rafmacalaba/datause-encoder-data huggingface dataset:
total allowances in 2030 are estimated to be 8,413 million (one allowance is equal to one ton of CO2 emissions). The total number of emission allowances does not change between the with and without linking the CGE model to information obtained from the bottom-up TIMES model. The information from the TIMES model reduces the baseline emissions and the gap between the emissions under the baseline and the NDC. Under the ETS, a total number of allowances in 2030 would be 70.3% of the baseline emissions in 2030 if the information obtained from the bottom-up model is not taken into account. This means there would be 28.8% reduction of national emission from the baseline under the ETS in 2030 under the no-linking case. Since the linking of CGE with the information from the bottom-up model reduces 15.5% of baseline emissions, reduction of emissions under the linked case would be 13.3% (28.8% - 15.5%) in 2030 under the linked case. The targeted level of allowed emissions under the NDC in 2030 remains the same under both cases. We assumed that the NDC target would be met by reducing emissions from the production sectors only. Emissions from the final consumption sectors (i.e., households, governments, capital goods) will not be capped, and they do not participate in the national ETS. However, these sectors also experience emission reductions due to indirect effects (because of increased consumer prices under the ETS). It also excludes emissions from private transportation because it is accounted for under the household sector in the CGE model, and the household sector does not participate in the national ETS.

Figure 3a and 3b present the allocation of allowances across the sectors and also across the provinces, respectively. Emission allowance received by a sector is highly sensitive to the criteria used for the allocation. If the emission is used as an allocation criterion, sectors with higher CO2 emissions, such as electricity, non-metallic minerals, metals, coal mining, would receive the relatively higher number of emission allowances. On the other hand, if the sectoral outputs are used as an allocation criterion, sectors with larger outputs, such as the service sector, the construction sector, the other manufacturing sector receive a relatively higher number of emission allowances. While the emission based allocation rule is preferable to emissionintensive sectors, the output-based allocation rule is preferable to less emission-intensive sectors. Higher allowance received means a higher opportunity to benefit from the emission trading. It is, therefore, the government’s priority how the emission allowances are distributed.

35
<!-- Start of picture text --> F igure 1<br>A djusted W eekly H ours W orked<br>50.0<br>45.0<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Source: O w n estim ates based on EN EU survey 1988 1994 1999<br>Primary Teacher Pub. Sch.Primary Teacher Priv. Sch.L-Sec Teacher Pub. Sch.L-Sec Teacher Priv. Sch.U-Sec Teacher Pub. Sch.U-Sec Teacher Priv. Sch.Univ. Teacher Pub. Sch.Univ. Teacher Priv. Sch.The Mix-Skilled GroupThe Agricultural GroupThe Low-Skilled Group<br><!-- End of picture text --> Based on ENIGH, the main and secondary occupations listed on Table 4 show hours-worked, adjusted hours-worked and the total number of hours-worked by region. Figure 2 shows that hours-worked vary across school levels, sectors and regions. Public primary school teachers work an average of 32 hours per week, while those in private schools work on an average of 34 hours. Public and private primary school teachers work significantly less than their counterparts. This pattern changes in lower and upper secondary school level, since public school teachers work more hours on average. On the other hand, _university teachers in public schools_ and _the mixed-skilled group_ work an average of 47 hours per week, while people employed in _the agricultural group_ or _the low skilled group_ work on an average of 43 hours per week. There is not a significant difference in the weekly hours-worked between public school teachers in urban areas and those teachers in the rural areas. In sum, total hours-worked for _the Professionals with a B.A in Education not teaching, the mixed-skilled group_ , _the agricultural group_ and _the low-skilled group_ is higher than the total hours-worked for teachers of basic education. This result also holds true in urban and rural regions.

