rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 9. Firm Efficiency and Government Facilitation: By Income Level</u>**

||**Poor**<br>**Pooled**|**State**|**Domestic**<br>**private**|**foreign**|**Rich**<br>**Pooled**|**State**|**Domestic**<br>**private**|**foreign**|
|---|---|---|---|---|---|---|---|---|
|loan help, city-ind mean|1.837***|1.548|3.824***|1.483**|0.702|0.089|0.973|0.943*|
||(0.496)|(1.361)|(1.010)|(0.643)|(0.464)|(1.277)|(0.667)|(0.544)|
|info facilitation, City-ind mean|1.048**|-1.084|0.542|1.847**|0.124|-1.351|0.161|0.228|
||(0.409)|(0.680)|(0.550)|(0.795)|(0.254)|(0.824)|(0.333)|(0.441)|
|Ln(firm age)|-0.488***|-0.290**|-0.574**|-0.515***|-0.385***|-0.265***|-0.352***|-0.471***|
||(0.090)|(0.127)|(0.289)|(0.106)|(0.068)|(0.087)|(0.114)|(0.110)|
|Ln(L)|-0.276|-0.405**|-0.431|-0.572|0.064|3.664***|-0.491***|0.162**|
||(0.349)|(0.182)|(0.898)|(0.783)|(0.039)|(0.074)|(0.180)|(0.075)|
|state|-0.329|-0.691||0.074|-0.345**|-1.062**||-1.033*|
||(0.268)|(0.638)||(0.789)|(0.149)|(0.427)||(0.530)|
|foreign|0.105|0.658||-0.114|-0.035|-1.214**||-0.399*|
||(0.211)|(0.665)||(0.417)|(0.115)|(0.563)||(0.242)|
|Ln(GDP per capita)|1.173***|1.290***|1.239***|1.379***|0.576***|0.656*|0.560**|0.547***|
||(0.198)|(0.374)|(0.350)|(0.354)|(0.148)|(0.337)|(0.221)|(0.203)|
|PS long tenure|-0.046|0.606|0.214|-0.145|0.184|0.114|0.090|0.214|
||(0.200)|(0.539)|(0.370)|(0.248)|(0.138)|(0.248)|(0.198)|(0.177)|
|CEO characteristics, access to finance, institutional<br>environment, efficiency of state machinery,|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|ind dummies * ln(K/L), lnL|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|r2_a|0.370|0.541|0.532|0.368|0.384|0.383|0.349|0.484|
|N|1,532|408|434|764|2,117|519|699|990|

*, ** and *** represent statistical significance at the 10, 5 and 1 percent, respectively.  White-heteroskedasticity-corrected standard errors, clustered at the city-industry level, in parentheses.

Rich cities are defined to be those with average GDP per capita above 12467 Yuan (i.e., the median) in 2005, and poor cities are the rest of our sample. Rich cities include Changchun, Changsha, Dalian, Hangzhou, Jiangmen, Kunming, Shenzhen, Wenzhou, Wuhan, Zhengzhou. Poor cities include Benxi, Chongqing, Guiyang, Haerbin, Lanzhou, Nanchang, Nanning, Xi’an.

The results based on labor productivity styled regressions are similar.

40
# Research Components of World<sup>BankEducation Projects,</sup> since 1982.

