rafmacalaba/datause-encoder-data huggingface dataset:
Also, by age group Figures 5a-c display notable differences across and within regions. Peak poverty rates of 85 percent for children aged 5-9 in rural areas in the North West in wave 3 stand in stark contrast to urban poverty rates for the 10-17-year old in the South West of below 10 percent. Even though rural poverty rates appear based on this data in wave 3 higher in the North West than in the North East, which currently receives a lot of attention from donor agencies, it needs to be kept in mind that the data from the North East is biased towards accessible areas. In the inaccessible rural conflict affected areas child poverty rates may likely be higher than the already high 70% rural child poverty rates in wave 3 in the accessible areas.

# _5.1.2 Multi-Dimensional Poverty in Nigeria_

As poverty goes beyond monetary poverty, this section presents poverty estimates based on different deprivations using the four waves of the GHS and the MICS 2016/2017 data set. The results of the individual level deprivations (education, food and health) are presented for different age groups, followed by the child deprivations (water, sanitation, shelter, information) measured at the household level over the complete child age range<sup>21</sup> . For each deprivation, first national level figures are presented for each GHS wave and the MICS data, distinguishing by urban and rural sector. Subsequently, estimates by geopolitical zone are presented by survey and sector. Finally, state level estimates based on the MICS data are displayed, with a further breakdown by gender for severe education and food deprivation.

## Education Deprivation

By reaching the age category 15-17 years, according to Figure 6a, around 1 in 10 children has never been to school and is not currently in school with little change over time. However, educational deprivation seems very much a rural phenomenon. Moreover, rural and northern do children start schooling later. Among the 5-9-year olds living in urban areas, around 10 percent has not yet been to school while still close to 30 percent of their peers in rural areas has never been to school. Between the north and the south this gap is even more extreme, 32 versus 3 percent respectively, as Figures 6 b-c display. Whereas the in Figure 6 presented GHS and MICS data suggest no clear change in educational deprivation over time, the Figures 6 b and c show that children in rural and norther areas have over time started to attend school at an earlier age.

While there has been a strong emphasis on girls’ education among the donor community with projects and policy recommendations targeted at increasing the rate of girls’ education around the world (see Bill

> 21 Severe information deprivation is presented for children of school going age 5-17, as it is considered that only in this age range children can actively consume and look for information.

18
Policy Research Working Paper 8506

# **Abstract**

Trade and participation in global value chains can play a key role in economic diversification and development. This paper deepens the discussion about productivity growth and upgrading in agriculture in Africa, and the role of national, regional, and international value chains in supporting such structural change. The analysis in this report is based on quantitative and qualitative surveys undertaken in 2016 in Ghana, Kenya, and Zambia, where 3,935 farmers, 60 aggregators, and 56 buyers in the maize, cassava, and sorghum value chains were interviewed in the three countries. The descriptive results show that farmers who were on a contract saw greater structural transformation; higher output; and better access to seeds, fertilizers, pesticides, technology, and extension services compared with farmers who were not on a contract. To identify more robustly the link between value chain participation and contract farming with productivity and upgrading, the paper looks at the relationship using a variety of empirical methodologies, ranging from ordinary least squares and probit regressions to propensity score matching. Based on the empirical evidence, the hypothesis that value chain participation leads to structural transformation cannot be confirmed. The paper does find evidence that formal or informal contractual arrangements that regulate the provision of inputs to production, such as fertilizer, technology, extension services, and market information, positively affect upgrading. It remains nevertheless important to understand the impact of government policies on the emergence of value chains given that value chains support contractual arrangements.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. The work was sponsored by the Office of the Chief Economist for the World Bank Regional Vice-Presidency for Africa (AFRCE). 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://www.worldbank.org/research. The authors may be contacted at ndihel@worldbank.org, agrover1@worldbank.org and chollweg@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
Fiscal deficits widened significantly in almost all countries during 2009, as a result of both automatic effects and countercyclical policy responses. The extent of fiscal expansion was larger in advanced economies than in emerging economies; it was smallest in low-income economies (table 2). However, economic growth in 2010 and 2011 was no stronger in advanced economies than emerging economies, suggesting that fiscal stimuli may have worked more efficiently in emerging economies, in the sense that recovery was accomplished with smaller fiscal expansion than in the advanced economies.

