rafmacalaba/datause-encoder-data huggingface dataset:
discussing the main theoretical arguments addressing the design of fiscal decentralization arrangements. Then we review the empirical literature on the effect of fiscal decentralization on economic outcomes. Finally, we discuss selected international practices.

# **4.1 What is the optimal level of fiscal decentralization?**<sup>25</sup>

## **4.1.1 Revenue decentralization**

Governments rely on several tax tools to collect revenues. In the case of mining, these tools include royalties, direct taxes (income, profit, and property taxes), indirect taxes (such as the value-added tax [VAT], sales and excise taxes, import and export duties, and payroll-based taxes), surtaxes, and licensing and user fees. The policy question is which of these taxes should be assigned to subnational governments. To inform this question, the literature on fiscal decentralization has already identified several principles for the optimal design of tax systems.

1. <u>Economic efficiency: This requires that the tax system does not affect economic decisions</u> such as how much or where to invest, produce, or consume. Decentralized tax systems can interfere with economic efficiency in at least two ways. First, geographic differences in tax systems may distort locational decisions linked to mobile goods and factors, such as capital and tradable goods. For instance, a state imposing a high corporate tax rate may hinder private investment. Second, in the presence of mobile factors, jurisdictions could compete among themselves by reducing taxes to suboptimal levels. This principle has an important implication. It suggests that taxes linked to mobile goods and activities should be decided centrally or at least coordinated among jurisdictions. Similarly, taxes linked to less mobile tax bases (such as property taxes) are more suitable to be assigned to local governments. Given the ex-ante mobility of mining, this principle suggests that mining taxes should be assigned to national- and regional- level governments instead of local governments.

2. <u>Equity: The tax system plays a crucial role in economic redistribution. This redistribution</u> is politically feasible when both net payers and net receivers (the winners and losers of

> principles can be applied to both mining and nonmining cases. For a discussion on principles for expenditure decentralization, see World Bank (2014).

> 25 This section is partially based on Otto (2001) and World Bank (2014). For additional work describing fiscal tools and principles to decentralize resource rents, see also Brosio (2006); Clark (1999); and Morgandi (2008).

34
Figure 2: FDI Inflows and GDP Growth Rates, 1994-2003 <!-- Start of picture text --> Regression statistics: GDP growth = 2.34 + 0.24 FDI inflows (T Statistic 3.11)<br>IRL<br>CHN<br>ALB<br>MOZ<br>INDKOR MLI UGADOM SVK HUN<br>IRNJPNIDNBGDBFACMRTURGRCITADZAGTMPAKSYREGYMWILKAZAFUSA ETHSENBWASLVDEUAUTGHAAUSPHL NOR TGOPRTISRFRATHAMARTUNFINBRAMEXESPCANGBRCRINGANZLMNGPERBGRMYS DNKPANCOGCHLSWENLD BOL SGP<br>SAUNER ARGJORCOLHNDCHEZMBECUNAMNICJAM<br>KENMDGURYPRY CIV<br>HTI PNG<br>VEN<br>SLE<br>ZAR<br>0 5 10 15<br>Annual average FDI inflows, % of GDP, 1994-2003<br>8<br>6<br>4<br>2<br>0<br>-2<br>Annual average per capita growth in % (1994-2003)<br><!-- End of picture text -->

# **3. Model Specification**

To analyze the interaction of foreign investment, regulations and growth, we could use the “standard” cross-country growth regression model that usually is specified as follows:

> where _y_ is the log of per capita GDP for country i and periods t and t-1, _X_ are a set of explanatory variables, including regulations, FDI inflows and other control variables, θ and γ ' are the coefficients to be estimated for the initial GDP per capita _yt-1_ and the control variables, respectively, and _e_ is the error term. By following this approach, however, we are likely to obtain biased estimates due to the well-known problems of cross-country growth regression, such as reverse causality, measurement errors, omitted variables or simultaneity.

9
**Table 1: Descriptive statistics on pay and tenure of professional staff, May 1997**

||Part I men|Part II men|Part I women|Part II women|
|---|---|---|---|---|
|Current salaries|100|95.3|86.6|82.5|
|Salaries at entry|100|91.4|86.4|80.6|
|Mean annual growth (%)|3.2|3.3|3.8|3.8|
|Mean tenure (years)|11.9|12.5|10.7|11.2|
|Number of staff|1356|857|537|250|
|(% of total staff)|_(45%)_|(29%)|(18%)|(8%)|

Note: Data on World Bank (IBRD) professional staff pertain to those in grades 22-30.

