rafmacalaba/datause-encoder-data huggingface dataset:
**<mark>Figure 6: Uplift costs</mark>**

<!-- Start of picture text --> Incentive scheme<br>introduced, during<br>700 1994/ 95<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Operational outturn Ancillary services Both categories combined<br>(£m, 1999 prices)<br>TSS costs excluding NGC incentive payments 1990/ 91 1991/ 92 1992/ 93 1993/ 94 1994/ 95 1995/ 96 1996/ 97 1997/ 98 1998/ 99<br><!-- End of picture text -->

**_Source: OFGEM documents such as NGC System Operator Incentives, Transmission Access and Losses Under NETA, consultation document (1999), available at_** **_<u>http://www.ofgem.gov.uk/temp/ofgem/cache/cmsattach/1447_nsodec.pdf</u>_**

Clearly, NGC gained substantially from the scheme. The incentive payment in 1994/95 was £22.5m and in 1995/96 £25m (in nominal prices which was the maximum allowed payment)<sup>8</sup> . However, the reduction in the two cost categories shown above from 1993/94 to 1995/96 was almost £260m, about 4% of the total cost of wholesale electricity in England and Wales. Thus, although NGC might appear to have been the principal beneficiary, the benefits to customers were substantially greater. Furthermore, by exposing its ability to manage these cost categories, NGC provided Offer with information it could use to set tougher targets in the future.

> 8 It is worth noting that the first payment was below the maximum available while the second was at the maximum.  Even in this second year, NGC’s performance did not result in costs substantially below the levels at which the maximum payment was triggered.   If this had happened, it would be reasonable to conclude that incentives were not the principal driver of the cost reduction.  The receipt of two payments close to the maximum suggests (but does not prove) efficient response to incentives on NGC’s part.

23
<mark>Public perceptions of climate change are the major determinants of countries’ climate and energy response. Understanding the drivers of concerns about climate change is essential to designing effective communication strategies, democratic policies, and socially robust technologies</mark> <u><mark>(Whitmarsh and Capstick 2018).</mark></u> <mark>This paper investigates recent trends in attitudes toward climate change in Eastern and Central Europe and Central Asia based on data from the 2016 and 2023 rounds of the Life in Transition Survey (LiTS). It shows that despite increasing attention from policy makers, scientists, and the media, the proportion of respondents willing to pay to combat climate change declined significantly between the two rounds. We propose and test several hypotheses that could explain the deterioration of public support for climate change policies in the region.</mark> <mark>This paper contributes to the literature on perceptions of climate change in several meaningful ways. I</mark> t is among the first to quantitatively demonstrate the growing influence of right-wing populism on support for climate policies across countries in Europe and Central Asia. It systematically tests several hypotheses to explain the decline in climate policy support, including competing priorities, personal experiences with extreme weather, and political polarization. The paper contributes to a better understanding of factors influencing public opinion on climate change. The paper is also one of the first to draw on data covering the period of profound transformations of the political and economic landscape in Europe and Central Asia, including as a result of the COVID-19 pandemic, the sharp increase in the cost of living, and the Russian Federation’s invasion of Ukraine.

<mark>The paper is organized as follows. Section 2 reviews the literature. Section 3 outlines our hypotheses. Section 4 describes the data and presents some descriptive results. Section 5 presents the main results of our multivariate analysis. Section 6 summarizes the paper’s main findings and proposes policy implications.</mark>

# **2.** **<mark>Literature review</mark>**

The decline in public concern about climate change in the late 2000s is reported by Ratter et al. <u>(2012)</u> in Germany, where the proportion of people who feel that climate change poses a threat almost halved (from 17 to 9 percent) between 2008 and 2011. Climate skepticism was reported to be on the rise in Great Britain (BBC 2010). A continuous decrease in concern about climate change 3
# **Table 11: Results with Core Spending and Non-core Expenditures (panel OLS)**

