rafmacalaba/datause-encoder-data huggingface dataset:
- De Haas, Ralph and Iman van Lelyveld, 2010. ―Internal Capital Markets and Lending by Multinational Bank Subsidiaries,‖ _Journal of Financial Intermediation_ , 19(1): 1-25.

- Demirgüç-Kunt, Asli, and Enrica Detragiache. 2005. "Cross-Country Empirical Studies of Systematic Bank Distress: A Survey." _National Institute Economic Review,_ 192(0):68– 85.

- Demirgüç-Kunt, Asli, Ross Levine, and Hong G. Min. 1998. "Foreign Banks: Efficiency, Stability and Growth." In Seongtae Lee, ed., _The Implications of Globalization of World Financial Markets_ . Seoul, Korea: The Bank of Korea.

- Dollar, David, and Mary Hallward-Driemeier. 2000. "Crisis, Adjustment, and Reform in Thai Industrial Firms." _World Bank Research Observer,_ 15(1):1–22.

- Dollar, David, Mary Hallward-Dreimeier, and Taye Mengistae. 2005. "Investment Climate and Firm Performance in Developing Countries." _Economic Development and Cultural Change_ 54(1):1–31.

- Fisman, Raymond, and Jakob Svensson. 2007. "Are Corruption and Taxes Really Harmful to Growth? Firm Level Evidence." _Journal of Development Economics_ 83(1):1–37.

- Giannetti, M. and Ongena, S., 2009a, ―Financial Integration and Firm Performance: Evidence from Foreign Bank Entry in Emerging Markets,‖ _Review of Finance,_ 13(2), 181-223.

- Giannetti, M. and Ongena, S., 2009b, ―'Lending by Example: Direct and Indirect Effects of Foreign Banks in Emerging Markets‖ Second Singapore International Conference on Finance 2008, AFA 2009 San Francisco Meetings Paper and ECGI - Finance Working Paper No. 221/2008.

- Herrmann, Sabine and Dubravko Mihaljek, 2011. ―The Determinants of Cross-border Bank flows to Emerging Markets: New Empirical Evidence on the Spread of the Financial Crisis.‖ Bank of Finland, Institute for Economies in Transition (BOFIT) discussion paper3/2011.

Iarossi, Giuseppe. 2006. _The Power of Survey Design_ . Washington DC: World Bank.

- Kamil, Herman and Kulwant Rai, 2010. ―The Global Credit Crunch and Foreign Banks‘ Lending to Emerging Markets: Why Did Latin America Fare Better?‖ IMF Working Paper WP/10/102.

- Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi, 2007. ―Governance Matters VI: Governance Indicators of 1996-2006.‖ World Bank Policy Research Working Paper 4280. Washington, DC.

- Mihaljek, Dubravko, 2010. ―Domestic Bank Intermediation in Emerging Market Economies during the Crisis: Locally Owned versus Foreign-owned Banks.‖ In BIS papers No. 54, The Global Crisis and Financial Intermediation. pp. 31-47.

37
# 2.4 The Drug Consumer

The problem of the drug consumer is more complex. It relies on the ‘rational addiction’ literature pioneered by Becker and Murphy (1988). The model generalizes the framework of Orphanides and Zervos (1995). The representative drug consumer (agent D) demands the illegal drug from the drug trafficker (n<sup>D</sup> ). His consumption of the legal good can be c<sup>D</sup> 0,t<sup>or</sup> c<sup>D</sup> 1,t<sup>dependingonwhetherheisnotoriscaughtconsumingtheillegaldrug(whichhappens</sup> with probability π<sup>D</sup> t<sup>).Then:</sup> where h<sup>D</sup> t<sup>istheamountofthelegalgoodproduced,φ</sup> t<sup>isaconsumptiontaxleviedbythe</sup> government, and 0 < τ<sup>D</sup> t<sup>≤1 is the penalty that is paid if caught consuming the illegal drug.</sup> At each period, the agent is endowed with one unit of time and derives utility from leisure.<sup>9</sup> This unit of time can be devoted to demand leisure (l1<sup>D</sup> ,t<sup>), to produce the legal good</sup> (l2<sup>D</sup> ,t<sup>),ortoreducetheprobabilityofgettingcaught(l</sup> 3<sup>D</sup> ,t<sup>).TheproductionfunctionhDis</sup> increasing and strictly concave in l2<sup>D</sup> ,t<sup>.TheprobabilityπD</sup> t<sup>=πD ¡</sup> g1<sup>D</sup> ,t<sup>, n</sup> t<sup>D, l</sup> 3<sup>D</sup> ,t¢ is increasing in the Þrst two arguments and decreasing in the third, where g1<sup>Disthelevelofgovernment</sup> expenditures destined to detect the illegal activity.