10
In correcting for selective reporting, the propensity scores (p-scores hereafter) are estimated using a probit regression for whether SP spending is data for a given country and year. In addition to (log) GDP per capita, the regressors are the log of the country population in that year and a variable measuring the availability of SP spending data for that country in prior years.<sup>29</sup> By including population size our reasoning is that public spending data have properties of a public good, such that lower average costs in more populous countries entail that the data are more likely to be available. The variable for past SP data is motivated by the assumption that <mark>past experience in providing SP spending data makes it less costly to currently provide such data.</mark> We then include a cubic function of the p-scores in the regressions for the SPEC as control functions for testing the selective reporting hypothesis.<sup>30</sup>

# **4.2 Results**

As can be seen from the “baseline” panel in Table 3, the slope coefficient in the simplest linear regression specification for the SPEC gives a semi-elasticity of 3.78 (implying a full elasticity of SP spending to GDP of 1.42, when evaluated at the mean SP share for 2019). This falls to a semi-elasticity of 1.37 (full elasticity in 2019 of 1.15) when the country effects are included. Qualitatively, this is what one would expect if the omitted country characteristics in the SPEC based on cross-country comparisons (such as Figure 1) tend to be jointly positively correlated with the SP share and GDP. Nonetheless, a positive and significant income effect on the SP share of national income remains when we allow for the latent country effects.

We still find a statistically significant non-linear income effect on the SP shares when we allow for country fixed effects in the PLREG estimator (the “baseline” panel in Table 3). Figure <u>4</u> compares the estimated nonparametric sub-functions, f̂(lnGDPit), with and without the country effects, where the f̂(lnGDPit) values controlling for the country effects are centered on SPit the overall mean of the country-specific means of ~~.~~ The positive slope is evident either way, GDPit but is attenuated considerably by introducing the country effects. Comparing the top quintile of

> 29 More precisely, this variable measures the share of previous country-year observations (from 1995 and up to the year previous to that of each observation) where SP spending data is not missing. When data are not missing in every year since 1995 the variable takes a value of one, and when data is always missing the variable takes a value of zero. The variable takes intermediate values whenever information in some years is missing and non-missing in others.

> 30 For a more general treatment of these methods of correcting for sample selection bias, see Das et al. (2003).

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# **4.3 Timeline and Data**

## **4.3.1 Study Timeline**

Figure 2 shows the study’s timeline and data collection phases. In the three study states, the MNM campaign was live for almost 5 months, from August 19th 2020 to January 6th 2021. To assess its effectiveness, we recruited two separate survey samples over distinct periods: (i) a longitudinal (experience) survey from early September 2020 to end of January 2021 to gauge effects _during_ the campaign; and (ii) a cross-sectional sample from January to late March 2021 to study accumulated and medium-term effects _after_ the campaign had ended. As outlined in Section 6, we also carried out an additional survey wave in April 2021 as part of an individual-level _feed_ experiment, aimed at distinguishing between limited effectiveness and limited reach of the ad campaign for vulnerable populations. Finally, we augmented these survey samples with administrative health facility records covering the period from four months prior to the campaign’s initiation (April 2020) to five months after its completion (May 2021).

Figure 2: Study Timeline <!-- Start of picture text --> Administrative data: recorded incidence<br>Preventative/response behaviors Self-reported incidence Analysis of<br>during campaign after campaign mechanisms<br>Longitudinal sample: Longitudinal sample: Cross-sectional sample: Individual-level<br>Recruitment & Baseline Endline Recruitment & Survey experiment<br>Experience surveys<br>(8 waves)<br>August 2020 January 2021 March 2021 April 2021<br>Ad campaign is live<br><!-- End of picture text -->

## **4.3.2 Survey Samples and Administrative Data**

During the campaign, the longitudinal (experience) survey assessed malaria-related preventive behaviors (such as bed net usage) and response actions (such as seeking medical care when experiencing fever symptoms). This survey involved a baseline questionnaire (before or at the start of the campaign), an endline survey (towards the end of the campaign and right after it), and a series of 8 short surveys in between, conducted every 17 days. All baseline respondents regardless of how many intermediate waves they completed, were invited to complete the endline.<sup>23</sup> To analyze potential impacts during the campaign, we filtered the data to include responses collected from September

> 23Hence, early enrollees had the potential to provide responses for all 8 experience surveys and the endline survey

14
Given the constraints discussed above, generating evidence to guide policy makers on the overall issue of how to improve investment efficiency appears to deserve a high rank on the list of applied research priorities.

# **References**

Cameron, A. C. and D. L. Miller. (2015). “A Practitioner's Guide to Cluster-Robust Inference”, _Journal of Human Resources_ , Vol.50, No. 2, pp.317-373.

