rafmacalaba/datause-encoder-data huggingface dataset:
show the diversity of choices made by provinces.

**Table 2.  Descriptive Statistics of Subnational Expenditure and Revenue Shares, 2003**

|**Expenditure shares**|**Mean**|**Std. Dev.**|**Maximum**|**Minimum**|**C.V.**|
|---|---|---|---|---|---|
|Province|28.0%|9.1%|49.4%|12.8%|0.33|
|Prefecture|24.4%|8.0%|48.9%|15.0%|0.33|
|County+ township|47.7%|8.2%|63.2%|30.3%|0.17|
|**Revenue shares**||||||
|Province|22.1%|8.2%|42.4%|11.4%|0.37|
|Prefecture|31.2%|6.8%|50.9%|19.0%|0.22|
|County+ township|46.7%|10.8%|65.2%|22.1%|0.23|

Note: Data exclude Beijing, Tianjin, Shanghai and Tibet. Source: China Prefectures, Cities and Counties Fiscal Statistics 2003.

These features of the current intergovernmental fiscal arrangements in China explain two notable outcomes in rural public finance in China **:** First, the wide variation in the fiscal environments faced by rural local governments in different regions, both in terms of how much autonomy they have in spending decisions, as well as resources available for meeting their expenditure responsibilities. Second, it is not a straightforward process for the central government to implement changes in rural policies since these changes are mediated by several intermediate levels of government.

# **2.2.  Expenditure Assignments**

Expenditure responsibilities are only defined in broad terms for central and “local” (i.e. sub-national) authorities in the Budget Law (1994). The law leaves it to the subnational governments to work out how to divide “local” responsibilities and revenues among the four levels. Table 3 shows the broad assignments under the Budget Law.

**Table 3.  Division of Responsibilities between the Central and Local Governments**

|**Central Government**|**Subnational Governments**|
|---|---|
|1. National defense|1. Localgovernment administration|
|2. Diplomacyand foreign affairs|2. Localpublic services|
|3. Military police|3. Locally financed basic infrastructure & technical<br>renovations|
|4. Keyconstructionprojects|4. Support to agriculture|
|5. Central Government administration|5. Urban maintenance and construction|
|6. Public services at the central level|6. Price subsidies|

10
|L. Adjustments for Fiscal Objectives<br>......................<br>26|
|---|
|**Taxes**ouiPubllc Znterprises<br>........................<br>26<br>Recurrent Cost Financing by Governments<br>.................<br>28<br>Investment Cost Financing<br>...............................<br>29<br>Earmarking<br>........<br>30<br>Link to Publie Ezpenditure Reviews<br>.32|
|F. Adjustments for Financial Objectives in<br>Public Sector Enterprise .33|
|IV.<br>CONCLUSIONS AND RECO)OEMDATIONS<br>.34|
|Summary of MajorLecommendations<br>.36|
|Footnotes .38|

# List of Tables

|Table|1.<br>Projects<br>with pricing<br>studies<br>................................<br>14|
|---|---|
|**Table**|2.<br>Percentofprojects using**shadow**prices<br>......................<br>17|
|**Table**|3.<br>Sourcesof project<br>**funds**in SARs.............................<br>29|
|**Table**|4.**Use**of**revenue covenants**<br>.....................................<br>33|

