rafmacalaba/datause-encoder-data huggingface dataset:
more equitable in the case of children attending private schools compared to those attending public schools.

Table 7: Government subsidy per child, by total family expenditure quintile and level of <u>education (private schools only), in $</u>

|Quintile|Primary|Secondary|Tertiary**|All levels|
|---|---|---|---|---|
|1st|126|195|33*|147|
|2nd|201|374|239|261|
|3rd|360|266|350*|308|
|4th|175|803|475|309|
|5th|296|311|469*|322|
|All (mean)|219|360|215|269|
|N|63|46|21|129|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages * less than 5 observations; ** Professional (technical) education institutions only In Tables 8 and 9, the information generated in Tables 4, 5, 6 and 7 is used to derive estimates of the percentage of family education expenses covered by subsidies across family expenditure quintiles. In both the cases of public and private school attendance, there is a clear tendency for the share of family education expenditure covered by subsidy to decline as one goes to higher family expenditure quintile. The subsidy system, therefore, seems to be progressive, and more so for private schools. In public schools, almost all household education expenditure per child by families in the lowest quintile is covered by subsidies, compared to a subsidy/expenditure ratio of 42 percent for families in the highest quintile. In private schools, the subsidy/expenditure ratio is 81 percent for families in the lowest quintile, compared to only 22 percent for families in the highest quintile. Nevertheless, this does not change the finding (Tables 4, 5, 6 and 7) that the more well off families (which spent much more on education of children) still receive a disproportionately large share of subsidy money, especially in the case of public school attendance. The fact that well to do families benefit from subsidies seems to relate to the system of allocating subsidies. At the primary level, the subsidy is linked to tuition fees 16
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38
the _same_ method on different data sets does _not_ guarantee a result such that the material standard of living represented by the resulting poverty lines is equivalent. Why this is so is something of a puzzle. Apparently the Engel curve relationship shifted over time.

Therefore, for consistent welfare measures we propose to use method II for comparisons over time, in which case the headcount poverty rate increased by between 6.52 and 8.14 percentage points, depending on whether the food prices are taken from either CPI or Susenas unit prices. This is keeping in mind that method II is something of a "worst case" scenario when food prices are increasing.

The same procedures mentioned above as in table 4 can also be applied by working backward. That is, we use an iterative method to set the poverty line and poverty rate in 1999, then we deflate that poverty line back to 1996 rupiah using methods I and II for the deflators. The results of using this _methodologically consistent procedure in 1999_ are very much higher levels of poverty as the "base case" is in 1999.

The results as presented in table 6 show that, using this procedures with CPI inflation, the poverty rate would have risen from 19.95 percent in 1996 to 27.13 percent in 1999 using actual share of food in consumption expenditures at the poverty line (method I) and from _[_ 7.55 to 27.13 percent using the share of food in the poverty basket (method II). This implies poverty rate increased by 7.18 and 9.58 percentage points by each method respectively using CPI. Again, they are consistent with the previous procedures, where poverty also increased by between 5.53 and 6.52 percentage points. Using Susenas unit prices, the implied increase in poverty rate is 21
# Table 5 (cont.) The Gains From Agricultural Tariff Liberalization Across Commodities Vietnam

