rafmacalaba/datause-encoder-data huggingface dataset:
merely declining to accept the e-invoice. The derogation for the buyers’ acceptance of e-invoicing was inevitable under compulsory ETI. However, there could also be criticism about such derogation. Kim and Sur contended that such ETI-issuing process without the buyers’ acceptance only favors suppliers when a legal dispute pertaining to a transaction, a payment, or an invoicing arises (2012). In this respect, Korea’s legal solution will be subject to future legal proceedings and rulings.

# **IV. ASSESSING KOREA’S SUCCESS WITH ETI**

Although only four or five years have passed since the introduction of compulsory ETI in Korea, this paper attempts to analyze what it has achieved and what effect it has had on tax compliance. First, an analysis is undertaken on ETI adoption rates, tax compliance cost reduction, and combating tax evasions and frauds using data provided by the NTS and previous research findings. Second, descriptive and statistical analysis are conducted on how Korea’s ETI affected tax compliance, the transparency of business transactions, tax service, and taxpayers’ burden in ETI compliance using survey results taken from private tax practitioners and business taxpayers in Korea.

## **1. Analysis of NTS Data**

## **Pace of ETI Adoption**

After the introduction in 2011, ETI adoption reached nearly 100 percent as shown in Table 3, a remarkable achievement from around 15 percent before the adoption.

Table 3: Pace of ETI Adoption

|Year|Number of<br>Compulsory<br>issuers (A)|companies<br>ETI<br>participation<br>(B)|B/A<br>(%)|Transacti<br>Total value<br>of<br>transactions<br>(C)|on value (KRW,<br>Value of ETI<br>transactions<br>(D)|trillions)<br>D/C (%)|
|---|---|---|---|---|---|---|
|2013|473,794|470,359|99.3|2,456|2,454|99.9|
|2012|448,996|445,252|99.2|2,421|2,416|99.8|
|2011|424,545|420,023|98.9|2,345|2,341|99.8|

Source: The National Tax Service, Korea; As a result of the rapid transition, penalties for late or non-transmitted ETI information to the NTS continued to fall as shown in Figure 6. Penalties assessed on corporate businesses’ voluntary reporting fluctuated but gradually declined as shown in the trend line. Other penalties pertaining to NTS charges and individuals voluntary reporting also headed lower.

32
# **1. Introduction and Motivation**

A large body of research shows that risks to human and cognitive development are not homogeneously distributed over the life-cycle. They are typically higher in earlier stages of life, with important long-term and sometimes irreversible consequences. In Egypt, as elsewhere, many of the critical inputs for early childhood development, such as the quantity and quality of food, early education and healthcare, and the availability of clean water and sanitation, are unequally distributed among children. For instance, Egypt’s children and youth, representing more than one-third of the country ́s population, face a higher risk of poverty than the general population; and the gap has widened during the past decade (World Bank, 2011). The unequal distribution of these factors contributes in turn to inequality in desirable early childhood development outcomes, and, as such, represents an important source of inequality later in life.

Children’s access to essential basic services or lack thereof will determine their education, health and labor market outcomes, and thus their income-earning potential as adults. Longitudinal studies show that investments in children from poor and vulnerable families can translate into higher earnings in adulthood, thus helping break the cycle of poverty. Nationally, more equitable access to basic services in the early years could lead to more human capital accumulation, which in turn could translate into higher economic growth (see Galor and Zeira, 1993 for theoretical evidence; Birdsall and Londono 1997 and LoÂpez et al., 1998 for empirical evidence). While the acceptable level of inequality of outcomes (such as income) in a society is debatable, policies to give all children equal opportunity, regardless of their socioeconomic background, are embraced across the political spectrum. Early childhood development interventions are thus generally considered as some of the few policy areas where the traditional equity-efficiency trade-off does not exist (Heckman and Masterov 2007). It is therefore important to understand how children’s opportunities develop and identify policy interventions that contribute to reducing the impact of predetermined factors.

