rafmacalaba/datause-encoder-data huggingface dataset:
_22 On the Dangers of Decentralization_ 4.6 Furthermore, there is the possibility that the fiscal policies of subnational governments will run counter to the fiscal policy of the central government. In many countries, local government policies are influenced by the political cycle: taxes increase immediately after local &lections, and expenditures increase immediately before election. But there is no reason why this political cycle would coincide with the business cycle. In practice, local governments may end up increasing local public expenditures or local taxes at the time when the central government is trying hard to depress expenditures or taxes.

4.7 This is what happened in the United Kingdom in the early 1980s. As the central government was trying to decrease public expenditures (for structural reasons as much as for countercyclical reasons), local governments were increasing them, thus frustrating the central government efforts. This was one reason for the introduction of the ill-fated poll tax in the United Kingdom, a tax that would make it politically more difficult for local governments to increase expenditures. The opposite is said to have happened in the United States as the time of the Great Depression, when subnational governments ran high budget surpluses, thereby countering the central government budget deficit (Perloff, 1985).

# Examples

4.8 The case of Argentina in the 1980s provides a good illustration of this 'fiscal perversity" (Perloff 1985) of subnational governments kWorld Bank 1990a). Argentina, a federal country, has always been decentralized. In the early 1980s, the expenditure decentralization ratio was well over one third (ignoring public enterprises). Provincial expenditures rose rapidly, reaching over 11.2 percent of GDP in 1986. But revenues of provincial governments actually dropped from 5.6 percent of GDP in 1980 to 5.0 percent of GDP in 1986. As a result, the (pre-transfer) fiscal deficit of the provinces in 1986 was 6.2 percent of GDP. This important deficit was either financed by transfers from the central government or by borrowing, in both cases inflationary. Transfers were (at least until 1988) mostly a _posteriori_ discretionary grants from the Ministry of Finance. This system in practice fostered provincial mismanagement, leading to a large central budget deficit. Borrowing by the provincial governments from the Central Bank or from provincial Banks (entirely controlled by the provinicial governments) was also a problem. "These provincial/national financial practices have contributed to unsustainable public sector fiscal and quasi-fiscal deficits, and their continuation would undermine national efforts to attain price stability and to promote sustainable economic development" (World Bank, 1990a, p.ii).

4.9 The 1988 reform of the Constitution of Brazil significantly reduced the freedom of the central government to conduct macroeconomic policies. Table 4.1 indicates the share of the central government in taxes and net-of-transfers expenditures before and after the reform.
|**Appendix Table 6: Ba**|**lance of Con**|**trols among**<br>Regression-|**Girls**<br>|
|---|---|---|---|
||Mean among<br>Control|<br>Adjusted<br>Difference|Robust Standard<br>Errors|
|White Child|0.30|0.04|0.03|
|Black Child|0.14|1.72E-3|0.02|
|Mixed Race Child|0.52|-0.05|0.03|
|Other Race Child|0.03|2.99E-4|0.01|
|Birthweight (in kilos)|3.15|-0.01|0.04|
|Birth Height (in cms)|49.03|-0.15|0.33|
|Planned Birth|0.35|-0.02|0.03|
|First Born|0.40|0.01|0.03|
|Age of the Mother at Birth|20.34|0.36|0.35|
|Prenatal Care|0.95|0.01|0.01|
|Natural Birth Delivery|0.69|-0.04|0.03|
|Premature Birth|0.12|0.01|0.02|
|Breastfed up to 6 Months|0.78|-0.03|0.03|
|Household per Capita Income|490.16|-10.97|26.64|
|Household Size|4.49|0.63|0.36|
|Age of Caregiver|30.30|-0.58|0.71|
|Caregiver can Read and Write|0.97|0.02|0.01|
|Caregiver Finished Middle School|0.67|-1.6E-3|0.03|
|Caregiver Graduated from High School|0.30|0.05|0.03|
|_N_Th bl ii hh<br>Caregiver has a College Degree|h<br>2.4E-3|d l il<br>0.01|bl i<br>0.01|

_Notes_ : The table investigates whether the treatment and control girls are comparable in terms of predetermined characteristics. The first column shows means among the control girls. The second column reports the coefficient on the treatment indicator from regressions of the dependent variable listed in the row on the treatment indicator and on dummies specific for childcare center × age group. The third column shows robust standard errors. _N_ = 1,005.

