rafmacalaba/datause-encoder-data huggingface dataset:
# **Tables and Figures**

Table 1. The Gender Wage Gap by Age Group

||**Month**<br>|**ly wages (with c**<br>|**ontractual hours**<br>|**of work)**<br>|
|---|---|---|---|---|
|**Panel (a)**|All|Teenagers<br>(15-19)|Young adults<br>(20-24)|Prime age<br>(25-29)|
|**Unconditional**|-0.1013***|-0.0559|-0.1125***|-0.1059***|
|**Conditional on: hours of work**|-0.0765***|-0.0847|-0.0831**|-0.0694**|
|**Conditional on: hours of work, education**|-0.0828***|-0.094|-0.0848**|-0.0776**|
|**Conditional on: hours of work, education, actual work experience**|-0.1645***|-0.1315|-0.2235***|-0.1516***|
|**Conditional on: hours of work, education, actual work experience, work**<br>**and study, self-employment, migration status, job search method, type of**<br>**contract, formal / informal work, moonlighting, job satisfaction, training,**<br>**union membership, firm’s size, goal in life, rural area**|-0.1204***|-0.0697|-0.1959***|-0.1279***|
|**Panel (b)**|M|onthly wages (wit|h actual hours of w|ork)|
||All|Teenagers<br>(15-19)|Young adults<br>(20-24)|“Young old”<br>(25-29)|
|**Conditional on: hours of work**|-0.0947***|-0.0852|-0.1081***|-0.0963***|
|**Conditional on: hours of work, education, actual work experience**|-0.0698**|-0.1395*|-0.1284*|-0.0612|
|<br>**Conditional on: previous variables, plus worked while studying, migration**<br>**status, job search method, goal in life, rural area**|-0.1531***|-0.2121**|-0.2244***|-0.0883*|
|<br>**Conditional on: previous variables, plus self-employment, type of contract,**<br>**formal / informal work, moonlighting, job satisfaction, training, union**<br>**membership, firm’s size**|-0.1275***|-0.0753|-0.2161***|-0.1337***|
|**Panel (c)**||Hour|ly wages||
||All|Teenagers<br>(15-19)|Young adults<br>(20-24)|“Young old”<br>(25-29)|
|**Unconditional**|-0.0489*|-0.1025|-0.0454|-0.026|
|**Conditional on: education**|-0.0536**|-0.0976|-0.0455|-0.0340|
|**Conditional on: hours of work, education, actual work experience**|-0.0353|-0.1565*|-0.0460|-0.0205|
|<br>**Conditional on: previous variables, plus worked while studying, migration**<br>**status, job search method, goal in life, rural area**|-0.0823**|-0.1412|-0.1154**|-0.0909*|
|**Number of observations**|1896|316|703|877|

_Note: * p<.1; ** p<.05; *** p<.01. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 25
_Table 8: Determinants of applicant’s CV being clicked on,_ _conditional on applicant making it to the longlist_

