Nunn and Puga (2012). A replication and extension using Stata.

 

RUGGEDNESS: THE BLESSING OF BAD GEOGRAPHY IN AFRICA

      A replication and extension

1.     Introduction and Replication[1]

Under normal conditions, rugged terrain negatively affects income because it can hamper trade and other productive activities like agriculture. Nonetheless, through quantitative analysis Nunn and Puga (2012) demonstrate that, in the case of Africa, there is a positive differential effect of ruggedness on income. Moreover, they explain that the reason of this positive effect is because ruggedness limited the long-term negative effects of slave trades.

The purpose of replicating this paper is to deepen the understanding on how geography, via its effect on history, can impact economic development. The following replication and extension paper is organized as follows section 2 analyses the effect of ruggedness on economic growth after year 2000. Section 3 analyses the effect of ruggedness on ethnic fractionalization (EF). Section 4 analyses the effect of ruggedness on corruption perception. Section 5 analyses the impact of ruggedness in both EF and CPI when including slave export intensity (SEI). Finally, section 6 presents the conclusions.

Nunn and Puga (2012) first use a basic regression model to explore the impact of ruggedness on GDP per capita in year 2000, this model includes the effect of ruggedness for the entire world, a control variable for African countries, and the interaction effect of ruggedness and African countries. In order to address for omitted variable differential effects, Nunn and Puga (2012) also include other controls like diamonds extracted per square kilometre, percentage of fertile soil, percentage of land with tropical climate, and distance to coast. After performing their initial regressions, they check for robustness of their model. They show, in all iterations, that their results are very robust.

On subsequent analysis they check for the effects of ruggedness by prevalent characteristics in Africa and the effects of ruggedness in different regions within Africa. On another model, they include SEI and conclude that SEI “fully accounts for the differential effect of ruggedness within Africa”. Finally, they analyse the effects of ruggedness, and SEI on rule of law, from these last set of regressions they conclude that SEI “adversely [affects] domestic institutions today”. All the replications are shown on Appendix 1. The replication has the same results as those shown on Nunn and Puga (2012). Therefore, the replications present no further implications.

2.     Extension One  – The effect of ruggedness on economic growth after year 2000

With the purpose of addressing for compression of history, Nunn and Puga (2012) also perform regressions using average annual income between years 1950 and 2000. Across all regressions they find a very robust, positive, and statistically significant differential effect of ruggedness in GDP. The first extension of this paper aims to analyse if the effect of ruggedness persists after year 2000. Therefore, regression analysis having GDP for years 2005, 2010 and 2015 as dependent variables is performed. GDP data is obtained from Maddison (2020).

All performed regressions have a positive and statistically significant effect of ruggedness in Africa and a negative effect of ruggedness in the world. However, in years 2005 and 2010 the differential effect is slightly negative, while in year 2015 is positive but close to zero. Moreover, the differential effect for all years gets progressively closer to zero. The results of the differential effect for years 2005, 2010, and 2015 could suggest that, as years pass by, the continuous negative impact of ruggedness in productive activities could eventually overcome the historical benefits of ruggedness in Africa. It should also be noted that between years the significance levels vary slightly. (See Table 1).

 

3.     Extension two – The effect of ruggedness on ethnic fractionalization

Enslavement in Africa was usually performed through raids and kidnapping by people from one ethnicity against another, sometimes even between people of the same ethnicity. (Northrup, 1978; Lovejoy, 2000). Research has suggested that EF is a significant factor of underdevelopment in Africa (Easterly, W. & Levine R., 1997; Lea, A., 2014). Concerning slave exports, Whatley and Gillezeau (2010) investigated the transatlantic slave trade and found a positive relation between ethnic heterogeneity and slave exports. Moreover, Nunn (2008) explains that slave procurement caused EF and state collapse. 

The second extension of this paper analyses the effect of ruggedness on EF. Since ruggedness reduced SEI and SEI increased EF, “hypothesis 1 (H1)” is that there is a negative correlation between ruggedness and EF. To perform this extension, EF for year 2010[2] will be computed using the approach of Alesina, Devleeschauwer, Easterly, Kurlat, and Wacziarg (2003) (See Appendix 2). Moreover, shares of ethnic groups will be retrieved from Drazanova (2019). Data on Drazanova (2019) had several ethnic group share duplicates, these duplicates were removed before computing EF. 

