rafmacalaba/datause-encoder-data huggingface dataset:
**-5-**

dominant. Informal loans contracted for these purposes, however, cannot be easily diverted to finance day-to-7ay consumptlon or production, because the lenders, mostly relatives and friends, can easily monitor compliance. The bulk of the fungible credit, defined as credit which is not granted for easily monitored purposes, In the study area thus comes from the formal sector (87 percent).

Given the dominance of formal credit, a key issue for tha present study is the extent to which its supply is a constraini on households' desired activities. The survey data collected permit an answer to this question. Borrowing households were asked if at the go ng rates of interest they would have liked more institutional credit than the amount they were actually granted. Households which did not borrow were asked the reason for not borrowing. The most common reason for not borrowing was availability of sufficient own resources. The borrowers who indicated a desire for more credit, and the non-borrowers who responded that they could not obtain credit, are classified as credit-constrained. As reported in Table 2, about 37 percent of the farm households in Gongzhuling were constrained by credit accordina to this classification. The liquidity position of credit-constrained households as compared to non-constra ned households is compatible with intuitive expectations: They have significantly lower deposils in financial institutions, and overall, their liquid resources per unit of land are 12 percent teIcA those of unconstrained households.

# lll. A **MODEL OF FARM** HOUSEHOLD CONSUMPTION, PRODUCTION AND INVESTMENT

Suppose the household considers the allocation of resources at its disoosai at :"e beginning of the production period between the following uses: (i) current consumpt on, investment; (iii) the purchase of variable inputs for current production (inclueing lator a'ncw fertilizers). Variable inputs, in combination with land and existing capital, will produce this perici s output. Investment will not mature by the time this period's output is produced, but Its contribution to the household's welfare may be accounted for through a valuation function wlich
# **References:**

- Aguilera, V., X. Del Carpio, C. Herrera, K. Macours, M. Enoe Moncada, C. Obregón, and R. Vakis, 2006.  “Estudio Cualitativo del Componente de Atención a Crisis del Ministerio de la Familia, Nicaragua”, mimeo, Johns Hopkins University.

- Alderman, H., J. Hoddinott, B. Kinsey, 2006. “Long Term Consequences of Early Childhood Malnutrition”, _Oxford Economic Papers,_ 58(3):450-474.

- Ashenfelter, O., and C. Rouse, 1998.  “Income, Schooling and Ability: Evidence from a New Sample of Identical Twins”, _Quarterly Journal of Economics_ , 113: 253-284.

- Basu, K., 1999. “Child labor: Cause, consequence, and cure, with remarks on international  labor standards”, _Journal of Economic Literature_ 37: 1083-1119.

- Becker, G. and N. Tomes 1976. “Child Endowments and the Quantity and Quality of Children”, _Journal of Political Economy_ 84(4), pp143-162.

Becker, G., 1991. _A Treatise on the Family_ , University of Chicago Press.

- Beegle, K., R. H. Dehejia, and R. Gatti, 2006. "Child Labor and Agricultural Shocks”, _Journal of Development Economics_ , 81:80-96.

- Behrman, J.R., 1988. "Intra-household Allocation of Nutrients in Rural India: Are Boys Favored? Do Parents Exhibit Inequality Aversion?" _Oxford Economic Papers_ , 40(1): 32-54.

- Behrman, J.R. and A. Deolalikar, 1988.  "Health and Nutrition" Chapter 14 in _Handbook of Development Economics_ , 631-711.

- Behrman, J.R., R. Pollack, and P. Taubman, 1982.  “Parental Preference and Provision for Progeny,” _Journal of Political Economy_ , 90(1):52-73.

- Behrman, J.R., R. Pollack, and P. Taubman, 1986.  “Do Parents Favor Boys?” _International Economic Review_ , 27(1):33-54.

- Behrman, J.R., and P. Taubman, 1986.  “Birth Order, Schooling, and Earnings,” _Journal of Labor Economics_ , 4(3): 121-145.