|Product|Aggregate<br>Gains|St.Dev.<br>Gains|Consumption<br>Efects|Income<br>Efects|Pro-Poor<br>Bias|Gains<br>Poor|Gains<br>Rich|
|---|---|---|---|---|---|---|---|
|Aggregate|1.99|0.82|5.52|-3.53|-1.70|1.04|2.74|
|Other fruit|0.05|0.13|0.22|-0.17|-0.20|-0.05|0.15|
|Other meat|-0.06|0.06|0.05|-0.11|-0.07|-0.09|-0.02|
|Other non-staple|0.06|0.01|0.06|0.00|-0.00|0.06|0.06|
|Other nuts|-0.03|0.05|0.02|-0.06|-0.05|-0.04|0.00|
|Other oil seeds|-0.00|0.00|0.00|-0.00|-0.00|-0.00|-0.00|
|Other oils/fats|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Other staple foods|-0.00|0.00|0.00|-0.00|-0.00|-0.00|-0.00|
|Other tobacco|-0.00|0.03|0.01|-0.01|0.01|0.00|-0.01|
|Other vegetables|-0.00|0.10|0.19|-0.19|-0.12|-0.07|0.05|
|Pepper|-0.05|0.07|0.00|-0.05|0.04|-0.02|-0.06|
|Pork|0.28|0.12|0.56|-0.28|0.20|0.37|0.18|
|Potato|0.00|0.02|0.01|-0.01|-0.01|-0.01|0.00|
|Poultry|0.03|0.05|0.09|-0.07|-0.08|-0.02|0.05|
|Qat (chat)|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Rice|-1.57|0.73|0.04|-1.61|-1.77|-2.35|-0.58|
|Safron|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Shrimp|-0.05|0.05|0.00|-0.05|-0.01|-0.04|-0.03|
|Soya|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Sugar|0.09|0.11|0.19|-0.10|-0.04|0.04|0.08|
|Tea|0.13|0.06|0.18|-0.04|0.07|0.15|0.08|
|Tomato|0.03|0.02|0.04|-0.00|0.01|0.03|0.02|
|Vanilla|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Vegetable oils|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Wheat|0.01|0.00|0.01|0.00|0.00|0.01|0.01|
|Wine|0.00|0.00|0.00|0.00|0.00|0.00|0.00|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the (country by country) elimination of Vietnam’s own agricultural import tariffs.

35
# **Table 17: Impact on the Regions of Russia of Energy Efficiency Standards to Reduce Carbon Emissions by 20 Percent, with decomposition of carbon emission effects. (Results are Percentage Change from Base Year unless otherwise indicated.)**

||**Overall**||**St. **||**North-**||||||**Far **|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**average M**|**oscow**|**Peters.**|**Tumen**|**west**|**North**|**Central**|**South**|**Urals**|**Siberia**|**East**|
|**Aggregate welfare**||||||||||||
|Welfare (EVas % ofconsumption)|-5.9|-5.5|-5.9|-13.2|-5.5|-6.8|-5.2|-5.9|-5.6|-5.5|-6.5|
|Welfare (EVas % ofGDP)|-2.7|-3.0|-2.7|-2.8|-2.5|-2.8|-2.4|-2.8|-2.5|-2.6|-2.9|
|**Carbon Dioxide Emissions and Deco**|**mposition**|||||||||||
|CO2price (ruble per tonofCO2)||0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|CO2emissions, decomposedinto:|-19.8|-20.2|-21.6|-20.5|-17.8|-18.2|-20.5|-20.7|-20.7|-17.0|-17.4|
|Outputeffect(% ofCO2)|-6.1|-5.5|-6.2|-7.4|-6.1|-6.5|-6.1|-6.1|-6.4|-5.8|-6.1|
|Compositioneffect(% ofCO2)|-6.0|-5.2|-5.8|-10.9|-5.1|-4.7|-5.4|-5.3|-5.9|-4.8|-3.8|
|Technique effect(% ofCO2)|-8.2|-10.1|-10.3|-1.9|-7.1|-7.5|-9.6|-9.9|-9.1|-6.9|-7.9|
|**Non-Carbon Dioxide Emissions**||||||||||||
|Sulphur Dioxide|-6.9|-18.1|-11.1|-17.3|-11.5|-6.9|-18.7|-18.3|-10.7|-3.8|-5.6|
|NitrogenOxide|-16.1|-20.5|-17.8|-10.9|-21.6|-17.0|-23.7|-19.6|-15.1|-13.6|-15.6|
|Hydrocarbons|-6.5|-10.4|-11.4|-4.5|-9.5|-6.3|-9.2|-7.1|-7.7|-6.6|-2.4|
|ParticulateMatter|-12.5|-20.2|-19.1|-7.6|-24.4|-14.8|-22.9|-21.5|-13.0|-9.8|-10.2|
|Volatile Organic Components|-6.8|-17.2|-11.8|-3.9|-9.2|-5.1|-16.5|-7.1|-8.1|-13.2|-10.0|
|Carbon Monoxide|-4.7|-7.4|-6.3|-4.0|-5.9|-4.0|-9.8|-6.0|-5.6|-3.2|-3.5|
|**Aggregate trade**||||||||||||
|Regional terms of trade (% change)|-1.5|19.9|19.3|16.5|19.4|18.8|19.5|19.7|19.4|19.6|19.4|
|Regionalexports (% change)|-7.5|-6.3|-8.5|-7.3|-7.3|-7.6|-7.9|-6.5|-8.3|-8.2|-8.0|
|Realexchangerate (% change)|-0.2|-0.1|-0.3|-1.4|-0.1|-0.5|-0.1|-0.3|-0.2|-0.3|-0.3|
|Internationalexports (% change)|-1.2|-6.2|-4.6|3.3|-8.8|-0.9|-9.1|0.3|-0.9|-3.2|-2.0|
|**Returnto primary factors (% change)**||||||||||||
|Unskilledlabor|0.0|0.7|0.5|-1.1|0.2|-0.2|0.5|-0.3|0.0|-0.4|0.0|
|Skilledlabor|-0.4|0.9|0.5|-4.6|0.5|-0.7|0.2|-0.2|-0.4|-0.4|-0.2|
|Nationalcapital|-3.4|-3.2|-3.5|-4.5|-3.3|-3.6|-3.2|-3.5|-3.3|-3.4|-3.4|
|Regional mobile capital|-3.1|-3.6|-3.8|-0.8|-3.9|-3.2|-3.7|-3.1|-3.2|-3.1|-3.5|
|Crude oil resources|-6.4|||-7.4|-4.7|-5.1||-5.0|-4.6|-5.4|-4.9|
|Naturalgasresources|-32.0|||-34.8|3.5|-6.6||-5.2|-2.3|-5.7|-1.1|
|Coal resources|-3.2|||||-3.5|-2.5|-3.4|-2.8|-3.3|-2.8|
|Specific capital indomesticfirms||-2.9|-3.2|-1.8|-3.7|-4.0|-3.5|-3.8|-3.9|-2.5|-2.5|
|Specific capital in multinational firms||-2.7|-2.3|3.3|-2.4|0.0|-0.1|0.9|1.3|1.6|-0.3|
|**Factor adjustments**||||||||||||
|Unskilledlabor(% changing sectors)|2.2|2.1|2.4|2.0|2.5|2.2|2.3|2.0|2.2|2.5|2.3|
|Skilledlabor(% changing sector)|2.7|2.2|2.7|4.0|2.7|2.6|2.7|2.2|2.7|2.8|2.8|
|**Source: Authors' estimates**||||||||||||