41
### **Box 6:  Need some guarantees in the process of changing/ amending license conditions**

According to the Communications Act, the CCK can, “...from time to time modify any conditions attached to the license.” Before making any modification, the CCK shall publish a notice in the Gazette, specifying the purposes and reasons for the modifications and inviting the licensee or any interested party to present written objections within sixty days.

The applicant can appeal to the Tribunal within 15 days from the date of the publishing of the notice in the Gazette. The Tribunal has to cause the decision concerning any appeal received in this way also within a 15-day period. Nevertheless, the CCK can enforce the license modifications and simply inform the operator in writing about the new set of conditions, if it intends to “remedy or prevent matters which operate (...) against the public interest.” _(Source http://mkaccdb.eu.int/study/studies/22.doc)_

# **5. Spectrum Implications in the Licensing Renewal Process**

In addition to the issues highlighted in this paper, and that surface from the renewal of telecommunications operator licenses, the renewal of mobile licenses presents additional challenges because they involve scarce and finite resources. Therefore licensing regimes and renewals need to take into account these challenges, and manage the distribution of resources in a manner that minimizes waste and promotes practices for the efficient use of resources.

## **_5.1 Renewal and Possible Reattribution_**

Since spectrum is a finite resource, the number of licenses—and therefore of entrants— is limited. At the time of license expiration, should new entrants be allowed to recompete for resources? Or should incumbents be allowed to claim the right to preserve their frequencies? In Morocco, for instance, the license of Medi Telecom clearly stipulates that renewal of mobile license will not be subject to competitive tendering. What impact will this have on competition? How to negotiate exit strategies and/or compensation for operators when these are required to exit the market? Many regulators in African countries found these questions to be controversial, and they were forced to change the rules of the game and modify spectrum allocation and spectrum fees to accommodate new entrants and to reflect the economic value of the spectrum even before the term of the license. Similar problems in the context of license renewal are to be expected, and a clear approach to these issues could become a determining factor for picky investors taking entry decisions.

**A balance is needed between protecting ongoing investment, and optimal exploitation of the spectrum resources.  In general, regulators tend to favor**

- 16 -
Table 9: Sibling and Intergenerational Correlations: Full Sample (16-20 year age cohort)

||All ch|ildren|Brothers|(Men)|Sisters (|Women)|
|---|---|---|---|---|---|---|
||1993|2006|1993|2006|1993|2006|
||||PANE<br>|L A<br>|||
|Sibling Correlations (SC)|0.688***|0.638***|0.697***|0.644***|0.856***|0.724***|
||(49.188)|(69.590)|(31.073)|(40.915)|(47.494)|(41.221)|
|Intergenerational Correlations<br>(IGC)|0.575|0.530|0.531|0.510|0.631|0.552|
||(78.28)***|(92.81)***|(55.90)***|(68.41)***|(59.55)***|(69.30)***|
|Proportion of SC explained by<br>|||||||
|IGC|0.519|0.403|0.404|0.404|0.465|0.421|
||||PANE|L B|||
|Controling for Caste|||||||
|Sibling Correlations|0.672***|0.607***|0.685***|0.615***|0.845***|0.703***|
||(45.672)|(62.080)|(29.305)|(36.714)|(43.284)|(37.210)|
|Intergenerational Correlations|0.552|0.493|0.512|0.472|0.601|0.514|
||(72.14)***|(82.88)***|(51.69)***|(61.17)***|(54.39)***|(61.54)***|
|Proportion of SC explained by<br>IGC|0.453|0.400|0.383|0.362|0.427|0.376|
|Controling for Neighborhood FE|||PANE|L C|||
|Sibling Correlations|0.404***|0.391***|0.458***|0.412***|0.537***|0.410***|
||(15.817)|(28.300)|(11.375)|(17.001)|(8.153)|(11.418)|
|Intergenerational Correlations|0.508|0.438|0.490|0.442|0.554|0.460|
||(66.11)***|(79.35)***|(49.75)***|(59.15)***|(48.17)***|(59.27)***|
|Proportion of SC explained by<br>IGC|0.639|0.491|0.524|0.475|0.571|0.516|
|No. of observations|13,140|27,966|8,098|15,274|5,042|12,683|