||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|
|---|---|---|---|---|---|---|---|
|Female|0.0245*|0.0156|0.0174|0.0180|0.0218|0.0224|0.0253|
||(0.0127)|(0.0149)|(0.0146)|(0.0145)|(0.0152)|(0.0151)|(0.0173)|
|Accounting||||<br>-0.0955***||<br>-0.0923***|<br>-0.0915***|
|||||(0.0238)||(0.0229)|(0.0230)|
|Marketing||||-0.0628***||-0.0714***|-0.0709***|
|||||(0.0238)||(0.0228)|(0.0229)|
|IT||||<br>0.2382***||<br>0.1706***|<br>0.1691***|
|||||(0.0301)||(0.0360)|(0.0361)|
|Istanbul Asia|||||||<br>0.0115<br>(0.0225)|
|Ankara|||||||<br>0.0116<br>(0.0263)|
|Total application size (100)||||||||
|Female * Istanbul Asia|||||||-0.0284<br>(0.0206)|
|Female * Ankara|||||||0.0005<br>(0.0252)|
|Female * Acct||||-0.0044||||
|||||(0.0131)||||
|Female * Mrkt||||<br>-0.0173<br>(0.0129)||||
|Female * IT||||<br>-0.0178<br>(0.0175)||||
|Female * Total application|size||||-0.0011|-0.0011||
||||||(0.0008)|(0.0008)||
|Vacancy and CV sectors m|atch||0.2272***||<br>0.2211***|<br>0.0664**|0.0664**|
||||(0.0221)||(0.0221)|(0.0263)|(0.0264)|
|Experience (in months)||0.0013|0.0015|0.0013|0.0015|0.0013|0.0013|
|||(0.0013)|(0.0013)|(0.0013)|(0.0013)|(0.0013)|(0.0013)|
|Age|-0.0160|<br>-0.0260*|<br>-0.0257*|<br>-0.0243|<br>-0.0277*|<br>-0.0262*|<br>-0.0263*|
||(0.0124)|(0.0157)|(0.0154)|(0.0153)|(0.0154)|(0.0152)|(0.0153)|
|Objective beauty||<br>0.0325|<br>0.0303|<br>0.0228|<br>0.0316|<br>0.0247|<br>0.0250|
|||(0.0246)|(0.0244)|(0.0242)|(0.0243)|(0.0242)|(0.0242)|
|Subjective beauty||<br>-0.0032|<br>-0.0085|<br>-0.0088|<br>-0.0080|<br>-0.0078|<br>-0.0077|
|||(0.0108)|(0.0106)|(0.0105)|(0.0106)|(0.0104)|(0.0105)|
|Total application size (100)|||||<br>-0.0034***<br>(0.0012)|<br>-0.0028***<br>(0.0010)|<br>-0.0034***<br>(0.0010)|
|Constant|0.8164**|0.9899**|0.9415**|0.9724***|<br>1.0102***|<br>1.0287***|<br>1.0280***|
||(0.3330)|(0.3845)|(0.3778)|(0.3741)|(0.3760)|(0.3724)|(0.3759)|
|N.obs.|10748|10748|10748|10748|10748|10748|10748|
|R-squared|0.001|0.001|0.025|0.047|0.030|0.051|0.051|

**Notes:** Models 1 to 7 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses.  Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant’s CV is clicked on. Variable _Female_ takes on the value 1 if applicant is female, 0 otherwise. Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Variables _Accounting_ , _Marketing_ and _IT_ denote the occupational clusters of vacancies (and so of applicants), and the baseline category is sales occupations. Variable _Total application size_ denotes the total number of applications for the vacancy. Symbols ∗∗∗, ∗∗, and ∗ indicate significance at the 1%, 5% and 10% level, respectively.

19
# **Supporting Carbon Tax Implementation in Developing Countries Through Results-Based Payments for Emissions Reductions**

# **1. Introduction**

The focus of this paper is to define and interpret methodologies for determining the size of payments by donor countries needed to induce low-income (L, or host) countries to implement or enhance domestic carbon taxes. Our particular objective is to study how much is needed to pay per unit of induced mitigation, for L countries to be willing to adopt the respective additional, donorsupported, carbon taxes.

The background for this work is that the current level of greenhouse gas (GHG) mitigation activity in L countries is far lower than what is required to reach the main mitigation targets for the 2015 Paris Agreement (PA). Comprehensive carbon pricing in many countries may well be a necessary component of policies to reach that goal. Today few L countries have any form of carbon pricing, and even fewer have broad-based carbon taxes. We assume that there is interest from the donor community to increase incentives for L countries to adopt carbon taxation, and as needed, to compensate these countries when doing so. The rationale is that comprehensive carbon pricing is the most cost-effective way to achieve substantial and meaningful GHG emissions reductions at manageable cost (see Appendix 3 for a summary of the rationale). In particular, it is a necessary condition for achieving renewable energy transition with deep involvement of the private sector.<sup>2</sup> Carbon taxation can also be beneficial for L countries by raising sorely-needed tax revenues.

How much L countries need to be paid for adopting comprehensive carbon taxation is less clear. This paper aims to clarify that issue, by deriving expressions for measures of the direct economic costs imposed on host countries when they implement comprehensive carbon taxation. Our discussion will focus mostly on factors explained by traditional economic theory, i.e., deadweight losses lowering production and consumption, but it will also recognize that other (more politicallyoriented) factors and issues can play a major role for many L countries.