2
# Social Impacts of Climate Change in Chile: A municipal level analysis of the effects of recent and future climate change on human development and inequality<sup>*</sup>

by Lykke E. Andersen<sup>a</sup> Dorte Verner<sup>b</sup>

**Keywords:** Climate change, social impacts, Chile. **JEL classification:** Q51, Q54, O15, O19, O54.

> * This paper forms part of the World Bank research project “Social Impacts of Climate Change and Environmental Degradation in the LAC Region.” Financial support from the Danish Development Agency (DANIDA) is gratefully acknowledged. The assistance of Yyanny Cruz Aguayo is greatly appreciated as are the comments and suggestions of Kirk Hamilton, Jacoby Hanan, John Nash, Maximillian Shen, and David E. Yuravlivker. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent.

> a Institute for Advanced Development Studies, Calle 21 #8350, Piso 7, Calacoto, La Paz, Bolivia. Phone/Fax: +591-2-214-6069. Please direct correspondence concerning this paper to <u>landersen@inesad.edu.bo.</u> b The World Bank, Latin America and Caribbean Region, Washington, DC. E-mail: <u>dverner@worldbank.org.</u>
# **Table 8: Intrahousehold heterogeneity of impacts by past academic achievement : all elegible households**

# **<u>Fixed Effects Model</u>**

||||Nu|mber of hours p|er week child|between 6 an|d 15 years work|ed in :||
|---|---|---|---|---|---|---|---|---|---|
|COEFFICIENT|nr obs/hh|non-<br>agricultural|agriculture &<br>livestock|<br>agriculture|livestock|chores|domestic<br>work|all econ<br>activity|all work|
|male*at least 1 grade behind|3228/1551|0.108|0.272|0.309|-0.0382|-0.245|-0.344|0.391|-0.199|
|||(0.16)|(0.96)|(0.99)|(0.15)|(0.34)|(0.27)|(1.01)|(1.21)|
|male*at least 1 grade behind*itt||-0.332|-2.734**|-2.560**|-0.132|0.683|0.966***|-3.087**|-1.438|
|||(0.21)|(1.21)|(1.22)|(0.29)|(0.44)|(0.34)|(1.27)|(1.43)|
|female*at least 1 grade behind||-0.0820|0.143|0.295|-0.168|0.231|0.220|-0.00201|0.449|
|||(0.11)|(0.58)|(0.54)|(0.18)|(0.39)|(0.31)|(0.61)|(0.85)|
|female*at least 1 grade behind*itt||-0.232|0.682|0.374|0.328|-0.194|-0.269|0.310|-0.153|
|||(0.22)|(0.73)|(0.64)|(0.27)|(0.45)|(0.43)|(0.74)|(1.04)|
|male*itt||-0.158|0.0639|0.401|-0.354|-0.707|-1.452***|-0.266|-2.425**|
|||(0.23)|(0.67)|(0.71)|(0.26)|(0.49)|(0.50)|(0.73)|(1.11)|
|P-value female*grbehind*itt=male*g|rbehind*itt|0.745|0.012|0.032|0.232|0.181|0.033|0.016|0.465|
|male*number grades behind|3192/1543|0.0151|0.218|0.184|0.0174|-0.0145|-0.181**|0.251|0.0555|
|||(0.043)|(0.45)|(0.47)|(0.055)|(0.13)|(0.076)|(0.45)|(0.49)|
|male*number grades behind*itt||-0.116*|-1.242**|-1.081**|-0.127|0.120|0.344***|-1.429***|-0.966*|
|||(0.058)|(0.50)|(0.51)|(0.095)|(0.15)|(0.10)|(0.50)|(0.54)|
|female*number grades behind||-0.00916|0.105|0.0908|0.0211|0.0909|0.0208|0.110|0.222|
|||(0.045)|(0.17)|(0.14)|(0.084)|(0.10)|(0.14)|(0.18)|(0.28)|
|female*number grades behind*itt||-0.123*|-0.0632|-0.0531|-0.0234|-0.0392|-0.0648|-0.287|-0.391|
|||(0.068)|(0.24)|(0.19)|(0.12)|(0.13)|(0.18)|(0.26)|(0.36)|
|male*itt||-0.200|0.514|0.778|-0.355|-0.464|-1.536***|0.190|-1.810|
|||(0.23)|(0.84)|(0.84)|(0.25)|(0.43)|(0.53)|(0.92)|(1.24)|
|P-value female*grbehind*itt=male*g|rbehind*itt|0.941|0.022|0.042|0.441|0.404|0.043|0.033|0.363|