**Table 2 General Government Fiscal Balance in Selected Economies, 2007–12** **<u>(Percent of GDP)</u>**

|_Type of economy_|_2007_<br>_(precrisis)_|_2008_|_2009_|_2010_|_2011_|_2012_<br>_(projected)_|
|---|---|---|---|---|---|---|
|Advanced economies|–1.9|–3.5|–8.9|–7.8|–6.6|–5.9|
|Germany|–0.5|–0.1|–3.2|–4.1|–0.8|–0.4|
|Japan|–2.5|–4.1|–10.4|–9.4|–9.8|–10.0|
|United States|–2.9|–6.7|–13.3|–11.2|–10.1|–8.7|
|Emergingeconomies|0.2|0.0|–4.5|–3.2|–1.8|–1.9|
|Brazil|–2.5|–1.3|–3.0|–2.7|–2.6|–2.1|
|China|0.9|–0.7|–3.1|–1.5|–1.2|–1.3|
|India|–5.2|–8.7|–10.0|–9.4|–9.0|–9.5|
|Mexico|–1.4|–1.1|–4.7|–4.3|–3.4|–2.4|
|Low-income economies|—|–1.0|–3.9|–2.0|–1.9|–3.4|

_Sources:_ IMF 2009, 2012. _Note_ : — not available.

The movement in the general government fiscal balance is the result of combined net public spending at the national and subnational levels. The combined deficit peaked in 2009 in almost all countries, after which there was some fiscal contraction. Expansionary fiscal stance could not be sustained in most cases, either because of the pro-cyclical behavior of subnational finances or because of already high fiscal deficit and debt levels before the crisis.

The capacity for further fiscal stimuli is related to the level of public debt and the need to contain it within sustainable limits. Emerging and lower-income countries are less debt stressed than advanced economies, and China is significantly less debt stressed than other emerging economies (table 3). However, this assessment needs to be reviewed in the light of full information, not yet available, on the considerable contingent liabilities of national and provincial governments in China, which are excluded from table 3.

13
dividing the sum across sectors and countries by the change in domestic emissions, using the following equation: _IC_ denotes import-origin countries, and _EC_ denotes export-destination countries. ∆ _EmissionD_ is the domestic emissions abatement. This formula assumes that any decrease in China’s bilateral exports will be perfectly offset by an equivalent increase in foreign production by the trade partner. This assumption tends to over estimates the leakage rate: In a more complicated model with trade response, the leakage rate would be even smaller. The data on emission intensities of each sector by country is from the Global Trade Analysis Project database (version 10) (2019).

# **5. Equilibrium**

The general equilibrium requires supply-demand balance in each period for each factor and produced goods. Under policies with emissions allowance trading, the allowance supply and demand must match as well. In each period, these requirements determine (a) the prices for the 31 sectors’ produced goods; (b) the wage rate; (c) the rental prices of capital, which differ across sectors (as well as subsectors in the electricity, cement, aluminum, and iron &steel sectors); (d) the four different rental prices of the natural resources, for these resources employed in the solar, wind, hydro, and nuclear electricity production subsectors, respectively; and (e) the CO2 allowance price.

# **6. Dynamics**

The model is solved as a mixed complementarity problem (MCP) with a Newton-based solver, and solves at one-year intervals from 2020 through 2035. Changes in equilibria from one period to the next depend on the increments to the stocks of labor and capital. There is one aggregate capital stock. As discussed earlier, domestic real investment in each period is set as a fixed share of GDP. The stock in the next period is aggregate real domestic investment in the current period net of depreciation over that period. The stocks of land and the four kinds of natural resources (wind, solar, hydro, and nuclear) are treated as fixed at the base year level.

55
**World Bank (2008c)** , “Cambodia: Towards an Agrarian Structure in Support of Growth, Employment, Equity, and Efficiency”, _World Bank Report_ .

