rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 9: Foreign ownership barriers**

<!-- Start of picture text --> 4.0 3.7<br>3.5<br>3.0<br>3.0<br>2.6<br>2.5 2.3<br>1.9 2.0<br>1.8<br>2.0<br>1.5 1.3<br>1.0 0.8<br>0.5<br>0.5<br>0.0<br>0.0<br>Albania CroatiaRomania EU15 OECD HungaryCzech Slovak MICs BulgariaPoland<br>2007 2007 Rep. Republic<br><!-- End of picture text --> _Source: see Figure 3. Note: Other MICs are Brazil, Mexico, Turkey. Values refer to 2007 for Croatia and Albania, 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see Annex I._

**This is therefore consistent with the finding that Croatia does not, in general, discriminate against foreign firms** (Figure 10).  The rights of foreign firms in Croatia to appeal and redress through competition agencies, regulatory bodies, trade policy bodies, or private rights of action is equal to those of domestic firms, although there are no specific provisions requiring that regulations, prior to entry into force, be published or otherwise communicated to the public in a manner accessible at the international level, which may put foreign players at a disadvantage relative to domestic competition.

**Figure 10: Discriminatory Procedures against foreign firms**

<!-- Start of picture text -->
1.4<br>1.2<br>1.2 1.1<br>1.0 0.9<br>0.8 0.7 0.7 0.7<br>0.6 0.5 0.5<br>0.4<br>0.3<br>0.2<br>0.0 0.0<br>0.0<br>BulgariaRomaniaPoland EU15 OECD Czech CroatiaAlbania MICs SlovakHungary<br>Rep. 2007 2007 Republic<br><!-- End of picture text -->

_Source: see Figure 3. Note: Other MICs are Brazil, Mexico, Turkey. Values refer to 2007 for Croatia and Albania, 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see Annex I._

# **3.3 Regulatory barriers to trade and investment**

**Croatia still has a plethora of regulatory barriers relating to international harmonization of standards and certification procedures** Whereas mutual recognition agreements with other countries are in force, there are no specific provisions which require regulators to recognize

11
Policy Research Working Paper 7489

# **Abstract**

In response to a growing interest in comparing inequality levels and trends across countries, several cross-national inequality databases are now available. These databases differ considerably in purpose, coverage, data sources, inclusion and exclusion criteria, and quality of documentation. A special issue of the _Journal of Economic Inequality_ , which this paper introduces, is devoted to an assessment of the merits and shortcomings of eight such databases. Five of these sets are microdata-based: CEPALSTAT, Income Distribution Database, Luxembourg Income Study, PovcalNet, and Socio-Economic Database for Latin America and the Caribbean. Two are based on secondary sources: All the Ginis and the World Income Inequality Database; and one is generated entirely through multiple-imputation methods: the Standardized World Income Inequality Database. Although there is much agreement across these databases, there is also a nontrivial share of country/year cells for which substantial discrepancies exist. In some cases, different databases would lead users to radically different conclusions about inequality dynamics in certain countries and periods. The methodological differences that lead to these discrepancies often appear to be driven by a fundamental trade-off between a wish for broader coverage on the one hand, and for greater comparability on the other hand. These differences across databases place considerable responsibility on both producers and users: on the former, to better document and explain their assumptions and procedures, and on the latter, to understand the data they are using, rather than merely taking them as true because available.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at fferreira@worldbank.org, nlustig@tulane.edu, and dan.teles@gmail.com.

_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
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 5: Individual Analysis Summary Statsitics