|Area|Cotuntry<br>and Loan/Credit<br>humber|Approved<br>I|Research<br>Components|Current<br>Status<br>of Studies,<br>inctuding<br>Completed<br>Documents/Data|
|---|---|---|---|---|
|ASIA|Nepal CR 1463-hEP|1984<br>||Primary Education. Survey<sup>of primary schoots in1</sup><br>20 other districts.<br>(ii) Study teacher education<sup>to improve</sup><br>training programs.<br>(iii) Evaluation surveys<sup>(paras. 3.33-6 p.16).</sup>|(i) Base-line data<sup>for Project schools(1985 Phase)</sup><br>- discusses impact of<sup>project in terms of student</sup><br>enrollment, drop-out<sup>andsurvival</sup><br>rates.EU-PEP 1969;<br>(ii) Evaluation report<sup>on materiats training workshop</sup><br>-<br>Centrallevetpreparation<br>andtesting-<br>provides<br>information on effectiveness<sup>of Educational</sup><br>Materials Training. EU-PEP<sup>1989;</sup><br>(iii) Evaluation strategies<sup>for proposed Primary</sup><br>Education<br>project<br>-<br>discusses institutionsand<sup>data</sup><br>gathering process in education<sup>sector.EU-PEP 1969</sup><br>(iv) Evaluation of materials<sup>training workshop for</sup><br>field<br>coordinators<br>and resource persons.<br>(v) Primary Education<sup>Project-</sup><br>an interim evaluative<br>study report (draft).<sup>CERID 1988</sup><br>(vi) Evaluation of district<sup>level PEPtraining</sup><br>-<br>Impact oftraining<br>on teacher'sprofessional<br>knowledge.<br>In process: Impact of<sup>training on teacher's cLassroom</sup><br>behavior;<br>Impact<br>of PEP irputs<br>on student<br>achievement|
|ASIA|Papua New Guinea<br>LN 2395-PNG<br>l|1984|Secondary<br>Education.<br>(i)<br>Study to review<br>the<br>relationship<br>betweenuppersecondary<br>education<br>and middle and higher level<sup>employment to</sup><br>recommend expansion rates<sup>and future curricutum</sup><br>options;<br>(ii) review retationships<sup>between primary,</sup><br>secondary<br>and higher<br>education<br>(pare.<br>2.04<br>~~~~~~~~~p.14).<br>I<br>(iii) Evaluation studies:<sup>teacher attrition,</sup><br>and inter-school transfer<sup>of teachers;</sup>|(i)<br>and(ii)status unknown;<br>(iii)<br>Status<br>report<br>on the<br>in-service<br>B. Ed (1985)<br>(doc. # 434.010);<br>(iv) Report on National<sup>Grade 6 Examinations as a</sup><br>predictor of Grade 10 certificate<sup>performance.</sup><br>(doc.<br>#434.009);<br><br>(v)<br>Study<br>on textbook<br>impact<br>-<br>Evaluation<br>11(1986)<br>(doc. # 434.113) and Textbook<sup>development report</sup><br>(doc. # 434.112)|

46
sufficient to operate an 800 MW nominal capacity combined cycle gas turbine (CCGT) power station for a period of 22 years on base load. Probable gas-in-place volumes at a confidence level of 50% are sufficient to operate a 1,600 MW CCGT plant for at least 25 years.

# **The Scope for Regional Integrated Resource Planning**

The scope for regional development and for optimizing the electricity system from a regional perspective is enormous. And the creation of SAPP clearly marked an important step toward realizing the benefits of such regional electricity planning.

South Africa’s Eskom--which accounts for over 80 percent of generation in the region—relies heavily on expensive (at least relative to hydro) and polluting thermal power from domestic coal. Trading arrangements between South Africa and the hydrorich northern countries could be mutually beneficial, providing cheaper electricity for the former and revenue boosting for the latter. Obvious examples for regional cooperation would include: the hydroelectric potential of the DRC, Mozambique, Zambia and Zimbabwe; the Pande, Temane and Kundu fields in Mozambique and Namibia; and the oil industry and hydroelectric potential of Angola.

The magnitude of potential gains from increased regional cooperation and trade is substantial. An important attribute of electricity trade is the improved reliability of supply, particularly when relying on hydro power that is vulnerable to drought. In the case of Zimbabwe, for example, the 1992 draught caused electricity shortages in 1993 that cost the country’s economy US$ 435 in lost export earnings--nearly 10 percent of its GDP.<sup>92</sup> A 1993 joint study by SADC and the World Bank estimated that optimal use of regional resources and installations could provide savings of US$ 1.6 billion over 10 years in comparison with a scenario under which each country would pursue its power development plans separately.<sup>93</sup> It should be noted that South Africa, which accounts for the bulk of the power produced in the region, was not included in that study. Thus, the potential gains available now through South Africa’s participation in regional planning are likely to be significantly higher.