As the illegal drug is potentially addictive, the long-lasting effects of past consumption of n<sup>D</sup> are summarized by the stock variable d<sup>D</sup> that has the following law of motion: where 0 < δ < 1 acts as a depreciation rate.

The momentary instantaneous utility of the individual is: where u (·) is increasing and concave in leisure and in the consumption of the legal and illegal goods. The third term is itself composed by two terms. The Þrst is θ<sup>D</sup> t<sup>= θ</sup> ¡g2<sup>D</sup> ,t<sup>, d</sup> t<sup>D</sup> ¢ and denotes the probability that the agent will experience the detrimental effects of the consumption of the illegal good and is decreasing in g2<sup>Dand increasing in the stock of illegal</sup> drugs.<sup>10</sup> The second is kt<sup>D=k</sup> ¡n<sup>D</sup> t<sup>, dD</sup> t ¢ and represents the detrimental side effects of past consumption. It is increasing in the Þrst argument and decreasing in the second. Thus, the second reßects the fact that the agent is rationally addicted in the sense that he knows that there are negative side effects to increased consumption.

> 9As this agent does not have to devote resources to produce illegal goods, we force him to derive utility from leisure to avoid trivial solutions for the labor supply.

> 10The difference between g1D<sup>and g</sup> 2<sup>Dis that the Þrst considers expenditures that the demand of the illegal</sup> good riskier for the consumer, while the second deals with expenditures that diminish the effects of its use (for example through educational campaigns or treatment for heavy users).

6
WPS4169

# **Quantitative Approaches to Fiscal Sustainability Analysis**

**A New World Bank Tool Applied to Turkey**<sup>**1**</sup>

Nina Budina World Bank nbudina@worldbank.org and Sweder van Wijnbergen University of Amsterdam s.j.g.vanwijnbergen@uva.nl

## **Abstract**

Fiscal sustainability analysis (FSA) is an important component of macroeconomic analysis. This paper reviews various quantitative approaches to FSA with a major objective to bring these approaches together and to present a user-friendly tool for FSA that reflects modern developments. We combine a dynamic simulations approach with a simplified version of the steady state consistency approach. We also incorporate two different methods to deal with uncertainty: user-defined stress tests and stochastic simulations. The tool goes further by evaluating the required fiscal adjustment as a consequence of the stochastic realizations of the exogenous variables. Furthermore, the FS tool incorporates an endogenous debt feedback rule for the primary surplus, a fiscal policy reaction function. Besides outlining the theoretical framework, we also present a case study for Turkey.

World Bank Policy Research Working Paper 4169, March 2007 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 This paper is to be used together with the technical instruction manual (WPS 4170), which provides step-by-step technical instructions for running the FSA tool, and includes mathematical appendices and a glossary.
policy choice. With the general very high costs of sewage systems, low-cost sanitation might still come out to be the better option to invest in to achieve the maximum of diarrhea reduction given a fixed budget.