CEDLAS and The World Bank. (2014). “A Guide to SEDLAC Socio-economic database for Latin America and the Caribbean.” http://sedlac.econo.unlp.edu.ar/eng/methodology.php Costa, F., and F. Gerard. (2015). “Hysteresis and the Social Cost of Corrective Policies: Evidence From a Temporary Energy Saving Program”, mimeo.

Davis, L., and P. Gertler. (2015). “Contribution of air conditioning adoption to future energy use under global warming”. _Proceedings of the National Academy of Sciences USA_ 112(19): 5962–5967.

Dinkelman, T. (2011). “The effects of rural electrification on employment: New evidence from South Africa”, _American Economic Review_ , Vol. 101(7) pp. 3078-3108 Estache, A., and M. Fay. (2009). Current Debates on Infrastructure Policy. Comm. Growth Dev., World Bank Publication.

Fay, M. and T. Yepes. (2003). “Investing in Infrastructure: What is Needed from 2000 to 2010?” World Bank Policy Research Working Paper No. 3102.

Ferreira, F. H. G., Messina, J., Rigolini, J., López-Calva, L.-F., Lugo, M. A. and R. Vakis. (2013). _Economic Mobility and the Rise of the Latin American Middle Class_ . Washington, DC: World Bank.

Galiani, S., P. Gertler and E. Schargrodsky. (2005). “Water for Life: The Impact of Privatization of Water Services on Child Mortality”, _Journal of Political Economy_ , 113: 83-120.

Gertler, P., O. Shelef, C. Wolfram, and A. Fuchs. (2016). “The Demand for Energy-Using Assets among the World's Rising Middle Classes,” _American Economic Review_ . Vol. 106, No. 6, pp. 1366-1401.

20
39 Graph 13 Stock Prices, Capital Flows and Domestic Credit. 1980 -2001

# **CHILE**

<!-- Start of picture text -->
1.0<br>0.5-<br>-0.5<br>-1.0  rT  1 . . . . . . I . I ..-<br>80 82 84 86 88 90 92 94 96 98 00<br>- Log of stock price index in real terns  j<br>1.2E+1  1<br>1.OE+1 1  -50-<br>8.OE+104<br>6.OE.10-<br>4.OE+10<br>2.OE+10-<br>O.OE+00 ....  ....  . . . .<br>80 82 84 88 88 90 92  94 96 98 00<br>Domesc  c  it  erms<br><!-- End of picture text -->

<!-- Start of picture text --> 3<br>2-<br>0<br>3,r1 -<br>80 82 84 86 88 90 92 94 96 98 00<br>Capita fows<br>60<br>40-<br>30,<br>20m<br>1<br>-100<br>-20<br>80 82 84 86 88 90 92 94 96 98 00<br>Real domesUc crdit grawl<br><!-- End of picture text -->
**_Energy demand models for policy_** **_<u>formulation</u>_**

Fig. A1.2: LEAP framework <!-- Start of picture text --> Demographics Macro-economics<br>Demand Analysis Integrated<br>Cost-benefit<br>Analysis<br>Statistical differences<br>Environmental<br>Loadings Transformation Analysis<br>Stock Changes<br>Resource Analysis<br>Environmental<br>Externalities<br>Non-energy sector Emission Analysis<br><!-- End of picture text --> Source: Heaps (2002)

# **A1.3.1.3 Top-down, econometric models**

# **DTI energy model**

For its energy forecasts and future carbon emission estimations, the Department of Business, Enterprise and Reform of the United Kingdom relies on an econometric model. The model covers both supply and demand sides but the demand is fairly elaborate. The demand model contains 150 econometric relationships to determine the demand in various sectors of the economy. The model follows the Error Correction Modelling approach and uses price and economic activity as main variables although time trends are 135
taxes to more growth-friendly revenue sources in Italy (IMF, 2020). A potential hypothesis could be the substitution of IRAP with the additional tax on IRES (CIT) to regions. In this case, it would be appropriate to carefully consider the redistributive effects deriving from the differences between the two taxes in terms of taxpayers (broader audience in the case of IRAP), the tax base (IRAP taxes profits and interest expenses, IRES only profits), and the territorial distribution of the latter.

