rafmacalaba/datause-encoder-data huggingface dataset:
# **III. KOREA’S ETI INTRODUCTION**

## **1. Key Drivers of ETI**

## **Strong ICT and E-government Development**

The introduction of ETI in Korea is intricately intertwined with advances in Korea’s ICT and e-government. After the government started to build the National Electronic Backbone Network<sup>27</sup> in early 1980s and the High Speed Information and Communication Network<sup>28</sup> in mid 1990s, Korea emerged as one of the world’s most advanced countries in ICT and e-government. According to the UN e-government survey, Korea’s e-government development index ranked 13<sup>th</sup> in 2003, 6<sup>th</sup> in 2008 and 1<sup>st</sup> in 2010, 2012 and 2014 (UN, 2014, 2012, 2010, 2008, & 2003). Korea’s ICT Development Index has ranked 1<sup>st</sup> since 2008 (International Telecommunication Union, 2013, 2012, & 2011). Household Internet access reached 98.1 percent in 2013.<sup>29</sup> It was the combination of strong ICT development, service enhancement through e-government, and the general population’s familiarity with ICT that ultimately provided the support needed for further e-government service and set the stage for the introduction of ETIs in Korea.

## **Korea’s Success with Electronic Tax Administration**

For tax administration, the NTS established in 1997 the Tax Integration System to collect, store, and analyze tax information from taxpayers and electronically connect the regional tax offices in support of centralized and integrated tax administration. After the Ministry of Finance and Economy<sup>30</sup> proposed amendments to the Framework Act on National Taxes to pave the way for electronic tax filing in 1999, the NTS set up Home Tax Service, an integrated web portal, in 2002 to provide e- filing, e-payment, and other tax services and reduce tax compliance costs. Using Home Tax Service, taxpayers can file tax return electronically, make tax payments including VAT and the Special Consumption Tax, and receive various tax administration services including business registration certificates, tax payment certificates, and tax-related filings such as application for deferment of tax collection 27 The first national ICT development project covered public administration, financial business, research and education, national defense, and security from 1983 to 1995. The aim of the project was to build a small and effective government providing better services and to develop the ICT industry. 28 The national project to build information and communication highways capable of delivering multimedia data (including sound, image, and movies) at high speeds lasted from 1995 to 2005. The project was aimed at supporting the information usage of governments, public institutions, and private businesses whiling improving the overall efficiency of the society and the country as a whole. 29 International Telecommunication Union (2014) Core indicators on access to and use of ICT by households and individuals (http://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx); 30 It was reorganized into the Ministry of Strategy and Finance in 2008.

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# **VI. CONCLUSION**

Agglomeration economies are one of the important factors to increase firm productivity, despite the falling costs of distance in recent years. Firms still prefer to be located close to one another at a particular locality. Unlike other developing countries, Africa has only a few industrial clusters, such as textiles in East Africa. There are a number of potential constraints on businesses in Africa. Among others, labor productivity, land access and infrastructure are among the important common constraints.

The paper recast light on firm location behavior in Africa. Using the recent firm registry database in Tanzania, particular attention was paid to the effects of infrastructure conditions, such as transport connectivity. To deal with the potential endogeneity caused by infrastructure placement as well as spatial autocorrelation among neighboring locations, the instrumental variable spatial autoregressive model was applied. The results indicate significant agglomeration economies. Thus, it is important to foster firm agglomerations and increase industrial competition. It is also found that improved local connectivity measured by road density and transport access to large cities can attract more firms. The global market connectivity is also found to be particularly important. Firms are more likely to select a place where the transport cost to the port is low.

From the methodological point of view, the paper found the importance of dealing with infrastructure endogeneity in the empirical model. According to the exogeneity test, infrastructure variables are likely endogenous. In addition, spatial autocorrelation is also found to matter to the firm location choice. As expected, there are positive externalities of firm location choice around the neighboring areas.
# How the 1981-83 Chilean Banking Crisis was Handled