The regression results are not significant when including all controls (See Table 2). However, in all the models that do not include the variable measuring the percentage of land surface that has tropical climates show significant results (See Table G in Appendix 3). It is plausible that results are not significant because of the reduced number of observations (observations dropped from 170 to 155). Nevertheless, as proposed in H1, the magnitude of the effect of ruggedness in Africa is negative.

 

4.     Extension three – The effect of ruggedness on corruption perception

Through quantitative research, Pak Hung (2001), showed that corruption has a negative impact on economic growth; as well he finds that corruption affects growth through political instability, that it reduces the share of private investment, and that if lowers human capital levels. Concerning slavery trades, Nunn and Wantchekon (2011) show that the descendants of people who were heavily raided experience lower levels of trust. Trust is directly related to corruption. Li and Wu (2010) argue that corruption tends to be more destructive and inefficient in countries with a low level of trust.  Moreover, interpersonal and political trust can be “both a cause and a consequence of corruption” (Morris and Klesner, 2010).

Extension three aims to analyse the effect of ruggedness on perception of corruption. To perform the regression Corruption Perception Index (CPI) data for year 2010 is retrieved from Transparency International (2010). Since ruggedness prevented raids, heavy raided areas have lower levels of trust, and there is a strong relation between trust and corruption, “hypothesis 2 (H2)” is that there is a positive correlation between ruggedness and the corruption perception index. Regression results are significant and confirm H2 (See Table 2). Moreover, the magnitude of the effect of ruggedness on CPI for the rest world is statistically significant to the 10% level but close to 0. It would be interesting to analyse the geographical effects in other regions and see if due to the harder conditions rugged terrain could have detrimental effects on perception of corruption.

5.     Extension Four – The impact of ruggedness in EF and CPI when including SEI

Extension four is performed with the in order to analyse what is the impact of ruggedness in EF and CPI once SEI is included in the model. “Hypothesis 3 (H3)” is that, regardless of the dependent variable (EF or CPI), the effect of ruggedness in Africa is mainly accounted by SEI . The performed regressions include all controls. Since EF and CPI data was gathered for year 2010, column 1 shows the results for a regression that has 2010 GDP as the dependent variable. Column 2 has EF as the dependent variable. Column 3 show the results for a regression with CPI as the dependent variable. The results are shown in table 3. As in the basic model, the results for the regression with EF as a dependent variable are not significant.

Columns 1 to 3 in table 3 show that the common effect of ruggedness almost remains unchanged. However, the magnitude of the effect of ruggedness in African countries gets very close to zero and is no longer statistically significant. These results confirm H3 and provide further support for explaining that the effect of ruggedness arises because of slave trades. Moreover, Columns 4 to 6 of table 3 report that there is an unconditional, significant, and negative relationship between ruggedness and slave exports. As well, the product of both the coefficient of ruggedness and the coefficient of SEI can be used to compute an alternative estimate of the indirect historic effect of ruggedness in CPI.

 

6.     Conclusions

Regressions using GDP of years after 2000, show that ruggedness in African countries maintains a positive correlation with economic growth. However, years 2005 and 2010 show a negative differential effect. Additionally, year 2015 shows a positive differential effect but the effect is minimal and close to zero. These results could suggest that, in the future, the persistent common negative effect of ruggedness in productive activities could overweight the previous benefits from ruggedness due to the restriction of slave trades in Africa. 

Extensions two, three, and four are performed with the intention to answer three hypotheses. H1 states that there will be a negative correlation between ruggedness and EF. H2 states that there is a positive correlation between ruggedness and the corruption perception index. H3 states that regardless of the dependent variable (EF or CPI), the effect of ruggedness in Africa is mainly accounted by SEI. All three hypotheses are confirmed. It should be noted that the results of regressions with EF as a dependent variable are not significant.

To conclude, Nunn and Puga (2012) research not only helps to have a better understanding of the causes and consequences of slave trades, but it also provides a ground-breaking approach for analysing geographical conditions. As with ruggedness, several geographical conditions are assumed to have unfavourable deterministic effects on economic growth. Nevertheless, Nunn and Puga (2012) show the importance of considering historical context when exploring the impact of geography in development.

 

References

Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S. & Wacziarg, R. (2003). Fractionalization, Journal of Economic growth, vol. 8, no. 2, pp.155–194.

 

Drazanova, Lenka. (2019). Historical Index of Ethnic Fractionalization Dataset (HIEF). Harvard Dataverse, V2. Available Online:  https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/4JQRCL [Accessed 4 March 2021]

 

Easterly, W. & Levine R. (1997). Africa's Growth Tragedy: Policies and Ethnic Divisions, The Quarterly Journal of Economics, Volume 112, Issue 4. Pages 1203–1250.