**Table 4: Child mortality**

||(1)||(2)||(3)||(4)||(5)||
|---|---|---|---|---|---|---|---|---|---|---|
||total||rural||urban||SSA||OTHER||
|Latrine<br>_Conf. Int._|0.954<br>_(0.920, 0.982)_|***|0.952<br>_(0.921, 0.982)_|***|0.936<br>_(0.874, 0.999)_|**|**_0.999_**<br>_(0.965, 1.035)_||0.867<br>_(0.825, 0.911)_|***|
|Flush<br>_Conf. Int._|0.862<br>_(0.813, 0.913)_|***|0.826<br>_(0.752, 0.907)_|***|0.839<br>_(0.766, 0.918)_|***|**_0.972_**<br>_(0.893, 1.059)_||0.794<br>_(0.734, 0.860)_|***|
|Well/pump<br>_Conf. Int._|0.955<br>_(0.922, 0.988)_|***|0.927<br>_(0.894, 0.962)_|***|**_1.056_**<br>**_(0.929, 1.200)_**||0.940<br>_(0.903, 0.979)_|***|**_1.004_**<br>**_(0.926, 1.059)_**||
|Piped<br>_Conf. Int._|0.885<br>_(0.847, 0.924)_|***|0.894<br>_(0.851, 0.940)_|***|0.884<br>_(0.777, 1.006)_|*|0.871<br>_(0.824, 0.92_|***<br>_0)_|0.906<br>_(0.829, 0.962)_|***|
|Female|0.983||1.001||0.926|***|0.978|*|0.996||
|First born|0.593|***|0.583|***|0.626|***|0.676|***|0.467|***|
|Birth interval|0.988|***|0.986|***|0.993|***|0.989|***|0.986|***|
|Breastfed|0.326|***|0.323|***|0.327|***|0.421|***|0.256|***|
|Educ.mother|0.944|***|0.952|***|0.931|***|0.959|***|0.926|***|
|Agemother|0.916|***|0.927|***|0.892|***|0.932|***|0.885|***|
|Age squared|1.001|***|1.001|***|1.002|***|1.001|***|1.002|***|
|Mother married|0.797|***|0.842|***|0.709|***|0.811|***|0.793|***|
|Household size|0.945|***|0.939|***|0.959|***|0.954|***|0.910|***|
|Electricity|0.851|***|0.829|***|0.905|***|0.857|***|0.850|***|
|Radio|1.006||1.001||1.002||0.993||1.009||
|TV|0.888|***|0.901|***|0.881|***|0.867|***|0.930|***|
|Fridge|0.826|***|0.871|***|0.846|***|0.929||0.794|***|
|Bike|1.030|**|1.041|**|0.991||1.044|***|1.033||
|Urban|yes||no||no||yes||yes||
|Country|yes||yes||yes||yes||yes||
|Year|yes||yes||yes||yes||yes||
|Season|yes||yes||yes||yes||yes||
|P|0.708||0.710||0.703||0.766||0.604||
|Observations|796219||533905||262314||365515||430704||