ANNEX 1: Review of Pricing and Cost Recovery in SARs: Coal ........... 40 ANNEX 2: Review of Pricing and Cost Recovery in SAks: Education ...... 45 ANNE 3: Review of Pricing and Cost Recovery in SARs: Fertllizer ..... 51 ANNEX 4: Review of Pricing and Cost Recovery in SARs: Health ......... 57 ANNEX 5: Review of Pricing and Cost Recovery in SARs: Highways ....... 63 ANNEX 6: Review of Pricing and Cost Recovery in SARs: Housing ........ 69 ANNEX 7: Review of Pricing and Cost Recovery in SARs: Irrigation..... 75 ANNEX 8: Review of Pricing and Cost Recovery in SARs: Oil & Gas ...... 83 ANNEX 9: Review of Pricing and Cost Recovery in SARs: Ports .......... 90 ANNEX 10: Review of Pricing and Cost Recovery in SAks: Power .......... 95 ANNEX 11: Review of Pricing and Cost Recovery in SARs: Railways ...... 103 ANNEX 12: Review of Pricing and Cost Recovery in SARs: Telecommunications ....... 110 ANNEX 13: Review ot Pricing and Cost Recovery in SARs: Water Supply and Sewerage ..117 We are grateful to the many Bank staff who reviewed the earlier draft of this paper. In particular, we wish to thank jeremy Warford, Dennis Anderson, Bela Balassa, Ezra Bennathan, David de Ferranti, Delbert Fitclhett, Frida Johansen, Maurice Mould, Emmanuel Jimenez, Robert Saunders, Mary Shirley, Yermal Shetty, Jee-Peng Tan, Mark Tomlinson and Bjorn Wellenius for their many useful comments and suggestions.
goods that would directly influence a woman’s LFP decision, women leaders’ impact on crime outcomes can also affect female LFP (Iyer et al 2012).

We examine this indirect impact of women leaders on female LFP using a 2SLS approach: cumulative exposure to women leaders at the state level up to the year 2001 is used to predict the level of key public goods in that year. We confirm that the length of exposure to women leaders affects the level of access to key public goods such as primary health centers, banks, paved roads and safe drinking water. Of these, our second-stage analysis finds that the first three have a significant influence on women’s LFP in the year 2004, overall and in specific categories of selfemployment.<sup>2</sup> This suggests that women leaders facilitate greater female LFP through indirect channels as well. Taken together, our findings provide encouraging support for the idea that women PRs can be a catalyst to improve both the supply of economic opportunities for women (through public employment) as well as women’s demand for labor force participation (through better public goods).<sup>3</sup> This study contributes to two specific strands of academic literature. Our work directly connects to prior studies of the impact of women’s political reservations in India. Secondly, it is also linked with a literature that examines women’s LFP in developing countries – including the declining trend in the Indian context (see Ghani et al 2013b, 2013c, 2013d). Finally, it also links to studies assessing the impact of the effect of MGNREGS on rural labor markets in India (see for instance Khera and Nayak 2009). More generally, this work builds upon earlier studies of the micro-level effects of policy on household labor supply and occupational choice. Overall we contribute to a much larger literature on the role and effect of policy and women’s advancement in less-developed countries, as well as wider interest on how to increase the effectiveness of social welfare programs in developing economies.

> 2 We have used the closest year to the latest available census data on public goods (2001) for which household level data on labor force participation is available, i.e. 2004.

> 3 We have not explored the impact of women PRs effects as role models on female LFP.  However, Beaman et al(2012) do find that they encourage higher levels of education and aspirations for girls’ working outside the home.