Robust t statistics in parentheses. Standard errors corrected for clustering at family level * significant at 10%; ** significant at 5%; *** significant at 1% 44
<u>Table 4: Reductions in the value of the time of trade</u>

||CHN|||||bri|||||xbr|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||BRIL<br>BD|BRIU<br>BD|BRIU<br>BBD|BRIUB<br>BTD|BRIUB<br>BTPD|BRIL<br>BD|BRIU<br>BD|BRIU<br>BBD|BRIUB<br>BTD|BRIUB<br>BTPD|BRIL<br>BD|BRIU<br>BD|BRIU<br>BBD|BRIUB<br>BTD|BRIUB<br>BTPD|
|World|1.0|1.9|2.9|3.0|3.0|1.0|2.2|3.7|3.7|3.7|0.4|1.3|1.5|1.5|1.6|
|BRI Area|1.8|3.0|5.3|5.4|5.4|1.4|2.7|4.8|4.8|4.8|0.7|1.8|2.4|2.4|2.4|
|Cambodia|2.7|2.9|3.9|3.9|3.9|4.3|4.6|8.8|8.8|8.8|3.0|4.1|4.2|4.2|4.2|
|China||||||2.1|3.5|5.6|5.6|5.6|0.7|2.0|2.2|2.2|2.2|
|Indonesia|0.1|1.1|1.4|1.4|1.4|0.4|2.1|2.3|2.1|2.1|0.1|1.5|2.0|2.0|2.0|
|Lao PDR|0.5|3.9|7.4|7.4|7.4|0.9|3.5|14.4|14.3|14.4|0.3|7.0|10.7|10.8|10.7|
|Malaysia|5.7|6.1|7.0|7.0|7.0|4.0|5.0|5.6|5.4|5.4|2.9|4.7|5.0|5.0|5.0|
|Philippines|0.0|0.4|0.5|0.5|0.5|0.7|2.0|2.1|2.1|2.1|0.6|1.8|1.8|1.8|1.8|
|Thailand|0.2|3.1|7.8|7.8|7.8|0.8|6.4|8.6|8.5|8.6|0.7|5.1|5.5|5.6|5.5|
|Vietnam|0.1|0.1|0.2|0.2|0.2|2.0|2.3|2.7|2.7|2.7|1.2|2.4|2.4|2.4|2.4|
|Rest of||||||||||||||||
|East Asia|1.2|2.0|5.8|5.7|5.7|0.6|2.6|4.1|4.1|4.1|0.3|1.3|1.4|1.4|1.4|
|Bangladesh|4.1|5.1|9.0|9.0|9.0|1.1|1.4|2.6|2.6|2.5|0.6|1.4|1.4|1.5|1.5|
|India|3.0|3.8|5.7|5.8|5.8|1.4|2.1|3.4|3.4|3.4|0.8|2.2|2.5|2.5|2.6|
|Nepal|3.5|4.3|20.4|20.5|20.5|0.5|0.9|21.9|21.9|21.9|0.4|1.5|10.0|10.3|10.3|
|Pakistan|3.3|3.3|3.3|3.3|3.3|1.8|3.1|5.3|5.0|5.0|1.0|2.4|4.7|4.7|4.7|
|Sri Lanka|2.3|2.9|3.9|3.8|3.8|0.8|1.2|2.6|2.6|2.6|0.6|2.3|2.4|2.5|2.5|
|Rest of S.||||||||||||||||
|Asia|2.7|2.9|8.2|8.3|8.3|1.8|2.3|11.2|11.2|11.2|1.6|2.9|10.1|10.3|10.3|
|EU ECA|2.3|2.3|2.3|2.2|2.2|0.6|0.9|2.1|2.1|2.1|0.1|0.2|0.3|0.4|0.3|
|Russian||||||||||||||||
|Federation|1.3|1.5|1.8|1.9|1.9|0.7|1.3|4.6|4.5|4.5|0.4|0.8|2.0|2.0|2.0|
|Poland|2.4|2.4|2.4|2.4|2.4|0.6|0.9|1.8|1.8|1.8|0.0|0.2|0.2|0.2|0.2|
|Rest of E.||||||||||||||||
|Europe|2.1|2.1|2.1|2.2|2.1|0.6|1.6|4.2|4.1|4.2|0.2|0.9|1.8|1.8|1.8|
|Kazakhstan|1.1|1.4|13.4|13.7|13.6|1.8|2.5|14.8|14.7|14.6|0.2|0.3|2.7|2.7|2.7|
|Kyrgyz||||||||||||||||
|Republic|25.5|25.9|38.3|36.9|37.0|8.4|10.2|26.4|26.4|26.4|5.1|6.0|17.3|17.3|17.3|

