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- Li, Wei, Lixin Colin Xu. 2002. “The Political Economy of Telecom Privatization and Competition” _Journal of Comparative Economics_ , 30(3), 439-62.

31
|**Appendix Table: The Determinants of Child and A**<br>!<br>|**dult Sickness**<sup>**in Peru,**</sup><br>I<br>|<sup>**1994 and 1997a**</sup><br>|
|---|---|---|
|<br>_________________________________________________|<br>Child Sickness|IAdult Sickness|
|_!_Median householdage|-0.015<br> <br>|-0.005<br>|<br>|
|<br> <br>**_**|.<br>_L__<br>_-4.22,_|_<br>_3._<br>_-233_|
|_____.___<br>**__**<br> <br>Native|-0.118<br> <br>|-0.071<br>|
||_0._<br>_087)_|_(-0.71)_|
|lMaximum education in household|-0.017<br> <br>|0.023|
|_____<br>_____|_-__~.(_<br>_2_|_.35)___|
|i Hours of public water supply icluster|<br>-0.000|-0.004|
|_ _ _._<br>_<br>_<br>_<br>_ w~~|~~~~~~~~~~~~E<br>_<br>L .|_<br>_<br>_87_|
|% of households in cluster with sanitation|-0.252<br>|0.068|
|_'i_<br>___ __________<br>_______________<br>____ _|___<br>_(-1.28)_|_(0.44)_|
|I % of households in cluster with gas/electricity|0.233|-0.304|
|_____<br>__,_<br>_<br>__<br>_<br>__.___|_(1.21)__-_<br>___||
|% of households in cluster with in-house water|0.007<br>|0.385<br>|
|[.<br>|_I.0.0_|_(2.71)_|
|___________________________<br>_______<br>% of households in cluster with electriclighting|__ _<br>0.142<br>|<br>0.261|
|L____<br>_____________<br>|-<br>_(0.8J5_|_(1.95)_|
|_!_Roof: concrete|<br>-0._106|-0.004_|
|iRoof:<br>wood<br>_________-0.115|!____|-0.351|
|!___________--_______________________<br>_1_|__<br> <br>_(-0.48)_|_(-1.91)_<br>___|
|Rf tiles|-0.168|0.010<br>|
|_||**_0_**_.10-1.36)_<br>_(IN_|
|Rooms per capita|-0.299|-0.101|
|t<br>______<br>____________|_6-2.38)_|_(-1.82)_|
|___________<br>Household<br>size<br>_|0.207|0.069|
|_!_<br> <br>.|_(7.80)_|_(3.20)_|
|______<br>________<br>Numberofbo-ys'inhousehold(5-14)|<br>-0.027|-0.010|
|_._ ___,_<br>__._____|_-3.52_|_-_|
|Number of girls in household (5-14)|-0.028|-0.023|
|ii|<br>(-3.~~~|~~~~~~~~~69)<br>_(-3.45)_|
|__<br>Number of elderly men in household (>60)|_<br> <br>-0.021|<br><br>-0.001|
|__________________<br>____________|__(-1.02)_|___(-0.08)_|
|Number of elderly women in household (>60)<br>wmnihoshl(60|0.019<br>________<br>___<br>.____|-0.031<br>_|
||_LO.87,)_<br>-<br>|_-j.76)_|
|Rural_<br>__|<br>L<br>0.245|0.273|
|________<br>_<br>_<br>______.(1.4|||
|_________________________<br>LConstant<br>_.__|-_15<br>_<br>-1.154|_-211).000_<br>-1.000|
|!<br>_<br>_<br>_<br>.__._|~~~~~~~~<br>_38) _|~~~~~(-4.<br>_-. 94)_|
|<br> <br>_Number ofhouseholds_|<br> ___<br>898<br>-|<br> <br>898|
|_Number of observations per household_|2|2|
|tit!!<br>_|156.87|96.19|

a Random-effects probit estimates. Dependent<sup>variables are, respectively, child and adult sickness dummies. t-ratios are given in</sup> parentheses below coefficient estimates.