_NOTE:_ Standard errors corrected for clustering at community level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Dependent variables corrected for gender-specific age trends by regression of outcomes in control on series of age dummies for each gender using a household fixed effects model. All estimates include gender dummy and household level fixed effects. All households with at least 2 children between 6 and 15. Academic achievement measured at baseline. age1015 is a dummy variable = 1 for children between 10 and 15.

28
link between human capital and migration. Section 4 examines the level of expenditure and structure of the economy, section 5 constructs measures of the Balassa-Samuelson adjusted real exchange rates for these economies and explores whether they are under or over-valued and section 6 looks at export performance. Section 7 concludes.

# **2. Trends in Migration and Remittances**

Data on annual net migration, per thousand of the population, for the eight FSU countries in Central Asia and the South Caucasus, in five-year periods beginning at the turn of this century, are shown in table 1. In the first decade of this century, net migration for Armenia and Georgia was negative (more migrants left than arrived) and averaged more than 1 percent of the population per annum; hence they incurred a drop of more than 10 percent of their population over the course of the decade. Since 2010, net out-migration from Armenia has reduced markedly, to less than a quarter of the levels in the previous decade. Net out-migration from Georgia accelerated in the first half of the second decade of the century, but then fell back sharply after 2015.

**Table 1 Net International Migration Rates per 1000 population (annual averages)**

||2000‐05|2005‐10|2010‐15|2015‐20<br>projection|2000‐20|
|---|---|---|---|---|---|
|Armenia|‐10.6|‐10.5|‐2.1|‐1.7|‐6.2|
|Azerbaijan|0.9|1.2|0.0|0.0|0.5|
|Georgia|‐12.1|‐13.7|‐14.9|2.5|‐9.6|
|Kazakhstan|0.6|‐0.4|1.9|0.0|0.5|
|Kyrgyz Republic|‐6.9|‐2.9|‐4.9|‐3.3|‐4.5|
|Tajikistan|‐2.5|‐2.0|‐2.5|‐2.2|‐2.3|
|Turkmenistan|‐5.4|‐2.5|1.9|‐0.9|‐1.7|
|Uzbekistan|1.9|‐1.0|‐0.4|‐0.3|0.1|

Source: Asian Development Bank The Kyrgyz Republic experienced heavy net out-migration in the first five years of the century, which then reduced in the second five years before increasing again after 2010. Net outmigration from Tajikistan has been fairly stable, at between 0.2 and 0.25 percent of the population per annum, since 2000.

Table 2 shows net cumulative migration over the 2000-18 period as a percentage of the 2018 labor force of each country. The two countries with the largest loss of population to net migration relative to their labor force were Georgia and Armenia. Since the turn of the century, net migration from Georgia and Armenia amounted to 37 percent and 27 percent respectively 3
intensity of smoking on smoker’s wages. To account for a potential endogeneity of a smoking experience with respect to wages, we instrument this variable with the instruments we use in our FIML estimations. The rational for the instruments is similar: we argue that parental smoking might affect the intensity of smoking and age at which an individual starts smoking but has no direct effect on individual wages. The results of this IV regression (available from the authors on the request) are similar to the results of OLS estimation, i.e., the effect of smoking experience on wages of current smokers and quitters is insignificant.