- 25 -
# MTEFs and Fiscal Performance:

# Panel Data Evidence<sup>*</sup>

Razvan Vlaicu<sup>¶</sup> Francesco Grigoli<sup>†</sup> Zachary Mills<sup>‡</sup> Marijn Verhoeven<sup>§</sup>

**Abstract**

In the last two decades more than 120 countries have adopted a version of a MediumTerm Expenditure Framework (MTEF). These are budget institutions whose rationale it is to enable the central government to make credible multi-year fiscal commitments. This paper analyzes a newly-collected dataset of worldwide MTEF adoptions during 19902008. It exploits within-country variation in MTEF adoption in a dynamic panel framework to estimate their impacts. The analysis finds that MTEFs strongly improve fiscal discipline, with more advanced MTEF phases having a larger impact. Higher-phase MTEFs also improve allocative efficiency. Only top-phase MTEFs have a significantly positive effect on technical efficiency.

## **JEL Classification:** E62, H51, H62.

**Keywords:** budget institutions, MTEF, fiscal discipline, allocative efficiency, technical efficiency, dynamic panel data analysis.

* We would like to thank Abdul Abiad, Nataliya Biletska, Jim Brumby, Stefania Fabrizio, Richard Hemming, Malcolm Holmes, Sam Ouliaris, Philip Keefer, Steve Knack, Ajay Tandon, and participants at the PREM Knowledge & Learning Forum and the World Bank MTEF Workshop. Haishan Yuan provided excellent research assistance. The views expressed in this paper are those of the authors and should not be attributed to the World Bank or its affiliated institutions, the IMF, its Executive Board, or its management.

† IMF, Fiscal Affairs Department, 1900 Pennsylvania Ave. NW, Washington, DC 20431, USA. E-mail: fgrigoli@imf.org.

‡ The World Bank, Europe and Central Asia Department, 1818 H Street NW, Washington, DC 20043, USA. E-mail: zmills@worldbank.org.

§ The World Bank, Poverty Reduction and Economic Management Vice Presidency, 1818 H Street NW, Washington, DC 20043, USA. E-mail: mverhoeven@worldbank.org.

¶ University of Maryland, Department of Economics, and Department of Government and Politics, 3105 Tydings Hall, College Park, MD 20742, USA. E-mail: vlaicu@econ.umd.edu.
-9-

old-age dependency and the surge of retirements that has occurred during the transition. Children's allowances account for an additional 2 percent. Property income and 'social benefits' each account for only a negligible proportion of household income.