For these reasons, it seems appropriate to verify the effects of this tax reform on the Italian economic system through the IRENCGE-DF model. Here, three different scenarios are simulated:

1. IRAP-baseline: Complete abolition of IRAP tax-financed by public deficit;

2. IRAP-IRES: Tax shift between IRAP and IRES with a uniform tax rate; 3. IRAP-VAT: Tax shift between IRAP and VAT.

The simulation of each scenario with the IRENCGE-DF model is straightforward. We already have seen that the model has clear national and regional tax streams. Moreover, depending on the closure rule, the tax rate may be endogenous to achieve some target.

To simulate the first scenario, we set to zero the IRAP tax and allow for a higher public deficit. In the second simulation, the Government revenue is fixed to the baseline scenario level, and we set the IRAP tax to zero. Next, we endogenize the IRES tax schedule to achieve the fiscal target using a uniform shift in the marginal tax rate. The model solves for a unique tax multiplicative adjuster applied to all activities. Finally, we simulate the third scenario using the same logic for the VAT instead of the IRES tax.

In the following figures, we present some results regarding these simulations. First, we look at the macroeconomic impacts on GDP in 2030 (end of time horizon for all simulations) regarding percentage change with respect to the baseline scenario. Figure 2 also illustrates the GDP dis-aggregated into its components: consumption, Government expenditure, investment, and net export. All three considered scenarios have a positive impact on GDP. The IRAPbaseline scenario (complete abolition of IRAP tax) has the highest impact on GDP. Figure 3 shows the same effects on GDP but for all the time horizons. The effects observed in the IRAP-baseline scenario are the highest: in 2018, there is more than a +0.35 percent increase in GDP compared to the Business as Usual (BaU) scenario. That increase is slowly decayed but persistently higher than in the other two simulations. Overall, we could consider the IRAP-IRES simulation has the one having no effect on GDP growth compared to the baseline scenario. The IRAP-VAT scenario, on the other hand, starts with a slightly negative effect 20
Figure 13. Decomposing the EACR Differences Across Countries (2011) <!-- Start of picture text --> Individual (FINDEX 2011) Household (LITS)<br>2<br>0.8<br>1<br>0.4<br>0.0 0<br>‐0.4 -1<br>‐0.8 -2<br>‐1.2 -3<br>‐1.6 -4<br>Senegal Argentina Algeria Mali Azerbaijan Bulgaria Tajikistan Moldova<br>Composition Equalization Scale<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base and LITS is household level. In FINDEX 2011, 141 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries. _Sources_ : FINDEX, LITS Turkey’s dissimilarity index in savings falls substantially when measuring at the household-level despite a similar coverage rate. As a result, Turkey has a significantly lower dissimilarity index than the benchmark countries (Figure 28). Part of this effect can be explained by some homogeneity in households. Even with this reduction, the composition of the d-index remains fairly stable as age of the household head and income of the household are the principal sources of dissimilarity. (Figure 29) The decomposition of the difference in the EACR using the LITS survey shows that households save at a more equitable level in Turkey compared to the benchmark countries (Figure 13); the equalization effects explains why the EACR is lower in the comparator countries of Azerbaijan, Bulgaria, Tajikistan, and Moldova.

# **iii. DEBIT AND CREDIT CARD USE**

Debit and credit card usage in Turkey is notably higher than the BRICS economies and the rest of ECA countries in 2011 (Figure 14). Over 42 percent of the lowest income quintile in Turkey has debit cards compared to around 13 percent of their counterparts in the BRICS and around 29 percent of other ECA countries. The difference is even greater in credit card usage. In Turkey, 30 percent of the lowest income quintile has credit cards. This value is under 10 percent in both the BRICS and rest of ECA. In fact, Turkey’s overall credit card usage is higher than that of the developed world in 2011. The high level of credit card utilization may form a financially dangerous situation especially for the poor who are more prone to having larger debt than their income.

Turkey’s gender discrepancy in debit and credit card ownership is parallel to the use of bank accounts. In 2011, debit and credit card usage rates for males are 81 percent and 64 percent. The same rates are 31 and 26 percent for females, respectively. Between 2011 and 2014, both debit and credit card ownership levels decrease for males and females. However, the fall is steeper amongst the males than it is for the females resulting in a reduced gender disparity.

