three main findings are highlighted. First, absence of registered rights generates large negative externalities, the size of which plausibly exceeds the cost of registering all land. By contrast, informality of lease contracts is a choice that may enable owners to evade regulatory obstacles that prevent them from renegotiating contracts to obtain more favorable terms. Second, while land market liberalization generated significant indirect benefits, gains are unevenly distributed. Furthermore, competition in sales markets remains limited, pointing to scope for measures—including reducing the transaction costs of selling land and accessing mortgage finance, improving publicity of pending land sales, and use of electronic auctions—to enhance the reforms’ impact on efficiency and equity. Third, size at the parcel, field, and farm levels is associated with higher per hectare prices, pointing to scope for market-based land consolidation and growth of medium-size farms to increase land values and productivity. Achieving this potential will require measures to limit speculative land acquisition and exercise of market power by making local land markets more competitive and using market-based land valuation as a basis for taxing land on a recurrent basis and any capital gains due to land appreciation.
Micro-Level Impacts of the War on Ukraine’s Agriculture Sector: Distinguishing Local and National Effects over Time
(Washington, DC: World Bank, 2024-08-21) Deininger, ,Klau; Ali, Daniel Ayalew; Kussul, Nataliia; Lemoine, Guido; Shelestov, Andrii
This paper uses remotely sensed and farm-level data to assess the micro-level impacts of the war in Ukraine. Remotely sensed, high-resolution data on areas of war-induced agricultural field damage in different periods are combined with crop cover data for a 2019–23 panel of about 10,000 village councils. Estimates suggest that there were significant negative effects of field damage on crop area, with persistent, direct impacts, the size of which increased over time. However, the economic losses due to conflict-induced increased transport costs reduced profitability by more than 60 percent, far surpassing the losses from direct crop damage in conflict areas. The lack of diversification into less transport cost sensitive, higher value crops—even in areas far from the conflict zone—points to constraints to adaptation and diversification. By increasing the resilience of farmers in non-conflict areas, removing such constraints could accelerate post-conflict recovery and complement efforts toward reconstruction in directly affected areas.
Using Satellite Imagery and a Farmer Registry to Assess Agricultural Support in Conflict Settings: The Case of the Producer Support Grant Program in Ukraine
(Washington, DC: World Bank, 2024-09-20) Deininger, Klaus; Ali, Daniel Ayalew
While cash transfers have emerged as an attractive option to minimize negative long-term impacts of conflict, the scope for targeting and assessing their impact in such settings is often challenging. This paper shows how a digital farmer registry in Ukraine (the State Agrarian Register) helped to target and evaluate such a program, using the country’s $50 million Producer Support Grant in a way that largely avoided mis-targeting. The analysis applies a difference-in-differences design with panel data from 2019–23 on crop cover at the parcel/farm level for the universe of eligible farmers registered in the State Agrarian Register. The findings suggest that the program significantly increased area cultivated, although the effect size remained modest. Impacts were most pronounced near the frontline and for the smallest farmers. The paper discusses the implications in terms of a more diversified menu of support options and the scope of using the State Agrarian Register to help to implement these options, as well as lessons beyond Ukraine.
Impacts of Transparent Online Auctions on Public Land Lease Revenue: Evidence from Legal and Administrative Changes in Ukraine
(World Bank, Washington, DC, 2022-10) Deniniger, Klaus; Ali, Daniel Ayalew; Neyter, Roman
Although millions of hectares of public land are transferred to private parties each year, often with unsatisfactory results, evidence on mechanisms to achieve better outcomes is scant. This paper analyzes the impact of a 2021 reform in Ukraine that -after earlier digitization efforts did not produce desired results- mandated use of transparent online auctions by local government to transfer rights to public agricultural land. The shift to a collusion-proof electronic auction system led to a near-instantaneous doubling of lease revenue. Had all public land Ukraine transferred since 2015 auctioned using post-reform mechanisms, local governments would have received incremental lease revenue of US$500 million per year. In countries with large public land endowments, legal and regulatory reform to ensure rights to public land are allocated competitively and in a decentralized way could possibly improve social, economic, and environmental outcomes.
