rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Views of Hungarian Health Actors**

||**Has cross-border purchasing**<br>**been growing in the last years?**|**Do you perceive foreign patients**<br>**as a good business opportunity?**|
|---|---|---|
|NHIF|Moderate|No (at least not for the public<br>sector)|
|Association of Human Private<br>HealthCareProviders|Moderate|Yes (though primarily for<br>marketing considerations)|
|Buda Health Center|Number and share of foreign<br>patients has been growing<br>considerably|Yes|

# **4 QUANTITATIVE DATA ON CROSS-BORDER PURCHASING OF HEALTH CARE**

There are few reliable data available on cross-border purchasing of health care between Austria and Hungary. The regional Austrian HIFs and the HVSV did not provide statistics that differentiate between the countries to which insured persons travel in order to receive health care treatment. The Hungarian NHIF, however, was able to deliver some differentiating data.

The gathered data do not provide a satisfying picture of the phenomenon of crossborder purchasing of health services. The number of Austrian patients seeking health care in Hungary within the EC regulatory structure is about 42,500. The number of Austrian patients seeking health care abroad world-wide and then claiming (partial) reimbursement from Austrian social insurance—estimated by the Austrian Federal Ministry for Social Affairs—revolves around 58,000 per year. An unknown number of patients travel to Hungary to receive mainly dental treatment and cosmetic surgery outside these established structures. Overall, it can be said that the number of Austrian patients treated in Hungary is quite modest.<sup>23</sup>

# **4.1 Data for Austria**

According to reports of the Administrative Commission on Social Security for Migrant Workers, in 1997, Austria spent 0.48 Euro per inhabitant on medical benefits consumed in other countries on average. A year later, this value rose to 1.87 Euro and amounted to 8.90 Euro in 2004.<sup>24</sup> According to rather old data from the Austrian Ministry for Social Affairs presented in Table 3 (1997–1998), very few patients, i.e. an estimated 850, travel each year to another country under the E112 Procedure (see Section 2.1.2), for the average costs of 5,523 Euro. The E112 authorization procedure is mainly used for hospital treatment.

> 23 Albreht, Pribaković and Štalc (2006) find the same for Austrians traveling to Slovenia to receive health care.

> 24 However, according to a representative of the Austrian Federal Ministry for Social Affairs, these data are highly questionable.

15
Table 1: Comparison of IE data to nationally representative surveys

|Variable|Source|Source data|IE data|
|---|---|---|---|
|_Demographics_||||
|% HH Head that are Female|DHS|23.4%|13.6%|
|AvgHH size|DHS|5.40|4.90|
|_Housing Characteristics_||||
|_Flooring material_||||
|Earth|DHS|80.9%|82.5%|
|Concrete|DHS|17.5%|14.9%|
|_Cooking fuel_||||
|Paraffin/Kerosene|DHS|0.5%|0.5%|
|Charcoal|DHS|8.5%|8.5%|
|Firewood|DHS|90.1%|91.0%|
|_Access_||||
|HH has access to improved water source|DHS|46.2%|33.3%|
|_Assets_||||
|Electric Generator|DHS|4.3%|1.4%|
|Radio|DHS|57.1%|67.7%|
|TV|DHS|5.3%|2.9%|
|Mobile Phone|DHS|51.3%|58.9%|
|Non-Mobile Phone|DHS|0.2%|0.4%|
|Iron|DHS|19.6%|13.5%|
|Refrigerator|DHS|1.2%|1.3%|
|Bike|DHS|50.9%|62.7%|
|Motorcycle|DHS|4.9%|5.1%|
|Car or Truck|DHS|1.1%|0.6%|
|_Net School Enrollment Rate_||||
|Pre-Primary|National Panel|21.0%|20.4%|
|Primary|National Panel|79.0%|67.9%|
|Secondary|National Panel|20.0%|16.2%|
|Higher|National Panel|1.0%|0.0%|
|_Health_||||
|_Anemia_||||
|% of children under 5 with Hemoglobin<br>less than 8 grams per deciliter<br>_Anthropometrics_|DHS|5.4%|2.1%|
|Height for Age 2SD Below|DHS|44.5%|47.5%|
|Weight for Height 2SD Below|DHS|4.8%|2.5%|
|Weight for Age 2SD Below|DHS|16.9%|15.4%|
|Height for Age 3SD Below|DHS|17.7%|17.1%|
|Weight for Height 3SD Below|DHS|1.3%|0.4%|
|Weight for Age 3SD Below|DHS|4.1%|3.2%|
|Height for Age Mean|DHS|-1.80|-1.95|
|Weight for Height Mean|DHS|0.00|0.02|
|Weight for Age Mean|DHS|-1.00|-1.03|