- Bourguignon, F., and P.A. Chiappori, 1994.  “The Collective Approach to Household Behavior” Chapter 3 in _The Measurement of Household Welfare_ , 70-85.

- Das Gupta, M., 1987. "Selective Discrimination Against Female Children in India." _Population and Development Review_ , 13:77-101.

- Del Carpio, X., 2008. “Does  Child  Labor  Always  Decrease  with  Income? An

- evaluation  in  the  context  of  a  development  program  in  Nicaragua”, The World Bank, Policy Research Working Paper Series 4694.

- Duflo, E., 2003.  “Grandmothers and Granddaughters: Old-age Pension and Intra-

- household Allocation in South Africa”, _World Bank Economic Review_ , 17(10): 1- 17.

Edmonds, E. 2006. “Child Labor and Schooling Responses to Anticipated Income in South Africa”, _Journal of Development Economics_ , 81(2), 386-414.

- Edmonds, E., 2006. Understanding Sibling Differences in Child Labor, _Journal of Population Economics_ , 19(4), October 2006, 795-821.

- Edmonds, E., 2007. “Child Labor” in the _Handbook of Development Economic, Volume 4._

Edmonds, E. and N. Schady, 2008. “Poverty Alleviation and Child Labor”, _World Bank Policy Research Working Paper_ No 4702. The World Bank, Washington DC. Emerson, P. and A. Portela Souza, 2008. "Birth order, child labor, and school attendance 19
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|---|---|
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|Irrigation and Watershed Management Project|Ziva Razafintsalama|
|Trade & Transport Facilitation in South East Europe Project(TTFSE)|Gerald Paul Ollivier|
|Trade & Transport Facilitation in South East Europe Project(TTFSE)|Gerald Paul Ollivier|
|Trade & Transport Facilitation in South East Europe Project(TTFSE)|Gerald Paul Ollivier|
|Trade & Transport Facilitation in South East Europe Project(TTFSE)|Gerald Paul Ollivier|
|Trade & Transport Facilitation in South East Europe Project(TTFSE)|Gerald Paul Ollivier|
|Trade & Transport Facilitation in South East Europe Project(TTFSE)|Gerald Paul Ollivier|
|Trade & Transport Facilitation in South East Europe Project(TTFSE)|Gerald Paul Ollivier|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Health Sector Support|Jane Chuma|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
|Decentralization and CommunityDevelopment Project|Alex Kamurase|
Policy Research Working Paper 7903

# **Abstract**

This paper explores the landscape, contributions, and determinants of sovereign wealth funds’ long-term investments in Sub-Saharan Africa. The study finds that of all regions, Africa receives the lowest share of investment from sovereign wealth funds, and the landscape is dominated by Asian funds. The investment strategies of sovereign wealth funds established by African countries tend to be to invest less domestically and more abroad, contrary to Asian funds. In addition, using an enriched simple mean-variance portfolio model with an exponential utility function, the analysis shows that the investment rate of return and political connections have a positive and significant effect on sovereign wealth fund investments, and risk exerts a negative but not significant effect. The paper confirms these results empirically, using a database that includes 26 sovereign wealth fund investments over 19852013. Hence, sovereign wealth funds investing in Africa care more about high returns and the political interests of their country of origin than the risk of their investment.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group and the Office of the Chief Economist, Africa Region. 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 diallobs@kfupm.edu.sa, ftchanatchana@worldbank. org, and azeufack@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
- _15_ -

from the Institute of World Economic and Politics. The Soviet essays are entitled: "The Strategy of Developments and Aid to Developing Countries," "Soviet Economic Aid: Quantitative and Qualitative Analysis," and "Aid in the Development of Education and Training of Personnel."<sup>2 7</sup> Not only do these chapters detail the kinds and direction of Soviet aid, but they explain and criticize past funding strategies. The jointly authored summary chapter by Elena Arefyeva and U.S. economist W. Donald Bowles includes a rather sweeping statement of the new collective wisdom: 'Today, in effect, East and West views both acknowledge the important role that markets can play in development, the crtical needs in agriculture, the stimulating effects of exports, and the importance of limiting the sphere of government activities."<sup>2 8</sup> Such language is, of course, consistent with the rhetoric and policy reforms that mark Gorbachev's domestic program.