# by Maurlclo Larrain

# Table of Contents

# Summary

||Summ|ary|1|
|---|---|---|---|
|II.|The C|risis and Its Origins|3|
||2.1|LAberalization and Growth|3|
||2.2|Macroeconomic Problems|5|
||2.3|Financial Sector Problems|9|
||2.4|Magnitude of the Banking Crisis and<br>Options|10|
||2.5|The1981-82 Interventions|12|
||2.6|The 1983 Banking Crisis|14|
|111.|Mech|anisms Used to Rehabilitate Problem Banks|14|
||3.1|Background|14|
||3.2|Debt Relief Measures|16|
||3.3|Purchase of Risky Loans by the Central<br>Bank|18|
||3.4|Recapitalhzation of Intervened Banks<br>("Capitalismo Popular)|19|
||3.5|Supervision and Prudential Regulations|20|
|V.|Resul|ts|22|
quality because the original procedures may have been performed with lower quality, resulting in infections or complications that require readmission.

We compare whether there was any difference in readmission rates under PPTP in 2008 compared to DTS in 2009. Figure 8 shows that for both appendectomy and hip replacement, there was little change in readmission rates. For appendectomy, the annual readmission rate in 2008 was 3.0% whereas in 2009 it was 2.5%. For hip replacement, the annual readmission rate in 2008 was 14.8% whereas in 2009 it was 15.2%. Unlike Shmueli et al. (2002), we do not find that readmission rates increase, suggesting that either quality stayed constant or hospitals did not increase readmissions to earn greater revenue. The fact that hospital budgets stayed relatively constant during this period may account for hospitals not changing their readmissions drastically, but the results do provide suggestive evidence that there were no unintended consequences, either on quality or efficiency.

To get a sense of whether hospitals are ―purposely‖ increasing readmissions in order to earn more revenue, we examine the length of stay of readmissions. Under a DRG system, a readmission (like an additional case) results in an additional payment, so hospitals may be incentivized to readmit high cost cases as opposed to extending a patient’s stay under the original case code. Under this scenario, readmissions would increase under a DRG system even if quality remained high. As a robustness check, we examine the length of stay of the readmissions to get a sense of whether reported readmissions are ―real‖ readmissions.

Table 4 shows that ALOS for readmissions for both appendectomy and hip replacement are slightly longer under DTS in 2009 than under PPTP in 2008. If it were true that hospitals were ―purposely‖ increasing readmissions, we might expect readmission lengths of stay to be shorter under DTS, a full DRG system, because hospitals have little incentive to keep patients longer once they have been admitted. Yet, the ALOS is longer in 2009, suggesting that perhaps hospitals are not gaming readmissions, and that the readmission rate is a valid proxy for quality.

Finally, did hospitals with greater improvements in efficiency also have poorer quality? Figure 9 examines this hypothesis by examining the percent change in ALOS (efficiency) from 2008 to 2009 against the percent change in readmission rate (quality). Each dot represents one hospital observation. No clear pattern emerges for either procedure as the observations are scattered throughout, suggesting that increasing efficiency was not 9
**Table 4: Effects of Parent Insurance among Ineligible Children with and without** **<u>Eligible Siblings</u>**

|Panel A: Ever Visit|Any<br>Provider<br>(1)|Pharmacy<br>(2)|EMP<br>(3)|Public<br>Facilities<br>(4)|Private<br>Facilities<br>(5)|
|---|---|---|---|---|---|
|Parent Enrolled|-0.189|-0.039|-0.015|-0.170|-0.203|
||(0.174)|(0.176)|(0.044)|(0.154)|(0.139)|
|Parent Enrolled*Eligible||||||
|Sibling|-0.073|-0.214|0.086|0.140|-0.197|
||(0.270)|(0.273)|(0.061)|(0.242)|(0.196)|
|Eligible Sibling|-0.023|-0.005|-0.013|0.001|-0.041|
||(0.057)|(0.058)|(0.012)|(0.051)|(0.043)|
|Observations|824|824|824|824|824|
|R-squared|0.032|0.019|0.035|0.043|0.014|
|P-value of Enrolled +||||||
|Enrolled*EligibleSib|0.208|0.231|0.086|0.876|0.005|
|Panel B: Number of Visits|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-1.972|-0.307|-0.016|-0.618|-0.981**|
||(1.284)|(0.668)|(0.065)|(0.506)|(0.467)|
|Parent Enrolled*Eligible||||||
|Sibling|0.496|-0.266|0.136|0.810|0.033|
||(1.712)|(0.910)|(0.090)|(0.742)|(0.541)|
|Eligible Sibling|-0.623|-0.173|-0.016|-0.134|-0.225|
||(0.389)|(0.195)|(0.016)|(0.157)|(0.151)|
|Observations|824|824|824|824|824|
|R-squared|0.005|0.018|0.029|0.012|-0.026|
|P-value of Enrolled +||||||
|Enrolled*EligibleSib|0.202|0.361|0.059|0.738|0.003|