Notes: DHS = 2010 Demographic and Health Survey; National Panel = Tanzania National Panel Survey (LSMS) 2010 - 2011 36
17 Mexico MFA

# **2.3** Testing Differences in Composition

Although the results in the previous section seem to indicate the possible existence of<sup>rent</sup> sharing, there are other porible explanations for the difference between the Mexican export price and the U.S. price. One possible explanation is a difference in the composition of the groups of apparel, with Mexican exports concentrating on the lower value MFA categories that make up the groups,<sup>and</sup> U.S. products concentrating on the high value end. As explained in the data appendix, each of the groups examined is an aggregation of categories in the MFA export data and different categories<sup>of</sup> U.S. production data. The problem faced when attempting to compare the unit value of U.S. production to the unit value of Mexican exports under the MFA is that at the most disaggregated levels the two category groupingc are not compatible. In aggregating Lhem to the more comparable group levels, some compositional bias is inevitable, and it may show up in the marginal rent-sharing parameter f3, the coefficient on pjtur, or in the intercept term. This section tests whether the price differences detected above can be explained entirely by this compositional bias. If composition bias cannot be ruled out, then the assumption of homogeneous product groups cannot be maintained<sup>and</sup> some accounting for product differences must be made.

The procedure for testing compositional differences follows that of Erzan, Krishna, and Tan (1991), with some modifications to allow for a more efficient estimation of the parameters<sup>and to</sup> explicitly test the assumptions underlying the test procedure. The aggregate prices can be decomposed into their production weighted components as follows: where the subscript i refers to the category belonging to apparel group j. Qijus is the quantity of<sup>U.S.</sup> output in category i of group j, and **QJUu** is total output in group j. Hence wijUs is the quantity weight cF category i in group j of U.S. production. Similarly, for Mexico: (3) pj<sup>m</sup> (Qj/Qj) = i Wij, i= ,...,n, j=1,...,6. i I
<!-- Start of picture text --> me | Pit Paty<br>GnrR Ph + Path<br><!-- End of picture text -->

me | Pit Paty GnrR Ph + Path

. Let F(-) be the new cumulative density function, and eMaX and emmn F(ae) = 1. There are eTMTM* = qeTM* and eTMTM = qemin =P, and F(e ) > F(ae ) = F(e ). Therefore, the denominator of <!-- Start of picture text --> Ji f(e)de = 1—- F(e*) < 1-<br><!-- End of picture text --> Ji f(e)de = 1—- F(e*) < 1- F(e*) Assuming that e4*>_~TMa~
WPS3366

# **Knowledge and Development A Cross-Section Approach**

Derek H. C. Chen and Carl J. Dahlman * The World Bank Washington DC 20433

## **Abstract**

This paper assesses the effects of knowledge on economic growth. By using an array of indicators, each of which represents an aspect of knowledge, as independent variables in cross-section regressions that span 92 countries for the period 1960 to 2000, the paper shows that knowledge is a significant determinant of long-term economic growth. In particular, we find that the stock of human capital, the level of domestic innovation and technological adaptation, and the level of information and communications technologies (ICT) infrastructure all exert statistically significant positive effects on long-term economic growth. More specifically with regard to the growth effects of the human capital stock, we find that an increase of 20 percent in the average years of schooling of a population tends to increase the average annual economic growth by 0.15 percentage point. In terms of innovation, we find that a 20 percent increase in the annual number of USPTO patents granted is associated with an increase of 3.8 percentage points in annual economic growth. Lastly, when the ICT infrastructure, measured by the number of phones per 1,000 persons, is increased by 20 percent, we find that annual economic growth tends to increase by 0.11 percentage point.