**Table** 2: **Descriptive** statistics **on** grade of **professional staff,** May 1997

||||||**Grad**|**e**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||**22**|**23**|**24**|**25**|**26**|**27**|**28**|**29**|**30**|**Total**|
|**Part I Men**|||||||||||
|**%**in grade currently|7|16|37|19|15|_3_|_3_|1|0|100|
|Mean years in grade|2.5|3.9|6.0|4.3|6.7|4.0|5.0|_3.0_|--|5.09|
|N|98|214|506|254|197|36|36|15|0|1359|
|**Part II Men**|||||||||||
|**%**in grade currently|8|21|38|16|11|2|2|1|0|100|
|Mean years in grade|3.3|3.6|5.7|3.7|6.7|3.8|4.4|3.1|7.9|4.7|
|N|69|182|328|141|91|17|21|7|1|857|
|**Part I Women**|||||||||||
|**%**in grade currently|17|29|32|10|8|2|1|1|0|100|
|Mean years in grade|2.7|3.1|4.6|2.9|3.8|2.3|4.6|2.1|--|3.6|
|N|93|158|174|51|42|11|_5_|3|0|537|
|**Part II Women**|||||||||||
|**%**in grade currently|22|33|31|7|_5_|0|1|0|0|100|
|Mean years in grade|2.4|2.9|4.7|2.9|4.5|1.5|2.4|--|--|3.4|
|N|_55_|83|77|18|13|1|3|0|0|250|
|**Total**|||||||||||
|**%**in grade currently|11|21|36|15|11|2|2|1|0|100|
|Mean years in grade|2.7|3.5|_5.6_|3.9|6.3|3.6|4.7|3.3|7.9|4.6|
|N|315|637|1085|464|343|65|_65_|25|1|3000|

37
Policy ReseaRch WoRking PaPeR 4704

# **Abstract**

Ownership and control over assets such as land and housing provide direct and indirect benefits to individuals and households, including a secure place to live, the means of a livelihood, protection during emergencies, and collateral for credit that can be used for investment or consumption. Unfortunately, few studies—either at the micro or macro levels—examine the gender dimensions of asset ownership. This paper sets out a framework for researchers who are interested in collecting data on individual level asset ownership and analyzing the gender asset gap. It reviews best practices in existing surveys with respect to data collection on assets at both the household and individual levels, and shows how various questions on individually owned assets can be incorporated with a minimum of effort and cost into existing multi-topic household surveys, using examples of three Living Standard Measurement Study surveys: the 1998–99 Ghana survey, the 2000 Guatemala survey, and the 1997–98 Vietnam survey questionnaires. The analysis shows that it is feasible to add a minimal set of questions to enable calculation of the gender asset gap. Adding a series of extra questions will permit a more satisfactory and nuanced analysis of asset acquisition, use, disposition, and valuation—information that is critical for policies promoting gender equality, poverty reduction, and economic growth.

This paper—a product of the Gender and Development Group, Poverty Reduction and Economic Management Network —is part of a larger effort in the department to measure and understand women's economic empowerment. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at cheryl.doss@ yale.edu, cgrown@american.edu, or deere@latam.ufl.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
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72
would expect to see increases in the food Engel curve among mixed-adult households, where bargaining is an issue, but not among female-only households, where there are no men to bargain with. This is in fact the pattern of program effects we find. We might also expect that the BDH would have the largest effects in mixed-adult households in which the initial control of women over resources was weak. Although there is no direct measure of bargaining capacity within the household in our data, the relative education of men and women is likely to be a reasonable proxy—see Frankenberg and Thomas (2003) for a general discussion, and Beegle, Frankenberg, and Thomas (2001) for an application. We show that, among mixed-adult households, BDH program effects are larger when women have less schooling than men.

The rest of the paper proceeds as follows. In section 2, we describe the data and the BDH evaluation experiment. Section 3 discusses our methodology and presents results. We conclude in section 4.

# **2. Program design, evaluation, and data**<sup>**1**</sup>

# A. <u>The BDH program</u>

Ecuador has had a nationwide cash transfer program in place for approximately a decade. The first of these programs, the _Bono Solidario_ , was created in 1998. Its goal was to make transfers to poor households as Ecuador recovered from an economic crisis. (GDP per capita contracted by 7.7 percent between 1998 and 1999.) As originally envisioned, transfers made by the _Bono Solidario_ were meant to be temporary. In practice, however, the program continued well after recovery from the crisis.

