title: An Investigation of the Macroeconomic and Monetary Effects of Dollarization in Eritrea

authors: Mengesha, Lula G.

subjects: Dollarization

abstract: Most countries of the developing world have voluntarily or involuntarily been involved in using foreign currencies and trapped their economies for either the best or worst outcomes of dollarization. For those countries that have fully dollarized their economies, dollarization has provided some benefits in terms of bringing economic stability. However, the benefits in these economies came with costs that have worsened the income inequality and poverty that exist within their economies. Similarly, some countries that rely partially on their own currencies and partially on foreign currencies have seen different outcomes of partial dollarization.
	This research has specifically examined the reliance of the Eritrean economy on hard currency. Despite numerous studies that have been conducted regarding the Eritrean economy, the dollarization of Eritrea is an important issue that has not been investigated. This research has uncovered what kind of dollarization the Eritrean economy has and also what factors drive the Eritrean dollarization. It has been found that there is partial dollarization with evidence of financial, sovereign liability and underground dollarization in the economy. Real dollarization is only manifested in the form of selling houses and lands in exchange for hard currency. It has also been discovered that banking practices, the structure of the interest rate, loans issued to the private sector relative to the government sector and the Eritrean monetary system, particularly the introduction of new currency, followed by the second war with Ethiopia in 1998, are some of the causative factors. 
	Furthermore, this research has investigated the impact of partial dollarization of the Eritrean economy on macro-economic variables with specific examination on the exchange rate volatility, inflation and the monetary policy transmission mechanisms. In each analysis, quarterly data starting from 1996 to 2008 is employed. The fundamental reason for selecting this time period for the study is the inability to obtain the data for some of the variables outside the specified time period. Exponential Generalised Autoregressive Conditionally Heteroscedasticity in mean (EGARCH) model is applied over real official exchange rate and on both nominal as well as real black market exchange rates. Dollarization measured by a hard currency index is then augmented in the variance equation of EGARCH-M (1, 1). The results show that partial dollarization of the Eritrean economy enhances the volatility of the real official exchange rate, the nominal and the real black market exchange rates.
To see whether partial dollarization reduces or produces inflation in the Eritrean economy, Vector Error Correction Models together with Dynamic Ordinary Least Squares (DOLS) are employed. The results indicate that inflation increases as a result of an increase in dollarization. This applies regardless of whether official or black market exchange rate data are used in the estimation. In terms of the short-run dynamics involved in the long-run relationship between dollarization and inflation, the speed of adjustment towards long-run equilibrium ranges from 7.2-7.6 percent per quarter. Both error correction terms are negative. This indicates that the adjustment process is stable and convergent towards the long-run equilibrium which implies that there is a guaranteed long-run relationship between inflation and the remaining variables including dollarization.
To examine the effectiveness of the channels of the monetary policy, Vector Autoregressive (VAR), Structural Autoregressive (SVAR) and Toda and Yamamoto (TY) models have been used. The results suggest that there is an effective exchange rate channel through the black market, an ineffective credit channel through the credit issued to the private sector and an effective credit channel through the credit issued to the government.  
In general, the contribution of this study can be summarized in three different areas: first, the measurement of dollarization; second, the determinants of dollarization; third, the effects of dollarization. Some of the policy implications of the findings are first, the Bank of Eritrea should address its exchange rate policy by revisiting the current official exchange rate and, at the same time, by addressing the growth of the black market foreign exchange. The Bank of Eritrea should also address its regulation on banking practices and the structure of interest rates. Second, the bank should create a conducive environment for the private sector to promote economic growth and minimise excessive government expenditure which requires fiscal discipline. Third, there should be an effort made to pave the ways for economic and financial cooperation with neighbouring countries.