||Tradable Sector|Sample|Nontradable|Sector Sample|
|---|---|---|---|---|
||mean|std. dev.|mean|std. dev.|
|Education|||||
|Illiterate|0.02|0.14|0.01|0.11|
|4th grade incomplete|0.13|0.33|0.10|0.30|
|4th grade complete|0.25|0.43|0.18|0.38|
|8th grade incomplete|0.19|0.39|0.14|0.34|
|8th grade complete|0.15|0.35|0.14|0.35|
|High School incomplete|0.05|0.21|0.06|0.24|
|High School complete|0.13|0.33|0.21|0.40|
|College incomplete|0.02|0.15|0.04|0.19|
|College complete|0.07|0.26|0.12|0.33|
|Female|0.24|0.43|0.32|0.46|
|Age|32.8|5.4|32.8|5.5|
|December 1989 Earnings (in 2010 R$)|1,911|2,459|1,838|2,670|
|1989 Yearly Earnings (in 2010 R$)|19,172|23,750|18,701|26,087|
|Average Annualized Earnings 1986-1989 (in 2010 R$)|19,010|21,043|18,077|21,653|
|Months formally employed per year|||||
|1990|10.2|3.4|9.9|3.8|
|1990-1995|8.2|3.8|8.2|3.9|
|1990-2000|7.1|3.7|7.2|3.9|
|1990-2005|6.4|3.7|6.6|3.9|
|1990-2009|6.0|3.7|6.2|3.9|
|Employed in a different region in 1994 than in 1989|0.09|0.29|0.11|0.31|
|Employed in a different region in 2000 than in 1989|0.11|0.31|0.12|0.32|
|Observations|587,575||973|,048|

RAIS data. Weighted to account for 15% sample of individuals in regions with more than 2000 traded sector workers in 1989 and 100% sample in other regions. All monetary values reported in 2010 R $ . In Dec 31, 2010, a US dollar was worth 1.66 Brazilian Reais.

67
# 9 Conclusions

We show that the present value of aggregate TFP growth, for a given initial endowment of capital, is a complete welfare measure for a representative consumer, up to a ...rst-order approximation. This result rigorously justi...es TFP, rather than technical change or labor productivity, as the central statistic of interest in any exploration of productivity, at all levels of aggregation. Importantly, the result holds even when TFP is not a correct measure of technical change, for example due to increasing returns, externalities, or imperfect competition. It also suggests that productivity decompositions should be oriented towards showing how particular features or frictions in an economy either promote or hinder aggregate TFP growth, which is the key to economic welfare. Our theoretical results point to a key role for the persistence of aggregate TFP growth, since welfare change is related to the entire expected time path of productivity growth in addition to the current growth rate. Finally, our derivation shows that in order to create a proper welfare measure, TFP has to be calculated using prices faced by households rather than prices facing ...rms. In modern, developed economies with high rates of income and indirect taxation, the gap between household and ...rm TFP can be considerable.

We use these central results to show that one can explore the sources of welfare change using both non-parametric index numbers and formal econometrics. The non-parametric approach has the great advantage of simplicity, and avoids the need to address issues of econometric identi...cation. Many interesting cross-country comparisons can be performed using the index-number approach, including calculating summary statistics of allocative e¢ ciency for each country based on ...rmlevel data. However, if one wants to ask how much of aggregate TFP growth is due to technical change, as opposed to scale economies or allocative ine¢ ciency, one does need to make additional assumptions and estimate production functions at the ...rm level. We show how one can decompose aggregate TFP growth in such a manner using ...rm-level data.

The results suggest that in the majority of OECD countries we have analyzed (Belgium, France, Great Britain, Italy, and Spain) most of productivity growth in manufacturing is accounted for by technology growth. This is particularly true for Great Britain and France. Moreover, aggregate distortions are quite important in many countries, such as Spain, Belgium, and Italy. Finally, the reallocation terms for primary factors or materials account for a small proportion of productivity growth in all countries over the period 1995-2005. We will explore in future research whether this result extends to other countries or time periods, or to other data sets less biased towards larger ...rms.

27
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**-18-**

lack of technical support and complementary infrastructure.12 Identification of the different socio-economic groups may not be easy in view of data and methodological limitations, but even in countries where data is poor, analysis of available information and technical knowledge can provide useful results. In Gambia, a qualitative estimate of the probable impact of the adjustment process on different socio-economic groups was made (see Box 2).

33. Information about specific groups may often be available from existing studies, NGOs or international agencies working in specific regions. For example, the UNICEF report "Situation Analysis: Women and Children' is an important source of information on vulnera.ble groups in a number of countries. In Chad an NGO, Medecins sans Frontieres (MSF), has surveyed and maintains information on the nutritional status of specific groups.

