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Garcia, M., and A.S. Rajkumar. 2008. _Achieving Better Service Delivery through_ _Decentralization in Ethiopia_ . African Human Development Series Working Paper No. 131, World Bank, Washington, DC.

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Gregg, P. 2012. “Pre-distribution as a new set of policy tools.” Policy Network,

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- Harford, T. and M. Klein. 2005. “Aid and the Resource Curse: How Can Aid Be Designed to Preserve Institutions?”  Public Policy for the Private Sector Note No. 291.  World Bank, Washington, DC.

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Houle, C. 2015. “Ethnic Inequality and the Dismantling of Democracy: A Global Analysis.” _World Politics_ , 67 (3): 469-505.

- Ishiyama, J. 2013. “The Politics of Appeasement? Politics and Federal Financial Disbursements: The Case of Ethiopia.” _Publius: The Journal of Federalism,_ 42 (1): 1-24.

- Khan, Q.M., J.P. Faguet, C. Gaukler, and W. Mekasha. 2014 _. Improving Basic Services for the Bottom Forty Percent: Lessons from Ethiopia_ . World Bank Studies. Washington, DC: World Bank.

Krugman, P. 2013. _End This Depression Now!_ New York: W.W. Norton

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Lefort, R. 2007. “Power – mengist – and peasants in rural Ethiopia: the May 2005 elections.” _Journal of Modern African Studies,_ 45 (2): 253–73.

26
<!-- Start of picture text --> Figure 4a: Covariate Effects for I-LI Gap, 2000 and 2005<br>95% Confidence Interval<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>0. 0 5 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95<br>-0.02<br>-0.04<br>-0.06<br>Percentile<br>2000 2005<br>Figure 4b: Returns Effects for I-LI Gap, 2000 and 2005<br>95% Confidence Interval<br>0.2<br>0.18<br>0.16<br>0.14<br>0.12<br>0.1<br>0.08 .<br>0.06<br>0.04<br>0.02<br>0<br>0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95<br>Percentile<br>2000 2005<br>Difference in LRPCE<br><!-- End of picture text -->
# **Appendix 1: Data and Statistical Appendix**

# **Sample**

The study uses a sample of 104 surveys from the September 2016 vintage of the Global Micro Database (GMD); only surveys between 2009 and 2014 are selected. The sample is composed of 89 developing countries. The GMD sample is lined up to represent the estimates of extreme poverty in 2013. When a survey of 2013 is unavailable, two surveys are selected, one before and one after 2013, and their results are weighted according to the relative distance to 2013 (Castañeda et al, 2016); if only surveys before 2013 are available, we use the latest available. The countries and years selected are showed in Table A1.

|**Table A1: Developing countries and survey years in GMD sample**|
|---|

|**Countries (89)**|**Survey years**|
|---|---|
|Armenia, Bulgaria*, Chile, China, Czech Republic, Dominican Republic*, Estonia,<br>Georgia*, Honduras*, Croatia, Hungary, Kazakhstan*, Kosovo, Lithuania, Latvia*,<br>Moldova, Montenegro*, Pakistan*, Poland*, Romania*, Serbia, Slovak Republic,<br>Slovenia*, Ukraine (24)|2013 only|
|Argentina, Bolivia, Brazil*, Colombia, Costa Rica*, El Salvador, Ecuador,<br>Mexico*,Paraguay*,Peru*, Uruguay, Vietnam(12)|2012 and 2014|
|Guatemala*,Indonesia (2)|2011and2014|
|Nicaragua (1)|2009 and2014|
|Albania*, Bhutan, Democratic Republic of the Congo*, Djibouti, Guinea*, Haiti,<br>Kyrgyz Republic, Cambodia, Lao PDR, Mongolia, Mauritius*, Panama*,<br>Philippines,Russian Federation, Sri Lanka*,Thailand,Turkey, Uganda*(18) 7|2012 only|
|Chad*, Republic of the Congo*, India*, Niger*, Senegal*, Sierra Leone*,<br>Tanzania*,Togo*(8)|2011 only|
|Bangladesh*, Ethiopia*, Guinea-Bissau*, Lesotho*, Madagascar*, Malawi* ,<br>Mali*, Nepal*, Nigeria*, Rwanda*, São Tomé and Príncipe*, Tunisia, Vanuatu,<br>South Africa*,Zambia*(15)|2010 only|
|Burkina Faso*, Botswana, Maldives, Nicaragua*, Papua New Guinea, West Bank<br>and Gaza, Sudan*, Swaziland*,Tajikistan,Tonga (9)|2009 only|

*Countries for which GMD has surveys’ PSU.