57
44%. There are significant differences in the composition of FDI inflows into developed and developing countries. Business services accounted for 40% of the total inward FDI stock in developing countries in 2003, compared to only 20% in the OECD.<sup>14</sup> Policy reforms, mostly implemented by governments autonomously, have complemented changes in technology in supporting the expansion of trade in services. Many countries have taken action to increase competition on services markets by liberalizing FDI, opening access to foreign competition in backbone sectors such as transport and telecommunications, and privatizing state-owned or controlled service providers. The most far-reaching liberalization and regulatory reform processes have been implemented by transition economies – illustrated by the time trend of an index of service sector regulatory policies developed by the EBRD (Figure 4). Similar trends pertain to developing countries, although comparable time series data are not available.

A recent survey undertaken by the World Bank of the extent of discriminatory policies restricting entry by foreign firms in specific services markets in 30 developing countries has found significant heterogeneity (Gootiz and Mattoo, 2007). Many sectors are open, especially for FDI. However, in many sectors various restrictions continue to be imposed, and some sectors are completely closed. The consensus view is that the tariff equivalents of prevailing restrictions are a multiple of those that restrict merchandise trade.<sup>15</sup> “Sensitive” sectors vary by country reflecting differences in comparative advantage and the legacy of past policies. Many countries maintain foreign equity or entry restrictions for certain services markets.<sup>16</sup> Moreover, barriers to entry in a number of services sectors, ranging from telecommunications to professional services, are maintained not only against foreign suppliers but also against new domestic suppliers. Liberalization can, therefore, lead to enhanced competition from both domestic and foreign suppliers.

> 14 Non-equity FDI (franchising, management contracts, leasing) is not captured in the forgoing statistics. 15 See e.g. Dee (2005). No comprehensive, cross-country, comparable datasets exist that allow a summary assessment of the prevailing levels of services trade and investment barriers.