Recent studies utilizing the optimization model RIEP (Regional Integrated Electricity Planning) developed at the University of Stuttgart quantified the benefits of regional co-operation in Southern Africa to savings of at least US$2.2 billion or 5 percent of the expected total costs over a period of 20 years. Under a scenario of high electricity demand growth, savings of over US$ 4 billion could be realized through regional planning. Approximately 40 percent of the estimated savings would result from lower investment costs and 60 percent from lower operating costs. The potential economic and social benefits of co-operation become even larger when environmental factors are

> 92 Robinson, P. 1994. _Economic Integration in Southern Africa_ .  Volume 2, Chapter 5.

> 93 World Bank. 1993. Joint UNDP/World Bank, Energy Sector Management Assistance Program (ESMAP).  SADC Project AAA3-8, Phase II.

59
point mus' he based on observations of water use from all and prices. Several water supply schemes were analyzed available sources. Otherwise,' as in two of the type C that way in the SAR. The calculated ERRs were between analyses, consumer surplus can be grossly overestimated. 5-27 percen,. There was not any cut-off level applied, and Both analyses aimed to determine only the average value all schemes were accepted, based on additional, of consumer surplus based r c ubic meters of water nonspecified, "unquafitifiable" health benefits. consumed. To do that, each calculated the area of the triangle under the demand curve between the quantity sold The third type D analysis divided the consumers by vendors and the quantity provided by the piped water into two groups, existing consumers (already served by system and therefore implicitly assumed that the "without the water sJvtem) and new consumers. Water demand project" consumption was not more than the amount of curves were estimated separately for each group. At first water pt rchased from vendors. As a result, the two two points on each demand curve were identified. One analysesurrivedatsimilarconclusions: theaveragevalue point was based on drinking/cooking water demand, of consumer surplus is about equal to the water tariff; pricedattheratechargedbywatervendors,whiletheother therefore, total benefits should approximate two times the point was based on projected per capita piped water use, sales revenue."<sup>5</sup> The third type D analysis divided the consumers point was based on projected per capita piped water use, priced at the expected average tariff under the project. Again, uiless vendors were the only water source used by the nonconnected consumers, these two points are not on One of the three type D analyses provided an However, since piped water consumption (per consumer) and (-0.8). was projected to be higher than existing water use, and since consumers generally assign a decreasing value to overestimated the true economic rate of return.

After estimating the demand curves, the average estimated for each group of consumers. Existing consumers' average willingness to pay was estimated at the higher than the projected tariff. If these consumers had been using other water sources in addition to vendors, then

**IS** that the project, as a result of a type B analysis, appears justified), the analysis ended with an ERR of 34 percent. surplus."

Altogether, only three type B and one type D 17 projects are not economically viable. Some of them could be justified on the basis of information in the SARs, information and analysis. However, rather that assessing the viability of these projects, this review aims to 6 To be exact, two additional assumptions are needed. It among the costs of existing water supply and that there is no producer surplus.

6
# **I. Are the South Asian Countries “Natural Trading Partners”?**

Triggered by their desire for export-led development, the South Asian countries began expanding their export orientation toward industrial countries, moving from basic agricultural exports to labor-intensive manufactured exports. During the 1980s and 1990s, international conditions—specifically the multilateral reduction of tariffs under the GATT and the diffusion of production and trade through foreign direct investment—helped the South Asian countries in their quest to enhance their economic development through manufactured exports. As barriers began to fall, industrial countries increasingly sought to rationalize production by transferring laborintensive manufacturing to low-cost production centers and South Asia became a premier beneficiary. The effect on the textile and apparel sector has been the most notable impact of this transformation as far as South Asia has been concerned. A commanding share of manufacturing and export revenues for all South Asian countries increasingly comprised textile and apparel exports to industrial countries. Although the Multi Fiber Agreement (MFA) regime governed the textile and apparel trade, the quotas extended by industrial countries provided an adequate incentive for South Asian countries to increase trade, via both domestic and international (quotaseeking) investors. The depreciation of real effective exchange rates in the region further assisted the South Asian countries in capitalizing on favorable international conditions.<sup>7</sup> At a broad level, the available (official) data of the major South Asian countries indicate that industrial countries continue to assume a major share of the region’s trade, while developing countries outside South Asia have been the second most important group, although their importance has been steadily diminishing as seen from Table 1, below. Intra-regional trade, meanwhile, remains low despite a revival in the late 1990s.