36
Gregorio Impavido, Alberto R. Musalem, and Dirnitri Vittas

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- 29 -
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

# **REFERENCES**

- [1] **Callund, David** , 2004, Pension Reform – Charting a Way Forward.  Processed

- [2] **Deloitte and Touche,** 2001 Review of Public Pension Arrangements.  Processed.

- [3] **GOU** , 2005, National budget framework paper for financial years 2005/06 – 2007/08.  Processed.

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- 78 -
# Table of Contents

# Page Number

|Ackn|owledgements|
|---|---|
|I.<br>Back|ground|
|A.|Origin of State Capitalism<br>1 - 3|
|B.|Economic Policies and Performance, 1950-80<br>3- 6|
|C.|Macroeconomic Performance in the 1980s<br>6- 10|
|D.|Dissatisfaction with SEE Performance<br>11- 16|
|E.|Strategic Options for Improving SEE Performance:<br>16- 19<br>Reforms and Privatization|
|{I.<br>Priv|atization|
|A.|Initial Strategy<br>20- 24|
|B.|Preparations<br>25- 27|
|C.|Initial Test Cases<br>28- 29|
|D.|Stepped Up Privatization<br>29- 32|
|E.|Change in Strategy in Response to Criticism<br>32- 38|
|F.|Planned Future Privatizations<br>38- 43|
|G.|Lessons<br>43- 47|
|H.|Opposition<br>47 - 51|
|I.|Medium-term Outlook<br>51- 56|
|Refe|rences<br>57- 59|
|Anne|x I.<br>List of State Economic Enterprises and Public Economic<br>Establishments<br>60-<br>63|
|Anne|x II.<br>List of State Economic Enterprises Transferred to the Public<br>Partnership Administration and Its Participations<br>64|
|Stat|istical Appendix<br>65-<br>73|

i
Table 1: Local Government Reform Initiatives

|**Concept**|**Reform Initiative**|
|---|---|
|Process|Eprocurement|
|Organization|Procurement Service Units (ULPs)|
|Capacity|Mandatory certifcation of ofcials<br>involved in tender committees|
|Monitoring|CSO oversight & complaint|
||handling mechanisms|

Our indicators of the four initiatives include the following:

1. Eprocurement

- Status of the implementation of e-procurement

- Number and value of packages procured through e-procurement between 2010 and 2010

2. ULPs

- Status of the establishment of ULPs

3. Mandatory certification of officials involved in tender committees

- Description of initiatives to develop the capacity of GoI officials

- Number of procurement accredited staff

4. CSO oversight and complaint handling mechanisms

- Description of CSO or community monitoring activities

- Status of the establishment of complaint handling mechanisms

The scan also collected data on the following three budget items for 2007 to 2010: total expenditure; procurement expenditure, or the total allocated for goods and services and capital expenditures; and the contribution of unspent budget to total expenditure.

We then commissioned structured case studies that are paired around three hypotheses relating to three factors that are associated with procurement performance: the quality of PFM systems for planning, budgeting, spending and oversight of public money; the quality of a local government’s 9
Forthcoming in the _World Bank Economic Review_

# **Foreign Investment and Productivity Growth in Czech Enterprises'**

Simeon Djankov World Bank Bernard Hoekman World Bank and CEPR JEL Codes: D24, F14, 052, P31 Keywords: Czech Republic, foreign direct investment; joint ventures, technology transfer Correspondence: Bhoekman(worldbank.org An earlier version of this paper was presented at the conference Trade and Technology Diffusion: the Evidence with Implications for Developing Countries, Fondazione Mattei, Milan April 18-19, 1998. We are grateful to Magnus Blomstrom, Caroline Freund, Ann Harrison, Roberto Rocha, Jim Tybout and three anonymous referees for helpful comments and suggestions.
|Subsidies_of which_|528|0.6%|...|
|---|---|---|---|
|Water|254|0.3%|Yes|
|Inputs for Agriculture|88|0.1%|Yes|
|Other Government Spending|11,515|12.5%|No|