> 16 In India, for example, professional services like accountancy and legal, retail distribution, postal, and rail transport services are formally closed to foreign participation.

12
# **Centralization, Decentralization, and Conflict in the Middle East and North Africa**

Mehmet Serkan Tosun<sup>*****</sup> University of Nevada, Reno and Serdar Yilmaz<sup>******</sup> World Bank

**JEL classification:** H77, H87, N45, O53

**Key words:** Fiscal decentralization, intergovernmental relations, Middle East and North Africa

The authors thank Lyn Squire, Blanca Moreno-Dodson Jennifer Keller and Robert Beschel for helpful comments and suggestions, Dilek Uz and Omid Harraf for excellent research assistance. The findings, interpretations, and conclusions are entirely those of authors, and do not represent the views of the World Bank, its executive directors, or the countries they represent. Corresponding author: Serdar Yilmaz. * Department of Economics, College of Business Administration, University of Nevada-Reno, Reno, Mail Stop 0030. Nevada 89557; <u>tosun@unr.edu; phone: +1-775-7846678.</u>

** Social Development Department, World Bank, 1818 H Street, NW, Washington D.C. 20433; <u>Syilmaz@worldbank.org; phone: +1-202-473-9350.</u>
4 behavioral and ecological adaptations of animals. This knowledge has been accumulated through thousands of years of human experimentation, as indicated by recent archaeological research in the Amazon (9-11).

Because of the variability of lowland tropi.cal environments, there has been a range of adaptations of the indigenous socie_ies from nomadic foragers to settled agriculturalists. Lowland indigenous groups have also adapted to new socio-cultural, as well as environmental, conditions. There are 4ocumLntF..i cases of these groups shifting their patterns of subsistence from foraging to horticulture, and from greater or lesser dependency on hunting and/or fishing. T1e environmental knowledge and adaptations if these societies are not static, but have changed as the lowland forest environments in which they live have evolved and changed (10, 12).

Despite this variability, there are certain cultural continuities which have organized indigenous societies and worldviews. Magical specialists or shamans, for example, play a significant role in the control of social behavior, as well as in the management and control of natural resources. Food, hunting and gardening taboos regulate social and reproductive practices, along with diet (13). Linked to these institutions and practices is a worldview which varies among peoples but which posits a unity between humankind and nature. To quote the Colombian anthropologist G. Reichel-Dolmatoff in reference to the Tukano of Colombia, Nature in their view is not a physical entity apart from man and, therefore, he cannot confront it or oppose it or harmonize with it as a separate entity. Occasionally man can unbalance it by his personal malfunctioning as a component, but he never stands apart from it. Man is taken to be a part of a set of supra-individual systems which -- be they biological or cultural-- transcend our individual lives and within which survival and the maintenance of a certain quality of life are possible only if all other life forms too are allowed to evolve according to their specific needs, as stated in cosmological myths and traditions (14).

These indigenous cosmologies give primacy to the symbolic configuration of space in both the natural and supernatural worlds. In some indigenous societies, for instance, the long-houses inhabited by living Indians are replicated in the living arrangements (and rituals) of the ancestors. The ancestors, along with
accounting matrixes (SAMs). The basic idea of these tools is to construct mathematical models of an economy and then calculate the change on economic outcomes associated with changes in some variables, such as the expenditure on a given basket of goods, or the output of an industry. Depending on data availability, these models can be built to describe regional and local economies, and thus inform about impacts at the subnational level.<sup>22</sup> Some countries, like Canada and the United States, routinely use I-O models to assess the exante economic impact of extractive industries.<sup>23</sup> These models have also been used to assess the impact of mining in a variety of contexts, such as in northern Chile (Aroca 2001), northern Sweden (Ejdemo and S ̈oderholm 2011), South Africa (Stilwell et al. 2000), and Europe (San Cristobal and Biezma 2006), among others.

Analyses based on I-O models and SAMs are useful for economic planning and ex-ante impact evaluation. Their predictions are informative of what the economic effect of a mining project could be. But a main limitation is that they do not tell what the effect actually is.