**World Bank (2008d)** , “A better investment climate to sustain growth in Cambodia: Second investment climate assessment”, _World Bank / International Finance Corporation Report_ . **World Bank (2008e)** , World Development Report 2009: Reshaping Economic Geography. **World Bank (2008f)** , Doing Business report 2009, September.

**World Bank (2009),** “Sustaining Rapid Growth in a Challenging Environment”, _Cambodia Country Economic Memorandum_ .

27
> **3.11 Structure of AFPs**

The Chilean pension fund market has been characterized since its inception **by** a small number of management companies and a high concentration ratio. When the system was first established, 12 AFPs were authorized. Several of these were established with the participation of large baking groups. Thus, Provida was related to Banco de Santiago, Santa Maria to Banco de Chile, Invierta to Banco de Conception, San Cristobal to the B.H.C. Bank, Alameda to the B.H.I.F. Bank and **El** Libertador to Banco Edwards.

Table 12

|Market|SharesandIndus|tryConcentration|
|---|---|---|
||**1981**|**1985**<br>**1990**|
|PensionFunds|||
|Provida|**33.8**|**28.6**<br>**26.0**|
|SantaMaria<br>|**27.6**|**22.9**<br>**19.2**|
|Habitat|**9.9**|15.2<br>**17.5**|
|Union<br>|12.4|10.4<br>8.4|
|**Summa**|4.5|10.2<br>**9.6**|
|Cuprum|**1.6**|**2.9**<br>**6.2**|
|**C2**|61.4|**51.5**<br>45.2|
|**C3**|**73.8**|**66.7**<br>**62.7**|
|C4|**83.7**|**77.1**<br>**72.3**|
|HHI|2220|**1795**<br>**1577**|
|Affiliates|||
|Provida|**32.9**|**29.9**<br>**29.0**|
|SantaMaria<br>|20.4|**18.8**<br>20.1|
|Habitat<br>|**5.7**|**11.8**<br>**17.1**|
|Union|19.4|**13.6**<br>**9.1**|
|Summa|4.4|**8.7**<br>**8.0**|
|Invierta|**9.1**|**6.7**<br>**3.9**|
|**C2**|**53.3**|**48.7**<br>49.1|
|**C3**|**72.7**|**62.3**<br>**66.2**|
|C4|**81.8**|74.1<br>**75.2**|
|HHI|2028|1714<br>**1729**|

> Source: Habitat **(1991)**

Following the banking crisis of **1982,** these AFPs became temporary wards of the State, although they were swiftly reprivatized. 40% of Provida was sold to Bankers Trust Pacific Ltd and **_51_ %** of Santa Maria to Aetna Insurance Company. The remainder of the shares of these 2 AFPs was sold to several thousand individual shareholders as part of the program known as "popular capitalism".

In **_1985,_** two AFPs -Alameda and San Cristobal- merged to create AFP Union with major participation **by** American International Group **(AIG).** Three new AFPs were established in recent years -Proteccion in **1986,** Futuro in **1988** and Bannuestra in **1990-** although the last-named was

**27**
Strobl (2012) analysis. All variables in monetary terms are measured in current Purchasing Power Parity (PPP), international dollars allowing for comparability between countries.

The average GDP per capita for the region is US$ 4,478, with an average annual growth rate of 3.6 percent in this period. This number masks important differences between countries. In the study period, Costa Rica and Panama experienced higher growth rates than the rest of the Central American countries. For example, in 2010, Costa Rica’s and Panama’s GDP per capita was twice the one of El Salvador’s and Guatemala’s, and thrice that of Nicaragua’s and Honduras’. While Panama grew at an average rate of 5.3 percent per year, Nicaragua (which suffered a political revolution in the studied period) grew by only 1.5 percent.

Table 1 presents descriptive statistics by decade of the variables used in the analysis. Central American countries have experienced trade liberalization in recent years, resulting in trade openness in the region growing from 0.47 to 0.76 between 1990 and 2000. There is, however, important variation between countries and across this time period: for example, Panama, the country with the highest openness index, trades 145 percent of its GDP, while El Salvador trades only 59 percent of its GDP on average. Also, in this period, the population in the region grew at an average rate of 2.1 percent, but this increase was far higher in urban areas, which has led to a concentration of the population in big cities. The size of the countries’ population is also variable, with Guatemala being the largest with 10 million inhabitants, while Panama, with 2.7 million inhabitants, has the smallest population.