_Notes: Weibull Survival Model. Reported coefficients are hazard ratios. Year-country fixed effects. ***: p<0.01, **: p<0.05, *: p<0.10. Robust standard errors are clustered at the cluster level. Conf. Int.: 95% confidence interval. The left out category in all specifications is surface water and open defecation._ In Table 4 we repeat the regressions shown in Table 3, but use child mortality, rather than diarrhea morbidity as the dependent variable. We estimate a survival model for under-5 22/36
_Table 11: Heterogeneity in plot endowment and management by gender of the plot manager_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|**Dependent variable:**Female plot manager (dummy)|Pooled|Ethiopia|Malawi|Mali|Niger|Nigeria|Tanzania|
|Any hired labor on plot|0.0342***|0.0336**|0.000582|-0.0254**|0.0644***|0.0473**|0.0560***|
||(0.00878)|(0.0145)|(0.0148)|(0.0104)|(0.0148)|(0.0216)|(0.0201)|
|Any inorganic fertilizer used|-0.00474|0.0159|-0.0149|-0.0206|-0.00489|-0.0373**|-0.00251|
||(0.00879)|(0.0125)|(0.0154)|(0.0138)|(0.0349)|(0.0176)|(0.0275)|
|Any organic fertilizer used|-0.0128|0.0127|0.0234|-0.0381***|-0.0375**|-0.0367*|-0.0167|
||(0.00924)|(0.0114)|(0.0186)|(0.00935)|(0.0150)|(0.0211)|(0.0309)|
|Plot is irrigated|-0.0332|0.0234|-0.0671|-0.0956***|-0.244**|-0.0418|-0.0718|
||(0.0246)|(0.0339)|(0.0703)|(0.0274)|(0.100)|(0.0610)|(0.0489)|
|Plot is intercropped|0.0184**|-0.0168|0.0698***|-0.0224|-0.0544***|0.0287**|0.0430*|
||(0.00844)|(0.0149)|(0.0129)|(0.0225)|(0.0206)|(0.0144)|(0.0225)|
|Plot is owned|0.0268***|0.0907***|0.0897***|-0.00327|-0.0335**|-0.00298|-0.00916|
||(0.00988)|(0.0173)|(0.0239)|(0.0189)|(0.0148)|(0.0181)|(0.0195)|
|Log plot area (ha)|-0.0423***|-0.0245***|-0.0524***|-0.0229***|-0.0461***|-0.0498***|-0.0619***|
||(0.00258)|(0.00297)|(0.00706)|(0.00363)|(0.00667)|(0.00567)|(0.00795)|
|Plot topographic wetness index|0.00105|-0.00154|0.00110|0.00154|-0.00162|-0.000648|0.00310|
||(0.00141)|(0.00271)|(0.00386)|(0.00135)|(0.00156)|(0.00176)|(0.00323)|
|Plot elevation (m)|-2.96e-05**|2.91e-07|-9.40e-05***|-0.000297***|0.000227**|-0.000188*|-5.01e-06|
||(1.17e-05)|(1.59e-05)|(3.27e-05)|(5.32e-05)|(0.000110)|(9.64e-05)|(2.13e-05)|
|Urban household|0.00322|0.0744**|0.0166|-0.0568**|-0.0634*|-0.0347|0.0214|
||(0.0153)|(0.0356)|(0.0350)|(0.0274)|(0.0378)|(0.0281)|(0.0268)|
|Household engaged in livestock farming|-0.0888***|-0.142***|-0.103***|0.00674|-0.00526|-0.00275|-0.0921***|
||(0.00932)|(0.0152)|(0.0147)|(0.0124)|(0.0200)|(0.0202)|(0.0195)|
|Household has access to electricity|-0.00878|-0.0308**|-0.0621*|0.00794|-0.0857**|0.0115|-0.0408|
||(0.0115)|(0.0144)|(0.0345)|(0.00813)|(0.0384)|(0.0205)|(0.0333)|
|Observations|118,311|40,243|17,932|31,138|8,803|7,773|12,307|
|Crop FE|YES|YES|YES|YES|YES|YES|YES|
|Country FE<br>Note: Average marginal effects from multivariate logit regre<br>parentheses. *** p<0.01, ** p<0.05, * p<0.1|YES<br>ssions. Base categor|NO<br>y for crop fixed effec|NO<br>ts is 'other crop'. Th|NO<br>e estimates are wei|NO<br>ghted to be nationa|NO<br>lly representative.|NO<br>Standard errors in|