34. "Priority Surveys' developed by SDA can also facilitate better identification of target groups and their specific needs. Priority Surveys are quick surveys using a relatively large sample and a questionnaire limited to vital areas concerning household living standards. Especially in countries where little data exists, they can provide a quick broad brush picture of the vulnerable groups and yield information on key variables relating to them. In Chad the preparation of the Social Development Action Program (SDAP) was based in part on a priority survey for N'Djamena conducted a few months before appraisal and completed in roughly four months at a cost of about US $40,000.

12/ In Malawi **the** proposed *xtension of **licenses** to grow certain export crops to smallholdern, In particular, burley tobacco, **is** expected to provide additional income for **some** smaliholdera.
# Research Components of World Bank Education Projects. since 1982.

|Area|Country and Loan/Credit<br>llumber|A|Research Components<br>|Current Status of Studies,<br>including<br>I<br>Completed Docimew ts/Date|
|---|---|---|---|---|
||||studies<br>focussing on;<br>(i)<br>higher education costs and financing;<br>(ii)<br>promotion of efficiency and quslity in<br>teacher<br>training;<br>(iii)<br>reform of accredition procedures;<br>(iv) the transferability<br>of semester credits...<br>(para. 2.13 p.21)||
|ASIA<br>ASIA|KoreaLK2427-KO<br>Korea LN 3037-KO|1984<br>1989|Programme<br>for ScienceandTechnology<br>Education. Study future development ofgraduat<br>study in science and technology (1984-5) (para.<br>2.03 p.10).<br>Technology Advancement. No research component.|Completed 1985.<br>e(i) "Study on the expansion plan of research<br>facilities<br>In graate<br>education"<br>Prepared by: Study<br>Team (1966);<br>(doc. S E05825)<br>(ii)<br>Korean Council of University Education<br>"Evaluation of ScienceandEngineering Education at<br>Undergraduate<br>and Gradtelevels inKorea."<br>(doc. * E05827)|
|ASIA|Lao PDR CR 2001-LA|1989|National<br>Poiytechnic<br>Institute.<br>Studies<br>and evaluation to assess:<br>(i) higher education programs (technical<br>nd<br>vocational) to improve efficiency;<br>(ii)<br>efficiency of secondary educationas<br>preparation for higher education;<br>(iii)<br>tracer study ofNPIgradtes;<br>(iv) exmine external efficiency ofNPI<br>program (technical<br>quality<br>of graduates,<br>salary level<br>of graduates. market perception<br>of grauates).|Nostudies<br>initiated.|

43
# **Table 2: The Varying Degrees of Formality Incidence of Registration with Federal Treasury and Payment of Taxes by Various Firm Characteristics**

|||All fi|rms||I<br>|Firms re<br>with loca|gistered<br>l treasur|<br>y|wi|r**Fi**<br>th busine|**rns**regi<br>ss guild|stered<br>or organi|zation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||#firrns<br>in this<br>group|% firms<br>in this<br>group|% regis<br>with fed<br>treasury|%<br>pay<br>taxes|firms|%<br>firms|%<br>fed<br>treas|%<br>pay<br>taxes|firms|%<br>firms|% fed<br>treas|% pay<br>taxes|% pay<br>guild/<br>org dues|
|1. All firms|9036|100.0|41.7|34.6|2273|100.0|78.8|74.0|2035|100.0|67.7|54.0|61.4|
|2. Paid workers>0|1748|100.0|73.1|61.3|690|100.0|91.6|79.6|573|100.0|88.0|72.1|57.4|
|2.A. and IMSS=0|1144|65.4|61.5|50.9|396|57.4|87.1|74.7|282|49.2|97.9|82.5|58.8|
|2.B. and IMSS>0|604|34.6|95.0|81.1|294|42.6|97.6|86.1|291|50.8|77.7|61.3|56.0|
|3. Inbusiness>3 years|5220|100.0|42.8|37.1|1365|100.0|79.1|76.3|1284|100.0|68.8|56.9|62.6|
|3.A. not in 1989 Census|3493|66.9|20.1|18.5|482|35.3|55.6|63.3|637|49.6|46.3|37.4|64.2|
|3.B. and in 1989 Census|1727|33.1|88.8|74.8|883|64.7|92.0|83.4|647|50.4|91.0|76.0|61.1|
|4. Pd wkrs>0, >3 years|1096|100.0|72.9|63.8|440|100.0|92.7|82.7|378|100.0|89.2|75.1|58.7|
|4.A. IMSS no, Census no|348|31.8|31.0|28.4|68|_15.5_|77.9|75.0|62|16.4|61.3|48.4|62.9|
|4.B. IMSS yes, Census no|79|7.20|86.1|74.7|26|5.91|100|80.8|34|8.99|97.1|73.5|50.0|
|4.C. IMSS no, Census yes|337|30.7|88.4|77.2|168|38.2|92.9|81.0|110|29.1|88.2|76.4|54.5|
|4.D. IMSS yes, Census yes|332|30.3|97.9|84.6|178|40.5|97.2|87.6|172|45.5|98.3|84.3|61.6|