The economic cost of smoking is large for an individual smoker and the implied societal cost of smoking could also be substantial. Taking a conservative estimate of the negative effect of smoking on wages for the prime-age males to be about 20 percent and calculating an aggregated loss in wages of 30 percent (about 400,000 workers) of current male smokers, every month the Albanian economy looses approximately 2.6 percent of its GDP because of negative consequences of smoking among the prime age males<sup>14</sup> .

# **5. Conclusions**

For working prime-age men in Albania, our analysis shows that wages of smokers are significantly lower than wages of individuals who never smoked. The simple comparison indicates an 8 percent wage advantage of non-smokers relative to smokers. However, smokers differ from non-smokers in both observable and unobservable characteristics. Consistent with other studies for developed countries, the wage penalty differs depending on how observed and unobserved traits of smokers and non-smokers are addressed. Controlling for the differences in observable characteristics in an OLS regression, the negative wage effect of smoking persists but is reduced to 4 percent. Taking into account both the differences in observable and unobservable characteristics of smokers and nonsmokers, the wage penalty for smoking is statistically and economically large: smokers experience wage reductions of 21-28 percent. The large wage reduction caused by smoking is robust to several specifications, including 2SLS and FIML estimations and

> 14 This simulation is based on figures from CIA (2006). According the official exchange rate the 2005 GDP in Albania was $8.52 B, the total population was 3,500,000 people, and the average monthly wage of nonsmokers is about $230.

16
Firms actively use these markets to obtain financing. On average, firms raise capital between two and three times per decade, with the average issuance accounting for 19 percent of a firm’s total assets.

Third, firms that raise funds in capital markets tend to grow at a faster pace than non-issuing listed firms, and at a faster pace than GDP (Table 1, Panel B). Over the period 2009-16, the median firm raising capital through equities or bonds grew 4.3 percentage points faster in terms of assets than the median non-issuing firm in that period. This growth differential is consistently positive over time and across different measures of firm performance. For example, over the same period, the annual growth rate of employment by issuing firms was 2.8 percent, whereas it was 0.2 percent in the case of non-issuing firms. As comparison, the average growth rate over time of the median listed firm in the median country is similar to the average GDP growth rate of the median country. For example, for the median listed firm the average growth of sales was 2.8 percent over the period 1991-99, 11.7 percent over 2000-07, and 0.4 percent over 2009-16. This is similar to the average GDP growth rate for the median country, which was 2.4 percent, 8.3 percent, and 0.5 percent, respectively, over the same periods.

# **3. Capital Market Financing and Firm Performance**

To assess the difference in performance between issuing and non-issuing firms, we employ an event study approach, where the dependent variable is a measure of firm growth around a five-year issuance window. We use a difference-in-differences regression strategy that evaluates the growth of issuing and non-issuing firms before, during, and after issuances. We focus on five measures of firm growth: the growth rate of total assets, sales, tangible fixed assets (PPE), intangible assets, and the number of employees. Whereas total assets will increase after a firm issues securities if the firm accumulates cash or other financial assets, increases in tangible fixed assets, intangible assets, and employees typically involve improvements in the productive capabilities of the business. To allow for potential differences 12
# **1 Introduction**

The increase in vertical specialization is changing the patterns of trade across the globe, as reflected in the location of production, trade flows and wages. Vertical specialization can be defined broadly as “the interconnectedness of production processes in a vertical trading chain that stretches across many countries, with each country specializing in particular stages of a good’s production sequence”, to put it in the terms of Hummels et al. (2001), and has been alternatively labeled as, to name a few, “outsourcing”, “fragmentation” or “intra-product specialization” in the international trade literature.

Despite some efforts towards collecting evidence of vertical specialization in advanced (mainly OECD) economies, there is little evidence of vertical specialization for developing economies. While Hummels et al. (2001) report results for Australia, Canada, Denmark, France, Germany, Japan, Italy, Netherlands, United Kingdom, United States, Ireland, Korea, Mexico and Taiwan for several years, Breda et al. (2008) replicate the exercise for Belgium, France, Germany, Italy, Netherlands, Spain and United Kingdom. This paper contributes to the existing literature in an attempt to narrow this gap providing comparable evidence of the level of vertical specialization in Argentina in 1997.