To facilitate comparisons with other countries, the sources of income are consolidated into four major types of income: wages and salaries, social transfers, selfemployment income, and other income (see Table 3). We have compared the sources of household income in 1992 with the same survey for 1989 and with some other comparator countries. Since 1989, wage income has declined as a source of income, reflecting the contraction of the state sector. Self-employment income has increased dramatically, nearly doubling due to the surge in small-scale retail establishments that have been a key feature of the transition. Social transfers have remained virtually unchanged.

Comparing Bulgaria with other economies reveals several stylized facts. First, wage income is less important in Bulgaria than in other Eastern European countries. This result is not surprising given the slow pace of structural reform, which has resulted in one of the highest unemployment rates in the region. Moreover, the share of wage income in Bulgaria is only about one half of the level of OECD countries. Second, the relative shortfall of wage income in Eastern European countries is compensated by a higher share of social transfers. Social transfers are over 40 percent higher in Eastern European countries compared to the OECD group. For Bulgaria, these transfers are roughly on par with other Eastern European countries. Third, Bulgaria's self-employment is a very large share of total
69

# **Table A1a**

## **Dependent Variable: Growth Rate of Real GDP Per Worker Educational Pillar Indicators**

|**Years : 1960 - 2000**|**Reg A1a.1 **|**Reg A1a.2 **|**Reg A1a.3 **|**Reg A1a.4 **|**Reg A1a.5**|**Reg A1a.6 **|**Reg A1a.7 **|**Reg A1a.8 **|**Reg A1a.9 **|**Reg A1a.10 **|**Reg A1a.11 **|**Reg A1a.12**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**<br>**Per Worker**|**0.2772*****|**0.2264*****|**0.2033*****|**0.2119*****|**0.2704*****|**0.2289*****|**0.2089*****|**0.2298*****|**0.2048*****|**0.1965*****|**0.2208*****|**0.2116*****|
|**Initial GDP (1960)**|**-2.3912*** **|**-3.9762*** **|**-4.2581*** **|**-3.7103*** **|**-3.6477*** **|**-3.6847*** **|**-4.0017*** **|**-3.6856*** **|**-3.9437*****|**-4.1992*****|**-3.6147*****|**-3.9131*****|
|**HC15**||**0.7106**|**0.5009**|**4.4661****|||||||||
|**HC15 with**<br>**educational returns**|||||**-0.0031**|**-0.1073**|**1.0498**||||||
|**HC15 with extended**<br>**educational returns**||||||||**0.2398**|**0.0287**|**1.1449**|**0.1777**|**1.5718****|
|**Sq of HC15**||||**-1.0965****|||||||||
|**Sq of (HC15 with**<br>**educational returns)**|||||||**-0.2023**||||||
|**Sq (HC15 with**<br>**extended**<br>**educational returns)**||||||||||**-0.2052***||**-0.2617****|
|**Log (Educational**<br>**Quality)**|||**0.6115**|**0.3719**||**1.1725**|**1.2351**||**0.7464**|**0.6449**|||
|**Log (Educational**<br>**Quality Extended)**|||||||||||**0.5066**|**0.3877**|
|**Log (Patents)**|**0.1524****|**0.1889****|**0.1833****|**0.1956*****|**0.1232**|**0.1126**|**0.0906**|**0.2067*****|**0.1973*****|**0.1845****|**0.2034*****|**0.1847****|
|**Phones**|**0.0041*****|**0.0039*****|**0.0038*****|**0.0040*****|**0.0047*****|**0.0037*****|**0.0040*****|**0.0047*****|**0.0042*****|**0.0044*****|**0.0042*****|**0.0044*****|
|**Constant**|**0.5058****|**-0.2149**|**-1.8680**|**-4.5304****|**1.1210****|**-2.7365**|**-4.1423**|**0.3499**|**-1.7359**|**-2.5023**|**-1.3481**|**-2.3031**|
|**R squared**|**0.6148**|**0.5444**|**0.5736**|**0.5912**|**0.6356**|**0.6557**|**0.6652**|**0.5410**|**0.5706**|**0.5801**|**0.5453**|**0.5607**|
|**Number of Countries**<br>*, **, *** denotes significance<br>Robust standard errors obtain|**107**<br>at the 90, 95 an<br>ed using White|**92**<br>d 99 percent c<br>'s correction fo|**85**<br>onfidence leve<br>r heterosceda|**85**<br>l respectively<br>sticity.|**53**|**53**|**53**|**92**|**85**|**85**|**92**|**92**|
Policy Research Working Paper 7234

