rafmacalaba/datause-encoder-data huggingface dataset:
Table 10: Impact on household per capita saving by poverty status and age of firstborn child

|**Dependent variable:**|**(**|**Household per capit**<br>**fnancial institutions + c**|**a saving in 200**<br>**ash + interest **|**7**<br>**earned)**||
|---|---|---|---|---|---|
||Above poverty line (APL)<br>(1)|Below poverty line (BPL)<br>(2)|Firstborn _≤_5<br>(3)|Firstborn _≤_10<br>(4)|Firstborn _≤_15<br>(5)|
|_Firstborn girl_ * _Expected dowry_|1881.66**|71.74|1353.38|1139.84**|707.71**|
||[679.91]|[114.13]|[996.04]|[417.54]|[325.12]|
|_Firstborn girl_|-5419.96|-74.37|-7955.71|-4666.32|-1899.76|
||[5895.24]|[422.49]|[5048.26]|[3293.17]|[3425.55]|
|N|1,461|1,172|795|1,510|2,328|
|Dep var mean for Firstborn boy|1415.33|135.28|836.10|856.67|669.87|
|CST Fixed efects|x|x|x|x|x|

NOTES: Columns (1) and (2) report the coefficients for specification (2) separately for APL and BPL households. The BPL status is measured at the time of survey. Columns (3)-(5) split the sample by the age of the firstborn child. The sample is restricted to households where all children have the same mother. The dependent variable is the sum total of per capita savings in financial institutions, cash savings, and interest earned in 2007. Each column is a separate regression. _Firstborn girl_ indicates that the first-born child of the household is female. Expected dowry (in INR 10,000) for a female (male) child is defined as the average dowry paid (received) by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Dep var mean is the mean of the dependent variable for the firstborn boy households. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.
**Table 4: Mis-targeting Between Different Allocation Criteria**

||**_Actual_**<br>**_DAU_**<br>**_2006_**|1. 2006<br>Fiscal<br>Gap<br>Formula<br>Design|2. 2005<br>Fiscal<br>Gap<br>Formula<br>Design|3. 2006<br>FG<br>Formula<br>with Exp<br>Need<br>Multiplied<br>byCosts|4. Poverty<br>Formula|5. Wage-<br>based<br>Distributi<br>on|6. 2006<br>FG<br>Formula<br>with<br>Hold-<br>harmless|7.<br>Populatio<br>n Formula|
|---|---|---|---|---|---|---|---|---|
|**_Actual DAU 2006_**<br>**_Allocation_**|**X**||||||||
|#1 : Pure Formula 2006|0.03|X|||||||
|#2 : Pure Formula 2005|0.02|0.01|X||||||
|#3 : Multiplicative Cost|0.05|0.03|0.04|X|||||
|#4 : Poverty|0.03|0.05|0.02|0.1|X||||
|#5: Wage Distribution|0.005|0.05|0.06|0.13|0.14|X|||
|#6: Pure Formula 2006 +<br>Hold Harmless|0.03|0.04|0.06|0.12|0.16|0.01|X||
|#7: Population|0.04|0.07|0.05|0.09|0.03|0.05|0.07|**X**|

Source: Authors’ calculation In addition to overall mis-targeting, the analysis of fiscal transfer should be concerned with those individual districts that are under-resourced. The mirror image to under-resourced districts, given an overall vertical pool, is the districts that receive too much transfer. Localities where the problem of too little transfers is most chronic would also be those where public services may not be provided owing to an excessive budget constraint, given prevailing expenditure roles & responsibilities. We draw on the poverty literature, and construct a further measure akin to a Foster, Greer and Thorbecke poverty gap index by summarizing the deviations from ideal allocations (in Figure 3 the ratio is 1) for those below 1 (Deaton 1997):

# **Fiscal Resource Gap* = 1/N Σ (1 – Xi)**<sup>**2**</sup> **, where X≤1** (1)

With (i) being the district, X the ratio, and N the total number of localities. The squared implies a greater weight for outliers, but can be suitably increased or decreased. Table 4 presents the pair-wise fiscal resource gap measure for all comparisons across the 20
Where INDIVIDUAL refers to data provided only by a single institution on individual _i_ . For example, total debt with a given bank (and not total system wide debt) is employed, and initial rating with a given bank (and not the average or worse rating across the system)<sup>9</sup> .