32
Table 2.3: SOUTH AFRICA, Percentage of observations lost due to missing values by industry and size

||Size|S|mall|Med|ium|La|rge|T|otal|
|---|---|---|---|---|---|---|---|---|---|
|Industry||#Obs|%Lost<sup>(d)</sup>|#Obs|%Lost|#Obs|%Lost|#Obs|%Lost|
|Food & beverages|Sampling frame<sup>(a)</sup>|22||80||87||189||
||Complete case<sup>(b)</sup>|13|40.9|49|38.8|69|20.7|131|30.7|
||With replacement<sup>(c)</sup>|14|36.4|66|17.5|82|5.7|162|14.3|
|Textiles & apparel|Sampling frame|12||43||120||175||
||Complete case|6|50|32|25.6|69|42.5|107|38.9|
||With replacement|10|16.7|33|23.3|101|15.8|144|17.7|
|Chemicals, rubber &|Sampling frame|42||119||118||279||
|plastics|Complete case|21|50|79|33.6|87|26.3|187|33|
||With replacement|29|31|111|6.7|101|14.4|241|13.6|
|Paper, edition &|Sampling frame|13||89||54||156||
|publishing|Complete case|10|23.1|65|27|45|16.7|120|23.1|
||With replacement|10|23.1|78|12.4|49|9.3|137|12.2|
|Machinery & equipment|<br>Sampling frame|47||252||256||555||
||Complete case|25|46.8|198|21.4|212|17.2|435|21.6|
||With replacement|35|25.5|222|11.9|241|5.9|498|10.3|
|Wood & furniture|Sampling frame|13||74||58||145||
||Complete case|7|46.2|55|25.7|39|32.8|101|30.3|
||With replacement|11|15.4|69|6.8|50|13.8|130|10.3|
|Non-metallic products|Sampling frame|13||23||30||66||
||Complete case|3|76.9|18|21.7|22|26.7|43|34.8|
||With replacement|6|53.8|18|21.7|26|13.3|50|24.2|
|Other|Sampling frame|27||63||57||147||
||Complete case|19|29.6|38|39.7|47|17.5|104|29.3|
||With replacement|25|7.4|50|20.6|51|10.5|126|14.3|
|Total|Sampling frame|189||743||780||1712||
||Complete case|104|45|534|28.1|590|24.4|1228|28.3|
||With replacement|140|25.9|647|12.9|701|10.1|1488|13.1|

Notes: (a) “Sampling frame” refers to the total number of observations (firms surveyed multiplied by the number of years of information).

(b) “Complete case” refers to the complete case of production function variables (sales, materials, capital and labor), missing values in other IC variables—other than production function— are not considered.

(c) “With replacement” refers to the sample after imputing IC variables according to the ICA Method; missing values in other IC variables—other than production function—are not considered. Notice that only observations with information available in at least one of sales, labor, labor cost, materials or capital, are imputed (d) “Perc. lost” refers to the percentage of observations lost with respect to the sampling frame. Source: Authors calculations with IC data.

12
13 of these effects. What is evident, however, is that despite the increase in real value of tax revenue due to the fall in inflation in 1992 and 1993, the more fundamental transitionrelated influences described above continued in these years to erode the traditional tax bases.

# 5. BULGARIA'S CHANGING TAX SYSTEM IN THE TRANSITION

How did Bulgaria respond in the transition in changing the tax system that had been inherited from socialism? We proceed now to describe briefly how the tax system evolved in the transition." The focus is on the three key taxes: profits tax, turnover tax (and its replacement, VAT), and the personal income tax.<sup>9</sup>

# A. The Profits Tax

As of mid-1993, state enterprises were taxed at the rate of 52 percent (consisting of a 40 percent standard tax rate accruing to the central government, 10 percent to the municipality in which the enterprise was located, and a 2 percent irrigation tax), and enterprises owned by a local municipality were taxed<sup>at</sup> the rate of 42 percent (the 40 percent standard rate to the It/ For a detailed description and evaluation, see The World Bank (1993).

iw Other taxes -- excises, property taxes, user charges, capital gains, and import duties -- have do not have the potential (and in the case of trade taxes are not desirable means, see George Fane, 1993) to resolve the transitional<sup>fiscal</sup> gap. Other tax possibilities include presumptive taxation (Tanzi and Casanegra de Jantscher, 1987).
Such substitutability is not restricted to the edible oil market; it extends to other commodity markets as well. For example, the co‐movement between oils and grains is high as well as evidenced by the high adjusted‐ _R_<sup>_2_</sup> s and ADF statis‐ tics for the regressions between wheat and maize with palm oil (panel III). Simi‐ larly, the co‐movement among grains is high (panel IV). Such findings imply that policy actions or other factors affecting one vegetable oil or grain market will eventually affect most other grain and oilseed markets.

# **3. Issues in the Use of Cotton Oil for Biofuels**

Given the growth of world demand for biofuels—which now consume an esti‐ mated 9 percent of global vegetable oil production—vegetable oils including cot‐ ton oil have often been mentioned as a possible new source of income for com‐ modity producers in Sub‐Saharan Africa.