Only those firms in business for more than three years could have been enumerated in the 1989 Census of businesses. IMSS refers to registration of the firm's paid workers with the Mexican Social Security administration. Only paid workers have to be registered with IMSS.

27
# **3.1 Data**

We obtained from the Albanian customs agency administrative data tracking imports at the transaction level for the period 2007-2012 (extracted through the ASYCUDA system). These highly disaggregated data cover a large number of variables that describe each import transaction. The variables available to us include an importing firm numeric identifier, the date (day/month/year) of the registration/submission of administrative documents and the date of the release/clearance of goods from customs, an HS-8 digit product code, the exporting country, import value, weight (in kilograms), mode of transportation, border post of entry, and the inspection channel (red, yellow, or blue). We also obtained from the Albanian customs agency a separate data set with information on customs infractions and penalties that was merged to the import transaction level data.

A key variable in our analysis - the time spent under control of the customs agency - is calculated as the difference between the time stamp associated with the filing of the import declaration and the time stamp for the release of the goods to the market. Since during the 2007-2012 period in Albania an import declaration - formally designated as single administrative document (SAD) - can only be submitted upon arrival of the freight to the customs office (electronic submission ahead of time was not possible), the variable defined above captures accurately the time spent in customs.<sup>24</sup> The ASYCUDA++ system that captured these data in the first half of our sample only retained the days of submission and clearance. The introduction of an updated IT system, ASYCUDAWorld, in 2009 allowed for a finer recording of the registration and clearance times for each import declaration in the latter half of the sample. For the period 2007-2012 we measure clearance times as a discrete number of days. For the sub-period 2010-2012 we have a continuous measure of clearance time, which we express in terms of (fractional) hours.

Another key variable in our analysis, the inspection rate, is also measured at the declaration level since that is the level at which customs officials decide the allocation to the red, yellow or blue inspection channels. As mentioned in Section 2, allocation of a declaration to the red channel implies the highest degree of inspection in the customs’ risk management system – involving

> 24 A single SAD may contain more than one transaction (e.g. more than one type of good is being declared for import).  But the clearance times we have available are for the clearance of the SAD, and so can apply to multiple transactions in cases where the declaration contains more than one transaction. We will often refer to a transaction as a shipment.

11
anal and vaginal sex, respectively (in Round 1, we only have a measure of total unprotected sex and so cannot di¤erentiate between unprotected anal and unprotected vaginal sex). In each round, each respondent was asked to keep the diary for a period of 3 months, though the ...rst few weeks were often not usable in the ...nal analysis due to reporting errors made as women were learning the diaries.

The diaries were extensively pre-tested by the authors, a research assistant, and the two enumerators to maintain respondent con...dentiality, meet norms of cultural sensitivity, and to ensure that respondents understood all of the questions. To ensure data quality, the two enumerators conducted diary checks roughly once a week, during which they checked for errors and resolved mistakes with respondents.