We use the scoring model to predict default probabilities for cases when banks do and do not share information. In Table 5 we construct tables using the same methodology as in Table 4, to demonstrate gains in access to credit and improved portfolio quality for a large and a small financial institution<sup>10</sup> . Information sharing is shown to significantly improve a bank’s ability to determine the likelihood of default for its customers. For a target approval rate of 40%, access to data from a PCR reduced the predicted default rate by 41% for a large bank, from 2.2% to 1.3% of the relevant portfolio. The results are even more impressive when repeated with a smaller financial institution – the expected default rate falls by 78% when PCR data are added to the institution’s own information for a targeted approval rate of 40%. Since larger institutions have a larger and potentially more varied portfolio to begin with, it is logical that smaller institutions would benefit more from sharing information. As the target approval rate is increased, the difference between the gains for a large versus a small bank is virtually eliminated: at a 60% target approval rate both institutions lower defaults by about 35% and at a target 80% approval rate by about 20%.

Data sharing can also enable both large and small lenders to increase their lending activities for a given level of risk or default. Panel B of Table 5 presents the results of this exercise with Argentine data where a default rate is targeted and then the number of borrowers who could be approved for that level of default is calculated. If a large bank was targeting a 5% default rate, then without information sharing it could extend credit to approximately 75% of the applicant pool compared with 84% if they had access to the PCR data – an increase of approximately 12%. For the small bank, the increase is even greater: approximately 18%.

> 9 We acknowledge that there may be an overstatement of the case for sharing information, as individual bank is not limited to the information supplied to the credit registry.  However, the variables used and supplied to the credit registry are normally used for credit scoring purposes and typically have a positive marginal effect in terms of the predictive power of scoring models and hence we believe that this remains a useful demonstration of the potential gains from sharing information.

> 10 We report results for arbitrarily selected large and small bank.  Similar results hold for other financial institutions.

11
Each firm produces a differentiated product with the standard Cobb-Douglas production function: s s s s 1−ααss ααss where s stands for firm-specific productivity; YY = AA LLs is the firm's laborKK s is the firm’s capital; and is the sector-specific capital share. AA LL ; KK Each firm maximizes current profits: ss αα s s s s s s where s s is the firm's value added (which is the firm's revenue minus the cost of intermediate YYs KKs ππ = (1 −ττ )PP YY −ww LL −(1 + ττ )RRKK inputs), and s and _R_ are the cost of one unit of labor and capital, respectively. The term denotes firm-specific output distortions that reduce firms' revenues. The firm-specific "capital" PP YY YYs distortions, which raise the cost of capital (relative to labor), are denoted as ww . ττ HK differentiate two productivity measures: TFPQ, which captures “physical productivity”; and KKs ττ TFPR, which captures “revenue productivity”: <u>s s s</u> T s = ~~1−α α~~ and T s = ~~1−α α~~ s s s s YY PP YY PPTT LL αssKK αss PPRR LL αssKK αss In the absence of distortions, TFPR should not vary across firms within each sector. This is so because more capital and labor are allocated to firms with higher physical productivity (TFPQ) such that their higher output results in a lower price, s ; this occurs up to the point where T s is equalized for all firms in the sector. On the contrary, it is normal for T s to vary across firms PP PPRR because different firms may have different productivity levels. PPTT Assuming monopolistic competition and CES technology, HK derive T s in terms of revenue data as: PPTT T s s σσ ~~,~~ σσ−1 s<sup>YYs</sup> s<sup>)</sup> ααss 1−ααss PPTT = AA = κκ<sup>(PPs</sup> where is a function of output quantity and price and can be normalized to 1. In line with HK, KK LL let’s define the efficient sector's productivity level (when all marginal products are equalized) as: ��� 1 κκ =(∑ s ) . MMss σσ−1 σσ−1 TFPQ is calculated from AAss ss=1 AA s , which contains elements of distortions, and is typically not observed in the data: PP )<sup>ααss</sup> = s s ) ~~�~~<sup>RR</sup> ~~�~~ ααss �<sup>wws</sup> ~~�~~ 1−ααss kks σσ (1 + ττ PP yys ss ss σσ−1 AA (1 −ττ αα 1 −αα 6
developed countries. In recent years, biogas cookstoves are also gaining popularity in rural areas of developing countries.