_Notes:_ The sample is ineligible children aged 12 and over (N=824). A sibling is considered eligible if they are 11 or under. Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status and Eligible Sibling*Parent Enrolled is instrumented with random assignment status*Eligible Sibling. The dependent variable in Panel A is whether or not the child has visited various providers over the past year. The dependent variable in Panel B is the number of times the child has visited various providers over the past year. Regressions control for household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

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percent of spending on food.<sup>10</sup> In urban areas, the shares are 35.0 and 15.1 percent respectively. Rice is also an important staple in urban centers (31.1 percent of spending on staples, and 13.4 percent of spending on food), but less so in the countryside (13.3 percent of spending on staples). The focus is on seasonality in maize and rice prices and whether this translates into seasonality in caloric intake. Bananas and cassava account on average only for 3.6 and 6.5 percent of total food spending among rural households (even less among urban households).

Monthly maize and rice price series from 20 regional markets in mainland Tanzania during 1995‐2013 (all regional capitals) were obtained from the national statistical offices.<sup>11</sup> Prices are expressed in local currency and deflated by the national CPI.<sup>12</sup> If the data set had been complete, there would have been 228 observations for each market (or 4,560 observations in total). Overall, 6.3 percent of the maize prices are missing and 5.4 percent of the rice prices, indicating that the prices are relatively complete. More than half of the markets are almost complete (more than 220 observations out of a maximum of 228). However, for some markets less than half of the possible observations are available, with minimums of 84 (maize) and 105 (rice). This underlines the importance of using a procedure for estimating seasonal impacts which is robust to the presence of these gaps.

For comparison purposes, seasonality in international prices for white maize and rice is also analyzed. The Johannesburg futures market (SAFEX) provides the reference price for white maize in southern and east Africa. This price is quoted in rand. For rice, the most commonly used reference price is the Bangkok spot price<sup>13</sup> which is quoted in US dollars. Monthly SAFEX

> 10 Staples comprise all cereals, tubers and starchy products whether consumed crude or processed. They include wheat, rice, sorghum, millet, maize, cooking bananas, Irish and sweet potatoes and yams. Pulses (beans, peas and oilseeds such as groundnuts) are not included. Shares are calculated from the Tanzania (2008‐2009) LSMS‐ISA household survey.

> 11 In a companion paper, Gilbert, Christiaensen and Kaminski (2015), provide a systematic analysis of the extent of price seasonality across an array of staple and non‐staple food and food products in seven African countries.

> 12 Price seasonality estimates using nominal prices are very similar.

> 13 White milled 5 percent broken. Source: IMF, _International Financial Statistics._

14
<!-- Start of picture text --> Figure 2a Sources of U.S. Imports, 2007 Figure 2b Destination of U.S. Exports, 2007<br>India<br>India<br>2%<br>2%<br>SSA<br>SAFTA SSA<br>3% ROW SAFTA ROW<br>0.5% 2%<br>10% 0.3% 13%<br>ASEAN<br>ASEAN<br>6% NAFTA<br>5% NAFTA<br>24%<br>26%<br>MENA MENA<br>6% 6%<br>China<br>6%<br>China EU27<br>EU27<br>14% 21% Japan<br>25%<br>7%<br>Japan LAC<br>7% LAC 8%<br>6%<br><!-- End of picture text --> ASEAN = Association of South East Asian Nations, EU = European Union, LAC = Latin America and the Caribbean, MENA = Middle East and North Africa, SA = South Asia, SSA = Sub-Saharan Africa, NAFTA = North American Free Trade Agreement, SAFTA = South Asian Free Trade Area.

# _Source_ : GTAP 8 Database.

Notes: NAFTA share consists of Canada and Mexico and does not include the United States. SAFTA share does not include India.