text: An Investigation of the Macroeconomic and Monetary Effects of Dollarization in Eritrea Most countries of the developing world have voluntarily or involuntarily been involved in using foreign currencies and trapped their economies for either the best or worst outcomes of dollarization. For those countries that have fully dollarized their economies, dollarization has provided some benefits in terms of bringing economic stability. However, the benefits in these economies came with costs that have worsened the income inequality and poverty that exist within their economies. Similarly, some countries that rely partially on their own currencies and partially on foreign currencies have seen different outcomes of partial dollarization.
	This research has specifically examined the reliance of the Eritrean economy on hard currency. Despite numerous studies that have been conducted regarding the Eritrean economy, the dollarization of Eritrea is an important issue that has not been investigated. This research has uncovered what kind of dollarization the Eritrean economy has and also what factors drive the Eritrean dollarization. It has been found that there is partial dollarization with evidence of financial, sovereign liability and underground dollarization in the economy. Real dollarization is only manifested in the form of selling houses and lands in exchange for hard currency. It has also been discovered that banking practices, the structure of the interest rate, loans issued to the private sector relative to the government sector and the Eritrean monetary system, particularly the introduction of new currency, followed by the second war with Ethiopia in 1998, are some of the causative factors. 
	Furthermore, this research has investigated the impact of partial dollarization of the Eritrean economy on macro-economic variables with specific examination on the exchange rate volatility, inflation and the monetary policy transmission mechanisms. In each analysis, quarterly data starting from 1996 to 2008 is employed. The fundamental reason for selecting this time period for the study is the inability to obtain the data for some of the variables outside the specified time period. Exponential Generalised Autoregressive Conditionally Heteroscedasticity in mean (EGARCH) model is applied over real official exchange rate and on both nominal as well as real black market exchange rates. Dollarization measured by a hard currency index is then augmented in the variance equation of EGARCH-M (1, 1). The results show that partial dollarization of the Eritrean economy enhances the volatility of the real official exchange rate, the nominal and the real black market exchange rates.
To see whether partial dollarization reduces or produces inflation in the Eritrean economy, Vector Error Correction Models together with Dynamic Ordinary Least Squares (DOLS) are employed. The results indicate that inflation increases as a result of an increase in dollarization. This applies regardless of whether official or black market exchange rate data are used in the estimation. In terms of the short-run dynamics involved in the long-run relationship between dollarization and inflation, the speed of adjustment towards long-run equilibrium ranges from 7.2-7.6 percent per quarter. Both error correction terms are negative. This indicates that the adjustment process is stable and convergent towards the long-run equilibrium which implies that there is a guaranteed long-run relationship between inflation and the remaining variables including dollarization.
To examine the effectiveness of the channels of the monetary policy, Vector Autoregressive (VAR), Structural Autoregressive (SVAR) and Toda and Yamamoto (TY) models have been used. The results suggest that there is an effective exchange rate channel through the black market, an ineffective credit channel through the credit issued to the private sector and an effective credit channel through the credit issued to the government.  
In general, the contribution of this study can be summarized in three different areas: first, the measurement of dollarization; second, the determinants of dollarization; third, the effects of dollarization. Some of the policy implications of the findings are first, the Bank of Eritrea should address its exchange rate policy by revisiting the current official exchange rate and, at the same time, by addressing the growth of the black market foreign exchange. The Bank of Eritrea should also address its regulation on banking practices and the structure of interest rates. Second, the bank should create a conducive environment for the private sector to promote economic growth and minimise excessive government expenditure which requires fiscal discipline. Third, there should be an effort made to pave the ways for economic and financial cooperation with neighbouring countries.

year: 2013

title: Time-of-flight perception pipeline for selective green asparagus harvesting: Theory and application