The conversion efficiency<sup>6</sup> of household cookstoves also varies widely by energy sources. The wide range of cookstove conversion efficiencies reported in the literature is compiled from variety of site-specific circumstances in developing countries (Table 1). Cooking fuels also differ in their energy densities. Modern fuels have high energy content per kg of fuel used, while traditional biomass fuels have low energy content. The use of biomass energy in inefficient or open stoves is considered a traditional way of cooking. On the other hand, natural gas, LPG, kerosene, electricity, and biomass energy used in efficient or less polluting stoves are considered modern ways of cooking. Other examples are also used in the literature, but the general idea is that traditional ways of using energy for cooking typically is inefficient and polluting, whereas the opposite is true of modern energy use for cooking. It is complex but important to understand how and why these different types of energy sources are used for cooking in varieties of cookstoves in different parts of developing countries.

**Table 1** : Typical conversion efficiency range of household cookstoves<sup>7</sup> by energy sources

|Fuel source|Energy content<br>(MJ/kg)|Conversion efficiency range<br> (%)|
|---|---|---|
|Traditional (open fire or mud) stoves|||
|Fuelwood|16|13-18|
|Crop residue (straw, leaves, grass, maize, wheat)|13.5|9-12|
|<br>Dung|14.5|12|
|Charcoal|30|10-22|
|Improved biomass cookstoves|||
|Fuelwood|16|23-40|
|Coconut shell (gasifier)|15.7|33-36|
|Crop residue (maize, wheat)|13.5|15-19|
|<br>Charcoal|30|20-35|
|Biogas|22.8 (MJ/m<sup>3</sup>)|50-65|
|Advance cookstoves|||

6 The analysis of cooking efficiency is challenging due to variations in individual appliances and the situations where different foods are prepared or different cooking styles are used (see e.g., Hager and Morawicki, 2013). In the literature, cookstoves efficiency, in general, is reported either as cooking, conversion, thermal, appliance or end-use efficiency. They all mean the same.

7 Different stove terminologies are used in defining cookstoves (World Bank, 2011b; Barnes et al., 2012). It is also difficult to distinguish between "improved" and "advanced" stoves. There are also several classifications of biomass cookstoves (see Kshirsagar and Kalamkar, 2014). In this paper, we refer to biomass based open-fire or mud stoves as "traditional" stoves. "Improved biomass" cookstoves refer to cookstoves that have better fuel efficiency and/or lower emissions than the traditional stoves. "Advanced" cookstoves refer to cookstoves that are freely available in the market and they are based on non-biomass energy sources.

10
Besides the general shortcomings of the PLSS for the analysis of training, (see Arriagada 1989) there is a gender-specific difficulty with the data set. The majority of women interrupt their labor force participation at some point in their lives. The lack of complete occupational histories is a significant constraint for estimating women's returns to education and training, because the survey does not offer any direct measurement of accumulated work experience. This is relevant because women who work may adjust their labor force activities to meet family needs in ways that may affect productivity, and therefore wage rates. Thus, women may restrict job locations, work schedules and number of hours of work. Using the usual proxy for experience (age - 6 - years of schooling) for prime-age males is probably appropriate, but using it for women is inadequate. As Mincer and Polacheck (1974) discuss, women tend to suffer more unemployment, have lower labor force participation, and work fewer hours when employed than men. Blinder (1976) illustrates how the use of the usual proxy for experience in inappropriate circumstances implies that every coefficient in the wage regression will be biased, and that no results on the direction of the bias can be predicted.