Individual payments made by the _Bono Solidario_ program were small—$15 per month per eligible family—but the scope of the program was large: In 2002, it accounted for approximately 0.75 percentage points of GDP. (Ecuador adopted the US dollar as national currency in January 2000.) Payments were intended to be made to poor households. However, because there were no clear selection criteria, households essentially signed up on a first-come, first-served basis. As a result, many poor households did not receive transfers, and a substantial fraction of transfers were received by non-poor households. In 1999, 49.8 percent of families in the poorest quintile received transfers, and 27.4 percent of families in the top two wealthiest quintiles received transfers. (These statistics are based on calculations from household survey data using the nationally representative 1999 Ecuador _Encuesta de Condiciones de Vida,_ the multi-purpose household survey intermittently conducted in Ecuador.)

> 1 This section borrows considerably from the descriptions in Schady and Araujo (2006) and Paxson and Schady (2007).

4
One study studies the effect of school size on student performance using TiMMS data for both developed and developing countries: Schütz (2006) finds significant size effects for 11 out of 51 countries, 8 out of these 11 are developing countries. Most of these significant effects are positive, suggesting that, if there is a relationship between size and performance, bigger schools appear to be typically better.

# II.2 _The evidence from transition countries_<sup>_9_</sup>

The literature on schools in transition countries is much less developed than the literature on either schools in developed countries or schools in developing countries. This paper is indeed the first that tries to summarize the existing literature on schools in transition countries. We divide what follows into two parts: we first focus on the cross-country studies that include transition countries. Next, we focus on single-country studies.

# _a. Cross-country studies_

There is a number of studies that do include data from transition countries, even though they do not focus on transition countries or have specific conclusions based on the estimations they obtain for transition countries.

Hanushek and Luque (2002) and Hanushek (2003), reviewing a hundred production function estimates covering 37 developed and developing countries<sup>10</sup> , state that there is no consistent evidence that more school resources and better teacher quality lead to better test results, since in the majority of studies these variables are found to be insignificant. Despite this, studies for developing countries find some positive relationship in a greater share of cases<sup>11</sup> (possibly because the initial level of education spending matters). Class size is most often found to be insignificant, but for transition countries, if significant typically positive. These authors

> 9 Table A1 and A2 in the appendix summarize the empirical findings of education-relevant factors in transition countries, and in developed and developing countries.

> 10 Among them eight transition economies: Hungary, Latvia, Lithuania, Poland, Slovak Republic, Czech Republic, Romania and the Russian Federation.

> 11 In another review article, Heyneman and Loxley (1983) suggest that school and teacher quality matters more for lower-income countries than for high-income ones. At the same time, family background matters for these countries less.

8
# TABLE 4: WEIG HIED FREIG HTRAIES FOR DIFFERENTCO MMO DIMES BE1WEEN DIFFERENTDESlINATO NSFOR IHE YEAR 1980-81

|WHE|AT|RIC|E|COTI|ON|SUG|AR|GROUN|DNUT|MUSCAR|DSEED|SUNFL|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|KMS|RS(QIL|KMS|RSYQ1L|KMS|RSYQ1L|KMS|RS(Q1L|<br>KMS|RS'QlL|KMS|ROQ1L|<br>KMS|
|300<br>705<br>880<br>1375<br>1386|<br>3.42<br> <br>6.76<br> <br>7.61<br> <br>9.89<br> <br>10.03|<br>390<br> <br>410<br> <br>540<br> <br>720<br> <br>870|<br>3.95<br> <br>4.1<br> <br>5.56<br> <br>&84<br> <br>7.45|<br>324<br>1362<br> <br>1546<br>1585<br>|<br>6.25<br> <br>21.15<br> <br>23.18<br> <br>23.75|<br>880<br> <br>1386<br> <br>1423<br> <br>1441<br>1634|<br>10.55<br> <br>14.84<br> <br>15.03<br> <br>15.24<br> <br>16.83|<br>200<br> <br>300<br> <br>739<br><br>|<br>3.87<br> <br>5.0<br> <br>11.2|<br>1348<br>8<br>1423<br>5|<br>12.68<br> <br>13.3|<br>547<br>9|
|1675<br>1850|<br>10.7<br> <br>11.49|980<br> <br>1060<br>1160<br>1480<br>1700<br>2070|<br>8.24<br> <br>8.09<br>8.83<br> <br>10.58<br> <br>10.82<br> <br>11.81|<br>||1663<br>2054|<br>17.03<br> <br>19.68||||||

SOURCESA **N** D OlES: The table isfrom Sharma, Pradeep K. (1991) Sharma calculatesthese freight ratesby applying the modal composition by distance givenin Annex3to the road and rail freightsfo example, if the dista nce a foodgra in istransported is 100.150 km, it is assumed that 20 percent iscarried by rail and 80 percent by roa 1981 have been projected forwardsand backwardsusing the indicesinAnnexes5and 6.