World Bank Policy Research Working Paper 3366, August 2004 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*The Knowledge for Development Program of the World Bank Institute.
Table 12. Proportion of SUTPs by NUTS2 regions in 2013

|||Percent of SUTPs in the<br>totalpopulation<br>|
|---|---|---|
|TR10|Istanbul|2.8%|
|TR21|Tekirdag|0.0%|
|TR22<br>|Balikesir<br>|0.0%<br>|
|TR31|Izmir|0.6%|
|TR32|Aydin|0.1%|
|TR33|Manisa|0.0%|
|TR41|Bursa|0.5%|
|TR42|Kocaeli|0.5%|
|TR51|Ankara|0.6%|
|TR52|Konya|3.5%|
|TR61|Antalya|0.4%|
|TR62|Adana|4.9%|
|TR63|Hatay|16.9%|
|TR71|Kirikale|0.1%|
|TR72|Kayseri|0.8%|
|TR81|Zonguldak|0.0%|
|TR82|Kastamonu|0.0%|
|TR83|Samsun|0.1%|
|TR90|Trabzon|0.0%|
|TRA1|Erzurum|0.0%|
|TRA2|Agri|0.0%|
|TRB1|Malatya|0.1%|
|TRB2|Van|0.1%|
|TRC1|Gaziantep|22.4%|
|TRC2|Sanliurfa|11.0%|
|TRC3|Mardin|11.3%|

_Sources_ : AFAD 2013, MOI, UNHCR 28
8 7. total quantity of metal compounds released or transferred (AVMETAL) In view of the fact that the different pollutions indicators produce fairly similar results and in order to avoid excessive repetition, we confine ourselves to the presentation of the results for the second indicator, i.e. the linear acute human health and terrestrial ecotoxicity (AVHUML).

For lack of better information, it had to be assumed that the US emissions figures by industrial ISIC class (which meajure discharges per dollar value of output), and the derived pollution indicators, were equally applicable to Mexican manufacturing activities. Moreover, it was assumed that the emissions per unit of output in each industry were constant over time. Consequently, changes in our estimates of the pollution intensity of the manufacturing industry are exclusively produced by structural changes in the composition of the activity basket. In other words, the effects of intra-activity changes and of technological changes in the emissions per unit of output, e.g. by cleaner production technologies, are not taken into account.

It is quite possible that the above mentioned assumptions have introduced an important bias in our estimates. In the first place, US emission figures were collected at a moment that pollution control measures had already been in place for some time in the US
_Tax Reform in Transition_ 3 raising the income threshold at which a positive tax payment must be made. In each case the objective is to obtain better or at least revenues equal to those that would be obtained under the 1992 system. Furthermore, the distributional consequences of the proposed tax reform are examined, taking into account the existing income distribution as reflected in the 1992 household budget survey.

The type of analysis and principles enunciated are potentially of wider interest to other EMEs for two reasons. First, the striking similarities in the pattern of income distribution in these economies recently reported by Milanovic (1995) and Hassan and Peters (1996). Second, a comparison of the income tax systems in these economies - as shown in section 2 - reveals that these systems suffer from similar deficiencies as Bulgaria's system, notably, low exemption level for the poor, large number of tax brackets, and high rates. So the analysis is potentially capable of providing useful direction for improving the design of personal income tax not only in Bulgaria, but also in other Eastern European countries contemplating tax reform.