Table A-1 Annex 1 presents the definitions and measurement of the variables used in the analysis and indicates how they enter the training (TR), employment (LF) and wage (W) equations.

# III. OVERALL PATTERNS OF TRAINING

This section describes the basic characteristics of the women in the sample according to their training and employment status, and reviews training activities by provider of training.

In Peru, training for direct employment as well as upgrading of the employed labor force is carried out by several decentralized public agencies with financial and educational autonomy, and by private or proprietary institutions. The administration of publicly provided training programs is

- 11 -
significant effect of the satisfaction with the government on the propensity to work without contract in Estonia, Latvia, Romania, Russia, Norway, Austria, the UK, Ireland, and Israel (these results are available on request).

# **6 Conclusion**

In this paper we have compared the prevalence of informal employment in 30 European countries using data from the European Social Survey. Overall size of informal employment decreases from the South to West to East to North, but the median prevalence of dependent informal employment is higher in the East than in the West. Yet there is a strong heterogeneity within these geographical areas. In particular, Western Europe is split into highly informal part (Ireland, the UK, and Austria) on one hand and the continental part (without Austria), where work without contract is quite rare, although informal selfemployment is more prevalent than in most of Eastern Europe. We have found a substantial within-country regional variation in informality in all countries except for Nordic countries, Hungary, Romania and Slovenia.

In all European regions, the dependent informality rate is inversely related to skills (measured in terms of either schooling or occupation). Both in Southern and in Western Europe, the highest dependent informality rate is found among immigrants from CEE and former Soviet Union, whilst in Eastern Europe this group is second after local born minorities without immigrant background. In Southern Europe (especially in Spain, Portugal, and Greece) immigrants not covered by the ―free movement of labor‖ provisions are more likely to work without contract than both natives and covered immigrants. Both in Eastern and Southern Europe, as well as in highly informal part of Western Europe, these not covered immigrants have significantly higher propensity to work without contracts also after controlling for individual characteristics. Students and persons with permanent health problems also are more likely to work informally, other things equal.

Our findings lend support to theoretical arguments that apart from low productivity, informality drivers include ―informal‖ social capital, low value placed on job security, and preference for flexible working time and/or substantial volatility of desired working hours.

We provide evidence that exclusion, lack of human capital and discrimination play important role in pushing employees into informality, whilst this seems not to be the case for informal self-employed. Both on average and after controlling for a rich set of individual characteristics, informal employees in all parts of Europe are having the largest financial difficulties among all categories of the employed population (yet they fare much better than 45
# THE TAXATION OF NATURAL RESOURCES: PRINCIPLES AND POLICY ISSUES

## by

## **Robin Boadway and Frank Flatters**

## **Queen's University, Kingston, Canada**

## **I.** INTRODUCTION

The raising of revenues from the economric activity associated with the exploitation of natural resources is virtually a universal phenomenon among the nations of the world. This can take several different forms. It may consist of taxes specific to the resources in question. It may involves special mreasures applicable selectively to the resource industries within more general systems of taxation (such as the corporation income tax). Or, it may consist of varying degrees of public owners.iip of resource property rights ranging from ownership of the resource being exploited which are sold or leased to private sector resoarce firms, to joint public-private ventures, to outright public ownership and operation of the resource firnms themselves. Our purpose in this study is to concentrate on the use of taxation measures by the public sector to extract revenues fromn resources industries, especially taxes specific to the resource sector. However, we will not be able to do so in isolation frorn these other measures, some of which represent relatively close substittutes for taxation. In this introductory section we set the stage for the subsequent analysis by outlined some general features of resource industries and resource taxation found across countries.