# **Abstract**

Drawing on a new set of nationally representative, internationally comparable household surveys, this paper provides an overview of key features of structural transformation— labor allocation and labor productivity—in four African economies. New, micro-based measures of sector labor allocation and cross-sector productivity differentials describe the incentives households face when allocating their labor. These measures are similar to national accounts-based measures that are typically used to characterize structural changes in African economies. However, because agricultural workers supply far fewer hours of labor per year than do workers in other sectors, productivity gaps disappear almost entirely when expressed on a per-hour basis. What look like large productivity gaps in national accounts data could really be employment gaps, calling into question the prospective gains that laborers can achieve through structural transformation. These employment gaps, along with the strong linkages observed between rural non-farm activities and primary agricultural production, highlight agriculture’s continued relevance to structural change in Sub-Saharan Africa.

This paper is a product of the “Agriculture in Africa—Telling Facts from Myths” project managed by the Office of the Chief Economist, Africa Region of the World Bank, in collaboration with the Poverty and Inequality Unit, Development Economics Department of the World Bank, the African Development Bank, the Alliance for a Green Revolution in Africa, Cornell University, Food and Agriculture Organization, Maastricht School of Management, Trento University, University of Pretoria, and the University of Rome Tor Vergata. 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 author of the paper may be contacted at ebm68@cornell.edu.

_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
sustainability will critically hinge on the government’s ability to stick to the revenue mobilization efforts.

# **_Scenario 2. Reduction of tax incentives toward the regional average_**

**The second alternative scenario explores a strategy in which the government boosts revenue collection by broadening the revenue base primarily by reducing tax exemptions.** Burundi has a large potential for boosting government revenues by rationalizing its extensive tax incentives. According to the recent World Bank study, the forgone revenue due to tax incentives was around BIF 130 billion—equivalent to 5 percent of GDP or 38 percent of total government revenues.<sup>26</sup> Compared to the neighboring countries, the figures are exceptionally high, and since the exemptions are mostly given exclusively to domestic firms, it is said to be not effective for attracting additional investments in the country.<sup>27</sup> With move to harmonize tax incentives high on the EAC regional integration agenda, rationalizing tax incentives thus has the potential of not just boosting government revenue but help toward improving investment climate and accelerate regional integration

**The simulation results suggest that the government’s accelerated effort in this area could**

**have a big impact on fiscal outcomes.** Results are reported in Figure 14 under “Higher tax revenue” scenario. We assume the authorities will gradually reduce tax exemptions from the current 7.1 percent of GDP to 5.2 percent of GDP by 2017, adjusting the tax exemption to GDP ratio halfway toward the regional average (3.3 percent). Reducing tax incentives would result in additional revenues of BIF 17.9 billion in 2013 gradually rising to BIF 127.4 billion in 2017 above those projected in the baseline. Revenue excluding grants will rise from 14.8 percent to 17.6 percent of GDP. Fiscal balance will begin to improve after 2014, approaching near balance by 2017. The government could build up fiscal buffer while lowering the level of public debt, potentially creating additional borrowing space for priority investments. With lower gross financing needs each year, the country’s debt to GDP will fall from 35.2 percent in 2012 to 25.8 percent in 2017, that is, 7.7 percentage points below the projected debt to GDP in the baseline (33.6 percent).

> 26 International Finance Corporation (2012).

> 27 The study also found that for domestic investors, too, tax incentives were not an important factor in decision to invest: “market potential” and “availability of infrastructure” were rated highly as motivation to invest while “tax incentives” was rated quite low. See IFC (2012).

20