The actual effect could be different from the predicted one for several reasons. First, by construction, economic models cannot account for all the possible mechanisms through which resource booms affect outcomes, such as pollution spillovers, structural changes, and migration. These mechanisms may be important. Second, I-O models usually do not take into account price-adjusting behavior or substitution effects, and assume that there is no scarcity of resources. Finally, the model parameters (such as multipliers and technical coefficients) may change as a consequence of the resource boom itself and thus render the model predictions inaccurate. This is the standard Lucas critique.

# **3.3 Main lessons**

The literature on the local impact of natural abundance is still at the early stages of development. Despite this, it has already provided some important insights.

First, similar to the country-level literature, it suggests that a local resource curse is not inevitable. On the contrary, there are some examples in which resource abundance does not have

> 22 I-O models and SAMs are available for several developed and developing countries. For a list of SAMs for developing countries, see http://www.ifpri.org/category/dataset-type/social-accounting-matrixes.

> 23 In Canada, the I-O analysis at the national and subnational level is performed by Stats Canada and provincial statistical agencies. In the United States, there are commercial I-O models at the national and regional level, such as IMPLAN and RIMS II.

32
workers because moonlighters report to work as many hours in the facility than those who do not hold a second job. It is more likely in this case that the lack of accountability and oversight drive moonlighting. Krishnan (1990) shows using the second wave of the US Survey of Income and Program Participation that husbands are less likely to moonlight when their wives also are in the labor force. We test a similar proposition here by including a dummy variable for the presence of at least one other wage earner in the health worker’s household.<sup>13</sup> We find that the presence of another wage earner has no significant impact on moonlighting.

# **5. Conclusion**

A large and growing literature strongly and consistently suggests that women are less corrupt than men. This paper, using a unique dataset on health workers in Tajikistan, shows that with equal power and equal opportunities, women are in fact just as likely as men to engage in corrupt activities. The result is obtained in an arguably ideal setting for testing gender differences towards corruption. Indeed, Tajik health workers operate within a risk-free environment which features a near-zero probability of corruption detection and no risk of sanction either through job loss of other non-pecuniary punishment. It is in these kinds of environments that the willingness to engage in corruption truly manifests itself allowing for an easier and more precise estimation of its determinants. Contrary to the literature, the paper also shows that female-headed health facilities experience the same incidence of informal charging. Although women appear to have the same propensity for extracting informal payments from patients, they are still shown to be less aggressive than men and collect smaller amounts. Women are also less likely than men to work outside the facility or hold a second job.

The paper presents some other interesting findings. For instance it presents evidence that workers who perceive themselves as unfairly rewarded for their effort will try to get even not by withdrawing effort but by engaging in corrupt activity if they have the opportunity. Another noteworthy finding that emerges is that workers who think that patients should get care for a fee are more likely to charge patients and charge significantly more. This may

> 13 We do not restrict this other wage earner to be the health worker’s spouse however.

23
41 Table 3: Pde.

||<br>"EXICO|Stripped<br>Pre?-<br>Operation<br>0.36|Prices<br>Post<sup>3</sup>-<br>Oeration<br>052|Average<br>Value<br>to Creditors<br>(AVC)'<br>041|
|---|---|---|---|
||PHILIPPINES|<br>0.40|.<br>052|.<br>046|
|COSTA<br>RICA|0.12|.<br>0.39|.<br>024|
|VENEZUELA|0.37|<br>0.61|.<br>050|
|URUGUAY|0.56|<br>074|.<br>060|
|TOTAL|0.37|.<br> <br>055|.<br> <br>044|
|Source:<br>Salomon<br>Br|others<br>and<br>Authors<br>c|.<br>'<br>alculations|<br>.|

> 3 divided stripping enhancements) 'The Average Value to Creditors (AVC) is defined as the post-operation value of the new portfolio. comprising post-operation debt claims operation.2is estimated The post operation price The pre operation by the models.asthe and payments face price market value is the average is of commercial value applicable the price right after the deal has teen finalized and bonds are exchanged, of debt in connection debt price after (column in the the deal 2 with buybacks month in (as also Table before 1) minus used the Brady net of rnw money for the the DRE (coLumn plan AVC) was anncunced minus provided, the 12 in Table prepayment as a fraction (March 2). See expressed as a stripped price. The stripped price equivalent 10, 1989).Lamdany of the face of collaterals and Underwood value of the debt tcolumn (1989) for altenative11 in TabLe (inclusive before 1),the of