# U.S. Reaction to Soviet 'New Thinking' About Reaional Security

38. U.S. attitudes toward recent Soviet pronouncements have been cautious. After all, the deep split in American political life still exists between those who instinctively view the Soviets as evil "power maximizers" bent on world domination and those who see their international behavior as another great power, a "mirror image" of the U.S.<sup>29</sup> The ratio of supporters for each point of view, however, appears to be rapidly charging. A national public opinion polling on this subject, done just prior to the 1988 U.S. presidential campaign by Daniel Yankelovich on behalf of the Public Agenda Foundation, indicates a broad based improvement in the public's attitude toward the Soviet Union. The shift, however, is tentative and the survey suggests that it will be sensitive to Soviet behavior in the developing world.<sup>30</sup> 39. Below are a few of the more specific findings:

- D R.M. Avakov, E.B. Arefyeva, E.A. Bragino-,a, L.Z. Zevin, M.P. Strepetova, N.A. Usakova, and A.I. Chekhutov. The three essays will appear in a book to be published by the Overseas Development Council, Washington, D.C., tentatively titled "Prospects for US-USSR Joint Efforts in Development Assistance."

- / Ibid. Chapter IV.

- / Charles Wolf, Jr., "Extended Containment," in Beyond Containment, Aaron Wildavsky, ed., (Westlake Village, CA; ICS Press, 1983), pp. 147-68.

- XQ/ U.S.-Soviet Relations in the Year 2010: Americans Look to the Future. Public Summit '88, A Report of the Public Agenda Foundation and the Center for Foreign Policy Development at Brown University. (New York. Public Agenda Foundation, 1988).
**Figure 2: Interest rate shocks**

# A. Real shocks

<!-- Start of picture text --> Percent Yield, annual change<br>Real shocks 2-year bond (RHS)<br>6 6<br>4<br>4<br>2<br>2<br>0<br>-2 0<br>-4<br>-2<br>-6<br>-4<br>-8<br>-10 -6<br>C. Inflation shocks<br>Percent Yield, annual change<br>10 Inflation shocks 4<br>8 2-year bond (RHS) 3<br>6<br>2<br>4<br>2 1<br>0 0<br>-2 -1<br>-4 -2<br>-6<br>-3<br>-8<br>-10 -4<br>-12 -5<br>E. Reaction shocks<br>Percent Yield, annual change<br>20 Reaction shocks 5<br>2-year bond (RHS) 4<br>15<br>3<br>10 2<br>5 1<br>0<br>0 -1<br>-5 -2<br>-3<br>-10<br>-4<br>-15 -5<br>1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br>1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br>1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br><!-- End of picture text -->

# B. Real shocks

<!-- Start of picture text --> Percent Percent, year-on-year<br>10 Real shocks Industrial production (RHS) 20<br>8 15<br>6<br>10<br>4<br>2 5<br>0 0<br>-2 -5<br>-4<br>-10<br>-6<br>-8 -15<br>-10 -20<br>D. Inflation shocks<br>Percent Percent<br>10 Inflation shocks 5<br>Inflation expectations (RHS)<br>3<br>5<br>1<br>0<br>-1<br>-5<br>-3<br>-10 -5<br>F. Reaction shocks<br>Percent Percentage point<br>Reaction shocks<br>15 3<br>Slope of fed fund futures curve<br>10 2<br>5 1<br>0 0<br>-5 -1<br>-10 -2<br>1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br>1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br>2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text --> _Sources:_ Bloomberg; Federal Reserve Bank of St Louis; Haubrich, Pennacchi, and Ritchken (2012); World Bank. _Notes:_ Figures reflect the year-on-year change in government bond yields. “Shocks” are 12-month moving averages of monthly shocks identified by the sign-restricted VAR model. Shaded area reflects NBER-dated recessions. D. “Inflation expectations” reflect 5-year breakeven inflation rate from January 2003 and the 5-year inflation expectations measure of Haubrich, Pennacchi, and Ritchken (2012) prior.