5
||Title|PPR WorkinaPpcr Sedes<br>Author|Date|Contact<br>forPaper|
|---|---|---|---|---|
|WPS189|Tbe Treatment of Companies under Cash<br>Flow Taxes: Some Administrative,<br>Transitional, and International Issues|Emil M. Sunley|May 1989|A BhalIla<br>60359|
|WPS190|Macro Performance Under Adjustment<br>Lending|Riccardo Faini<br>Jaine de Melo<br>Abdel Senhadji-Semlali<br>Julie Stanton|April 1989|M. Ameal<br>61466|
|WPS191|Openness, Outward Orientation, Trade<br>Liberalization and Economic Perform-<br>ance in Developing Countries|Sebastian Edwards|June 1989|M. Ameal<br>61466|
|WPS192|Inflation, Price Controls and<br>Fiscal Adjustment: Tne Case of<br>Zimbabwe|Ajay Chhibber<br>Joaquin Cottani<br>Reza Firuzabadi<br>Michael Walton|April 1989|A. Bhalla<br>60359|
|WPS193|Voluntary and Involuntary Lending:<br>A Test of Major Hypotheses|Peter Nunnenkamp|June 1989|L. Chavarria<br>33730|
|WPS194|Efficient Debt Reduction|Jeffrey Sachs|May 1989|L. Chavarria<br>33730|
|WPS195|How Has the Debt Crisis<br>Affected Commercial Banks?|Harry Huizinga|May 1989|L. Chavarria<br>33730|
|WPS196|A Review of Alternative Debt Strategies|Eugene L. Versiuysen|May 1989|E. Hadjigeorgalis<br>33729|
|WPS197|Differentiating Cyclical and<br>Long-Term Income Elasticities<br>of Import Demand|Fernando Clavijo<br>Riccardo Faini|May 1989|K. Cabana<br>61539|
|WPS198|Equity in Unequal Deductions:<br>Implications of Income Tax Rules in<br>Ghana and Nigeria|Chad Leechor<br>Robert Warner|May 1989|A. Bhalla<br>60359|
|WPS199|Private Sector Assessment: A Pilot<br>Exercise in Ghana|Samuel Paul|May 1989|E. Madrorna<br>61712|
|WPS200|Women and Development: Objectives,<br>Framework, and Policy Interventions|T. Paul Schultz|April 1989|J. Klous<br>33745|
|WPS201|How Much Fiscal Adjustment Is Enough?<br>The Case of Colombia|William R. Easterly|||
|WPS202|Financial Policies, Growth, and<br>Efficiency|Alan H. Gelb|June 1989|M. Raggambi<br>61696|
Figure B.8: Number of municipalities: verification <!-- Start of picture text --> CE MG<br>185<br>180 840<br>175<br>820<br>170<br>165 800<br>2009 2011 2013 2015 2017 2019 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>PB PR<br>223 400<br>221 395<br>219<br>390<br>216<br>385<br>213<br>380<br>210 375<br>2009 2011 2013 2015 2017 2019 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>RS SP<br>500 645<br>400 640<br>635<br>300<br>630<br>200<br>625<br>100<br>620<br>2010 2012 2014 2016 2018 2020 2008 2010 2012 2014 2016 2018 2020<br>Year Year<br>Municipalities from our data Municipalities from Siconfi<br>Municipalities<br>Municipalities<br>Municipalities<br><!-- End of picture text -->

**Notes:** This figure compares the number of municipalities in our dataset and the number of municipalities in SICONFI in verification data.

55
# **Weathering a Storm: Survey-Based Perspectives on Employment in China in the Aftermath of the Global Financial Crisis**

John Giles, Albert Park, Fang Cai, and Yang Du JEL Codes: J60, J63, J64, O23, O53 Key Words: Global Financial Crisis, Employment, China, Asia The authors gratefully acknowledge support for this paper and underlying research from the Gender Action Program and Knowledge for Change Trust Fund. The authors are grateful for comments on earlier drafts received from Andrew Mason, David Newhouse Adam Wagstaff and participants in the IZA-World Bank Conference on Employment and Development held in Capetown, South Africa in May 2010. John Giles is Senior Labor Economist in the Development Research Group at the World Bank and Research Fellow at IZA; Albert Park is Professor of Economics at Hong Kong University of Science and Technology and Research Fellow at IZA; Fang Cai is Professor and Director of the Institute for Population and Labor Economics at the Chinese Academy of Social Sciences (CASS-IPLE) and Yang Du is a Professor at CASS-IPLE.
from 0 to 1 keeping all other RHS variables at their mean.<sup>21</sup> In our case, all marginal effects but the HHI are the outcome of discrete changes in the independent variables.<sup>22</sup> For the three significant variables _HHI_ , _EQ_ and _EB_ , the marginal effect showed high values for outcomes “allowed” and “conditioned.” Controlling for everything else, a percentage increase in the _HHI_ reduces in more than 0.14 percentage points the probability of a merger being allowed and at the same time, increases the probability of it being conditioned in more than 0.12 percentage points, _ceteris paribus_ . On the other hand, for a case to be blocked, the commission will need to deem more important the presence of equity share holders—of other firms within the relevant market ( _EQ_ )—where the would-be merger is taking place. Everything else being equal, the presence of this effect in a merger case reduces its probability of being allowed by more than 54 percentage points.