xxx
**<mark>Table 1. Employment of women</mark>**

|Variables|VHLSS<br>2010|VHLSS<br>2012|VHLSS<br>2014|VHLSS<br>2016|
|---|---|---|---|---|
|% working|91.8|93.2|92.5|93.5|
||(0.4)|(0.5)|(0.5)|(0.5)|
|% in a wage-earning job|30.9|33.4|35.5|37.6|
||(0.7)|(1.0)|(1.0)|(1.1)|
|% self-employed in a nonfarm job|16.5|14.6|13.7|18.0|
||(0.6)|(0.7)|(0.7)|(0.8)|
|% self-employed in a farm job|44.4|45.1|43.4|37.9|
||(0.8)|(1.0)|(1.0)|(1.1)|
|% in a skilled job|45.0|47.2|49.3|53.5|
||(0.8)|(1.0)|(1.0)|(1.1)|
|% in a formal job|15.1|18.6|21.5|23.7|
||(0.7)|(0.8)|(1.0)|(0.9)|
|Number of working hours per month|180.0|187.2|188.7|188.0|
||(1.3)|(1.5)|(1.6)|(1.6)|
|Hourly wage (thousand VND)|18.1|19.5|20.4|24.2|
||(0.8)|(0.7)|(0.5)|(0.0)|
|Monthly wage (thousand VND)|3252.4|3554.0|3845.2|4404.3|
||(89.1)|(99.1)|(79.9)|(0.0)|
|Yearly wage (thousand VND)|39013.0|41878.6|46334.3|52749.0|
||(1434.5)|(1331.3)|(1131.8)|(0.0)|

Note: This table reports the employment variables of women with children age 1 to 5. Variables of wagepaying jobs, skilled jobs, and formal jobs are defined using the main occupation over the past 12 months. Employment consists of wage-paying employment, self-employed non-farm work, and self-employed farm work.

Wages are defined as the total wages, including main and secondary jobs. Standard errors of the mean are in parentheses. Wages are measured in 2016 prices. Source: Estimation from VHLSS 2012, 2014, and 2016.

33
# **5. Conclusion**

**This paper analyzed the sustainability of Burundi’s fiscal position in the medium term in view of various risks to which the country is exposed.** Studies in the past have highlighted key sources of macro vulnerability in the country,<sup>31</sup> which include those arising from uncertainty in global commodity prices, dependence on rain-fed agriculture, and dependence on volatile foreign aid. Internally, uncertainty about the implementation of the government’s fiscal reforms is a key risk. Until now, the studies have not quantified the size and impact of the risks to the country’s fiscal sustainability.

**The paper finds that Burundi does not have much room to maneuver.** Even with a strong growth projected in the baseline scenario for 2013–17, the government will continue to struggle to create fiscal space and reduce debt from its currently high level. The dynamics is mainly explained by falling grants as a share of GDP combined with persistently high current expenditure, mostly the wage bill. Although the government is expected to limit wage growth in real terms, unless additional revenue measures are introduced to offset the falling grants, it will struggle to reduce its deficit. While the deficit is expected to be largely financed by external concessional loans, debt will remain high as a share of GDP, and will likely keep the country at “high risk of debt distress.”

**External risks relating to aid, rainfall, and coffee prices could significantly increase the country’s deficits and debt.** Stochastic simulations show that, under very adverse shocks to rainfall or coffee prices, the country’s debt could reach 39 to 41 percent of GDP by 2017—about 5 to 7 percentage points above the projected ratio of debt to GDP under the baseline. Aid shocks could have a bigger impact—with debt to GDP reaching as high as 50 percent under adverse shocks—but the estimates are the least statistically reliable because of the time series is short and because its historical volatility in part reflects endogenous shocks (e.g., reform implementation) as well as exogenous shocks (donors’ behavior).

**The analysis suggests that the government should focus on building buffers, increasing flexibility, and reducing exposure to risk.** Countries that are exposed to shocks should build buffers to cope with the shocks. Smaller fiscal deficits would allow the government more flexibility to use discretionary policies to respond when hit by a shock. Larger foreign reserves would function as a shock absorber in response to negative external shocks. Lower and more stable inflation would give the authorities greater scope for monetary loosening and fiscal stimulus without triggering inflationary expectations and wage demands. Countries with lower risk of debt distress will have greater access to both concessional and non-concessional emergency financing.

> 31 Lim and Rugwabiza (2009), World Bank (2011), IMF (2012), and IMF and World Bank (2013).

26