authors: Peebles, Matthew Christopher Scott

subjects: Asparagus

abstract: Generally declining labour markets, coupled with a significant increase in the projected global population have raised concerns over a potential food scarcity crisis. In response to this, the agricultural industry is currently undergoing a technological revolution, especially with respect to robotic harvesting.
Green asparagus, due to its unique physiology and labour intensive harvesting requirements, has been a long-time crop of focus in this field. Despite the relatively large body of work surrounding the development of selective robotic asparagus harvesting solutions, no such machine has yet ascended to the level of being commercially competitive. A critical component of a prospective selective robotic asparagus harvester, that is missing from contemporary machines, is a robust vision system, capable of detecting, and localising, harvest-eligible spears in real-time from a commercial asparagus field.
This thesis presents a novel perception pipeline for time-of-flight images that can achieve this task, and describes the implementation of the proposed perception pipeline into two functional robotic harvesters, AHR-1 and AHR2, capable of harvesting green asparagus spears in a real-world commercial setting.
The proposed perception pipeline achieves spear detection by firstly, segmenting the soil plane from an input pointcloud of an asparagus row, generated with a time-of-flight camera. Two plane detection methods, namely RANSAC and a modified version Hyun’s method (MHM) were investigated for this purpose. A detailed evaluation of the performance of each method on cluttered scenes revealed both RANSAC and MHM to be suitable for soil plane segmentation of asparagus beds, with both methods demonstrating similar RMSE error across a variety of scenes. The stability of model predictions made by RANSAC was generally found to be lower than that achieved by MHM, particularly for high-clutter scenes. This was determined to be due to the non-deterministic nature of RANSAC coupled with high degrees of soil plane occlusion in high clutter scenes.
Following soil plane removal, non-asparagus points pertaining to rocks, weeds, and other field debris are then filtered from the scene. This is achieved by coarse filtering input points based on the output of a FRCNN model. The various FRCNN models utilised in this work were trained with a novel dataset of labelled images, collected from various asparagus farms throughout New Zealand and California, USA. Evaluation of these models revealed a typical maximum F1 score of 0.73, providing reasonable frame-by-frame identification of asparagus features.
The remaining point clusters, representing each asparagus spear in the scene, are then filtered to remove flying pixels; a typical artefact of time-of-flight imaging. A novel geometric filter, named the closest point filter (CP filter), was developed for this task. Based on laboratory testing, it was found that this filter achieved a 68% reduction in the mean standard deviation of intra-cluster distance with respect to ground-truth positions, resulting in a significant improvement in the accuracy of base point predictions.
The proposed perception pipeline is implemented inside a ROS framework, and deployed on two robotic harvesting platforms. The first platform, AHR-1, was developed as a proof-of-concept system. This system provided a wealth of knowledge which was utilised to develop a more complete prototype asparagus harvester, AHR-2. The design of AHR-1 and AHR-2 was largely informed by the shortcomings of existing asparagus harvesting robots from the literature, particularly with respect to their harvesting, and detection strategies.
The literature surrounding robotic asparagus harvesters does not provide an adequate method for objectively evaluating the performance of such systems. Consequentially, performance metrics pertaining to existing robotic harvesters are relatively opaque, particularly with respect to the selective nature of the harvesting task. These inadequacies necessitated the development of a novel evaluation method for the evaluation of a selective asparagus harvester, which was utilised to measure the performance of both AHR-1 and AHR-2.
Several field trials of AHR-1 and AHR-2 were conducted on farms throughout New Zealand and California, USA. The resulting analysis revealed that AHR-2 achieved state-of-the-art performance, harvesting 45.9% of all harvest eligible spears with a precision of 87.2% at its nominal ground speed of 0.3m/s. The vision system detected 97% of all harvest-eligible spears with a precision of 74.5% at this speed. When operating at a ground speed of 0.7m/s AHR 2’s harvesting rate fell to 22% with 95% precision. The corresponding drop in the vision systems detection rate was relatively small, dropping to 92.5% with 87.2% precision at a ground speed of 0.7m/s. From this it was concluded that the vision system outperformed the available hardware. The perception pipeline proposed by this thesis achieved state-of-the-art performance at a variety of ground speeds. The pipeline demonstrated a robustness to the unstructured nature of commercial asparagus fields, providing spear locations which successfully facilitated the robotic harvest of green asparagus spears.