_Source: International Monetary Fund; Uganda Annual Budget Performance Report 2016/17; Ministry of Education and Sports (2017)_

# 2.4.4. In-kind Transfers

- <u>Education: Education expenditure includes recurrent wage and recurrent non-wage and</u> development funding for education and represents the largest budgetary allocation of the expenditures covered here.  Uganda eliminated primary schooling fees in 1997 (mainly in public schools) under the Universal Primary Education (UPE) policy. Ten years later, it attempted to do the same for secondary schooling fees under the Universal Secondary Education (USE) initiative which, in addition to eliminating fees in public schools, adopted a public-private-partnership with capitation grants to selected private schools (Wodon, 2016).

In 2016/17, Uganda spent around 2.4 percent of GDP on education. Expenditure in pre-primary and primary schools totaled around 1,200 billion UGX, or about 50 percent of the total education sector budget or 1.3 percent of the GDP, with approximately 8.7 million students enrolled in 2016/17. The shares of the total sector budget for secondary and tertiary education are much lower at approximately 16 and 20 percent, respectively. Approximately 1.4 million students and 87,000 students are enrolled in secondary and tertiary education (respectively), which indicates that tertiary education is a very expensive proposition in Uganda; while secondary education enrolls over 16 times more students as tertiary, it receives a budget allocation that is equivalent to only 80 percent (approximately) of the tertiary education budget allocation. These data come from the Ministry of Finance, Planning and Economic Development’s Annual Budget Performance Report (MoF, 2017) and the Uganda Ministry of Education and Sports’ Sector Annual Performance Report (Ministry of Education and Sports, 2017).

- <u>Health: Health expenditure in 2016/17 reached 1.3 percent of GDP, and mainly includes</u> expenditure in public primary health care and hospitals, which provide basic free health care services to the population. This expenditure mainly takes the form of grants to local governments, which include payments of wages to health workers, funding for service delivery operations, and construction and rehabilitation of health facilities (Jellema et al., 2017). Primary health care expenditure was close to 50 percent of total health expenditure and includes recurrent grants to local governments and medicines, while public hospitals received an allocation of 33 percent, including medicines. These data come from the Ministry of Finance, Planning and Economic Development’s Annual Budget Performance Report (MoF, 2017) and the Ministry of Health Annual Sector Report 2016/17 (MoH, 2017).

14
is available). Moreover, the latest available year for which data is available varies signi fi cantly across countries (see second column of Table 1). To enhance country and time coverage would allow to improve upon the precision of the estimates.

We obtained the entire UNCTAD TRAINS dataset through the World Bank’s WITS system. This dataset contains detailed information on various types of NTBs (more than 30 different types of NTBs are identi fi ed). As discussed earlier, we included in our measure of Core NTBs: Price control measures (UNCTAD Trains code 6100 6200 and 6300), Quantity restrictions (UNCTAD Trains code 3100 3200 3300), Monopolistic Measures (UNCTAD’s Trains code 7000) and Technical Regulations (UNCTAD’s Trains code 8100). The dataset was updated using information provided by the WTO’s Trade Policy Reviews and in the case of the European Union by the EU Standard’s Database built by Groupe d’Economie Mondiale at Science Po (Paris).<sup>14</sup> The Core NTB variable used in the estimation of equation (8) takes the value 1 when a given country imposes one of the Core NTB measures in a six digit tariff line, and zero otherwise. This obviously suffers from the drawback that we do not have a measure of the restrictiveness of a particular NTB in a particular country. However, instead of making ad-hoc assumptions regarding the restrictiveness of each NTB, we will rather rely on our estimation procedure to impute the restrictiveness of each measure in each country. Rather than the statistician deciding how restrictive is each measure we will let the data “decide” which type of NTB and in which country has the most restrictive impact on imports.