> 2 See discussion in World Bank 2019a, and Strand 2020; see also an extensive literature which includes Acemoglu et al 2016, Aghion et al 2016, Cramton et al 2017, Tvinnereim and Mehling 2018, and Andersson 2019. A consensus in this literature is that comprehensive carbon taxation is necessary but not sufficient for “deep decarbonization”, which also requires support to renewable energy investments, in the L countries and globally.

2
Policy Research Working Paper 9947

# **Abstract**

Does the digitization of transactions in an economy increase tax compliance? This paper studies the effect of financial incentives on the adoption of electronic payment technology and on tax compliance by firms. Exploiting administrative data and policy variation from Uruguay, the paper shows that i) consumer value-added tax rebates for credit and debit card transactions trigger an immediate 50 percent increase in the number of card transactions, ii) firms’ use of card machines increases only on the intensive margin, and iii) tax compliance is unaffected. Endogenous card machine adoption and a low share of card sales in total reported sales can rationalize the findings.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at abrockmeyer@worldbank.org and msaenzsomarriba@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
Busso et al. (2012) and Levy (2018). The estimated growth rates for informal plants do not vary significantly across definitions (from 77% in our extensive margin definition, to 75% under a stricter classification, and 81% in the intensive margin definition), but estimated growth rates among formal establishments are significantly higher under these alternative definitions, from 136% in our extensive margin definition, to 265% under a stricter classification, and 240% in the intensive margin definition of Busso et al. (2012) and Levy (2018).

# **3. Model**

We build on the model developed by GPV where entrepreneurs run bigger plants by investing in their entrepreneurial ability over their life cycle. Our main innovation is introducing in this framework a tax on output that some plants can escape from by operating informally due to incomplete enforcement.

## **3.1 Environment**

In every period, a group of agents (cohort) endowed with entrepreneurial ability _z_ is born. This initial endowment of entrepreneurial ability is drawn from the (exogenous) probability density function _f_ ( _z_ ) with cumulative distribution function _F_ ( _z_ ) and support <u>[</u> _<u>z, z</u>_ <u>].</u> Agents live for _J_ periods. Each agent maximizes the expected present value of lifetime utility derived from consumption: where _β ∈_ (0 _,_ 1) is the discount factor, _cj_ denotes the consumption of an agent at age _j_ , and _u_ ( _c_ ) = log( _c_ ).

Agents have one unit of time which they supply inelastically until they reach their retirement age _JR < J_ . Along their life-cycle, agents choose to be workers (working for a wage for someone else) or entrepreneurs, and following GPV, this decision is not reversible. Furthermore, each period, entrepreneurs choose whether to operate formal plants, i.e., complying with all taxes, or informal plants, i.e., escaping from taxation.

Production of the single final good of the economy takes place in plants that combine entrepreneurial ability, _z_ , capital, _k_ , and labor, _n_ , as follows: 10
producing unbiased estimates, avoids the measurement errors inherent in the use of price indexes. About one-quarter of the estimates reported by Disdier and Head (2008) includes these country fixed-effects. As before, a “distance puzzle” obtains when the distance effect, θ<sup></sup> , takes larger values when estimated from more recent data as exemplified in figure 1.

Economic and econometric arguments have been advanced to explain the presence of the puzzle. Unfortunately, Disdier and Head (2008) cannot test for each effect separately from the estimates reported in their metaanalysis. For convenience, we categorize these arguments under the headings of sample composition, zeroes in the data and incorporation of multilateral resistance and look for the sensitivity in the estimates of θ<sup></sup> to these three set of controls, using the meta-analysis dataset of Disdier and Head (2008).