We will focus on a particular aspect of disintegrated production processes, namely, one that looks at imported goods and services used as inputs in the production of export goods and services. In this sense, the imported content of exports comprises a narrow concept of this phenomenon. Following this approach to vertical specialization, Hummels et al. (2001) develop a measure of vertical specialization based on data from input-output tables, which measures imported inputs embodied in goods that are exported. Here we follow the same approach and apply their methodology to measure vertical specialization in Argentina for 1997. We use the 1997 input-output tables for Argentina, compiled by the National Institute of Statistics and Census (INDEC). We estimate the imported content of Argentine exports for 17 aggregate sectors of economic activity and for the country as a whole, as well as some other related indicators. To our knowledge, this is the first study to perform such an exercise applying a comparable methodology using data for Argentina or any South American country.

The paper is organized as follows. In section 2 we outline the estimation methodology. Section 3 briefly describes the data and presents the results. Section 4 concludes with some final remarks.

# **2 Methodology**

We follow Hummels et al. (2001) in their measures of vertical specialization (VS). For country _k_ and sector _i_ a first measure of VS can be defined as follows: ∙ (1) **VS** _ki_ = [(imported intermediates)/gross output] exports.

2
# **APPENDIX: ADDITIONAL TABLES**

|**Table A1.1: Firs**|**t Stage- IV- Co**|**lumns 1 to 3**||||
|---|---|---|---|---|---|
|**Column in IV**|**1**|**2**|**3**|**3**|**3**|
|Dependent<br>Variable:|**Inputs per**<br>**worker**|**Program**<br>**Dummy**|**Program**<br>**dummy**|**prog***<br>**inputs per**<br>**worker**|**Prog*capital**<br>**per worker**|
||(1)|(2)|(3)|(4)|(5)|
|**_Factors_**||||||
|Capital-per-|0.152**|-0.015|0.033|0.204|0.116|
|worker||||||
||(0.06)|(0.02)|(0.04)|(0.36)|(0.33)|
|Inputs-per-||0.026|0.072|0.545|0.414|
|worker||||||
|||(0.03)|(0.06)|(0.49)|(0.46)|
|**_Activities_**||||||
|PCB|0.472|0.314***|0.354***|3.267***|3.298***|
||(0.33)|(0.12)|(0.12)|(1.06)|(0.99)|
|HCB|0.89***|-0.035|-0.026|-0.276|-0.219|
||(0.29)|(0.10)|(0.10)|(0.88)|(0.83)|
|Gravel|<br>-2.120***|<br>0.129|<br>0.127|<br>1.147|<br>1.540|
||(0.68)|(0.23)|(0.26)|(2.28)|(2.15)|
|Wood|-0.306|0.198**|0.182*|1.666*|1.149|
||(0.30)|(0.10)|(0.10)|(0.86)|(0.81)|
|Wall|<br>-0.540|<br>0.091|<br>0.086|<br>0.914|<br>1.622|
||(0.51)|(0.17)|(0.17)|(1.52)|(1.43)|
|Structural|<br>0.875|<br>0.028|<br>0.053|<br>0.354|<br>1.199|
||(1.83)|(0.59)|(0.59)|(5.23)|(4.92)|
|Electrical|0.152|0.049|0.028|0.216|0.018|
||(0.69)|(0.22)|(0.22)|(1.98)|(1.86)|
|Sanitary|-0.908|0.024|0.049|0.492|-0.176|
||(0.56)|(0.18)|(0.18)|(1.63)|(1.54)|
|Finishing|-0.101|-0.133|-0.134|-1.163|-1.312|
||(0.63)|(0.20)|(0.20)|(1.79)|(1.69)|
|Production-|0.522|-0.307**|-0.334**|-3.237***|-3.081***|
|inputs||||||
||(0.47)|(0.14)|(0.14)|(1.24)|(1.16)|
|Other|1.472***|-0.159|-0.150|-1.370|-1.606|
||(0.39)|(0.15)|(0.15)|(1.35)|(1.27)|
|**_Instruments_**||||||
|Labor-at-start|0.121***|0.164***|0.581*|2.186|1.682|
||(0.03)|(0.05)|(0.28)|(2.52)|(2.37)|
|Labor-at-|||-0.023|0.094|-0.104|
|start*capital||||||
||||(0.03)|(0.24)|(0.22)|
|Labor-at-|||-0.028|-0.215|0.028|
|start*inputs||||||
||||(0.02)|(0.20)|(0.19)|
|Constant|7.063***|-0.005|<br>-0.760|<br>-5.168|<br>-2.951|
||(0.55)|(0.28)|(0.59)|(5.24)|(4.93)|
|N|148|148|148|148|148|
|R2|0.381|0.387|0.398|0.414|0.395|
|Adjusted R2|0.321|0.322|0.325|0.342|0.321|

