rafmacalaba/datause-encoder-data huggingface dataset:
In line with the macroeconomic behavior, moderate poverty decreased from 2008 to 2018. However, in the same period extreme poverty increased slightly. As of 2018, Grenada’s poverty rate had decreased by almost 13 percentage points compared to 2008 (the previous year for which data were available). In contrast, extreme poverty in Grenada increased by 1.2 percentage points (figure 1, panel a). Regarding inequality, we observe that during this 10-year period, the Gini coefficient, which measures the inequality of the income distribution, increased from 0.37 to 0.40 (figure 1, panel b). This increase in inequality from 2008 to 2018 may imply some degree of deterioration of conditions for the lower part of the consumption distribution, which is also shown in the increase of the extreme poverty rate during this period.

**Figure 1 Poverty and Inequality Measures**

<!-- Start of picture text --> (a) Poverty and Indigence (%) (b) Gini Index<br><!-- End of picture text --> <!-- Start of picture text --> 37.7<br>32.1 0.45<br>0.40<br>24.8<br>0.37<br>12.9<br>2.4 3.6<br>1998 2008 2018 1998 2008 2018<br>Extreme poverty Total poverty Gini<br><!-- End of picture text --> _Sources_ : Government of Grenada, Central Statistical Office, Survey of Living Conditions and Household Budget Survey (SLCHBS) 1998, 2007–08, and 2018–19.

Grenada’s fiscal position has varied over the years, partly due to natural and external shocks. Following a modest performance in 2009–13, revenues and grant collection shifted to an upward trajectory in 2014, growing by an average of 11.2 percent annually until 2019. Taxes accounted for 83.2 percent of total government revenue between 2009 and 2021 (figure 2). Tax revenue comprises direct and indirect taxes levied on individuals and businesses. The direct taxes are taxes on income and profits and on property, while taxes on domestic goods and services and on international trade and transactions comprise the indirect taxes. For the purposes of this paper, we focus on the PIT under direct taxes and the value-added tax (VAT) and excise tax under indirect taxes, because these have the most significant impact on individuals and households while also being crucial income earners for the government, as shown in figure 3. PIT in 2021 accounted for 53.4 percent of Taxes on Income and Profits and 10.2 percent of tax revenue. The VAT accounted for 83.8 percent of Taxes on Domestic Goods and Services and 35.0 percent of total tax revenue in 2021. Because the VAT is charged to consumers at the point of purchase, it has implications for individual and household consumption. The excise tax is charged at a fixed rate per quantity of specific goods (imported and locally manufactured goods) referred to as “excisable goods” and accounted for 2.8 percent of tax revenue in 2021.

4
- 28 -

# _3. IMETODS FOR_ ESTIMATING THE sUDSTK XTABLxTT or FUELS

In studying the potential for fuel substitution<sup>there are two distinct</sup> approaches available. The econometric acoroach<sup>is essentially historical and</sup> relates actual experience of fuel switching, usually at a rather<sup>aggregate level,</sup> to the various factors which may have produced these<sup>changes. The enaJngerina</sup> annraach typically is more prospective, in the spirit of<sup>project appraisal, and</sup> seeks to describe for a specific situation the cost conditions under which one fuel or another would be used.

Econooetric 8tudies of Fuel Substitutability Many econometric studies of fuel switching and fuel use have been published and these form a natural starting point<sup>for an analyLsi of methods of</sup> measuring fuel substitutability. Interest in the use of fuels began with a series of studies in the 1970. which investigated the Lapacts of the rise in real energy prLces following the first oil shock.<sup>Acrucial point for such studies</sup> was the extent to which energy was a substitute or complement to the other inputs. The methodology developed ln these **studies** was later extended to disaggregating between the different fuels so that it became possible to estimate the degree to which one fuel was switched for another as well as the degree to which fuels were substituted in total against other factors of production.