**Table II: Constrained Model: Order Lo** **<mark>g</mark> it**

||)<br>,<br>(<br>_Z_<br>_H_<br>_V_<br>_V_ =|)<br>,<br>,<br>(<br>_X_<br>_Z_<br>_H_<br>_V_<br>_V_ =|
|---|---|---|
|ln(HHI)|1.03***|1.20***|
||[3.52]|[2.95]|
|HIST|0.54|0.85|
||[0.79]|[1.23]|
|EQ|2.54***|2.24**|
||[2.73]|[2.08]|
|EF|0.52|0.76|
||[0.86]|[1.27]|
|IMP|-1.07*|-1.11**|
||[1.80]|[2.08]|
|EB|1.31***|1.20***|
||[3.41]|[3.22]|
|MKT||-0.11|
|||[0.17]|
|FRG||-1.48***|

21 In the presence of several binary RHS variables, it is possible to estimate the marginal effects setting all other RHS dummy variables at their most likely binary value. However, it is not clear that this method is preferred over the conventional one; the expected value of a binary variable is still its simple mean and this can also be interpreted as a probability.

22 Notice that the significance of the marginal effects though associated with is not necessarily equal to the significance of the estimated coefficient ( β ). In our case, all those variables that showed a significant coefficient had also a significant marginal effect; therefore _t_ -statistics for marginal effects are excluded from Table II.

17
children’s education are relatively poorer. To explore further the basic correlations in the data, we report a series of OLS regressions with alternative controls.

As households living in a village face similar choice in terms of school access and quality, we cluster standard error at the PSU level. This is also motivated by the fact that the ...rst stage of strati...ed random sampling used in NHSC 2010 selected 300 PSUs from the IMPS sample frame of 1000 PSUs, as discussed above in the data section. All standard errors reported in this paper are clustered at PSU level if not reported otherwise.<sup>30</sup> All regressions also include regional dummies (six regions called ‘divisions’) to account for any spatial di¤erences.<sup>31</sup>

# (6.1) Propensity to Pay Bribes: OLS Results

The ...rst four columns in Table 2 provide OLS estimates of the coe¢ cients of per capita income in the regressions of propensity to pay bribes. The Probit estimates are similar to those reported in Table 2, and thus omitted for brevity. The results reported in column (1) are from a simple bivariate speci...cation where propensity to pay bribe is regressed on per capita household income alone. The speci...cation in column (2) controls for household head’s age, gender and religion (a dummy if head is muslim). We also include household size and number of school-age children as additional controls as these variables may a¤ect a household’s need and ability to pay bribes. The OLS estimates in columns (1) and (2) indicate a negative and statistically signi...cant e¤ect of per capita household income.

The results in the next column of Table 2 (i.e., column (3)) shows the estimate when we add PSU ...xed e¤ect to speci...cation in column (2). The PSU ...xed e¤ect controls for spatial heterogeneity in endowment (such as soil quality), variations in prices and wages, and also heterogeneity across schools.<sup>32</sup> The estimates indicate a statistically signi...cant and negative e¤ect of income, although the magnitude is smaller.

Column 4 adds household head’s education and occupation to the speci...cation in column

> 30 As discussed before, PSU is a geographic unit approximately equal to a one square Km in our data set. All the conclusions in this paper remain valid if we cluster the standard errors at the Thana level which is a somewhat larger geographic unit than the PSU.

> 31 Note that although Rangpur became the 7th division at the beginning of 2010, the NHSC 2010 data are organized based on the six divisions before 2010.

> 32 In rural Bangladesh, it is extremely unlikely, if not impossible, to have more than one schools in a PSU (approximately one square Km area on an average). Thus PSU ...xed e¤ect can alternatively be interpreted as school ...xed e¤ect.

26
substantial problems for the operation of the NRFs. In several cases, the integration of the fund’s operation with overall fiscal policy has proven problematic, and despite the operation of a fund, the stabilization of expenditure has been elusive. Moreover, there is evidence that funds may have been most difficult to operate when the extent of reliance on windfall has been important.

<!-- Start of picture text --> Equatorial Guinea: Oil Revenue (excluding grants)<br>1997 - 2006<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1997-2001 2002 2003 2004 2005 2006<br>% of GDP<br><!-- End of picture text --> Source: Equatorial Guinea Authorities and Bank’s staff and IMF.