Note: *, **, *** indicate significance at the 10%, 5% and 1% level respectively

-22-
- 39 -

# REFEENCES

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- Gomez-Castellanos, Luisa and George Psacharopoulos. 1990. "Earnings and Education in Ecuador: Evidence from the 1987 Household Survey." Economics of Education Reiew 9(3):219-27.

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- Mincer, J. 1974. Schooling. Experience and Earnings. New York: Columbia University Press.

- Fiszbein, Ariel and George Psacharopoulos. 1991. "A Cost Benefit Analysis of Educational Investment in Venezuela, 1989." Views from LATHR, Latin America and the Caribbean Technical Department, The World Bank.

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**Table 5b. Concentration indexes of selected MNCH services in Ethiopia (2000-2014)**

|Year|CI|SE|norm CI|SE|abs CI|SE|
|---|---|---|---|---|---|---|
|_Measles Vaccination_|||||||
|2000|0.242|0.029|0.331|0.040|0.065|0.008|
|2005|0.142|0.023|0.224|0.036|0.052|0.008|
|2011|0.096|0.016|0.223|0.036|0.055|0.009|
|_Full Immunization_|||||||
|2000|0.345|0.044|0.403|0.051|0.050|0.006|
|2005|0.173|0.035|0.220|0.044|0.037|0.008|
|2011|0.223|0.031|0.297|0.042|0.056|0.008|
|ANC|||||||
|2000|0.474|0.030|0.529|0.034|0.049|0.003|
|2005|0.467|0.025|0.530|0.028|0.056|0.003|
|2011|0.447|0.022|0.532|0.026|0.071|0.004|
|2014|0.382|0.024|0.503|0.032|0.092|0.006|
|Contraception|||||||
|2000|0.496|0.039|0.534|0.042|0.036|0.003|
|2005|0.382|0.019|0.453|0.023|0.060|0.003|
|2011|0.243|0.014|0.347|0.019|0.073|0.004|
|2014|0.120|0.014|0.218|0.026|0.054|0.006|
|_SBA_|||||||
|2000|0.665|0.037|0.705|0.040|0.038|0.002|
|2005|0.698|0.034|0.740|0.036|0.040|0.002|
|2011|0.650|0.028|0.722|0.031|0.065|0.003|
|2014|0.526|0.030|0.622|0.035|0.081|0.005|

Source: Authors’ compilation from the DHS (2000, 2005, 2011 & 2014) data. Note: CI is concentration index; norm CI is normalized concentration index; and abs CI is absolute concentration index.

The CIs in all the three undernutrition indicators show widening pro-rich inequality; as data from the DHS 2000-2014 show, the CI in absolute value more than doubled for all undernutrition indicators. This is also illustrated in Figure 2a, which compares concentration curves in two periods. The concentration curves for the latest survey are on the top of the concentration curves for the earlier survey showing the increase of wealth-related inequalities in favor of the better off. The test of dominance between concentration curves in Figure 2a shows that the upward shift is significant in 26