All econometric studies have made certain assumptions<sup>about the nature of</sup> the use of energy which are central to the models<sup>developed but which are quite</sup> different in spirit from the approach of engineering studies, and it is important to highlight theme before the more detailed description<sup>of the approach is given.</sup> The central assumption is that there **is**<sup>a continuous degree of</sup> substitutability between the various inputs. Small changes in external<sup>forces,</sup> such as relative prices, will then produce small changes in the pattern of energy use and fuel choice. This assumption of continuous substitutability, although very convenient for modelling, does not correspond<sup>to a description of what</sup> happens at any particular site. In particular cases fuel prices may have to change a great deal before there is any switch to<sup>a different fuel, as will be</sup> discussed below. The principle defence for the econometric<sup>approach is that for</sup>
<!-- Start of picture text --> «oo<br>a<br>a<br>2<br>Lo<br>&<br>5<br>2<br>8<br>Ee<br>Fa<br>3<br>s<br>r=4<br>8<br>oN<br>a<br>a Zimbabwe<br>Congo, Dem Rep<br>o<br>1960 1980 2000 2020<br><!-- End of picture text -->
new production facilities than it is to refurbish existing facilities. Both of these factors would suggest that the competitiveness effects of tightening up environmental policy would not be large in developing countries. On the other hand, if there are increasing returns or learning spillovers in the abatement process, or if substantial investments in infrastructure are required, then marginal abatement costs could be higher in a developing country.<sup>7</sup> It is likely that the net result of these two effects would vary across pollutants and sectors; but at this stage we have little or no evidence on the issue.

The applicability of the empirical work on plant location to an international context is also open to question. For example, early studies used US state-level data and found that environmental policy had little or no effect on which state that a firm chose to locate a new plant. However, studies such as Becker and Henderson (2000) found that environmental policy does have an effect at the county level. One interpretation is that environmental policy does not have much influence on which state a firm chooses as a site for its production; but that once a state is chosen, the firm looks for a location within that state that is attractive from a cost perspective, and that at this stage environmental policy could matter. If this interpretation is correct, then the implications of studies that find that county-level regulations within the U.S. affect plant location may not have much relevance for the effects of environmental policy on international location decisions.

There is as yet very little work that attempts to test the strong version of the pollution haven hypothesis, which is that trade liberalization causes pollution-intensive industry to systematically move to countries with weak environmental regulation. Antweiler et al. (2001) calculate the elasticity of the increase in ambient SO2 concentrations with respect to an increase in openness to trade for a large set of countries. The strong version of the

> 7 Another way to pose the question is to ask whether the response to pre-existing regulations has given developed countries first mover advantages in the ability to respond to more stringent environmental regulations, or whether instead the opportunities to learn from the experience of developed countries allows developing countries to "leapfrog" past developed countries by more easily adopting environmentally friendly production techniques.  The leapfrogging literature is discussed later in the paper.

13
s� �1 �s� in the case with no explicit measure of natural resource rents, and s� �1 �s� �s� when such a measure does exist. s� is, in an important sense, too large when data on resource rents are lacking.

If we assume that we have no direct measure of natural resource rents then the traditional approach to TFP growth measurement has been to calculate,

# g� �g� ��s�g� �s�g��

This formalizes the statement above – TFP growth is measured as the difference between the growth rate of GDP and a weighted average of the inputs to production. In this formula the weights are, quite naturally, the share of the factors in value added, s� and s�.

If we have measures of the natural resource rents and so can measure the share of rents in value added s� and the growth rate of resource inputs g�, then s� is correspondingly lower and we can write the formula for TFP growth measurement including natural resource inputs as, g� �g� ��s�g� �s�g� �s�g�� Now the weighted average of inputs to production includes the contribution to natural resources. These two formulae are the basis for measuring TFP growth with and without natural resource inputs in this study.

Recent work at the OECD has provided an approach to treating natural resources as production factors, which they have applied to selected countries, mainly OECD member countries, using World Bank data on minerals and energy (Brandt et al. 2017; See Annex I for the OECD approach to TFP). An important finding, derived below and by Brandt et al. (2017), is that there is a systematic relationship between TFP growth measured using only factor data on capital and labor, and TFP growth that also measures natural resources as a factor of production. If we denote TFP growth excluding natural resources as g���� and with natural resources as g����, it will be shown below that Where s� is the GDP share of natural resource rents, while g� and g� are the growth rates of fixed capital and natural resources measured in volume terms. Given the high resource dependence of developing countries, this formula suggests there are potentially significant biases when measuring TFP growth in developing countries while ignoring natural resources as production factors.