In order for women to keep these self-reports, it was of course necessary that they could read and write Kiswahili, one of the o¢ cial languages in Kenya and the language used in the diaries. Literacy levels in the sample were relatively high: 95% of the sample could read and 88% could write Kiswahili (Table 1). To avoid losing illiterate women, a special e¤ort was made to keep them in the sample. Each illiterate woman was assigned a peer educator who met with her daily to read the diary questions to her and ...ll in the answers for her.

In addition to the diaries, a background questionnaire was also administered. This survey included questions on family background, household characteristics, education, migration, land and durable good ownership, access to credit and savings, knowledge of HIV/AIDS, attitudes towards sex work, and other related topics. To compensate respondents for keeping the diaries, we paid women 1,000 Kenyan shillings (US $14) in Round 1, and 1,500 Kenyan shillings (US $21) in Round 2.<sup>10</sup> Of the 248 women that were sampled, we obtained complete, usable data from 192 of them (77%). The other women could not be included because they refused to participate or stopped ...lling out the diaries during the sample period, because they moved to another area and could not be traced, or because they kept the diaries poorly or did not ...ll them completely. In total, the ...nal dataset consists of 192 women, 19,041 transactions, and 12,526 sex worker days.<sup>11</sup>

> 10 Round 2 participants were compensated slightly more because the diaries were more detailed and took more time to complete.

> 11 The breakdown of the ...nal sample by round is 84 women in Round 1 and 91 in Round 2, with 17 women

9
-5-

The next sections are organized as follows. Section II summarizes trends in military expenditures since the early 1970s and reviews the empirical literature on the relationship between military spending and economic growth. Section III outlines our extension of a standard neoclassical growth model and the estimation technique used. Section IV first presents standard cross-section estimates of the effect of military spending on investment and economic growth and then contrasts them with the results of our panel-data estimation. Section V describes two simulation experiments that help to indicate the rough order of magnitude of the longer-run Peace Dividend from military spending cuts. We first simulate the long-run effects that will eventually become visible as a result of the developments in military spending that have already taken place in various geographic regions during the latter half of the decade of the 1980s. We then simulate the potential effects of further declines in military spending that might be expected to occur in the future in various regions if a lasting global peace could be secured. Section VI concludes.

# II. Data and EmRirical Research on Military SRending

Throughout this paper we define the _military spending ratio,_ m, as the ratio of total military spending to GDP; the data used here are those published by the Stockholm International Peace Research Institute
for further improvements, and may find it more difficult to achieve them. But this also suggests that the scope for PFM reform is only to a limited extent constrained by country characteristics, which are largely outside the (short-term) control of decision-makers in government and other PFM reform stakeholders. This is also consistent with the starting point for the follow-up work under the study, which is that country-specific constellations and dynamics critically influence when and to what extent a strengthening of PFM systems happens.

To check the robustness of the main findings, we run the first-differences model using CPIA-13 instead of average PEFA scores as a dependent variable (see Table A3.2).<sup>48</sup> Due to the higher number of countries available (124 countries as compared to 40), our regression fit improves significantly with country characteristics and changes in country characteristics accounting for around 40 percent of the variance in PFM quality, which is similar to that in the cross-sectional analysis. Having lower initial PFM quality has a robustly significant effect, similar to the results when using PEFA data. The biggest change is that using CPIA data a higher rate of population change emerges as having a significant negative effect on CPIA-13 as the dependent variable, suggesting that countries with high population growth have been among those achieving more limited PFM reforms. Being a SIDS and being resource dependent assume a small negative effect, significant at the 10 percent level using this wider set of countries, which is consistent with the findings from the cross-sectional analysis. Changes in GDP per capita appear to have a larger effect than the cross-sectional results with regards to growth. Among the political variables, the CPIA results show a small negative effect of being fragile and a positive effect of having programmatic parties.

> 48 The analysis is based on a comparison between CPIA data for 2005 and 2012, i.e. broadly the same time-frame as that for which PEFA data is available; but with a wider country coverage.

35