 

Harvard University (n.d.). Publications. Ruggedness: The Blessing of Bad Geography in Africa. Nathan Nunn. Available Online: https://scholar.harvard.edu/nunn/publications/ruggedness-blessing-bad-geography-africa [Accessed 27 February 2021]

 

Lea, A. (2014). National Versus Ethnic Identification in Africa: Modernization, Colonial Legacy, and the Origins of territorial Nationalism Research Note. World Politics 66 World Pol.

 

Li S. & Wu J. (2010). Why some countries thrive despite corruption: The role of trust in the corruption–efficiency relationship. Review of International Political Economy, Volume 17, Issue 1. Pages 129-154.

 

Lovejoy, P. (2000). Transformations in Slavery: A History of Slavery in Africa, 2nd ed. Cambridge University Press.

 

Maddison Project Database, version 2020. Bolt, J. & van Zanden. J. (2020), Maddison style estimates

of the evolution of the world economy. A new 2020 update

 

Morris S. and Klesner J. (2010). Corruption and Trust: Theoretical Considerations and Evidence From Mexico. Comparative Political Studies. 2010;43(10):1258-1285

 

Northrup, D. (1978). Trade without Rulers: Pre-Colonial Economic Development in South-Eastern Nigeria. Oxford, UK, Clarendon Press.

 

Nunn, N. (2008). The Long-Term Effects of Africa’s Slave Trades. Quarterly Journal of Economics 123:1, 139–176.

 

Nunn, N. & Wantchekon L. (2011). The Slave Trade and the Origins of Mistrust in Africa. American Economic Review.

 

Nunn, N. & Puga D. (2012). Ruggedness: The Blessing of Bad Geography in Africa. Review of Economics and Statistics. 2012; 94 (1): 20-36.

 

Pak Hung (2001). Corruption and Economic Growth. Journal of Comparative Economics. Volume 29, Issue 1, March 2001, Pages 66-79.

 

Transparency International (2010). Corruption Perception Index. Available Online: https://www.transparency.org/en/cpi/2010  [Accessed 6 March 2021]

 

Whatley, W. & Gillezeau, R. (2010). The Impact of the Transatlantic Slave Trade on Ethnic Stratification in Africa. American Economic Review 101(3):571-76.

Appendix 1 – Replication tables and figures from Nunn and Puga (2012)

 

Figure A – Ruggedness and GDP in African Countries.

Figure B – Ruggedness in Non-African Countries

Appendix 2 – Ethnic Fractionalization computation

 

Following Alesina, Devleeschauwer, Easterly, Kurlat, and Wacziarg (2003) approach, Ethnic Fractionalization is computed using the following formula:

where s_ij is the ethnic share group i (i = 1… N) in country j

 

 

Appendix 3 – Differential effect of ruggedness on Ethnic Fractionalization

 

---

[1] The data and do files were retrieved from Nunn’s Harvard University website (Harvard University, n.d.). However, there was no coding for the scatter plot graphs or the tables output. As well, the coding of the regressions that included ‘standard controls’ had to be modified to work properly.

[2] Year 2010 is chosen because it is the year that has more observations for EF and CPI.

A tuber for Ghanaian development: Influence of agro-processing activities in the lives and livelihoods of cassava farmers.

 "To Ghana and the Ghanaian people. Thank you for making me feel like home".  After doing a 4 month fieldwork in Ghana I successfully defended my thesis. The thesis has now been published in Lund University LUP and can be found under the following link: https://lup.lub.lu.se/student-papers/search/publication/9095319 

The study explores the influence of agro-processing activities in the lives and livelihoods of cassava farmers in five communities located in four districts in the Ashanti and Volta regions in Ghana. The qualitative research is done by performing thirty-six semi-structured interviews to farmers and stakeholders along the cassava value chain. The research concludes that agro-processing activities allowed cassava farmers to generate additional income and later invest it to improve their lives and livelihoods and those of their household members. Specifically, the investigation indicated that the additional income was predominantly generated through two channels: First, benefiting all farmers, through the influence of agro-processing activities in the use of new farming technologies, farm-size increases, and improved marketing opportunities. Second, benefiting those farmers who were processing cassava, through the additional income obtained from selling agro-processed products as opposed to raw cassava. In addition, the study found that the social development area in which farmers invested the most was education, followed by health, and infrastructure. As well, the research revealed that agro-processing activities positively influenced increasing opportunities for women and development of partnerships. 