The remainder of the paper comprises three sections. Section 2 reviews the 1992 personal income tax structure and potential and actual revenue performance, utilizing the 1992 income distribution data. This section also compares Bulgaria' s personal income tax system with those in other economies, notably transitional economies. Section 3 presents and discusses the counterfactual analysis of alternative tax structures with equal or better revenue raising potential and re-distributional impact. The analysis also takes into account the existing high tax evasion level. Conclusions and thoughts on broader implications of the analysis for tax reform in other EMEs are given in the final section.

# **_2. The_ Need** **_For Revenue_**

The evolution of Bulgaria's tax revenues and fiscal balance relative to GDP since 1991 are shown in Table 1. The table shows that the country experienced a decline of tax revenue/GDP -- from nearly 38 percent of GDP in 1991 to 28 percent of GDP in 1996, and is
inputs is higher in host countries with lower trade costs, among other things (wages, tax rates, etc), while exports by affiliates show similar correlation patterns.

On the other hand, Ma and Van Assche (2010) analyze the role of trade costs on intra-GPN trade using a comprehensive dataset on China’s processing trade regime, enabling the authors to map the location of input production, the location of processing, and the location of consumption. Using a three-country industry-equilibrium model with heterogeneous firms from two advanced countries, the authors find that Chinese processed exports not only depend on _downstream trade costs_ (export distance), but also on _upstream trade costs_ (import distance), and the interaction of both. Unfortunately, trade logistics costs are only captured indirectly through the distance to the suppliers and customers of the firm, and then using oil prices.

Another approach to modeling GPNs employs Input-Output tables. These tables link the input with the output of an industry in different countries or domestically, as a customer and supplier of intermediate goods (Escaith et al., 2010). For instance, Hummels et al. (2001) use this methodology to compute the degree of vertical specialization for OECD countries, attributing 30 to 40 percent of exports (OECD and World) to vertical specialization. On the other hand, they develop a multi-stage production model, allowing higher specialization due to comparative advantage, and multiple trade barriers as the different stages take place in different countries. Hence, the model predicts that small reductions in trade barriers provide strong incentives for vertical specialization. However, the I-O approach does not come without setbacks, namely the need for improvement in the quality of data and the frequency with which the tables are updated.

Lastly, GPNs in recent years have been examined thoroughly from the perspective of outsourcing. However, the main focus in this case is somewhat different from the previous approaches, and so is its relevance for trade facilitation. In a nutshell, most of this body of knowledge has been focused on 6
hypothesis that the increased transparency (the publication of announcements) resulted in more bidders for state contracts. Unfortunately, to conclude this one needs to know that some other factor was not responsible for the increased propensity to bid for state contracts. For example, overall economic reforms could have resulted in an across-theboard increase in the number of firms competing in the state procurement market, including trade liberalization resulting in greater imports.

**Table 5: Bidders for Polish government procurement contracts, 1996-2001**

|Item|1996|1997|1998|1999|2000|2001 (first<br>halfonly)|
|---|---|---|---|---|---|---|
|Number of announcements in the_Bulletin of_<br>_Public Procurement_|34136|44657|53147|55262|70234|37983|
|Value of contracts announced in the_Bulletin_<br>_of Public Procurement_, (US $ billion)||13.6|17.5|17.7|23.0|11.1|
|Mean number of offers for each<br>announcement (that resulted in the selection of<br>anoffer.)*|3.64|4.16|4.70|5.40|5.15|5.50|

_Note_ : * Approximately 20 percent of announced procurements were “invalidated” _Source_ : Lemke (2003, page 119).

Moreover, this evidence alone cannot explain why the average number of bidders for services and supplies contracts fell after 1999—even though the additional transparency brought about by the publication is presumably unchanged (or possibly enhanced if the published announcements became more precise over time.) Furthermore, even if the hypothesis is to be believed, this evidence does not reveal by what percentage (if at all) the prices paid by the state fell as a result of more firms bidding for public contracts. Without such estimates of the resulting savings one cannot compare these benefits with the costs of publishing announcements about procurement opportunities and the costs of verifying their accuracy. To reiterate: the objective of the foregoing discussion is not to criticize any one study or attempt at procurement reform. Rather it is to point out that the available empirical evidence is unsatisfactory for policymaking purposes, and also to indicate what types of questions on procurement practices in developing countries need to be addressed.