Overall, natural resource endowment if well managed is a blessing rather than a curse for low-income countries to escape the poverty trap and a sound resource-led development strategy could make a difference. However a volatile and uncertain fiscal revenue source renders fiscal management, budgetary planning, and the efficient use of public resources difficult. This is the case particularly when it makes up a large share of total revenue as it is the case of Equatorial Guinea and suggests that the country should constantly follow a strict fiscal discipline to better manage increasing surpluses while promoting the development process. Hence it is critical to assess the policies, institutions and capacity for the management of Equatorial Guinea’s oil dominated economy.

22
improvements (Table 12). Perhaps the scale of the required funding (and the magnitude of the estimates) explains why only limited progress has been made to date. Indeed, the Fisheries Department has identified the need for an integrated approach involving funding from the Government of Kenya, fish processors, local communities and donors (DF, 2001). A draft joint proposal by stakeholders representing BICs, BMUs and the Fisheries Department on Infrastructural Development of Fish Landing Sites in Kenya has subsequently been developed.

A number of investments have been made in beach improvements, although these have not always been co-ordinated effectively and have proceeded in a piecemeal fashion with funding through a variety of donors. Box 6 and Figure 27 provide illustrations of some improvements that have been made to date and confirm the widely varying standards that remain across individual landing sites.

**<u>Table 12. Estimated average cost of landing beach improvements:</u>**

|**Improvement**|**Cost**<br>**(US$)**|
|---|---|
|**Basic Requirements**||
|Fencing|6,400|
|Banda|51,300|
|Potable watersupply|6.400|
|Fish handling equipment|3,200|
|Paving|25,600|
|Toilets|3,800|
|Cold boxes|3,800|
|_Sub-total_|_100,500_|
|**Further Improvements**||
|Coldroom|44,900|
|Electricity supply|51,300|
|Icemaking plant|51,300|
|Accessroad upgrading|38,500|
|Landing jetty|38,000|
|_Sub-total_|_224,500_|

_Source: Fisheries Department; AFIPEK_ The LVEMP, for example, has invested in some improvements at landing beaches. Most notably, a stone banda with tables and a store has been established at Mbita beach at a cost of US$1.03 million. However, this beach is still in need of a perimeter fence and cooling facilities. Some fencing has been provided for other beaches.

69
- 25 -

National data collection systems have been simplified. And evaluators are no longer faced with the problems associated with massive federal education programs and have turned their attention to activities at the individual school level. This reflects not only the accountability pressures and demands from both state and federal levels, and from communities, that are being placed on local school districts and schools, but also the lessons derived from extensive input-output research on education undertaken within the past several years. School effectiveness studies have clearly pointed to the importance of the educational experience at the local school and to the critical role played by the human resources there -- the students and teachers (Murnane, 1982). This new interest in the educational program at the relatively smaller educational unit of the local school district or individual school, has increased the importance of monitoring and routine information collection at that level: The object of traditional evaluation designs is changing, from a "programmatic intervention" focus to a focus on "schooling" itself. In other words, the "program" for evaluation focus is now the ongoing constellation of daily activities and outcomes constituting the program of the local school. This suggests the need for ... a considerable variety of information ... designed to facilitate any number of evaluative purposes from appraising the impact of specific programmatic interventions, to informing organizational and instructional planning and development activities, to, perhaps most importantly, monitoring the periodic health of the school work and learning environment. (Sirotnik, 1987, p.77) The establishment of information bases at the school and district level has been supported by the rapid diffusion of computer technology and software.

The purposes of the collection and development of information have also changed significantly. Accountability has become the paramount concern at the local district level and the school principals are coming under increased pressure to produce demonstr...ble successes (see Fetler, 1986). As one evaluator described the current situation: ... the data for program improvement emphasis of the late sixties and seventies has been largely replaced by the accountability movement of the eighties. In the late sixties and seventies most school districts were involved in some type of curriculum development and had staff to do program improvement work; today, the level of curriculum development in school districts has dropped dramatically. Principals, as managers of school units are being held accountable for student achievement and consequently are being given more freedom in terms of the programs and strategies that they implement in their buildings. When