Table 1 Descriptive Statistics: Macroeconomic Variables

|Decade|Statistics|GDP PC|GDP PC<br>Growth|Investment|Openness|Population|
|---|---|---|---|---|---|---|
||Mean|2,947|2.3%|16.3%|65.7%|4,241|
|1983‐1990|SD*|1,006|5.9%|5.3%|31.4%|1,955|
||Min|1,532|‐25.9%|4.3%|17.0%|2,081|
||Max|5,470|10.6%|28.1%|146.8%|8,966|
||Mean|4,091|3.8%|22.4%|89.6%|5,311|
|1991‐2000|SD*|1,836|3.7%|6.0%|39.9%|2,450|
||Min|1,463|‐6.1%|13.6%|41.5%|2,441|
||Max|8,471|13.4%|40.4%|178.6%|11,085|
||Mean|6,090|4.3%|22.9%|97.7%|6,385|
|2001‐2010|SD*|3,074|3.1%|5.1%|29.5%|3,015|
||Min|1,922|‐3.9%|13.6%|57.2%|2,952|
||Max|12,983|11.9%|33.2%|161.2%|13,550|
||Mean|4,478|3.6%|20.8%|85.7%|5,389|
|Total|SD*|2,545|4.3%|6.2%|36.4%|2,673|
||Min|1,463|‐25.9%|4.3%|17.0%|2,081|
||Max|12,983|13.4%|40.4%|178.6%|13,550|

*Standard Deviation

10
Policy Research Working Paper 6821

# **Abstract**

This paper uses long panel survey data spanning over 20 years to examine the dynamics of microcredit programs in Bangladesh. With the phenomenal growth of microfinance institutions representing 30 million members with over $2 billion of annual disbursement over the past two decades, it is important to understand the dynamics of microcredit expansion and its induced impact on household welfare. A dynamic panel model is used to address a number of issues, such as whether credit effects are declining over time, whether market saturation and village diseconomies are taking place, and whether multiple program membership, which is rising as a consequence of microcredit expansion, is harming or benefiting the borrowers. The paper’s results confirm that microcredit programs have continued to benefit the poor by raising household welfare. The beneficial effects have also remained higher for female than male borrowers. There are diseconomies of scale caused by higher levels of village-level borrowing, especially for male members. Multiple program membership is also growing with competition from microfinance institutions, but this has rather helped raise assets and net worth more than it has contributed to indebtedness.

This paper is a product of the Agriculture and Rural Development Team, Development Research 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 authors may be contacted at skhandker@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
|Industry|4.6|2.4|5.4|4.8|3.9|3.5|
|---|---|---|---|---|---|---|
|Tertiary Sector|5.4|4.4|4.6|4.1|4.3|5.5|

Source: Senegal country sources and World Bank Growth rates in agriculture and industry decreased between the two periods across the regions being compared, owing partly to the financial crisis that occurred between 2008 and 2009. However, the halving of the growth rates in Senegal, in contrast to shaving off rates in the other representative economies, is the result of differentially significant challenges to the sectors in Senegal.