# _Analytical framework_

The framework we use for estimating TFP growth with natural resources as a factor of production is inspired by the recent work of the OECD. In what follows we adapt the approach 4
<!-- Start of picture text --> Lao PDR (log difference)<br>3<br>2<br>A<br>0<br>-1<br>-.2<br>01 02 03 04 05 06 O7 08 09 10 11 12<br><!-- End of picture text --> Thailand (log difference) <!-- Start of picture text --> 3<br>2<br>4<br>0<br>-1<br>-.2<br>01 02 03 04 05 06 O7 08 09 10 11 12<br><!-- End of picture text --> Vietnam (log difference) <!-- Start of picture text --> 12<br>.08<br>.04<br>.00<br>-.04<br>-.08<br>01 02 03 04 05 06 O7 08 09 10 11 12<br><!-- End of picture text -->
# **Table 11: Rescaled SW responses using Post Vignettes**

|(1)<br>PCE|(2)<br>PCE +<br>vignettes|(3)<br>PCE +<br>household<br>controls||(4)<br>PCE +<br>household<br>controls +<br>vignettes||
|---|---|---|---|---|---|
|Coef.<br>se|Coef.<br>se|Coef.|se|<br>Coef.|se|
|Log expenditure per capita<br>0.466***<br>0.034|0.559***<br>0.035|0.553***|0.039|0.646***|0.040|
|_Household Demographics_||||||
|Female headed household||-0.157***|0.052|-0.163***|0.052|
|Age of household head||-0.011|0.009|-0.009|0.009|
|Age of household head squared||0.000|0.000|0.000|0.000|
|<br>Household size||0.062***|0.013|0.063***|0.013|
|Number of children||-0.003|0.017|0.004|0.017|
|Number of older adults||-0.047|0.045|-0.034|0.046|
|Number of migrants||0.051*|0.028|0.067**|0.028|
|<br>_Ethnicity (Reference: Tajik)_||||||
|<br>Uzbek||0.032|0.045|0.067|0.046|
|Russian||-0.422***|0.161|-0.431***|0.156|
|Other||-0.488***|0.124|-0.613***|0.145|
|_Education (Reference: No Education)_||||||
|Primary||-0.208*|0.113|-0.086|0.113|
|Basic||-0.218**|0.110|-0.184*|0.109|
|General Secondary||-0.155|0.105|-0.112|0.105|
|Special Secondary||-0.147|0.112|-0.083|0.113|
|<br>Technical Secondary||-0.154|0.116|-0.089|0.116|
|<br>Higher Education||0.091|0.113|0.162|0.113|
|<br>Graduate School||0.602**|0.257|0.468*|0.253|
|_Employment Characteristics of Household, Head’s_|_Occupation (Refere_|_nce for Occu_|_ation: U_|_nemployed)_||
|<br>Number of employed||<br>-0.012|<br>0.018|<br>0.007|0.018|
|<br>Agriculture / Fishing / Forestry||0.057|0.065|0.041|0.066|
|<br>Manufacture / Mining||0.034|0.088|0.034|0.090|
|Services||0.333**|0.133|0.404***|0.124|
|Construction||-0.095|0.076|-0.070|0.078|
|Public Administration / Education / Health||0.097|0.067|0.108|0.067|
|Sales and Services||0.201***|0.062|0.229***|0.063|
|Other||-0.084|0.103|-0.142|0.114|
|_Agriculture (Reference for holdings: No Land)_||||||
|<br>Small Holding||0.055|0.064|-0.006|0.066|
|Medium Holding||0.113|0.080|0.071|0.080|
|<br>Large Holding||0.135*|0.077|0.133*|0.079|
|<br>Any livestock, poultry, etc||-0.004|0.055|-0.005|0.058|
|<br>_Geography (Reference: Dushanbe)_||||||
|<br>Sogd Urban||-0.099|0.067|-0.086|0.070|
|<br>Sogd Rural||-0.150*|0.077|-0.200**|0.078|
|<br>Khatlon Urban||-0.497***|0.085|-0.287***|0.089|
|Khatlon Rural||-0.525***|0.072|-0.527***|0.072|
|RRP Urban||-0.242**|0.108|-0.316***|0.109|

