rafmacalaba/datause-encoder-data huggingface dataset:
WPS4013

# **The Role of Agriculture in Poverty Reduction**

# **An Empirical Perspective**

Luc Christiaensen, Lionel Demery and Jesper Kühl<sup>1, 2</sup>

## **Abstract** :

The relative contribution of a sector to poverty reduction is shown to depend on its direct and indirect ‘growth effects’ as well as its ‘participation effect’. The paper assesses how these effects compare between agriculture and non-agriculture by reviewing the literature and by analyzing cross-country national accounts and poverty data from household surveys. Special attention is given to Sub-Saharan Africa. While the direct growth effect of agriculture on poverty reduction is likely to be smaller than that of non-agriculture (though not because of inherently inferior productivity growth), the indirect growth effect of agriculture (through its linkages with nonagriculture) appears substantial and at least as large as the reverse feedback effect. The poor participate much more in growth in the agricultural sector, especially in low-income countries, resulting in much larger poverty reduction impact. Together, these findings support the overall premise that enhancing agricultural productivity is the critical entry-point in designing effective poverty reduction strategies, including in Sub-Saharan Africa. Yet, to maximize the poverty reducing effects, the right agricultural technology and investments must be pursued, underscoring the need for much more country specific analysis of the structure and institutional organization of the rural economy in designing poverty reduction strategies.

## **JEL classification** : D3, O1

**Keywords** : Agriculture, Economic Growth, Poverty Reduction, Sub-Saharan Africa

World Bank Policy Research Working Paper 4013, September 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1The paper is prepared as part of a multi-country empirical investigation to re-assess the role of agriculture in poverty reduction in Sub-Saharan undertaken by the Africa Region in the World Bank and financed under the Norwegian ESSD Trust Fund.  The case studies, undertaken in Ethiopia, Kenya, Madagascar and Tanzania, take a mainly microeconomic perspective, and typically utilize household and farm surveys.  A companion paper synthesizes the results of these studies.

> **2** The authors are at the World Bank and can be contacted at **<u>lchristiaensen@worldbank.org</u>** <u>,</u> **<u>ldemery@worldbank.org, jesper_kuehl@yahoo.dk.</u>** Comments by Martin Ravallion and Derek Byerlee helped improve an earlier draft and are gratefully acknowledged.
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newly constructed units. In Russia, because of the lack of private urban land, local governments “participate” in the majority of new construction projects, extracting an unknown share of public rental units from developers.<sup>46</sup> The new construction programs in Romania and Lithuania are small and targeted to certain groups (low income, students). Recently, associations of banks and developers in Poland have been discussing a new PPP scheme, which would produce new rental housing by private sector partners, which would be made available to municipalities on longterm lease basis and used by them for social rental housing policy objectives. Municipalities would help mostly in terms of providing access to serviced building lots dedicated to such construction.

# **Rental Housing Demand**

In the tenure choice literature discussed earlier, rental housing demand is seen primarily as a function of income and life-cycle circumstances of the household involved. In the analysis of the studied countries that follows, the focus is on exploring those empirical relations. Other important demand determinants, such as deeper household type differentiations, income risk factors (proxied by professional status), and wealth and access to finance, are referred to future research. A brief review is made of available evidence concerning the impact of intra-country migration and the mismatch of available dwelling attributes and demand. These are the two factors expected to show high empirical significance for the housing sectors in transition; unfortunately, data problems are encountered here, calling again for better census information or in-depth rental housing surveys in the future.

# **Demand Structure – Life Cycle and Income**

# **_Rental Housing Demand and Life Cycle_**

# _Private Rental Sector_

In the urban economies of transition countries,<sup>47</sup> the private rental sector is a pivotal source of supply for “housing starters” (Figure 5). These are young single households or couples between the ages of 16 and 29 - and young families between the ages of 30 and 44 with at least one child.<sup>48</sup> This is consistent with the pattern in market economies.<sup>49</sup> Poland has the highest share among housing starters with 24 percent, followed closely by Russia, Serbia, and Armenia. Even in Romania, with the smallest overall private rental sector share, 12 percent of starters are recorded as private renters. The high Lithuanian figure of 34 percent could

> 46    Municipalities in Russia control access to land and continue “spot zoning,” which forces developers to relinquish some new dwellings to municipalities in exchange for land access and necessary zoning.

> 47  The urban-rural income divergence is very large in these countries—especially Poland, Romania, and Russia in this study—and thus must be cancelled to arrive at meaningful results. Hence, we report only data for urban areas.

> 48  Owing to availability constraints, data for Lithuania are added under a different definition, which yields a lower age average within each category.

> 49  See Chiuri and Jappelli (2000).