15
However, since prices are not likely to vary in a community and there is no time variation with cross sectional data, it could be argued that price effects get subsumed into ε _,_ the unobserved community effects (Alderman, Hentschel and Sabates, 2002). How the data from the Ethiopia DHS is used to construct these variables is explained next.

# _Variables_

Data from the Ethiopia DHS constitute the primary basis for the empirical analysis of the present study. The discussion here focuses on how this data has been used to construct variables used to proxy for _Y_ , household resources, and _E_ , environmental conditions, in equation (3) above.

# <u>Individual characteristics</u>

Several control variables are included to control for heterogeneity at the individual level. Individual specific controls include the child’s gender, age, and the mother’s height. The age variable is included as a dummy variable for different age groups to control for the cumulative effect of malnutrition. Mother’s height is included to proxy for the child’s genetic endowment. Previous research suggests mother’s height is associated with a child’s birth weight and has a stronger impact on a child’s height than father’s height.

# <u>Household resources</u>

No income or expenditure data is collected as a part of the DHS surveys. In the absence of such data, Filmer and Prichett (1998) have suggested the use of a wealth index, which can be constructed from information regarding housing characteristics and the possession of household durable goods. This is approach adopted in this study. The wealth index serves as a proxy for the household’s long-run wealth or economic status. Its reliability is dependent on having the appropriate weights for the assets. Using principal component analysis, factor scores are generated for each type of asset and determine the weight these assets receive in the construction of the asset based wealth index. The idea is to extract from a large number of variables the orthogonal linear combinations of the variables that best capture the largest amount of information common to all of the variables (Filmer and Pritchett, 1998).

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where _g L_ is the rate of growth of the labor force. The wedge between the social and private rates of return to R&D is thus composed of two components: the first component is generated by taxes and the domestic R&D externality, as captured by the term Ω , whereas the second term is associated with the rate of growth of firms, which in the model is equal to the rate of growth of the labor force, and arises because of an assumption in the model that new firms are born with a productivity equal to the average productivity of existing firms. Equation (3) determines the relative A level of a country given its measured levels of _k_ , and the two tax parameters τ and φ .

# _Calibration_

For the calibration, we follow Klenow and Rodríguez-Clare in having α = 1/ 3, γ = .0085 , δ = .008, _r_ = .0086 , _g_ = ε = .0015 , λ= .038, and μ = .055 . The interested reader can consult that paper to understand the details of this calibration. Here we just provide a brief explanation. The values used for the parameters α , γ , and δ are standard in the literature. The interest rate is obtained by noting that with a tax rate of 25% in the U.S. (i.e., τ = .025 ) and given data for the capital-output ratio and the relative price of investment in the U.S., then equation (2) implies _r_ = .0086 . The steady state growth rate of A*, _g_ , is obtained from the average growth of TFP in the OECD for the period 1960-2000. We assume that ε = _g_ to generate reasonable steady state properties. Finally, parameters λ and μ are calibrated to U.S. data. In particular, these parameters are set so as to have that the social rate of return to R&D in the U.S. be three times the net private rate of return given an R&D subsidy of 20% (i.e., φ = − 2.0 ), and given an R&D investment rate in the U.S. of 2.5%.<sup>8</sup>

> 8 Assuming that the U.S. has a 20% subsidy on R&D may be questioned for two reasons. First, because although this is the statutory rate (see Hall and Van Reenen, 2000), the effective rate is much lower. Second, because since we are considering a broad concept of R&D, then the actual rate would be even lower. It turns out, however, that this is not too relevant for our main conclusions. We recalibrated the model with a U.S. R&D tax of 0%, and the results do not change in any significant way.

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Table 2 Squared coefficient of variation of prices, **19501986**

|||||perventages|
|---|---|---|---|---|
|Commonacdity|<br>195<br>5-yr<br>MA|0-69<br>price<br>change|19<br>5-yr<br>MA|70-86<br>price<br>change|
|Cocoa|4.4|6.3|4.4|7.8|
|coffee|1.4|2.6|5.8|12.3|
|Tea|.05|1.2|3.6|5.3|
|Sugar|12.3|15.2|14.4|22.1|
|<br>Cotton|0.4|1.7|1.7|3.6|
|Jute|4.0|4.8|1.4|3.2|
|Rubber|2.6|5.8|3.2|5.3|

Source: Table 1 47