# **2.3. Sensitivity of Distance Elasticity estimates in existing empirical literature**

_Composition Effects_ . Composition effects appearing through the elasticity of substitution at the product level, have been invoked most.<sup>10</sup> Two recent studies shed some light. Estimating the distance elasticity of bilateral trade for 700 manufactured products in a sample including developing and industrialized countries, Berthelon and Freund (2008) find no evidence that changes in the composition of trade across manufactured commodities accounts for the distance puzzle (but they give some evidence that for 40% of industries distance became more important). However, compositional changes could take place between broader categories of products. In this vein, Melitz (2007) finds supporting evidence for the argument that there might have been a shift in trade patterns from comparative-advantage-based to intra-industry trade in differentiated products with intra-industry trade mostly among North-North countries that share similar characteristics. Distance has a positive impact on comparative-advantage-based (Ricardian) trade since differences in endowments /productivities are positively correlated with distance while it has a negative impact for trade in differentiated products. Then, if the share of trade based on comparative advantage decreases (which has certainly been the case if one considers the evolution of the share of agricultural trade in total trade), the negative impact of distance on trade will increase mechanically. In a sample including developed and developing countries, Melitz (2007) shows that when he introduces the 10 Disdier and Head (2008) discuss three channels that will yield different estimates of θ in theory-based gravity models: differences in σ across products, differences in the response of trade costs to distance across goods ρ and differences in productivity across firms (see also the discussion in Anderson and Van Wincoop, 2004, p.726).

8
with Australia in terms of power sector emissions share and coal consumption per capita. We hypothesize that the substantial weight on the economies of Israel, Gaza and West Bank may reflect similarities in climate and average income levels. We explore the sensitivity of the results to excluding certain weights in Figure 3.

Table 1: **Covariate balance**

|Variables|Treated|Synthetic|Donor Pool Avg.|
|---|---|---|---|
|Emissions share (power)|0.52|0.47|0.40|
|Emissions share (transportation)|0.21|0.21|0.17|
|Emissions share (other)|0.28|0.33|0.43|
|Log of gdp per capita (USD 000s)|3.68|3.42|2.46|
|Btus of coal per capita (Hundred MMs)|1.04|0.46|0.14|
|Btus of ng per capita (Hundred MMs)|0.50|0.34|0.50|
|Btus of petroleum per capita (Hundred MMs)|0.94|0.97|0.65|
|Emissions per capita in 2001|2.58|2.33|0.23|
|Emissionsper capita in 2007-2008(avg.)|3.00|3.14|1.49|

Notes: Table reports pre-period values of covariates in Australia (‘Treated’ column, _N=1_ ), synthetic Australia (‘Synthetic’ column) and donor pool (Donor Pool Avg. column, _N=33_ ). Each row displays the covariate average over the period 1998-2008 unless otherwise indicated. See Section 3 for details.

Table 2: **Nonzero synthetic control weights**

|Weight|Country (Unit)|
|---|---|
|0.39|Taiwan|
|0.30|Israel, Gaza and West Bank|
|0.26|Canada|
|0.05|Hong Kong SAR|

Notes: Table reports nonzero weights in synthetic Australia. See Section 3 and Appendix B for details.

To build confidence in the empirical strategy, Figure 3 shows a leave-one-out sensitivity test of the result from Figure 2. To produce the figure, each of the four economies with nonzero weights in Table 2 was dropped from the donor pool and the synthetic control was re-estimated. The resulting counterfactuals are plotted in gray. The results show that the counterfactual path of Australian emissions does not qualitatively depend on the choice of the economies listed in Table 2. The 16
<!-- Start of picture text --> Response to Cholesky One S.D. (d.f. adjusted) Innovations + 2 S.E.<br>Response of z to D(ffr) Response of D(tbeugdp) to D(ffr) Response of D(tbusagdp) to D(ffr)<br>0s 006 06 A,<br>‘oo! \\Neen 4 \\\ 004 7F/\\<br>mL \ \<br>.002 ee 002 \ ‘ 002 IC<br>/ra \\ aper \ penne<br>-,002 7 002 \ f .002 fYs\va<br>12345678 902 2 123 45678 9 0 i 2 1234567890 2<br>Response of D(bmedp) to D(ffr) Response of D(domint) to D(ffr) Response of gygap to D(ffr)<br>Pe nn 0024. “oo,<br>vo 0016 \ 015 / NN<br>~04 { \ A,<br>74 0012 \ 010 ——<br>-08 a 0004 a 5 —<br>/ Ten / ae<br>“12 / en le a ee<br>12345678 902 2 123 45678 9 0 i 2 1234567890 2<br>Response of infl to D(ffr)<br>.0000 _—<br>-0010 ZL<br>0015 //<br>.SS /i<br>1234567890 2<br><!-- End of picture text -->
# Table 2: Tenant Baseline Summary Statistics