|**_Dependent variable: Growth rate_**|**_of GDP per capita_**<br>||||
|---|---|---|---|---|
||Fast growing<br>countries<br>**(1)**|Comparison<br>group<br>**~~(~~2)**|Whole sample<br>pooled<br>**~~(~~3)**|Whole sample<br>with dummies<br>**~~(~~4)**|
|Private investment|0.08|0.114|0.163|0.106|
|(% of GDP)|~~(~~1.297)|~~(~~1.009)|(3.525)***|(2.048)**|
|Initial human capital|0.012<br>~~(~~1.353)|-0.017<br>(-1.435)|-0.011<br>(-1.804)*|-0.003<br>(-0.363)|
|Tax revenue<br>(% of GDP)|-0.158<br>(-1.474)|-0.045<br>(-0.363)|-0.036<br>(-0.436)|-0.068<br>(-0.833)|
|Other revenue|-0.306|-0.372|-0.105|-0.206|
|(% of GDP)|(-2.449)**|(-1.644)|(-1.434)|(-2.437)**|
|Core expenditure (-1)|0.449|-0.072|-0.001|...|
|(% of GDP)|(2.675)***|(-0.765)|(-0.018)||
|DFAST*Core expenditure (-1)<br>(% of GDP)|...|...|...|0.169<br>(1.698)*|
|DCOMP*Core expenditure (-1)|...|...|...|-0.019|
|(% of GDP)||||(-0.274)|
|Non-core expenditure (-1)|-0.052|0.112|0.099|0.086|
|(% of GDP)|(-0.262)|~~(~~1.011)|~~(~~1.185)|~~(~~1.039)|
|Budget surplus<br>(% of GDP)|0.298<br>(2.813)***|0.276<br>(2.332)**|0.167<br>(2.367)**|0.225<br>(3.034)***|
|Inflation - consumer price index|-0.212<br>(-5.631)***|-0.013<br>(-1.067)|-0.024<br>(-2.471)**|-0.017<br>(-1.733)*|
|Growth rate of GDP per capita (-1)|0.364|0.251|0.280|0.272|
||(3.375)***|(3.122)***|(4.363)***|(4.277)***|
|No. of observations|122|167|289|289|
|Adjusted R2|0.396|0.102|0.312|0.322|

Note: The estimation method is the ordinary least squares for panel data. Annual data are used. (-1) indicates variables lagged one period. D-FAST is the dummy variable which is 1 for fast growing countries and 0 otherwise. DCOMP is the dummy variable which is 1 for the comparison group and 0 otherwise. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients.

42
this effect is not significant. A decrease in concentration after closed cases are economically meaningful. Nocke and Whinston (2022) for example find that harms to consumers may outweigh potential efficiency gains from mergers when the HHI increases by 200, among industries that already have a low HHI of 1500-2500.

Changes in informality can explain this unexpected result. Columns (3) and (4) show specifications where the left-hand side variable is the SDID change in the concentration ratios, respectively CR4, the sales share of the four largest firms, and CR1, the sales share of the largest firm, each expressed as a percentage ranging from 0-100. The terms _τ_ 0<sup>_CR_4</sup> and _τ_ 0<sup>_CR_1</sup> are quantitatively small and statistically insignificant, suggesting the decrease in the HHI in closed cases is not coming from changes in the market shares of the largest four firms or the largest firm declining by a large amount and must be due to decreased concentration of sales among smaller (informal) firms. Columns (5) and (6) report specifications where the outcome change is in measures of legal and illegal informal employment proposed by Levy Algazi (2010) that can be measured in the Economic Census. Legal informal employment is unremunerated labor,<sup>16</sup> illegal informal employment is remunerated labor without employer contributions to social security. In Column (5) the left-hand side variable is the change in the percentage of workers in establishments with workers but no recorded remuneration. This indicates that workers are compensated out of profits, as in the case of a family business. Here we see _τ_ 0 _legalinformal_ = 1.445 (0.643), suggesting closed cases see a 1.445 percentage point increase in the share of workers operating in firms that record no remuneration. In Column (6) the left-hand side variable is employer contributions to social security as a percentage of the wage bill. The term _τ_ 0 _illegalinformal_ = -1.504 (0.551), suggesting closed cases see a statistically significant 1.504 percentage point decrease in employer contributions to social security, relative to the wage bill. These results suggest that investigators target industries with decreasing concentration of sales due to increased market shares of informal companies. These results paint a nuanced picture of the relationship between antitrust and concentration. Overall, antitrust investigations tend to target industries where firms are exiting and the share of informal employment is rising, however, these industries are not undergoing significant changes in the market share of the largest firms. There is significant treatment effect heterogeneity across Columns (2) - (6) indicated by the _γ_<sup>2</sup> term, indicating there is no hard and fast rule about how market structure may be affected by antitrust. Moreover, on average, antitrust sanctions do not seem to alter market structure, except in the case of the share of employment in legal informal firms. We now turn to how these trends relate to the distribution of productivity.