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Policy Research Working Paper 8080

# **Abstract**

This paper studies the gender-based differences in access to and return from economic activities in the rural non-farm economy (RNFE) using panel datasets from Uganda and Ethiopia. The results show that female-headed households have limited access to paid employment and self-employment in the sector, particularly in some industries.

These households also earn lower returns from RNFE than male-headed households, and the gross return gap is much higher in Uganda than in Ethiopia. Furthermore, endowment differences do not explain the return gap in Ethiopia, and only partially explain the gap in Uganda.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at HFuje@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# PPR Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS216 Price and Quality Effects of<br>Vers-Revisited: A Case Study of<br>Korean Footwear Exports|Jaime de Melo<br>L Alan Winters|June 1989|M. Ameal<br>61466|
|WPS217 Public Debt, North and South|Helmut Reisen|||
|WPS218 Public Finance, Trade and<br>Development: The Chilean Experience|Vittorio Corbo|July 1989|A. Oropesa<br>61758|
|WPS219 Rural Credit in Developing Countries|Avishay Braverman<br>J. Luis Guasch|June 1989|C. Spooner<br>37570|
|WPS220 Capacity Building for Policy Analysis|Samuel Paul<br>David Steedman<br>Francis X. Sutton|July 1989|E. Madrona<br>61712|
|WPS221 How Does Uncertainty About the Real<br>Exchange Rate Affect Exports?|Ricardo J. Caballero<br>Vittorio Corbo|June 1989|A. Oropesa<br>61758|
|WPS222 Why Stabilisation Policies in Zambia<br>Did Not Succeed|Christopher Colclough|||
|WPS223 Overvalued and Undervalued Exchange<br>Rates in An Equilibrium Optimizing<br>Model|Jose Saul Lizondo|||
|WPS224 The Economics of the Government<br>Budget Constraint|Stanley Fischer|May 1989|S. Fischer<br>33774|
|WPS225 Targeting Assistance to the Poor<br>Using Household Survey Data|Paul Glewwe<br>Oussama Kanaan|June 1989|B. Rosa<br>33751|
|WPS226 Inflation and the Costs of<br>Stabilization: Historical Cases,<br>Recent Experiences and Po;icy Lessons|Andres Solimano|||
|WPS227 Institutional Reforms in Sector<br>Adjustment Operations|Samuel Paul|||
|WPS228 Economic Perfornance of Developing<br>Countries in the 1980s|Robert Lynn<br>F. Desmond McCarthy|||
|WPS229 The Effect of Demographic Changes on S<br>for Life-Cycle Motives in Developing|aving|||
|<br>Countries|Steven B. Webb<br>Heidi Zia|July 1989|E. Khine<br>61765|
|WPS230 Unemployment, Migration and Wages<br>in Turkey, 1962-1985|Bent Hansen|July 1989|J. Timmins<br>39248|
|WPS231 The World Bank Revised Minimum<br>Standard Model|Doug Addison|May 1989|J. Onwuemznc-<br>Kocha<br>61750|
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the same job. Similarly, if workers are credit constrained, mandated benefits such as severance payments may be welfare enhancing (Alvarez and Veracierto, 2001).<sup>20</sup> In our empirical work we will divide the informal sector in two: informal wage earners and self-employed. This distinction may be important if, following some authors, there is duality within the informal sector (e.g., Fields, 1990, 2005, Maloney, 2004). While it is true that there is a group of informal workers who could be working in the formal sector if that was their choice, there is another group which operates in a segmented labor market, queuing for a formal sector job (as in the more traditional view of the informal sector; e.g., Dickens and Lang, 1985).<sup>21</sup> In the first model we presented in this section there is perfect mobility between the formal and informal sectors, while in the second model there is some mobility but workers may be forced to queue for a job in the formal sector while being unemployed. We do not model an informal sector completely segmented from the rest of the economy (but we allow for it in the empirical work).

# **4. Data**

The paper explores several sources of data. First, we use administrative data on the enforcement of labor regulations (in 2002), collected by the Department of Inspections at the Ministry of Labor for our project. This data contains information on the number and location of regional labor offices, number of inspected firms, number of fines issued in each city, and number of inspectors per state. Our measure of enforcement is the log number of inspections in each city (plus one) minus the log of the number of firms in the city (log inspections per firm in the city).

Second, we compute several city level labor market indicators using the 10% sample of the Brazilian Census in 2000, containing detailed information on labor market outcomes for 15 million individuals. In particular, we compute the share of workers who are registered, unregistered, or self-employed, the share of non-employed, average wages for each type of worker, and measures of income and wage inequality in the city (including several percentiles of the income and wage distributions, and the city 90-10 income and wage ratio). We also compute similar statistics for individuals in different gender, age and education groups. Table A2 reports

> 20 Several authors consider non-competitive models of the labor market, in which firms have some monopsony power. In these models mandated benefits can increase the bargaining power of workers, allowing them to increase their total compensation package (e.g., Saint-Paul, 1995, Ljunqvist, 2002). As a response, there can be an increase in the wage of informal workers to keep them indifferent across sectors.