In my thesis acknowledgements, apart from Ghana, I present my gratitude:

        -To God, for your continuous source of strength to face and overcome the challenges of life. 

        -To my father, for showing me through example that education and hard-work are the pillars

          of a man. I love you. 

        -To my mother, for giving me all her love and consequently giving me the capacity to love. 

          I adore you. 

        -To my program director, for your trust to make me part of the Ghana research project. 

        -To my thesis supervisor, for your academic advice, but most importantly for your spiritual 

          support and friendship. 

        -To the direct contributors of this research. Especially to Martin, Magnus, Ibrahim, Prince,                      Godwin, Eric, Jacob, Dela, Sylvester, Promise, Bempah, Elvis, Francis, Israel, and all the                     interviewees that took part of the study. Thank you for sharing your knowledge with me. 

        -To all the ones that have been by my side during this academic journey. 

        -To the Amazonian natives who domesticated cassava and to all the cassava farmers around 

          the world. Thank you for your hard-work and for providing food for us. 

"There was a time when there was no cassava on earth. Kashiri (the moon) had plenty of cassava on his farm. One day he came down to earth and fell in love with an indigenous Matsigenka woman. He wanted to marry her and thus he provided humans with a gift: the cassava stem." 

Cassava legend from the Matsigenka indigenous people from Peru. 

 Source: compiled by Arias (2003), translated by author

A Dark or Bight Green Future? Agricultural Transformation in Ethiopia towards 2050

 

A Dark or Bight Green Future?

Agricultural Transformation in Ethiopia towards 2050

Between 1983 and 1985, Ethiopia experienced one of its worst episodes in modern history with a famine that caused 1.2 million deaths and 2.5 million displacements (Gill, 2019: 44). In addition, the famine produced severe negative economic impact with GDP per capita drops of -5.9% in 1984 and -13.9% in 1985 (World Bank, n.d.a.). Researchers like Webb, Von Braun, and Yohannes (1994: 1) stated that famine in Ethiopia had “evolved into an almost structural problem” and that absence of human capital, lack of farming technology, predisposition to production variations, and inaccessibility to major markets were some of the underlying factors that could not be solved with short-term crisis responses. Despite low expectations in the country’s future, Ethiopia was able to recover and since 2004 the country has not had a single year of economic contraction (World Bank, n.d.a). In fact, the country’s average annual GDP growth rate (at 7.3%) exceeds the average growth rate of Sub Saharan African (SSA) when excluding high income countries (at 4.3%) (World Bank, n.d.a.).

Considering Ethiopia’s unexpected recovery, but not forgetting the country’s famine and previous economic struggles, it is relevant to inquire how Ethiopia’s future will look towards year 2050. This essay will focus in the agricultural sector and two of the “certainties” towards 2050: continuous urbanization and a growing population. Under this context, the essay will answer the following research question:

How could urbanization and a growing population impact agricultural transformation and food security towards 2050?

 

The World Bank (n.d.b.) estimates that by 2050, Ethiopia’s urban population will increase by 221.6% and that the urbanization share will go from 21.7% to 39.1% (See Figure 1). A growing urban population could be a double edge sword as labour productivity could increase, through increasing labour supply; but if no new opportunities are generated, a large part of the population could become unsatisfied and -consequently- promote social instability (Todaro, 1997). Moreover, rural migrants who lack jobs tend to engage in low-productivity non-tradable informal service jobs (Fox, Thomas, & Haines, 2017); a sectorial transition that could hurt the economy in the long-term. Another doubtful advantage is that, by demanding more quantity and higher-quality of food products, growing urbanization could push for modernization of agri-food systems (Reardon, Tschirley, Minten, Haggblade, Liverpool-Tasie, Dolislager, Snyder, & Ijumba, 2015); however, if the country is not able to produce -or acquire- enough food then it could run into food security issues (Andersson Djurfeldt, 2015).