> 16Typically unpaid family members working in the business.

29
|**Table A3: Cross‐Sectional Prod**|**uctivity Relati**<br>Bangladesh|**onships b**<br>Chile|**y Country**<br>Ghana|Kenya|Mexico|Nigeria|Sri Lanka|
|---|---|---|---|---|---|---|---|
|**Panel A: Labor Productivity**||||||||
|Business Practices Score|1.140***|0.944***|0.661*|0.973***|1.001***|2.636***|1.604***|
||(0.150)|(0.338)|(0.337)|(0.098)|(0.056)|(0.284)|(0.127)|
|Log(Paid Workers)|0.825***|0.720|0.817***|0.325**|0.619***|0.437***|0.636***|
||(0.030)|(0.722)|(0.126)|(0.126)|(0.071)|(0.062)|(0.066)|
|Log(Owners Hours)|‐0.061*|0.667***|0.325**|0.625***|0.465***|0.201***|0.358***|
||(0.034)|(0.100)|(0.152)|(0.056)|(0.027)|(0.072)|(0.056)|
|**Panel B: Total Factor Productiv**|**ity**|||||||
|Business Practices Score|0.471***|0.650*|0.121|0.572***|0.874***|2.481***|0.862***|
||(0.129)|(0.334)|(0.279)|(0.094)|(0.055)|(0.287)|(0.126)|
|Log(Paid Workers)|0.540***|0.256|0.510***|0.215*|0.603***|0.402***|0.484***|
||(0.030)|(0.570)|(0.116)|(0.125)|(0.069)|(0.064)|(0.070)|
|Log(Owners Hours)|0.005|0.592***|0.113|0.382***|0.416***|0.190***|0.210***|
||(0.031)|(0.104)|(0.128)|(0.056)|(0.027)|(0.071)|(0.056)|
|Log(Capital Stock)|0.048***|n.a.|0.263***|0.057***|0.128***|‐0.038|0.090***|
||(0.011)|n.a.|(0.040)|(0.016)|(0.008)|(0.027)|(0.014)|
|Log(Inventories)|0.303***|0.223***|0.177***|0.278***|n.a.|0.192***|0.190***|
||(0.016)|(0.063)|(0.036)|(0.016)|n.a.|(0.036)|(0.017)|
|Sample Size<br>Notes:<br>Robust Standard Errors in Pare<br>percent levels respectively.<br>All regressions also include se<br>factors of production.<br>n.a. Capital stock not available|1724<br>ntheses, *, **<br>ctor dummies<br>in Chile data,|146<br>, *** indic<br>, and contr<br>Inventori|321<br>ate signific<br>ols for zer<br>es not avail|3413<br>ance at the<br>o or missin<br>able in Me|8585<br>10, 5, and<br>g values of<br>xico data|1499<br>1<br>the|2458|

38
# **2.3 Linear Empirical Best Predictor Models**

We utilize the _povmap_ package in R to generate the Empirical Best Predictor (EBP) estimates.<sup>10</sup> This is an updated version of the _emdi_ package (Kreutzmann et al., 2019), which implements the models described in Molina and Rao (2010) with additional features. We implement a “sub-area level model,” in which we estimate the model at the sub-area level before aggregating to the area level for the final predictions. While we include descriptions of the other estimators in Appendix C, we discuss some details of the sub-area model, since it is one of the most common methods for small area estimation of welfare.

The subarea-level model is a model of the form: where _G_ ( _ysar_ ) is a transformation of outcome _y_ for sub-area _s_ in area _a_ in region _r_ ,<sup>11</sup> _Xsar_ is a vector of sub-area-specific geospatial features, _Xar_ is a vector of area-specific geospatial features – which may include region dummies – _ηar_ is an area-level random effect, assumed to be normally distributed and conditioned on the sample data, and _εsar_ is a classical error term assumed to be normally distributed. When predicting assets, no transformation is used, implying that _G_ ( _ysar_ ) = _ysar_ . When predicting poverty rates, the arcsin transformation is used, implying that _G_ ( _ysar_ ) = arcsin � _ysar_<sup>0</sup><sup>_._5�</sup> .