It is useful to begin with some discussion of the types of resources themselves. Natural resources consist of the various materials endowed uipon a nation by nature which are u.seful in the production of goods and services. It is cornmon to classify natural resources as being of two broa(d types, though the distinctiorl is sometimes ambiguous. They are the following: _Renewable Resources._ Renewable resources are those that can generate a continuous flow of output for an indefinite period of timrie. They include such things as fisheries, forests, hydro-electricity, water supplies, clean air and agricultural land. In each case, as some of the resource is taken for econormic use, the resource can replenish itself by natural or artificial means A characteristic feature of renewable resources is that the level of flow of resource that can be sustained is an endogenous variable. It can depend upon the stock of the resource that is maintained, upon natural rates of renewal of the resources (e.g.. biological rates of growth) and upon coinservation and husbandry practices of those exploiting the resources (e.g., replantirng of forests.

1
3. Mincer equation: Log (Labor income) = f(X), where X stands for individual and household characteristics After estimating the parameters of these three equations, it is possible to estimate poverty and social exclusion indicators for all households in Poland. For this analysis, we use the 2010 Poland Household Budget Survey (HBS 2010).<sup>18</sup> The overall poverty impacts of meeting the 2020 target of 85 percent pre-school enrollment of 4-year-olds are not large. The at-risk-of-poverty rate for the entire 2010 population—estimated at 18.2 percent—would decrease by 0.27 percentage points. However, the impacts on specific sub-groups are significant. To illustrate, three scenarios are considered below (see Table 11 and Table 12). First, 20- to 59-year-old women with children observe a 5.1 percentage point increase in employment rates and a 1.3 percentage point decrease in poverty rates when childcare increases from 30 to 85 percent of 4- year-olds. Second, 20- to 59-year-olds with children observe a 3.0 percentage point increase in employment rates and a 0.7 percentage point decrease in poverty rates when childcare increases from 30 to 60 percent in large cities first. Third, 20- to 59-year-olds with children observe a 3.2 percentage point increase in employment rates and a 1.2 percentage point decrease in poverty rates when childcare increases from 30 to 60 percent in rural areas first.

# **Table 11: Simulated employment rate impacts resulting from changes in childcare enrollment among women (aged 20 to 59) with children (aged 2 to 6)**

|**Target scenarios**|Enrollment<br>(%)|Employment<br>(%)|_Percentage_<br>_point change in_<br>_employment_|_Employment_<br>_relative_<br>_change_|
|---|---|---|---|---|
|No policy|30.1|55.2|_x_|_X_|
|85% of 4-year-olds|79|60.4|_5.14_|_1.09_|
|60% of all children—increase childcare access in|||||
|large cities first|60|58.3|_3.03_|_1.05_|
|60% of all children—increase childcare access in<br>rural areas first|60|58.5|_3.25_|_1.06_|
|100% ofallchildren|100|62.7|_7.42_|_1.13_|

_Source:_ Bonch-Osmolovskiy and Topińska (2012).

**Table 12: Simulated poverty and social exclusion impacts resulting from changes in childcare enrollment among households with women (aged 20 to 59) with children (aged 2 to 6)**

|**Target scenarios**|Income<br>poverty<br>(%)|Low<br>work<br>intensity<br>(%)|_Percentage_<br>_point_<br>_change in_<br>_income_<br>_poverty_|_Low work_<br>_intensity_<br>_abs (pp)_<br>_change_|
|---|---|---|---|---|
|No policy change (actual figures)|21.2|6.2|x|X|
|85% of 4-year-olds|19.9|5.4|_−1.34_|_−0.80_|
|60% of all children—increase childcare access in large cities first|20.5|5.9|_−0.68_|_−0.28_|
|60% of all children—increase childcare access in rural areas first|20.0|5.7|_−1.21_|_−0.54_|
|100% of all children|19.2|5.2|_−2.04_|_−1.01_|

18 The HBS 2010 rather than EU-SILC is used because it provides more recent information on social benefits, including childcare-related variables.