# **2.2. COLLAPSE OF THE PRIMARY SECTOR**

The agriculture sector faced declining productivity that was driven by a combination of adverse climatic conditions and commodity price shocks. As a Sahelian country, Senegal is inherently vulnerable to unreliable rainfall. Agriculture remains largely rain-fed and irrigation infrastructure is not particularly strong, exposing crop production to the ravages of flooding and droughts. The sector also suffered from adverse food price shocks emanating from the commodities market, the impact of which is magnified by the inefficient domestic market. Between 1995 and 2013, agricultural growth was negative in 6 of 19 years, with contractions as severe as 35 percent in 2002 and 28 percent in 2011. Real value-added per worker in agriculture fell from $380 during 19952005 to $362 in 2006-2013 in Senegal (a decrease by 5 percent) while it increased from $561 to $641 in WAEMU (an increase by 14 percent) and from $500 to $653 (an increase by 30.6 percent) in SSA.<sup>8</sup> Crop production, fishing and industrial (or export) agriculture, which on average jointly accounted for two-thirds of the primary sector all experienced decline in growth during 2006-2013, and thus in contribution to overall growth (Figure 1). The decline is most pronounced in industrial agriculture where growth was negative in four of eight years, reaching as low as -45 percent in 2011, in effect slowing down growth from 6.3 percent in 1995-2005 to 0.9 percent in 2006-2013, and contracting by -3.4 percent in 2014. Thus, its contribution to GDP growth reversed from +1 percent to -2 percent during the respective periods. This weakness is attributable to high production costs, including energy cost, and lack of competitiveness in both input and output markets.

> 8 World Bank Development Indicators 2014.

7
# Table of Contents

|||Eg|
|---|---|---|
|I.|Introduction .........................<br>.....<br>......<br>.<br>.|01|
|II.|Development and Growth of Kenyan i-iorticultural Exports<br>..... . . .<br>.|05|
|III.|The Characteristics of the Private Sector .......<br>. . . . . . . . . . . . .<br>.|10|
|IV.|Competition in Private Agricultural Markets<br>.......<br>. . . . . . . . . .<br>.|16|
|V.|Private Firms and the Organization of Crop Procurement<br>..... . . . .<br>.|21|
||a. Application of Transaction Cost Economics .......<br>. . . . . . . . .<br>.<br>b. Crop Procurement Arrangements for Processed Fruit and Vegetables<br>.<br>c. Procurement Arrangements for Fresh Horticultural Commodities . <sup>. ..</sup>|24<br>28<br>33|
|VI.|Conclusion .......................<br>.....<br>.....<br>....<br>.<br>.|38|
||Bibifography .....................................<br>|.<br>40|

i
food reallocation within the household (Jacoby, 2002). Analyzing a school feeding program in the Philippines, Jacoby (2002) finds virtually no intra-household reallocation of calories in response to the feeding programs on average, but reallocation was more pronounced in poorer households of the sample. Jacoby (2002) explains his finding by arguing that either the specific feeding program (snacks) is not substitutable with household food, or it is the reflection of a “labeling effect” as argued by Kooreman (2000) using Dutch data<sup>1</sup> .

In environments with low enrollment rates, perfectly targeting children who are enrolled might not be the best policy objective if one wants to reach most of the malnourished children. First, nutritionists generally believe that the window of opportunity for nutrition is during gestation and the first two years of life (Bhutta et al., 2008). Hence making sure that the food distributed reaches younger children may be important by its own right. Second, parents may selectively enroll the healthiest and the best fed of their children (e.g. Alderman, Hoogeveen, and Rossi, 2009; Glewwe and Jacoby, 1995). If so, it may be desirable to use THR to reach children 2 who are not enrolled since they are likely to be the ones who need the intervention the most .

# 3 Program Description and Research Design

School canteens which provide meals to the students attending school were first introduced in Burkina Faso by the Catholic Relief Service/Cathwell (a non-governmental organization) in the mid 1970’s in the aftermath of severe famine spells which affected the Sahel region of West Africa. Dry take home rations are a more recent intervention, also initiated in Burkina Faso by the Catholic Relief Service/Cathwell; Students who attend school on a regular basis receive a food ration (flour) that they can bring back home each month. Take home rations are targeted to girls. Starting from the 2005-2006 school year, after a reorganization of the operational zones of

> 1 Using Dutch data, Kooreman (2000) shows that the marginal propensity to consume child clothing out of exogenous child benefits is larger than the marginal propensity to child clothing out of income from other sources. He then argues that households act this way because the benefits have been “labeled child benefits”.

> 2 In Ghana, Glewwe & Jacoby (1995) find that malnourished children start school later and complete fewer years of school as compared to better nourished children. Therefore holding age constant, at any given time malnourished children are more likely to be out of school than well nourished children.

7