Quantifying War-Induced Crop Losses in Ukraine in Near Real Time to Strengthen Local and Global Food Security
(Washington, DC : World Bank, 2022-07) Deininger, Klaus; Ali, Daniel Ayalew; Lemoine, Guido; Yailimova, Hanna
This paper uses a 4-year panel (2019–2022) of 10,125 village councils in Ukraine to estimate direct and indirect effects of the war started by Russia on area and expected yield of winter crops. Satellite imagery is used to provide information on direct damage to agricultural fields; classify crop cover using machine learning; and compute the Normalized Difference Vegetation Index (NDVI) for winter cereal fields as a proxy for yield. Without conflict, winter crop area would have been 9.14 rather than 8.38 mn. ha, a 0.75 mn. ha reduction, 86% of which is due to economy-wide effects. The estimated conflict-induced drop in NDVI for winter cereal, which is particularly pronounced for small farms, translates into a 15% yield reduction or an output loss of 4.2 million tons. Taking area and yield reduction together suggests a war-induced loss of winter crop output of 20% if the current winter crop can be harvested fully.
How Urban Land Titling and Registry Reform Affect Land and Credit Markets: Evidence from Lesotho
(World Bank, Washington, DC, 2022-05) Deininger, Klaus; Ali, Daniel Ayalew
Using spatial fixed effects and time-varying controls, this paper draws on complete registry data for 1981–2019, supplemented by satellite imagery, to analyze impacts of urban land titling for some 40,000 grid cells in Lesotho. Beyond confirming the short-term impacts on female co-ownership and investment, previously reported, the paper documents medium-term impacts on land sale and mortgage market activity and women’s participation in these markets. Although titling was instrumental in ensuring the effectiveness of an earlier legal reform that allowed women to be co-owners of land, the credit and land market effects are due not to titling but to changes in policy to reduce the transaction cost of registering land that took effect just before titling started. Downward shifts in the time required to register transactions support this interpretation. The paper concludes by discussing what the evidence implies for design and evaluation of property registration programs.
Using Machine Learning to Assess Yield Impacts of Crop Rotation: Combining Satellite and Statistical Data for Ukraine
(World Bank, Washington, DC, 2020-06) Kussul, Nataliia; Deininger, Klaus; Lavreniuk, Mykola; Ali, Daniel Ayalew; Nivievskyi, Oleg
To overcome the constraints for policy and practice posed by limited availability of data on crop rotation, this paper applies machine learning to freely available satellite imagery to identify the rotational practices of more than 7,000 villages in Ukraine. Rotation effects estimated based on combining these data with survey-based yield information point toward statistically significant and economically meaningful effects that differ from what has been reported in the literature, highlighting the value of this approach. Independently derived indices of vegetative development and soil water content produce similar results, not only supporting the robustness of the results, but also suggesting that the opportunities for spatial and temporal disaggregation inherent in such data offer tremendous unexploited opportunities for policy-relevant analysis.
Using Satellite Imagery to Create Tax Maps and Enhance Local Revenue Collection
(Taylor and Francis, 2020) Ali, Daniel Ayalew; Deininger, Klaus
Although taxes on land and property have many desirable attributes, the challenge of ensuring completeness of tax rolls and currency of valuations preclude their effective use to support urbanization and service provision in many developing countries. The example of Kigali shows how building footprints and heights generated from high-resolution satellite imagery, together with sales prices and routine statistical data, allow to assess and improve coverage and design of property tax systems. We show that only 40% of potential land lease fee revenue (of US$ 4.9 million) was collected and that moving to 1% value-based tax would increase revenue almost 10 times while being less regressive than the current system. While this could allow reducing the tax burden for low-income groups, exemptions should be applied with caution based on careful empirical analysis.
Does Title Increase Large Farm Productivity? Institutional Determinants of Large Land-Based Investments’ Performance in Zambia
(World Bank, Washington, DC, 2021-06) Ali, Daniel Ayalew; Deininger, Klaus
The importance of well-functioning land markets for structural transformation via labor movements to the non-agricultural sector, growth in farm size, and the ability to use land as collateral for credit and has long motivated Government efforts at reducing the transaction costs of registering and transferring land and supporting large farm formation. Rigorous evidence on the effect of such measures has, however, been scant. This paper explores the impact of institutional arrangements on productivity for a representative sample of 3,000 large farms in Zambia, one of the earliest African adopters of such policies to close this gap. Instrumental-variable (IV) regressions suggest title has no effect on productivity, investment, or credit access and reduces rather than improves rental market participation. Measures to harness Zambia’s potential include improving title quality through exclusive use of digital registries; imposition of a land tax on state land to incentivize productive rather than speculative land use and cancel outdated legacy documents; and greater involvement of traditional authorities in record maintenance and land management, possibly via land tax revenue sharing.