text: Time-of-flight perception pipeline for selective green asparagus harvesting: Theory and application Generally declining labour markets, coupled with a significant increase in the projected global population have raised concerns over a potential food scarcity crisis. In response to this, the agricultural industry is currently undergoing a technological revolution, especially with respect to robotic harvesting.
Green asparagus, due to its unique physiology and labour intensive harvesting requirements, has been a long-time crop of focus in this field. Despite the relatively large body of work surrounding the development of selective robotic asparagus harvesting solutions, no such machine has yet ascended to the level of being commercially competitive. A critical component of a prospective selective robotic asparagus harvester, that is missing from contemporary machines, is a robust vision system, capable of detecting, and localising, harvest-eligible spears in real-time from a commercial asparagus field.
This thesis presents a novel perception pipeline for time-of-flight images that can achieve this task, and describes the implementation of the proposed perception pipeline into two functional robotic harvesters, AHR-1 and AHR2, capable of harvesting green asparagus spears in a real-world commercial setting.
The proposed perception pipeline achieves spear detection by firstly, segmenting the soil plane from an input pointcloud of an asparagus row, generated with a time-of-flight camera. Two plane detection methods, namely RANSAC and a modified version Hyun’s method (MHM) were investigated for this purpose. A detailed evaluation of the performance of each method on cluttered scenes revealed both RANSAC and MHM to be suitable for soil plane segmentation of asparagus beds, with both methods demonstrating similar RMSE error across a variety of scenes. The stability of model predictions made by RANSAC was generally found to be lower than that achieved by MHM, particularly for high-clutter scenes. This was determined to be due to the non-deterministic nature of RANSAC coupled with high degrees of soil plane occlusion in high clutter scenes.
Following soil plane removal, non-asparagus points pertaining to rocks, weeds, and other field debris are then filtered from the scene. This is achieved by coarse filtering input points based on the output of a FRCNN model. The various FRCNN models utilised in this work were trained with a novel dataset of labelled images, collected from various asparagus farms throughout New Zealand and California, USA. Evaluation of these models revealed a typical maximum F1 score of 0.73, providing reasonable frame-by-frame identification of asparagus features.
The remaining point clusters, representing each asparagus spear in the scene, are then filtered to remove flying pixels; a typical artefact of time-of-flight imaging. A novel geometric filter, named the closest point filter (CP filter), was developed for this task. Based on laboratory testing, it was found that this filter achieved a 68% reduction in the mean standard deviation of intra-cluster distance with respect to ground-truth positions, resulting in a significant improvement in the accuracy of base point predictions.
The proposed perception pipeline is implemented inside a ROS framework, and deployed on two robotic harvesting platforms. The first platform, AHR-1, was developed as a proof-of-concept system. This system provided a wealth of knowledge which was utilised to develop a more complete prototype asparagus harvester, AHR-2. The design of AHR-1 and AHR-2 was largely informed by the shortcomings of existing asparagus harvesting robots from the literature, particularly with respect to their harvesting, and detection strategies.
The literature surrounding robotic asparagus harvesters does not provide an adequate method for objectively evaluating the performance of such systems. Consequentially, performance metrics pertaining to existing robotic harvesters are relatively opaque, particularly with respect to the selective nature of the harvesting task. These inadequacies necessitated the development of a novel evaluation method for the evaluation of a selective asparagus harvester, which was utilised to measure the performance of both AHR-1 and AHR-2.
Several field trials of AHR-1 and AHR-2 were conducted on farms throughout New Zealand and California, USA. The resulting analysis revealed that AHR-2 achieved state-of-the-art performance, harvesting 45.9% of all harvest eligible spears with a precision of 87.2% at its nominal ground speed of 0.3m/s. The vision system detected 97% of all harvest-eligible spears with a precision of 74.5% at this speed. When operating at a ground speed of 0.7m/s AHR 2’s harvesting rate fell to 22% with 95% precision. The corresponding drop in the vision systems detection rate was relatively small, dropping to 92.5% with 87.2% precision at a ground speed of 0.7m/s. From this it was concluded that the vision system outperformed the available hardware. The perception pipeline proposed by this thesis achieved state-of-the-art performance at a variety of ground speeds. The pipeline demonstrated a robustness to the unstructured nature of commercial asparagus fields, providing spear locations which successfully facilitated the robotic harvest of green asparagus spears.

year: 2021

title: Aroha

authors: Maxwell, Te Kahautu

abstract: Waita Tira (choral), Lyrics composed by Te Kahautu Maxwell and Performed by Ōpotoki Mai Tawhiti (Kapa Haka group)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

text: Aroha Waita Tira (choral), Lyrics composed by Te Kahautu Maxwell and Performed by Ōpotoki Mai Tawhiti (Kapa Haka group)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

year: 2023-02-22