The available literature is a stronger on the factors that impede national reform initiatives on public procurement. For the most part, this strand of literature makes no 18
The analysis on the welfare improvement in Table D-2 shows that the standard deviations of the welfare improvement in terms of EV are generally small, compared to the mean values. For example, even the wide confidence interval of 4.5 standard deviation (i.e. 95% confident) range covers $260 billion for Case 1 and $170 billion for Case 2. The large welfare gains to APEC economies estimated here, therefore, appear to be generally sound.

# **Table D 2: Sensitivity Analysis on Equivalence of Variation by Changing Armington Elastiticies (Billion UD$)**

|||Case 1|||Case 2||
|---|---|---|---|---|---|---|
||Base|Mean|S.D.|Base|Mean|S.D.|
|AUS|1|1|0|0|0|0|
|CAN|-1|-1|0|-1|-1|0|
|CHL|0|0|0|0|0|0|
|CHN|110|110|6|80|80|5|
|HKG|6|6|0|3|3|0|
|IDN|11|11|0|8|8|0|
|JPN|2|2|1|2|2|1|
|KOR|8|8|1|6|6|1|
|MEX|22|22|0|19|19|0|
|MYS|13|13|0|12|12|0|
|NZL|0|0|0|0|0|0|
|PER|3|3|0|2|2|0|
|PHL|18|18|1|14|14|1|
|RUS|118|118|9|75|75|4|
|SGP|4|4|0|3|3|0|
|THA|61|63|4|54|55|5|
|CTP|3|3|0|3|3|0|
|USA|1|1|3|-2|-2|3|
|VNM|22|22|0|19|19|0|
|EU15|-4|-4|4|-10|-10|3|
|ROW|8|8|1|3|3|1|
|APEC|402|--|--|297|--|--|
|World|406|--|--|290|--|--|

(Note) see Table D-1 above.

40
Table 1: Satellite Indicators by Resolution and Availability

|Indicator|Source|Time-frame|Coverage|Resolution|Availability|
|---|---|---|---|---|---|
|_A. Population_||||||
|Population|Census||National||public|
|Population|HIES||1421 villages||public|
|Population|Facebook||National||public|
|Population|WorldPop||National||public|
|Population|GPW||National||public|
|Population|LandScan||National||public|
|Population|GHSL||National||public|
|_B. Other sources_||||||
|Night time lights|VIIRS|2014|National|Low|public|
|Elevation|ASTER||National|Low|public|
|Slope|ASTER||National|Low|public|
|Tree cover|Hansen et al. (2013)|2000|National|Low|public|
|Tree cover gain and loss|Hansen et al. (2013)|2000 to 2014|National|Low|public|
|Built-up area|GUF, GUF+|2012, 2015|National|High|public|
|Built-up area|GHSL|2014|National|High|public|
|Built-up area|Facebook|2015|National|High|public|
|Built-up area|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Cars|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Shadows|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Roof type|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Road type|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Agricultural land type|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Paddy land type|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|NDVI|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|
|Other Contextual Indicators|Engstrom et al. (2017)|2011, 2012|55 sub-dist.|High|proprietary|

Note: HIES=the Household Income and Expenditure Survey; GPW=Gridded Population of the World; GHSL=Global Human Settlement Layer; GUF= Global Urban Footprint; VIIRS= Visible Infrared Imaging Radiometer Suite; ASTER=Advanced Spaceborne Thermal Emission and Reflection Radiometer; NDVI = Normalized Difference Vegetation Index. Engstrom et al. (2017) use proprietary imagery from DigitalGlobe to derive features and textures for 55 sub-district divisions. “Other Contextual indicators” include SURF, Pantex, and Histogram of Oriented Gradients. The time frame refers to the year(s) during which the satellite images were obtained.