Figure 1. Ethiopia - Historical and Projected Urban Population

Source: Author’s own elaboration with World Bank (n.d.b.) data

 

Despite Ethiopia’s urban population percentage is low in comparison to the rest of the world (Vandercasteelen, Tamru, Minten, & Swinnen, 2016), increasing urbanization rates and a growing population are not an unknown phenomenon to the country. In fact, from 1991 to 2019, Ethiopia’s urbanization percentage went from 12.9% to 21.6% and overall population grew by 290.7%, a higher growth than the one projected towards 2050 (World Bank, n.d.a.). In the same period, the agricultural sector’s value-added contribution declined by 25.1 percentual points, while the value-added contribution of the industrial and service sector respectively increased by 17.5 and 7.4 percentual points (World Bank, n.d.a). Similarly, agriculture’s employment share decreased by 10.2 percentual points; with most of the labour

transferring to the service sector (See Appendix). Even though there has been a reduction in agriculture’s value-added contribution and employment share, the sector’s nominal contribution increased by 273.1%; moreover, with a 66.6% employment share and a 33.5% value-added contribution to the overall GDP, the importance of the agricultural sector remains high (World Bank, n.d.a). If Ethiopia can replicate the agricultural sector and structural transformation trends from 30 years ago towards the next 30 years, then 2050 looks promising.

Furthermore, while analysing the rural-urban migration patterns in Ethiopia, Dorosh, Thurlow, Worku Kebede, Ferede, and Taffese (2018) recognized that sustained investment in agriculture and the agri-food system is critical to promote social and financial inclusion in the short-term, but concluded that investing in cities, after the mid-2020s, will produce faster overall economic growth and higher pro-poor growth. Moreover, Vandercasteelen, Tamru Beyene, Minten and Swinnen (2018) proposed that, in Ethiopia, proximity to bigger cities, significantly benefits agricultural production, as producers can obtain better access to markets, acquire up-to date information, and reduce transaction costs. Nevertheless, Ayele and Tarekegn (2020) raise concern on the influence of urbanization on peri-urban agriculture in Ethiopia, as unorganized urban expansion has promoted the construction of low-quality housing on agricultural land, thus minimizing land accessibility for peri-urban farmers. Moreover, they found a significant positive correlation between urban related land loss and grain production decrease.

These contrasting findings are relevant, as urbanization and agricultural transformation could have detrimental effects on each other but are not necessarily dualistic. Consequently, it should be noted that in order for farmers to take full advantage of the urbanization process, there is a need of government involvement, targeted agricultural policies, and a well-planned urbanization strategy. Concerning public investment in the agricultural sector, Rohne Till (2021) highlights the commitment and central role of the state in Ethiopia’s agricultural transformation. She explains that spending in infrastructure and extension has been a priority, while spending on irrigation, R&D, and input subsidies has been modest. As well, in accordance with the agricultural trends shown in the appendix and discussed in this paper, she argues that agricultural growth started in Ethiopia in the 1990s, particularly through the implementation of the Agricultural Development Led Industrialization (ADLI) strategy since 1993. Moreover, she concludes that subsequent agricultural transformations could be achieved by replicating Ethiopia’s policies focus (Rohne Till, 2021).

Regarding the impact of increasing urbanization in food security, Matuschke and Schmidhuber (2009) constructed current and projected food density maps for years 2005 and 2050 for South and East Asia and SSA. Through their analysis they encountered that countries like Ethiopia will develop higher food density areas which will pose a threat to all dimensions of food security (See Figure 2). Nevertheless, food insecurity is already present in rural Africa, as migration is largely pursued by low productivity smallholder farmers who, despite living in low food density farming areas, are not even able to feed themselves (Andersson Djurfeldt, 2015). Migration then could become an opportunity instead of a threat as farmers and migrants could exploit the new stablished linkages between rural farming and urbanization.

Figure 2. Food Density Maps for Ethiopia

Source: Author’adaptation of Matuschke and Schmidhuber (2009) food density maps for SSA

Concerning rural-urban proximity, Bryan, Chowdhury and Mobarak (2014) performed randomized control trials through which they provided a stipend to rural farmers -so they could cover traveling expenses to market their products in the next urban area- and found that the additional obtained income from marketing their products allowed farmers to provide an extra meal per day for every family member for three months. Additionally, simulating the impact of rural-urban migration in rural towns in Ethiopia, Dorosh and Thurlow (2012) encountered that the rural-urban wage gap would decrease, rural agricultural products could be sold at higher prices, and rural farmers would experience lower underemployment. As a consequence, rural farmers would obtain more profit and could reinvest their earnings promoting the development of non-farming economic activities (Haggblade, Hazell, & Reardon, 2007).

Furthermore, Arslan, Egger, and Winter (2019) propose that increased urban investment in rural areas can encourage higher rural-urban migration, as rural workers would still look for urban employment and would be able to migrate through the obtained benefits from increased agricultural production; in fact, they found that rural-urban migration patterns up to 2015 are positively correlated with higher agricultural productivity and increasing shares of non-agricultural employment. Consequently, also in rural areas, urbanization and agriculture could experience a positive bidirectional relationship where -through increased incomes- urbanization could encourage rural development, generate additional rural-urban migration, and promote further structural transformation.