# **Lineup**

The lineup procedure followed three steps: 1) Calibrate the sample of each survey to represent the national population in 2013 according to UNDESA. The calibration is made to represent UNDESA’s population divided by 5-year age groups and gender. The survey weights are calibrated using maximum entropy estimation, and then rescaled to equal the exact UNDESA figures.<sup>14</sup> The number of children aged 15-17 within the 15-19 age-group is determined by each survey’s age distribution within the group. For Kosovo we use population estimates

> 14 Command maxentropy in Stata®14. This technique is used so as, to the extent possible, maintain constant weights within households. See Wittenberg, Martin. “An Introduction to maximum entropy and miminum cross-entropy estimation using Stata”. The Stata Journal 10 (2010) for a description of the methodology.

20
<!-- Start of picture text --> Occupational Composition of Formal and Informal Employees<br>In European Countries, by Region. 2004-2009<br>Non-Manual, High-Skilled Non-Manual, Low-Skilled<br>IL<br>GR<br>East North South West East North South West<br>Formal Informal Formal Informal<br>Manual, High-Skilled Manual, Low-Skilled<br>FR<br>E S FR<br>IL<br>East North South West East North South West<br>Formal Informal Formal Informal<br>60 60<br>40 40<br>20 20<br>0 0<br>in Employment, %<br>Share of Occupation<br>60 60<br>40 40<br>20 20<br>0 0<br>in Employment, %<br>Share of Occupation<br><!-- End of picture text --> Formal: Employee with a contract; Informal: Employee without a contract. Self-employed excluded Data not weighted by countries' population: A worker from any country is equally likely to be sampled Source: Calculation with ESS data

# **Figure 10 Shares of manual and non-manual occupations among formal and informal workers in European Regions**

Recall (see, e.g. European Commission, 2008, p.104: Table 8 in Annex to Chapter 2) that a worker with higher education holding a manual or low-skilled non-manual occupation, is considered _over-qualified_ ; secondary-educated workers employed in elementary occupations are also over-qualified. Figure 11 provides a cross-country comparison of prevalence of over-qualification among formal employees and two categories of shadow workers. Overall, there is a quite strong positive correlation between the three overqualification rates. However, in countries such as Ukraine, Russia, Slovenia, Denmark, Norway, Spain and Switzerland, employees without contracts are much more likely than their formally employed colleagues to have education beyond the level their job require. This could indicate either a lack of demand for skilled workers, or a skills mismatch between what is learned and what is demanded on the labor market at a level high enough to justify the cost of hiring formally. Given that the trends in favor of more skilled workers (both in terms of education and occupation) found in the structure of European employment, as well as in the structure of job creation (European Commission, 2009: Figures 2-4), the skill mismatch is a more likely explanation.

30
42

||Russia|Slovakia|Slovenia|Spain|Sweden|Switzerland|Turkey|UK|Ukraine|US|
|---|---|---|---|---|---|---|---|---|---|---|
|Barley|0.38|0.68|0.68|0.91|0.77|0.77|0.68|0.91|0.68|1.38|
|Beef|0.19|0.20|0.20|0.70|0.70|0.70|0.20|0.70|0.20|0.70|
|Cotton|-|-|-|-|-|-|0.15|-|-|0.20|
|Egg|0.15|0.10|0.10|0.20|0.35|0.35|0.10|0.20|0.10|0.35|
|Hazelnut|-|-|-|-|-|-|0.50|-|-|-|
|Maize|0.54|0.66|0.66|1.06|-|1.11|0.66|-|0.66|0.80|
|Milk|0.15|0.14|0.14|0.16|0.15|0.15|0.14|0.16|0.14|0.16|
|Oat|0.38|0.68|-|0.91|0.77|0.77|-|0.91|0.68|-|
|Oilseed|-|-|-|-|-|0.27|-|-|-|-|
|Pigmeat|0.18|0.50|0.50|0.80|0.60|0.60|-|0.80|0.50|0.86|
|Potato|-|0.68|-|0.91|0.77|-|0.68|0.91|0.68|1.38|
|Poultry|0.25|0.25|0.25|0.90|0.65|0.65|0.25|0.90|0.25|0.56|
|Rapeseed|-|0.72|-|0.35|0.27|-|-|0.35|-|-|
|Rice|-|-|-|0.50|-|-|0.15|-|-|0.25|
|Rye|0.38|0.68|-|-|-|-|-|-|0.68|-|
|Sheepmeat|-|0.28|0.28|0.90|0.47|0.47|0.28|0.90|-|0.70|
|Sorghum|-|-|-|-|-|-|-|-|-|1.38|
|Soybean|-|0.13|-|0.16|-|-|-|-|-|0.30|
|Sugar|0.15|0.30|0.30|0.50|0.29|0.29|0.30|0.50|0.30|0.24|
|Sunflower|0.37|0.72|-|0.35|-|-|0.72|-|0.72|-|
|Tobacco|-|-|-|-|-|-|0.50|-|-|-|
|Wheat|0.29|0.37|0.37|0.57|0.53|0.53|0.37|0.57|0.37|0.49|
|Wine|-|0.50|-|0.50|-|-|-|-|-|-|
|Wool|-|-|-|-|-|-|-|-|-|0.70|