18
**Table 2 Continued. Mapping:** **_Accounting Services_**

|**Australian categories**|**Weight**|**Score**|**Weighted score**|**The World Bank data**|**Weight**|**Score**|**Weighted**<br>**score**|
|---|---|---|---|---|---|---|---|
|**Multi-disciplinary practices**|**0.05**|||**This category has been removed so**<br>**that the weights sum to one**||||
|Prohibition on partnership or<br>association with other professions|0.05|1||||||
|Majority partnership required|0.05|0.5||||||
|No restrictions|0.05|0||||||
|**Advertising, marketing and**<br>**solicitation**|**0.05**|0.05||**Proxy: Minimum wage/salary**<br>**restriction**|**0.1**|||
|Prohibition of advertising, marketing<br>and solicitation|0.05|1||Minimum wage/salary is required for<br>ICT, IPE, and SSE<br>|0.1|1||
|Restrictions apply to some groups or||||Minimum wage/salary is required for<br>at least one of ICT,I PE, and SSE||||
|activities|0.05|0.5|||0.1|0.5||
|General legal requirements|0.05|0||Minimum wage/salary is not required<br>for ICT, IPE, and SSE|0.1|0||
|**Fee setting**|**0.05**|||**Proxy: Minimum wage/salary**<br>**restriction **||||
|Minimum and maximum fees for all<br>||||Minimum wage/salary is required for<br>ICT, IPE, and SSE||||
|groups in the profession|0.05|1|||0.1|1||
|Restrictions apply to some groups or||||Minimum wage/salary is required for<br>at least one of ICT,I PE, and SSE||||
|activities|0.05|0.5|||0.1|0.5||
|Setting fee freely|0.05|0||Minimum wage/salary is not required<br>for ICT, IPE, and SSE|0.1|0||
|**Licensing requirements on**<br>**management**|**0.02**|||**Proxy: Restrictions on Greenfield**<br>**branches”**|**0.04**|||
|At least a majority of managers must be<br>nationals or residents|0.02|1||Greenfield branch is not allowed|0.02|1||
|Directors and managers must be locally||||||||
|licensed|0.02|0.5|NA|||||
|Directors and managers must be<br>domiciled|0.02|0.25|NA|||||
|No restrictions|0.02|0||Greenfield branch is allowed|0|||
|**Other restrictions**<br>Rii hii ll|**0.02 **|||**Proxy: Restrictions on Greenfield**<br>**branches” **||||
|estrctons on rng oca<br>professionals|0.02|0.33||Greenfield branch is not allowed|1|||
|Restrictions on the use of firm’s<br>international names||||||||
||0.02|0.33||Greenfield branch is not allowed|1|||
|Government procurement —<br>restrictions towards foreign suppliers|0.02|0.33||Greenfield branch is not allowed|1|||
|No restrictions|0.02|0||Greenfield branch is allowed|0|||
|**Temporary movement ofpeople**|**0.01**|||**Temporary movement ofpeople**|**0.01**|||
|No temporary entry|0.01|1||No limitation on the duration of stay<br>initially allowed for IPC|0.01|1||
|Temporary entry of up to 30 days|0.01|0.75||Duration of stay initially allowed for<br>IPC is up to 1 month|0.01|0.75||
|Temporary entry of up to 60 days|0.01|0.5||Duration of stay initially allowed for<br>IPC is up to 2 month|0.01|0.5||
|||||Duration of stay initially allowed for|0.01|0.25||
|Temporary entry of up to 90 days|0.01|0.25||<br>IPC is up to 3 month||||
|Temporary entry over 90 days|0.01|0||Duration of stay initially allowed for<br>IPC is over 3 month|0.01|0||
|**Total**|**1.05**||||**1**|||

26