Because the random effects are conditioned on the survey data, they are shrunk towards zero, with the shrinkage factor depending on the relative estimated variances of the random effect _η_ and the ideosyncratic error term _ε_ . After estimating the model’s coefficients, estimates for areas are generated by calculating _E_ � _G_<sup>_−_1</sup> ( _ysar_ )�, conditional on estimated parameters _β,_<sup>ˆ</sup> ˆ _ση_<sup>2,and</sup><sup>_σ_ˆ</sup> _ε_<sup>2.Weincludebothsurveyweightsand</sup> population weights, with the latter taken from WorldPop estimates rather than the censuses. The software calculates measures of uncertainty using 100 parametric bootstrap replications. For more details on the model, we refer readers to Tzavidis et al. (2018) and Molina and Rao (2010).

As with any regression model whose main goal is prediction, EBP models can be prone to overfitting. This is especially true in our case, where we have several hundred possible features from which to choose. To help prevent overfitting, we select features using lasso, implemented using the R package _glmnet_ (Friedman et al., 2010). We select the optimal lambda using cross-validation.

> 10The package is a spin-off of the EMDI package developed by Ifeanyi Edochie and colleagues and available for download at: https://github.com/SSA-Statistical-Team-Projects/SAEplus.

> 11The level at which we are interested in predicting outcomes is the area, described above.

11
insurance. Members (n-29) indicated their benefits and disadvantages. Non-members (n-21) indicated why they did not join. Members were interested in seeing ambulatory care covered at a modest increase in premium. Those not joining had diverse reasons, including living outside the catchment area.

Table A.10 Swamwda Survey: Attitudes

|.**....**.........<br>...........................................<br>No<br>Survey<br>question<br>X Resp|. of<br>ond.|
|---|---|
|Can you increase<br>your premium<br>from 125<br>to 200 Zaire to cover nmbulatory<br>**care?**|26|
|Yes<br>80.8X<br>No<br>19.2Z||
|why did you decide to be a meber?<br>Cheapest way to get care<br>89.72|29|
|Quality of careisbetter<br>**3.4X**||
|solidarity<br>3.4X||
|I thought<br>it was oblieatory<br>3.42<br>Other<br>**3.4X**||
|What benefits do you receive from your<br>membership?||
|The personnel seesmefaster<br>**37.0X**|27|
|I have better<br>health<br>3.72||
|i want to support the**institution**<br>**3.72**||
|Quality<br>of care is better<br>**22.22**||
|Other<br>**3.7X**<br>||
|No response<br>29.6X||
|Whatare the disadvantages or problem of<br>insurance scheme?||
|Too expensive<br>6.9X|29|
|Itisnecessary<br>to come for ftollow<br>up 17.2X<br>visits||
|No response<br>75.92||
|Why are you not a member<br>of the insurance<br>scheme|?|
|<br>I have no money<br>16.02|21|
|I didn't know at the time of<br>5.02<br>Other<br>**75.0O**||

27
Our methodology involves two major steps. The first step would be to identify the urban base through which revenues can be generated. Due to non-availability of data on GCP for ULBs or any other reliable data on variables which can act as proxies for urban tax bases, we have followed a simple straightforward method for estimating the gross city products (GCPs). We have used per capita non agricultural component of gross district domestic products<sup>24</sup> for the respective districts in which the ULBs are located<sup>25</sup> and multiplied them by the population of each of the ULBs to get a proxy for the GCP of the local government (Figures 1 and 3, Table A 2)<sup>26</sup> .

Once the revenue base for measuring revenue capacity is estimated, the next task would be to choose an appropriate rate which can be applied to the base specified. Choosing a ‘standard’ rate that maximizes the revenue is very difficult because ULBs collect tax and non tax revenues which encompasses a lot of categories. We have taken the ratio of own revenue<sup>27</sup> to the estimated GCP as a benchmark and to be consistent with the worldwide evidence on under-performing local governments, have applied a positive margin<sup>28</sup> to this ratio to estimate the ‘standard’ rate (Table 11). These rates are multiplied with the GCPs to get the maximum own revenue capacity figures. The existing levels of transfers<sup>29</sup> for each

# 24 Published by Central Statistical Organisation.

25 See Table 1 for the details of the Districts in which the ULBs are situated 26 This implies that the per capita domestic product across municipalities of a district is the same, but with the data constraint this is the best way to construct a proxy for GCP at the municipality level in India. The rationale for using the non-agricultural component is that possibility of pursuing agricultural activities in the urban areas is minimal.

27 For Pune we have not taken the actual own revenues but have estimated the own revenues without octroi taking into consideration the possibility of abolition of octroi in near future. For this we have deducted the octroi component of own revenues from actual own revenues and taken the ratio of own revenues without octroi to GCP.