Sources: Authors’ compilation from Roningen (2001) and Tyers and Anderson (1992, Appendix Tables A2 to A4).
Table-¶ (annual average, percent)

||1979-82|1982-86|
|---|---|---|
|Reschedulers|||
|Argentina|-0.74<br>|0.38|
|Brazil|3.36|4.03|
|Chile|1.42|-0.37|
|Costa Rica|-1.08|1.63|
|Ecuador<br>|3.84<br>|1.98<br>|
|Mexico|6.12|0.06|
|Morocco|3.31|3.83|
|Peru|2.77|-1.39|
|Philippines|4.57|-1.16|
|Uruguay|0.97|-2.25|
|<br>Yugoslavia|2.11|0.72|
|Non-Reschedulers|||
|Colombia|3.16|2.67|
|Egypt|2.50|4.63|
|India|2.75|5.19|
|Indonesia|6.54|3.61|
|Israel|2.64|1.98|
|Korea|4.28|8.47|
|Malaysia|7.37|3.94|
|Venezuela|-0.08|-0.22|

Notes: Average growth of Mexico, Morocco, Peru and Indonesia is for 1982-85. Average growth of Egypt is for 1982-84.

Source: International Financial Statistics (various years).

25
# **Acronyms**

ARIP: Agricultural Reform Implementation Project CES: Constant Elasticity of Substitution CSE: Consumer Subsidy Equivalents CGE: Computable General Equilibrium DIS: Direct Income Support DSI: State Hydraulic Works, Republic of Turkey EU: European Union FAO: Food and Agriculture Organization IFPRI: International Food Policy Research Institute GDRS: General Directorate of Rural Services GDP: Gross Domestic Product GTAP: Global Trade Analysis Project LSCB: Lower Seyhan-Ceyhan Basin OECD: Organization for Economic Co-operation and Development PSE: Producer Subsidy Equivalents QHS: Quantitative Household Survey REC: Regional Environment Center SAM: Social Accounting Matrix SPO: State Planning Organization, Republic of Turkey TACOGEM-W: Turkish Agricultural Computable General Equilibrium Model with water TurkSTAT: Turkish Statistical Institute UFT: Undersecretariat of Foreign Trade, Republic of Turkey VA: Value-added WUA: Water User Associations WTO: World Trade Organization 2
consumption data while the other does not and the two surveys do not produce the same statistics are much more common for a variety of reasons. We consider the application of poverty imputation in such contexts by relaxing Assumption 1 and analyze the LFS in this section. Jordan’s Department of Statistics is responsible for implementing both the HEIS and the LFS, but different departments within this agency are in charge of each survey and thus conduct them independently according to their different mandates. The HEIS collects consumption data and is implemented biannually, while the LFS does not have consumption data but collects data on labor statistics more frequently on a quarterly basis.

# **IV.1. Making the Different Survey Designs More Comparable**

The violation of Assumption 1 implies that, the estimated distributions for the common variables for the two surveys in the same period may be different and not representative of the same underlying population. We propose to “standardize” the distributions of the variables in survey 2 by those in survey 1 in Proposition 3 below.<sup>24</sup>

# **Proposition 3: Standardizing common variables in surveys of different design**

_Assume that survey 2 has the same design over time, is collected more frequently than survey 1, and that the time periods data from the former are available include the periods that data from the latter are available. Assume further that the overlapping variables between the two surveys follow a normal distribution such that x_ 1 _t_ ~ _N_ ( μ 1 _t_ , σ 12 _t_ ) _and x_ 2 _t_ ~ _N_ ( μ 2 _t_ , σ 22 _t_ ) _, for t= 1, ..., T. We can standardize the variables in survey 2 according to survey 1 for both the overlapping periods and other periods as follows._ i) _For the overlapping period t, the standardized variables x_ 2 → ,1 _t_<sup>_in survey 2 are given by_</sup>

> 24 Note that these standardization procedures only require identifying the consumption survey as the benchmarking survey in terms of producing poverty estimates, and do not require that all the sample statistics produced from this survey be considered as more “correct” and/ or replace those from the other.