7
Table 1: Data availability and sources

||t=1|obs|t=2|obs|source|
|---|---|---|---|---|---|
|Albania|2008|5044|2012|6083|ECAPOV|
|Bosnia and Herzegovina|-|-|2013|19203|LFS|
|Croatia|2004|6076|2009|6224|LFS|
|Kosovo|-|-|2012|587025|ECAPOV|
|Macedonia|2007|43187|2011|38078|ECAPOV|
|Montenegro|2007|2184|2011|2142|ECAPOV|
|Serbia|2006|10038|2010|8614|ECAPOV|
|Turkey|2007|21308|2013|25524|ECAPOV|

In Self-employment (Table 3), the gap is also apparent in all countries, with the exception of Croatia, where men are underrepresented in both 2004 and 2009. The self-employment gap increases in Albania, Serbia, and Turkey and falls in Macedonia. The largest gap takes place in Macedonia in 2007, where only 28 women work as self-employed for every 100 men. Finally, there is a very clear gender gap in the category Employers (Table 4) where women are always underrepresented. Turkey has the largest gap in 2007, when only about 12 women worked as employers for every 100 men. The gap has decreased over time in Albania, Croatia, Macedonia, and Turkey; it has increased in Montenegro, and remained roughly constant in Serbia.

Table 2: Gender Gaps in Labor Force Participation (%)

||_t_= 1|_t_= 2|
|---|---|---|
|Albania|28.1|50.2|
|Bosnia and Herzegovina||38.4|
|Croatia|18.1|20.5|
|Kosovo||68.6|
|Macedonia|33.1|34.8|
|Montenegro|31.4|34.6|
|Serbia|31.3|31.8|
|Turkey|58.9|48.7|

# 3 Aggregate eects of the gender gaps in the Balkans and Turkey

## 3.1 Theoretical benchmark

In order to help interpret the numerical results of Section 4, we present here a brief summary of the model developed in Cuberes and Teignier (2014a). Their paper develops a general equi4
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and whether it is perceived in the same way by both parties. Joint decision making has been found to correlate negatively with IPV in several country studies, including Ebrahim and Atteraya (2019), Friedemann-Sánchez and Lovatón (2012), Gage (2005), Hindin and Adair (2002) and Svec and Andic (2018). This literature primarily focuses on the wife’s perception of the process of decision making and how egalitarian she thinks this process is. While one analysis included both the husband’s and wife’s responses (Zegenhagen et al. 2019), we go beyond this and include covariates that reflect not only their responses, but whether or not they give the same responses. This allows us to explore the direction of disagreement and its asymmetric effect on IPV.

A separate body of interdisciplinary research offers a theoretical explanation for why joint decision making may be associated with lower IPV. In the management, political science and conflict resolution literatures, joint decision making is highlighted as a tool for diffusing responsibility and reducing decisional conflict by sharing risk. For example, in her typology of political blame management strategies, McGraw (1991) features ‘horizontal diffusion of responsibility’, whereby political leaders avoid conflict in the face of a decision gone awry by arguing that their decision was a joint product of a group of individuals. Thompson (1980) discusses the blame-mitigation strategy of emphasizing ‘collective responsibility’. Shared decision making between doctors and patients is also highlighted as a tool for reducing decisional conflict—and — hence, malpractice lawsuits in the medical literature (Hoffmann et al. 2014, Kremer et al. 2007).

Our paper builds on existing work by examining the relationship between joint decision making and physical, sexual and emotional IPV across 12 Sub-Saharan African countries, using nationally representative Demographic and Health Surveys (DHS) for 31,243 couples. We find a strong and significant relationship between joint decision making and all three types of IPV. Compared to joint decision making, sole decision making by the husband is associated with a 3.3 percentage point (20.6 percent) increase in 3
The slow post-crisis recovery in the developed world, coupled with the relatively rapid recovery in the developing one, has underscored the future economic potential of emerging markets. Projections of post-crisis M&A volume by emerging market firms suggest that future M&A activity, while moderating somewhat, is likely to remain fairly robust. Emerging market firms are thus likely to be at the forefront of this process of global economic convergence, and are fast becoming a potent force for globalization in their own right. Future research in this area will do well to study how the economic behavior of such globalized emerging market multinationals may differ from those of advanced country corporations, beyond their choices in cross-border M&A.

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