28 It is to be noted that for each UA the median value of the ratio of own revenue to GCP of all ULBs is taken as the benchmark rate. The margins added to this rate vary across UAs. The margins are decided on the basis of the growth rate of own revenues in the ULBs of a UA in the past five years, the disparity in the own revenue to GCP ratios amongst the ULBs in the UA and political feasibility.

> 29 For Pune we have estimated the transfers component in the absence of octroi by adding an estimated compensation from the higher government with abolition of octroi. The compensation is generally given as a percentage (varying between 5-10% for previous cases of abolition of octroi in India) of the average of the past three years octroi collection in the city. We have estimated the compensation with 7.5% in case of Pune.

29
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# **Figures 11c and 11d. Government Revenue and Growth: Morocco**

<!-- Start of picture text --> Fig. 11c. Goverment Gov. budget gains as % GDP<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>2022 2024 2026 2028 2030 2032 2034 2036<br>$25 carbon tax phase out fuel subsidies<br><!-- End of picture text --> <!-- Start of picture text --> Fig. 11d. Growth contribution of carbon tax<br>versus removing fossil fuel subsidies<br>0.035<br>0.03<br>0.025<br>0.02<br>0.015<br>0.01<br>0.005<br>0<br>2020 2022 2024 2026 2028 2030 2032 2034 2036 2038<br>Baseline GDP growth GDP growth with $25 carbon tax<br>GDP growth: remove subsidies<br><!-- End of picture text -->

**Note.** Fig. 11a. For a modest carbon tax of $5 per ton of CO2, removing or leaving small fossil fuel subsidies makes little difference. This is indicated by the coincidence of the yellow and gray lines, suggesting that subsidy must be very small and inconsequential. Fig 11b. A large Carbon tax of $50 per ton of CO2 makes some difference, where lines bifurcate. Again however, subsidy, or lack thereof, makes little difference as indicated by the coincidence--this of the orange and blue lines--suggesting that, once again, subsidy must be very small and inconsequential.  Also, government budget benefits from a $25 carbon tax more than subsidy removal, but the effect is less dramatic than in Lebanon or even Egypt (Fig. 11c). Growth effect of both policies shows same short run dip and long run convergence to the baseline as in other countries, but the dip is much shallower (Fig. 11d)

33
demonstrated by one study in which sixth graders from Estonia reported on their teachers’ emotional support in their first three years of schooling. Students with the highest task persistence had teachers who were high on emotional support and low in psychological control in first grade (Kikas & Tang, 2018). _Teach_ included the following three teacher behaviors that capture the extent to which teachers encourage perseverance in their students: _1. Teacher acknowledges students’ efforts – rather than focusing only on results, students’ intelligence, or natural abilities_ _2. Teacher has a positive attitude toward student challenges_ _3. Teacher encourages goal setting_ _1. Teacher acknowledges students’ efforts – rather than focusing only on results, students’ intelligence, or natural abilities_ Effective teachers recognize students’ efforts, not only work products. In doing so, teachers can promote students’ “growth mindset” in learning; that is, the belief that intelligence is malleable rather than a fixed attribute of the child. Dweck and colleagues led a series of investigations on growth mindset; they recognized that praise molds students’ beliefs about themselves, their motivation, and performance (Dweck, 1999, 2002, 2013). This finding is supported by numerous experimental and longitudinal studies in both parenting and classroom contexts. For example, the longitudinal experimental study of Gunderson, Gripshover, Romero, Dweck, Goldin‐Meadow, & Levine (2013) demonstrated that parents who praised children’s effort at 14–38 months had encouraged children to adopt an incremental motivational framework at seven to eight years of age. Similarly, studies of parents who view their children’s failure as debilitating and focus on children’s performance and ability rather than children’s learning promote a fixed mindset rather than a growth mindset (Haimovitz & Dweck, 2016). In other words, teachers who help children see their ability as malleable, through their instructional practices, attribute success to hard work, encourage challenges, and generate strategies for improvement use behavioral practices that can promote a growth mindset in students (Park, Gunderson, Tsukayama, Levin, & Beilock, 2016).

Praise such as “Good boy” and “You are so clever” can undermine students’ motivation to show effort and performance (Dweck, 1999; Mueller & Dweck, 1998). Dweck and colleagues repeatedly found that students with a growth mindset show higher 32