28
- 25 -

VIII. Noland (1987) 47. Having complained that the Saxonhouse studies were based on a wrong theory (the H-0 theory) and that Bergsten/Cline and Balassa studies had no underlying formal models, Noland (1987) relied on the Helpman-Krugman trade theory. Noland's model specifically involved two kinds of international trade - the usual Heckscher-Ohlin inter-industry trade and intra-industry trade in differ-o'ntiated manufactures. The volume of inter-industry trade was posed to be a function of differences in factor endowments while the volume of intraindustry trade was theorized to be a function of countries' relative size similarity.

48. In Noland's model, thus, the volume of trade was to be explained by variables relating to country size and factor endowment. Specifically, three explanatory variables were utilized: (a) GDP as a scale variable (data were adjusted for purchasing power); (b) per capita income as a proxy of the capital-labor endowment ratio; and (c) a GDP-per-capita-related index devised as a measure of endowment similarity.!' In addition, two "resistance" variables were included --transportation costs and a dummy for participation in the EC. Dependent variables were (i) exports, (ii) imports and (iii) total trade (exports plus imports).

# 1/ Noland defined the endowment similarity measure as follows:

GDPCAPEQj = -[(q)ln(q) + (l-q)*ln(l-q)]/ln2 <!-- Start of picture text --> GDPCAP<br>GDPCAPj + GPDCAPW<br><!-- End of picture text --> j = home country where GDPCAPw indicates world per capita income, calculated from the countries in the sample (excluding the home country). GDPCAPEQ reaches its maximum when home and partner country per capita incomes are equal and its minimum as q approaches 0 or 1.
# **1. Introduction**

Based on 2007 data, only 76 percent of primary school-aged children were enrolled in SubSaharan Africa, which is the lowest net enrollment rate of all developing country regions (United Nations 2010).<sup>1</sup> The estimated 31 million not enrolled African children represent over 45 percent of the world‟s total of out-of-school children. Over half of the countries in Sub-Saharan Africa do not provide legal guarantees for free primary school education. In settings without universal free primary education and where most children engage in child labor activities, as is true in many African countries,<sup>2</sup> understanding how parents decide how to allocate limited resources across siblings is important as these decisions have long-term impacts on each child‟s future earnings, health, and overall welfare. Schooling decisions depend on parent perceptions about the returns to school for a child, the opportunity cost of sending that child to school, the child‟s ability, and the presence and characteristics of the child‟s siblings. To raise levels of school enrollment, it is critical to understand these links between child ability, school enrollment, and child labor.

An extensive literature examines the source of inequalities for children‟s educational investments within a household building on seminal work by Becker and Tomes (1976) that highlights the tradeoff between child quantity and their „quality.‟ In making decisions about schooling and child labor, parents have information about their children‟s abilities that is often not available to researchers, a fact that partly explains why much of the empirical research on the

> 1 This compares with an average net enrollment rate of 89 percent in developing regions and 96 percent in developed regions. These enrollment rates in Africa still represent an enormous improvement since 1998 when African net enrollment rates were only 58 percent. In Burkina Faso, the focus of this paper, the net enrollment rate was 35 percent in 2000 and improved to 55 percent by 2007 (UN Statistics Division). Based on this paper‟s data from Burkina Faso, only 24 percent of households with multiple primary school aged children enroll all of their children.

> 2 Based on the UNICEF Multiple Indicator Cluster Survey End of Decade Assessment, almost three-fourths of all rural primary school-aged children in developing countries were involved with some type of work that included helping with the family farm or business or providing domestic household services such as cooking, sweeping, doing dishes, or fetching water (Edmonds and Pavcnik 2005).