24
**Table 1D:** Summary Statistics: Consumer Pyramids Data

||||**Panel A:**<br>**Rural**<br>**Adults**|||
|---|---|---|---|---|---|
|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|Female|0.47|0.5|0|1|388273|
|Literate|0.73|0.44|0|1|388273|
|Age|35.33|13.97|15|65|388273|
|LFP|0.43|0.5|0|1|388273|
|Muslim|0.1|0.3|0|1|388273|
|SC/ST|0.34|0.47|0|1|388273|
|Unhealthy Individual|0.02|0.14|0|1|388273|
|On Medication|0.02|0.12|0|1|388273|
|Has Cell Phone|0.56|0.5|0|1|388273|
|Has Health Insurance|0.03|0.18|0|1|388273|
|Has Bank Account|0.68|0.46|0|1|388273|
|Has Credit Card|0.01|0.08|0|1|388273|
||||**Panel B:**<br>**Rural**<br>**Workers**|||
|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|Unemployed|0.01|0.09|0|1|167582|
|Farm|0.53|0.5|0|1|167582|
|Non-Farm|0.47|0.5|0|1|167582|
|Non-Farm Informal|0.3|0.46|0|1|167582|
|Non-Farm Formal|0.16|0.37|0|1|167582|
|Wage Labour|0.31|0.46|0|1|167582|
|Small Business|0.02|0.15|0|1|167582|
|Business Owner|0.04|0.2|0|1|167582|
|Self-Employed Professional|0.04|0.18|0|1|167582|
|White Collar|0.05|0.22|0|1|167582|

_Notes_ : This table presents summary statistics based on the Consumer Pyramids data in 2015. Panel A restricts the sample to rural individuals aged between 15 and 65; Panel B restricts the sample to rural individuals aged 15-65, who also participate in the labour force. _Wage Labour_ combines wage labourers and industrial workers; _Non-Farm Informal_ includes wage labourers and small business owners; _Non-Farm Formal_ includes industrial workers, business owners, self-employed profesions and white collar workers.

42
where the subscripts _c_ and _t_ represent country and time period, respectively. _I_ represents informal employment, proxied by the ratio of self to total employment, as reported by the International Labor Organization. _Y_ denotes the average level of income, as measured by the log of per capita GDP. _Flex_ represents business flexibility, proxied by the Fraser Institute index of credit, labor, and regulatory flexibility. _Law_ measures the enforcement of contracts and the prevalence of the rule of law and the efficiency of the police and judicial systems, proxied by the International Country Risk Guide index of law and order. Finally, _Govex_ is the ratio of government expenditures to GDP and attempts to measure the government’s ability to monitor and enforce formal taxes and regulations; we assume, therefore, that this ability is linked to the availability of government’s financial resources. The variables _Flex, Law,_ and _Govex_ are measured as country averages due to their stability over time and, for _Flex_ and _Law_ , to the incompleteness of their data in the time dimension.

# Short Run

As mentioned above, the short run is modeled as an error-correction process, where informal employment changes in response to output shocks and in order to close the gap that separates it from its long-run equilibrium level. A simple formulation of the short-run process is given by: where _LRdev_ represents the deviation of informal employment from its trend value, as derived from the long-run equation; and ∆is the difference operator denoting the (proportional) change with respect to the previous year’s value.<sup>3</sup> In this formulation the

> 3In the case of informal employment, the proportional difference is computed as the absolute difference divided by the previous year’s value: ( _It − It−_ 1) _/It−_ 1. In the case of GDP per capita, the proportional difference is simply computed as the difference of the logs.

13
Policy ReseaRch WoRking PaPeR 4665

# **Abstract**

Multidimensional auctions are a natural and practical solution when auctioneers pursue more than one objective in their public-private-partnership transactions. However, it is difficult to achieve auction efficiency with multiple award criteria. Using auction data from road and railway concessions in Latin America, the probability of renegotiation this paper estimates by a two-stage least squares technique with a binary selection in the first-stage regression. The findings show that auctioneers tend to adopt the multidimensional format when the need for social considerations, such as alleviation of unemployment, is high. This implies that such political considerations could hinder efficiency and transparency in auctions. The analysis also shows that the renegotiation risk in infrastructure concessions increases when multidimensional auctions are used. Rather, good governance, particularly anti-corruption policies, can mitigate the renegotiation problem.

This paper—a product of the Economics Unit, Finance, Economics and Urban Development Department—is part of a larger effort in the department to understand and examine efficiency and effectiveness in public infrastructure procurement. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at aiimi@worldbank.org.