||Units<br>(1)|N<br>(2)|Mean<br>(3)|Median<br>(4)|SD<br>(5)|
|---|---|---|---|---|---|
|**Demographics and water access**||||||
|Household size|No.|4178|3.23|3.00|1.57|
|Household total income|US$|3208|136.77|100.00|137.71|
|Compound total income (at compound level)|US$|1673|1308.27|985.50|1307.96|
|**Compound has piped water**||||||
|Yes|%|2097|73.06|-|44.38|
|Yes, but not working|%|2097|6.29|-|24.29|
|No|%|2097|20.65|-|40.49|
|**Main source of water**||||||
|Piped into compound|%|1844|72.78|-|44.52|
|Public tap/standpipe|%|1844|5.15|-|22.11|
|Water kiosk, tube well, or borehole|%|1844|20.23|-|40.18|
|Other|%|1844|1.84|-|13.46|
|**Rental market**||||||
|Deposit was paid|%|3958|46.97|-|49.91|
|Deposit amount|US$|1841|29.80|25.00|19.35|
|Monthly rent amount|US$|4154|25.16|25.00|16.77|
|**Does the rent include water?**||||||
|Including with unlimited water usage|%|3164|79.52|-|40.36|
|Including with limited water usage [1]|%|3164|13.53|-|34.21|
|Not including water bill|%|3164|6.95|-|25.44|
|**Type of tenancy agreement**||||||
|Written agreement|%|3920|7.63|-|26.55|
|Verbal agreement|%|3920|68.52|-|46.45|
|No agreement|%|3920|23.75|-|42.56|
|Rental agreement is month-to-month|%|2985|99.53|-|6.83|
|Years living in compound|Years|4176|2.98|2.00|3.78|

_Notes_ . 2015/2016 baseline data from a sample of 2 to 3 tenant households reported in this table. Responses include property owners when they live in the compound. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise stated. [1] Incurs additional charge after certain amount.

31
system. This paper explores the situation in which partial funding is implemented through a multi-pillar system in which the funded and unfunded parts (or pillars) are separated, each mandatory and each having different objectives and managerial arrangements.

The unfunded or tax-financed pillar provides a defined benefit, has the object of providing a social safety net, and is controlled by the government, which has the power to redistribute. Having a straightforward poverty-prevention goal, this pillar is more likely to achieve it than under current single-pillar systems, where poverty-alleviation is but one of several competing goals, many kinds of redistributions go on, and they are difficult to observe and measure (see World Bank 1994 for empirical evidence on the experience of single-pillar systems).

The funded part, in contrast, is privately managed and set up as a defined contribution plan. The investment managers may be chosen by workers (Chile and Argentina) , employers (Switzerland) or joint union-employer boards (Australia). Given the absence of independent unions, the fact that many employers are state enterprises, and the importance of facilitating labor mobility, the former is probably the best course in China today. This pillar is ideally suited for maximizing the rate of return on peoples' retirement saving.

Specifically, these two pillars take the following forms in this paper: 1) a basic benefit ( 25% of the local average wage)--designed to keep the old out of poverty, and financed out of broad payroll or general tax revenues (Pillar I); and 2) a defined contribution (8.5% of wages) that goes into individual accounts--fully funded, invested competitively by managers who are chosen by the workers themselves, and eventually turned into an annuity or gradual withdrawals upon the worker's retirement (Pillar II) ." One of the biggest risks of pension reform in China is the probable resistance from local and central governments toward private competitive management of pension funds--in which case many of the supposed benefits may be lost.

# **Risk in the Single-Pillar and Multi-Pillar Systems**

> 8 The flat benefit might be pro-rated by years of covered service, at the rate of, say, .6% per year. Prorating would not change the results of these simulations, so long as the average benefit remained 25% of the local average wage. The organization and regulation of the investment management of the defined contribution pillar are discussed generically in World Bank 1994.

**21**