F. “Slope of the federal funds futures curve” reflects the 2-year ahead USD overnight index swap less the current effective federal funds rate.

23
First, to construct financial records from scratch and obtain relevant anchors (for subsequent adjustment of firm profits), respondents are required to systematically build estimates for 13 major cost categories (i.e., high-level mental accounts). At the start of the _Money Out_ section, the survey instructions clearly define what is meant by firm costs: “In the next section, we are going to ask you some questions about all the money that went _out_ of your business in the last month (past 30 days). Please remember to focus on all of the money you spent in your business to pay for bills, expenses, and salaries in the past 30 days.” Each of the major cost categories is represented as a separate section in the electronic survey tool.

Next, one at a time, each major cost category is divided into its relevant sub-categories (i.e., lowlevel mental accounts) with detailed questions used to obtain how much money was spent last month on individual cost components. Particular attention is paid to getting an accurate measure of both the amount and the frequency (e.g., daily, weekly, monthly) of each sub-category of costs. This subdivision of costs helps correct any mismatch in timing of when costs are incurred relative to their corresponding revenue stream. The disaggregated components are then automatically converted into monthly values and aggregated up to create an estimate for that category’s total monthly cost. Once all sub-category estimates (i.e., component costs) are obtained, then a within category _Major Cost Summary_ screen is displayed on the survey interface (see Figures 4 and 5 for examples on Materials and Communication costs, respectively).

The respondent is prompted to review a table listing the monthly values for each sub-category as well as the aggregated estimate of the overall category’s total cost last month. At this juncture, the respondent can confirm her final estimate of the category’s total monthly cost or return to any of the previous cost questions to make further adjustments. After any adjustments, the respondent returns to the _Major Cost Summary_ screen, reviews the updated cost information, and confirms her final cost estimate or continues to make adjustments. The survey only proceeds to the next section after the respondent confirms her final monthly estimate for the major cost category. It is through this intra-category anchoring, aggregating and adjusting that a final estimate is obtained for each of the 13 major cost categories outlined below.

14
58 companies and 120 oil marketing companies (NPA 2015). The media reported evidence of “stiff competition at the pump” soon thereafter ( _Africa Report_ 2015). As one indication of continuing adverse effects of price subsidies, the Ghana Chamber of Bulk Oil Distributors reported at a petroleum conference in November 2015 that the weight of the subsidies that the government had not yet reimbursed was straining the marketers’ ability to access funding to compete and sustain operations effectively (GhanaWeb 2015b).

The disruptions to gas supplies from Nigeria via the West Africa Gas Pipeline have forced greater reliance on oil and substantially increased the cost of power generation, leading to large subsidies in the power sector. Tariffs below cost recovery in turn have caused payment arrears to Nigeria, which in turn have contributed to the volume of gas supplied being cut, intensifying reliance on oil. After raising power tariffs in October 2013 by 78.9 percent for all consumer categories except residential consumers consuming less than 50 kWh a month, the Public Utilities Regulatory Commission has been refraining from adjusting prices fully to reflect costs. For example, for the quarterly adjustment effective April 2015, the Commission stated that the average calculated tariff increase needed was 31.6 percent but it decided to limit the increase to 2.6 percent. For the adjustment effective July 2015, the calculated required average increase was 51.7 percent but the actual increase implemented was 0 percent <u>(www.purc.com.gh).</u>