The second type of NTB included is agricultural domestic support. This was obtained from WTO members noti fi cations during the period 1995-1998 (see Hoekman, Ng and Olarreaga, 2004 for a discussion of the construction of this variable). Domestic support is measured in dollars and is a continuous variable so it enters in log form in the estimation of (8). Only 158 tariff lines at the six digit of the HS are affected by domestic support in at least one WTO member.<sup>15</sup> One may wonder why we do not use estimates of producer subsidy equivalents (PSE) as traditionally done in the literature rather than estimating the AVE of these subsidies. The reason is twofold. First, production data at this level of disaggregation is not available, which precludes calculating PSE. Second, PSE cannot be directly compared to tariffs as they only affect the production side,

> 14 See Shepherd (2004).

> 15 One problem is that China’s agriculture support is not included as it was not a WTO member during that period.

15
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Policy ReseaRch WoRking PaPeR 4729

# **Abstract**

In determining whether a country’s higher education system should be expanded, it is important for policymakers first to determine the extent to which high private returns to post-secondary education are an indication of the scarcity of graduates instead of the high unobserved ability of students who choose to attend post-secondary education. To this end, the paper identifies the returns to schooling in urban China using individual-level variation in educational attainment caused by exogenous city-wide disruptions to education during the Cultural Revolution from 1966 to 1976. For city-cohorts who experienced greater disruptions, children’s educational attainment became less correlated with that of their fathers and more influenced by whether their fathers held administrative positions. The analysis calculates returns to college education using data from the China Urban Labor Survey conducted in five large cities in 2001. The results are consistent with the selection of high-ability students into higher education. The analysis also demonstrates that these results are unlikely to be driven by sample selection bias associated with migration, or by alternative pathways through which the Cultural Revolution could have affected adult productivity.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department studying the relationship between economic and political shocks, educational attainment and the returns to education. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jgiles@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
sulting in distinct regional patterns centering around large urban centers. These distinct patterns of specialization characterize not only farming but also di ff erent types of nonfarm activities.

A number of recent studies have demonstrated empirically the presence of such zones of specialization in rural areas. For instance, Fafchamps and Shilpi (2003), using household level data from Nepal, fi nd that both wage and self-employment in nonfarm activities are heavily concentrated in close proximity to large urban centers and somewhat concentrated around local markets.<sup>3</sup> Fafchamps and Shilpi (2005) report a signi fi cant e ff ect of proximity to cities on rural diversi fi cation and specialization along with the presence of some agglomeration economies around the cities. However, none of these studies analyzed the possible feed-back from agriculture to nonfarm activities.

The main objective of this paper is to examine whether and how the farm and urban linkages jointly shape the spatial pattern of non-farm activities observed in the context of a developing country, Bangladesh. To this end, we develop an empirical framework which combines these two separate approaches to examining urban-rural and farm-nonfarm linkages. The use of a comprehensive framework incorporating both types of inter-linkages helps to portray much richer picture of the e ff ect of infrastructure on non-farm activities. Better infrastructure can lead to a relocation of tradeables to cities and towns, reducing the density of such activities in rural areas. By extending the size of the market, better infrastructure, on the other hand, can facilitate agricultural productivity growth and induce growth of nonfarm activities in rural locations<sup>4</sup> . The overall impact of better infrastructure will thus depend on the relative strength and interaction of these inter-linkages.<sup>5</sup>

> 3 Although the study uses cross-section data, it utilizes an instrumental variables approach to correct for endogenous placement of roads.

> 4 The rural areas may attract more nonfarm activities as a result of infrastructure improvement if cities or towns are too congested and have higher living costs.

> 5 Agricultural productivity, by a ff ecting rural wages, can also trigger relocation of non-farm activities over space.

3