_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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16 We next turn to Tables 7 and 8, which present data on the size of budget deficits/surpluses for different government levels.

**Table** 7. **Budget Balance by Level of Government as Percent of GDP, on a Cash Basis**

|**Budget Balance / Year**|**1992**|**1993**|**1994**|**1995**|**1996**|
|---|---|---|---|---|---|
|**Consolidated Budget Balance**|**-18.77**|**-9.43**|**-9.18**|**-4.54**|**-8.41**|
|- federal|-20.56|-10.59|-9.80|-4.21|-7.61|
|- consolidated regional|1.79|1.15|0.62|-0.34|-0.80|
|-regional|0.93|0.72|0.27|-0.28|-0.67|
|- local|0.86|0.43|0.35|-0.05|-0.13|

**Table 8. Budget Balance before Transfers by Level of Government as Percent of GDP, on a Cash Basis**

|**Budget Balance / Year**|**1992**|**1993**|**1994**|**1995**|**1996**|
|---|---|---|---|---|---|
|**Consolidated Budget Balance**|**-18.77**|**-9.43**|**-9.18**|**-4.54**|**-8.41**|
|- federal|-19.15|-8.04|-6.20|-2.15|-5.03|
|- consolidated regional|0.39|-1.40|-2.98|-2.40|-3.37|
|-regional|2.31|N/A|0.08|0.37|-0.25|
|- local|-1.42|N/A|-2.44|-2.38|-2.84|

Between 1992 and 1996, the federal budget deficit, measured on a cash basis, fell from 20.6% of GDP in 1992 to 7.6% in 1996. At the same time, subnational governments have succeeded to maintain cash budget surpluses up to 1995, when both local and regional governments run into deficits. In 1996, local governments deficit as percent of GDP stood at 0.1 %, whereas the corresponding figure on regional level was 0.7%.

# 5. Decentralization at the regional level

This section is focused on the distribution of tax assignments and expenditure responsibilities between local and regional governments in Russian regions. In contrast to the previous section that analyses relative shares of three government levels, tables below describe a relative role of local governments in consolidated regional budgets, i.e. excluding the federal level.

Table 9 presents average values of local governments' shares in the four main shared taxes: the Profit Tax, the VAT, the PIT and Excises, together with selected summary statistics for the period 199296. Over this period the collective share of these four taxes in local governments' total tax revenues fell from 87.4% in 1992 to 58.9% in 1996.
income losses were greatest among the poorest households, the estimates are not statistically significant.

**Table 6. Correlates of Total Income Decline**

||Total inco<br>|me decline<br>|
|---|---|---|
|Sample|Household Head’s<br>Work and Sector|Household<br>Business Sector|
|Self-employed, farm<br>Self-employed, non-farm<br>Temporary/seasonal workers|-0.069<br>[0.102]<br>0.197<br>[0.080]**<br>0.348<br>[0.071]***||
|Own farm business||0.101<br>[0.111]|
|Own non-farm business<br>Own farm and non-farm business||0.262<br>[0.091]***<br>0.167<br>[0.090]*|
|Between $2.15 and $3.65|-0.145|-0.058|
||[0.153]|[0.115]|
|Between $3.65 and $6.85|-0.029<br>[0.124]|-0.015<br>[0.088]|
|Above $6.85|-0.030|-0.016|
||[0.132]|[0.098]|
|Female<br>Adult|-0.005<br>[0.049]<br>0.294<br>[0.129]**||
|Constant|0.605<br>[0.177]***|0.715<br>[0.109]***|
|Countries<br>Observations|ETH, MWI, NGA<br>972|ETH, MWI, NGA<br>1,106|
|R-squared|0.257|0.178|
|<br>Robust standard errors in brackets. Omitte<br>male, young, don’t own business.<br>*** p<0.01, ** p<0.05, * p<0.1|d categories: Wage emplo|yee, less than $2.15,|

# _Which types of household enterprises were more likely to report income declines?_

We now limit the sample to household enterprises and the self-employed. We continue to use both definitions of household enterprises. The household business definition allows us to look into differences among farm and non-farm enterprises, while the household head definition also allows us to explore how gender and age of self-employed household heads are correlated with the propensity of income declines. Table 7 shows the correlates of farm and non-farm income declines from linear probability models when pooling data from the four countries with complete data: 17