> 21 In the empirical work we will not be able to rigorously distinguish between upper and lower tier informal workers. However, there is a suggestion in the literature that informal wage earners belong in the lower tier, while part of self-employed workers are likely to be in the upper tier (e.g., Bosch and Maloney, 2006).

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14. Chaney, Thomas (2005) “Distorted Gravity: Heterogeneous Firms, Market Structure, and the Geography of International Trade,” mimeo, University of Chicago.

15. Coe, David, Arvind Subramanian, and Natalia Tamirisa, with Rikhil Bhavnani (2002) “The Missing Globalization Puzzle,”IMF Working Paper 02-171.

16. Demirguc-Kunt, Asli, and Ross Levine (2001) “Financial Structure and Economic Growth: A Cross-Country Comparison of Bank, Markets, and Development,” Cambridge, MA: MIT Press.

17. di Giovanni, Julian, Andrei Levchenko, and Romain Ranciere (2005) “Trade and Growth at Industry Level,”unpublished.

18. Do, Quy-Toan, and Andrei Levchenko (2004) “Trade and Financial Development”, World Bank Working Paper 3347.

19. Eichengreen, Barry, and D.A. Irwin (1998) “The Role of HIstory in Bilateral Trade Flows,” in Je¤rey Frankel (ed.), The Regionalization of the World Economy, Chicago: University of Chicago Press.

20. Evans, Carolyn (2003) “The Economic Signi...cance of National Border E¤ects,”The American Economic Review, 93(4), 1291-1312.

21. Feenstra, Robert C., Robert E. Lipsey, Haiyan Deng, Alyson C. Ma, and Hengyong Mo (2005) “World Trade Flows: 1962-2000,”NBER Working Paper No. 11040.

22. Frankel, Je¤rey, and David Romer (1999) “Does Trade Cause Growth?” American Economic Review, 89, 379-399.

23. Frankel, Je¤rey, with Ernesto Stein, and Shang-Jin Wei (1997) “Regional Trading Blocs in the World Economic System,”Washington DC: Institute of International Economics.

24. Heston, Alan, Robert Summers, and Bettina Aten, (2002) “Penn World Table Version 6.1," Center for International Comparisons at the University of Pennsylvania (CICUP).

25. Hummels, David (2001) “Towards a Geography of Trade Costs,” mimeo Purdue University.

26. Ju, Jiandong, and Shang-Jin Wei (2005) “Endowment versus Finance: A Wooden Barrel Theory of International Trade,”IMF Working Paper No. 05/123.

27. Kletzer, Kenneth, and Pranab Bardhan (1987) “Credit markets and patterns of international trade.” Journal of Development Economics, 27, 57–70.

21
nearest road is computed as the distance between the centroid of the city where this firm is located and the nearest road. In our sample, the average distance from a household or a firm to the closest main road differs according to the data set used: 2.31 kilometers (WBES), 9.70 kilometers (DHS),and 15.2 kilometers (LSMS). The average distance from a main road to a household is 13.43 kilometers in rural areas and 3.60 kilometers in urban areas (DHS). There is heterogeneity in terms of households’ proximity to a main road by level of development; in less-developed countries, the average distance is 11.46 kilometers; by contrast, the average distance is 7.50 kilometers in more-developed countries.

# **3.1.5 Internet Data**

We use the Hamilton data from Africa Bandwidth Maps on long-distance opticalfiber nodes from 2009 to 2019. The map depicts Africa’s terrestrial, satellite, and submarine cable transmission networks in great detail. The Hamilton map includes fiber-optic networks (operational, under construction, planned, and proposed), microwave networks (operational and planned), and submarine cables (operational, under construction, planned, or proposed) for over 300 network operators in more than 50 African countries, and for 72 submarine cable systems. Geographic coordinates of fiber-optic nodes are included in the data set. We have information on the year each fiber node became operational. Figure (A2) in the Online Appendix depicts the evolution of the optical-fiber network over time, showing a significant increase in the number of optical-fiber backbones from the year 2009 (see Figure (A3) too). In fact, by 2009, a large majority of African countries already had their terrestrial internet backbones connected to submarine cables. We also use information from Mahlknecht (2014) on submarine internet-landing points and arrival dates. Figure (A4) shows the gradual arrival of submarine internet on the African coast since 2008, with the East African Marine System (TEAMS) being the first cable that landed and was made operational in Sub-Saharan Africa in October 2009. We combine these data with the location of optical-fiber nodes to determine a location’s connectivity to the internet and to submarine (or high-speed) internet.

14