# **India**

India ended the gasoline price subsidy in June 2010 and began raising diesel prices by Rs 0.5 (US$0.01) per liter a month in January 2013 (Figure 14) in order to reduce price subsidies. Because of exchange rate fluctuations, this price increase was not always sufficient to keep up with world prices, but the precipitous fall in global petroleum product prices in the last few months of 2014 enabled the government to deregulate diesel prices in October while lowering them on the domestic market. At the same time, the government raised excise duties on gasoline and diesel in succession in October, November, and December 2014. The excise duty on gasoline with 92 RON was increased from Rs 9.48 (US$0.16) per liter in September 2014 to Rs 17.46 (US$0.28)/liter in December 2014, and that on diesel from Rs 3.56 (US$0.06) per liter to Rs 10.26 (US$0.16) per liter during the same period. The government also increased the “clean energy cess”—a form of carbon tax levied in India as an excise duty under section 83 (3) of the Finance Act, 2010—on coal from Rs 50 (US$0.83) per tonne to Rs 100 (US$1.66) per tonne starting in July 2014, and the budget for the fiscal year beginning in April 2015 doubled the cess again to Rs 200 (US$3.20) per tonne.

**Figure 14: History of diesel prices in New Delhi, India**

<!-- Start of picture text --> Rs60 $1.50<br>Rs40 $1.00<br>Rs20 Rs/liter, left axis $0.50<br>US$/liter, right axis<br>Rs0 $0.00<br>Jan‐13 Jul‐13 Jan‐14 Jul‐14 Jan‐15 Jul‐15 Jan‐16<br><!-- End of picture text --> _Source:_ http://www.iocl.com/Products/HighSpeedDiesel.aspx.
for improving the productivity of all enterprises, especially for those in the agriculture and household enterprise sectors. Moreover, economic diversification requires a greater supply of higher-level skills, including critical technical and scientific skills.

# **The Skills Base of the Tanzanian Workforce**

Workforce skills are the set of competencies needed to carry out the tasks and duties of a given job.<sup>10</sup> These competencies include cognitive (such as basic literacy and numeracy), non-cognitive skills (such as teamwork, communication, language, IT and other “soft” skills), as well as job- or occupation-specific skills. Skills are acquired through a sequence of education, training and labor market activities, with skill formation proceeding in stages, each building on the previous one.<sup>11</sup> Few countries, developing countries in particular, have comprehensive information on these different competencies, and research is only now beginning to develop comparable indicators of the workforce skills needed for employment and growth (World Bank, 2014c).<sup>12</sup> Skills are not equivalent to education,<sup>13</sup> but for the most part pre-employment skills are acquired largely through education and training, especially for jobs in the formal sector. With on-the-job experience and possibly additional formal training, skills of the currently employed workforce can be significantly higher than what can be expected from their education levels. However, in general, it is accepted that there is a certain matching of education and occupations, on the grounds that people below a particular level of education would not be able to enter certain jobs, despite experience and training in their current jobs<sup>.</sup> We therefore start by looking at what kinds of skills are available to Tanzanian firms, using educational attainment as a proxy for skills. Figure 1 shows the growth in average years of education of the population aged 25 and over in Tanzania along with a sample of other countries in Sub-Saharan Africa (SSA) and Vietnam. Tanzania’s average is about 5 years of education, behind Kenya with 6 years of education and other middle-income countries of SSA with 8 to 10 years of education. It is estimated that it would take Tanzania 45 years to attain an average of 8 years of education at the pace witnessed over the most recent 20-year period.

**Figure 1:  Average Years of Education in Tanzania and Selected Countries**

> 10 See the ILO’s (2012, page 11) definition of skills in the International Standard Classification of Occupations, 2008 (ISCO-08).

> 11 This follows the World Bank’s Skills Toward Employment and Productivity (STEP) framework described by Banerji et al. (2010).

> 12  STEP household and employer surveys have been rolled out in several countries since 2012 including (among others) Sri Lanka, Vietnam, Armenia, Colombia, Lao PDR, Ghana and Yunnan Province in China.

> 13 For example, a job requiring engineering skills and competencies cannot be filled by a person with basic education even after 20 years of experience or training.

6