Moreover, as the process of urbanization evolves, agri-food systems will become progressively more important (Arslan et al., 2019). For instance, Minten, Habte, Tamru, and Tesfaye (2018) found that, in Ethiopia, due to urban consumer’s rapid increased expenditure in dairy products the quantity of dairy processing companies tripled; as well, they found that about one third of the milk drank in the cities came from the urban farming sector. In addition, they researched upstream impact produced by increasing demand and found that, in milk production, there is more utilization of cross-bred cows, enhanced accessibility to livestock services, and milk-yield improvements.

Particularly, in the context of SSA, despite there has been sectorial employment share transfers, the different sectors productivity has remained stagnant or even decreased (See Appendix). The stagnation or decrease in productivity has been mainly due to the transfer of agricultural labour into a low productivity service sector. In contrast, between 1991 and 2019, Ethiopia has experienced labour productivity increments in all sectors, with the service, industry, and agricultural sector, respectively increasing by 225.7%, 356.6%, and 66.5% (See Appendix). Maintaining the described trends will be critical to achieve a successful agricultural transformation towards 2050. In that sense, investing in the implementation of efficient agri- food systems will be key, as manufacturing and services agro-processing activities provide opportunities for a gradual structural transformation that would allow for progressive productivity increases.

Moreover, regarding both productivity and food security, Shiferaw, Kassie, Jaleta and Yirga (2014) studied 2000 farm households in Ethiopia and found that households that adopted improved wheat varieties experienced higher yields and a significant increase in food security. Similarly, Heck, Campos, Barker, Okello, Baral, Boy, Brown, and Birol (2020), emphasized the importance to develop biofortified crops in order to help close the food security gap in the future. Particularly they highlight the 2016 drought in Ethiopia, in which higher quality seeds of potatoes and sweet potatoes -accompanied by enhanced seed management information- were provided with the intention to mitigate future demand shortfalls during crises (International Potato Center, 2019). Therefore, towards 2050, investing in research for the agricultural sector will be critical to improve both the yield and nutritious value of crops in order to ensure food security in Ethiopia.

In summary, in the context of Ethiopia, this paper explores the impact that two of the “certainties” towards 2050 -urbanization and a growing population- could have in agricultural transformation and food security. The paper addresses the two sides of the coin by recognizing challenges but also highlighting opportunities. For example, with regards to urban expansion, it brings attention to the problem of reduced land accessibility for farmers as housing is being built in peri-urban farming locations. Nevertheless, it emphasizes that farm proximity to urban areas reduces transaction costs and facilitates market accessibility for farmers.

Similarly, it acknowledges that a growing population will pose food security challenges in high density urban areas; but stresses that the increasing demand will also generate opportunities to develop a more efficient agricultural sector. As well, it proposes that there could be a positive impact in productivity as linkages between agriculture and new manufacturing and services agro-processing activities will be generated. Lastly, it argues that to satisfy the higher-quality and better nutrition demand there will be a need to engage in R&D for the agricultural sector.

To conclude, during the last 30 years Ethiopia’s trends show evidence of a positive agricultural transformation. If the country manages to replicate the observed trends for the next 30 years, address the mentioned challenges, and take advantage of the proposed opportunities, then the future towards 2050 looks promising. In this regard, the paper stresses the need of direct government involvement with targeted investment and policy implementations in favour of the agricultural sector and agricultural related activities. As during previous years, Ethiopia’s government support will be critical to ensure a successful structural transformation and food security for the country. Moreover, urbanization should not randomly occur, but there should be a well-planned strategy so that urbanization does not hurt the agricultural transformation process but that it rather benefits it.

Notes

*Implications of the current conflict are not addressed on this essay. 

*Essay written for the "EKHT 41 – Agricultural Transformation in the Development Process" course at Lund University. 

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APPENDIX

Figure 3. Ethiopia – Sector’s Value-Added (% GDP)

Source: Author’s own elaboration with World Bank (n.d.a.) data

Figure 4. Ethiopia – Sector’s Employment Share (% of Total Employment)

Source: Author’s own elaboration with World Bank (n.d.a.) data

Figure 5. SSA – Sector’s Value-Added per worker

Source: Author’s own elaboration with World Bank (n.d.a.) data

Figure 6. Ethiopia – Sector’s Value-Added per worker

Source: Author’s own elaboration with World Bank (n.d.a.) data