Opinions differ about the virtue of using food commodities to produce biofuels: the practice is viewed as a problem by some (consumers of food com‐ modities who pay higher prices) and as an opportunity by others (commodity producers who receive higher prices, as well as the biofuel industry). Of the three key sources of biofuels, sugarcane‐based ethanol, produced mainly in Brazil, is the most profitable. Maize‐based ethanol, produced mainly in the US, ranks second. Edible oil‐based biodiesel, produced mainly in the EU, ranks third. The consensus is that, with the exception of sugar cane‐based ethanol, production of biofuels would not have been profitable were it not for the various policies and mandates in force.

Demand for biofuels can affect the cotton oil market through three chan‐ nels. First, it may directly increase demand for cotton oil as a biofuel feedstock. Second, it may increase demand for competing oils, and so indirectly raise the demand for cotton oil. Third, it may increase the demand for other commodities and thereby, through the reallocation of resources, affect the cotton oil market as well. To analyze the effect of biofuels on the cotton oil market thus requires a look at each of these three channels.

First, the country studies summarized later in this paper find that biofuel production in SSA countries, based on either cotton oil or other sources, is un‐ likely to be profitable at currently foreseeable energy prices, at least within the next decade. Most of the African cotton‐oil producers surveyed believed that they could not profitably produce biodiesel from cotton oil unless it were heavily subsidized. Mitchell (2010) concluded that even sugarcane‐based ethanol may not be profitable in SSA without incentives (although such technology is profita‐

**_Page 14_**
for professionals and technical experts. Female employees earn a lower compensation, especially in the auxiliaries’ category. Both the marital status and the location dummy have a positive impact on total compensation. Finally, experience positively affects compensation but its impact is more evident for technical experts and auxiliaries.

# **Table A2.2. Estimation results**

|**Variables**|**Professionals**|**Technical**<br>**Experts**|**Auxiliaries**|
|---|---|---|---|
|**Public Sector Dummy**|**-0.032**|**-0.119*****|**-0.150*****|
|Secondary Education|0.782***|0.254**|0.223***|
|Non Completed Post-Secondary|0.068|0.252***|0.141***|
|Completed Post-Sec (non University)|0.344***|0.035|0.067|
|Completed Post-Sec (University)|0.427***|0.328***|0.041|
|Postgraduate|0.294***|0.452***|"o"|
|Age|0.018|0.031***|0.027***|
|Age Squared (/100)|-0.011|-0.017|-0.016*|
|Female|-0.194***|-0.151***|-0.396***|
|Married|0.069**|0.114***|0.149***|
|Urban|0.038***|0.155***|0.139***|
|Observations|1633|2178|1892|
|R-squared|0.185|0.237|0.187|
|*** p<0.01, ** p<0.05, * p<0.1, "o" when omitted|for collinearity|||

The absence of a significant private sector premium for the professionals’ category is surprising and deserves further attention. We looked at the different professions included in the sample of professionals and realized that results could be biased by the composition of the sample. We found that a large share of employees belonging to the group of professionals is constituted by teachers (66.4 percent). Teachers work mainly in the public sector and earn significantly less than the other professionals in the sample. In addition, compensation is particularly low for teachers working in the private sector. They earn 53 percent less than other professionals working in the public sector and 57 percent less than other private sector professionals (see the last two columns of Table A2.3). Therefore, it is difficult to compare them with other professionals, at least when focusing on compensation.

The lower compensation of teachers is probably biasing the estimation of the log-wage equation. To verify this, we re-estimated the model after excluding teachers from the group of professionals. Results of the analysis confirm our suspects. Coefficients estimated differ according to the sample considered. This highlights the heterogeneity of a sample which put together teachers with the rest of professionals. In particular, the private sector premium grows significantly after excluding teachers from the sample up to the point of being higher than private sector premia estimated for the technical experts and auxiliaries’ categories. Implications of these results are twofold: compensations for teachers in the 25