2
|**Table 5: Impacts on Business Ownership an**|**d Wage W**<br>Round 2<br>Cu|**ork for Po**<br>Round 3<br>rrently Ow|**tential Ow**<br>Round 4<br>n a Busine|**ners**<br>Round 5<br>ss|Ever own<br>a business <br>Round 5|Closed<br> Business<br>Round 5|Round 2<br>C|Round 3<br>urrently a W|Round 4<br>age Work|Round 5<br>er|
|---|---|---|---|---|---|---|---|---|---|---|
|_ITT_|||||||||||
|Assigned to Cash if finish Training|0.201***|0.140***|0.0261|0.0244|0.0708|0.0634*|-0.0309|-0.0587**|-0.00920|-0.000515|
||(0.0465)|(0.0483)|(0.0501)|(0.0485)|(0.0436)|(0.0383)|(0.0282)|(0.0281)|(0.0325)|(0.0317)|
|Assigned to Training only|0.0904*|0.0628|0.0364|-0.0217|0.0123|0.0166|-0.0252|-0.0753***|-0.0210|-0.0180|
||(0.0466)|(0.0490)|(0.0499)|(0.0488)|(0.0446)|(0.0365)|(0.0286)|(0.0275)|(0.0327)|(0.0311)|
|_TOT_|||||||||||
|Received Training & Cash|0.290***|0.203***|0.0384|0.0360|0.104*|0.0935*|-0.0449|-0.0862**|-0.0138|-0.000779|
||(0.0628)|(0.0658)|(0.0704)|(0.0689)|(0.0614)|(0.0548)|(0.0392)|(0.0394)|(0.0465)|(0.0450)|
|Received Training Only|0.122**|0.0848|0.0495|-0.0298|0.0161|0.0219|-0.0338|-0.102***|-0.0286|-0.0244|
||(0.0606)|(0.0638)|(0.0652)|(0.0643)|(0.0584)|(0.0478)|(0.0370)|(0.0357)|(0.0428)|(0.0406)|
|Firms|607|601|582|609|620|609|588|587|547|606|
|P-value for testing equality of treatments|0.020|0.113|0.842|0.357|0.173|0.244|0.830|0.470|0.714|0.584|
|Controlgroupmean<br>_Notes:_Results shown are for OLS regressio<br>TOT instruments receipt of treatment with|0.381<br>ns of outc<br>random a|0.477<br>ome on tre<br>ssignment.|0.555<br>atment as<br>Robust st|0.573<br>signment.<br>andard err|0.688<br>All specific<br>ors in paren|0.155<br>ations inclu<br>theses , ***|0.096<br>de random<br>p<0.01, *|0.116<br>ization str<br>* p<0.05, *|0.116<br>ata dumm<br>p<0.1|0.115<br>ies.|

38
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pursued a variety of reforms in the area of service delivery. Decentralization of the responsibility for social services to local governments, delegation of management responsibilities to communities, creation of special agencies to run high-priority programs (social funds, conditional cash transfers, etc.), contracting out to NGOs and non-state providers, and introduction of management reforms within public bureaucracies are examples of this trend.

Slowly, evidence of the impact of such reforms is emerging. Two recent examples involve the case of Conditional Cash Transfers (Fiszbein and Schady, 2009) and school accountability reforms (Bruns, Filmer and Patrinos, 2011). A key message of all such reviews is the well-known argument that „context matters‟. Quite often, though, it is not clear in which way it matters –making the statement more a source of frustration than a helpful reminder of the need to consider country conditions.

This paper is based on the premise that effective institutional reforms for service delivery require a carefully-considered institutional “fitting” process as opposed to transplantation of “international best practices.” We seek to ask how the specific institutional contexts of a given country limit options for service delivery reforms, and therefore how a feasible set of reform options may vary systematically across countries.

In _Citizens, Politicians, and Providers_ (Fiszbein 2005) we reviewed the experience with service delivery reforms in Latin America. We first mapped (see Figure 3) the range of reforms in service delivery (both for social and infrastructure services) using the accountability framework first developed in the 2004 World Development Report (World Bank 2003). The WDR presented a by-now familiar accountability framework focused on 4
Figure 4.

# DISTRIB. OF STOCK FUNDING RATIO

<!-- Start of picture text --> PERCENTAGE<br>60<br>50<br>40<br>30-<br>20-<br>10-<br>o 1-0 '<br>-20 o 20 40 60 80 100 120 140 160 IBO 200 220 ___240 260 280 300 320 340<br><!-- End of picture text --> STOCK FUNDING RATIO (1) IF RATIO LE -20 THEN MIDPOINT--20