> 7 Pigato _et al_ (1997).

6
De Soto, H. (1989). **The Other Path** . London: Harper and Row.

Dell'Anno, Roberto (2022). Theories and Definitions of the Informal Economy: A survey. _Journal of Economic Surveys 36_ (5): 610-1643.

Distinguin, I., C. Rugemintwari, and R. Tacneng (2016). Can Informal Firms Hurt Registered SMEs’ Access to Credit? _World Development_ 84(August): 18-40.

Dollar, D., M. Hallward-Driemeier, and T. Mengistae (2006). Investment Climate and International Integration. _World Development_ 34 (9): 1498-1516.

Duchin, R., O. Ozbas, B. A. Sensoy (2010). Costly External Finance, Corporate Investment, and the Subprime Mortgage Credit Crisis. _Journal of Financial Economics_ 97(3): 418–435.

Elgin, C., M. A. Kose, F. Ohnsorge, and S. Yu (2021). Understanding Informality. CERP Discussion Paper 16497, Centre for Economic Policy Research, London.

Farrell, D. (2004). The Hidden Dangers of the Informal Economy. _McKinsey quarterly_ 3: 26-37.

Ferreira, L.M. and C.J. Cândido (2021). Factors Influencing Firm Propensity for ISO 9001 Withdrawal: Evidence on Decertification Tendency and Antecedents. _International Journal of Production Economics_ 233: p.108024.

Fikru, M. G. (2014). Firm level determinants of international certification: Evidence from Ethiopia. World Development 64, 286–297.

Fisman, R. and J. Svensson (2007). Are Corruption and Taxation Really Harmful to Growth? Firm Level Evidence. _Journal of Development Economics_ 83 (1): 63-75.

Goedhuys, M. and L. Sleuwaegen (2013). The Impact of International Standards Certification on the Performance of Firms in Less Developed Countries. _World Development_ 47: 87-101.

Goedhuys, M. and L. Sleuwaegen (2016). International Standards Certification, Institutional Voids and Exports from Developing Country Firms. _International Business Review_ 25(6): 1344-1355.

Guasch, J.L. (2007). **Quality Systems and Standards for a Competitive Edge.** World Bank Publications.

Hudson, J. and M. Orviska (2013). Firms’ adoption of international standards: One size fits all? Journal of Policy Modeling 35 (2), 289–306.

ILO (2018). Women and Men in the Informal Economy: A Statistical Picture. Third edition. Geneva: International Labor Office. International Labor Organization.

33
or approximately US$17.30 per resident of a WTO member country. The primary reason that the figure is larger on the export side is that our estimates suggest that export time in customs is more responsive to policy reform than is import time. Noting that the export gains are more speculative, we add these to the estimated gains for imports to get an estimate of the gains from TFA implementation that arise through the reduction in time at customs. This procedure implies an upper bound estimate of $210 billion, which is approximately US$33.31 per year per resident of WTO member countries.

Our estimates lie in the lower range of estimates in the literature. This reflects both methodological differences as well as choices about the source of the welfare gain. Our estimate relies on a single channel of welfare gains, reductions in time spent in customs. By our reckoning this channel is the most plausible and is likely to be the largest source of gains from trade facilitation. One of the reasons that the gains are relatively small is that much of world trade occurs in countries that will see little reform (i.e. developed countries such as those in the OECD). Furthermore, most of the countries with large implied policy changes do not trade very much. As a result the bulk of the estimated global gains from TFA implementation in our model occur in large trading countries that still have some room for improvement in their trade facilitation policies. We report welfare gains for the five countries that account for the largest shares of global gains in Table 8. China accounts for nearly one-fourth of the global gains on the import side and 30 percent on the export side.<sup>44</sup>

# **7 Conclusion**

In this paper we use data on trade facilitation policies to estimate the impact of policy on customs performance. Our primary outcome variable of interest is the time spent in customs, as reported in the Doing Business - Trading Across Borders indicators. Since these data are reported in large discrete units, days, we adopt a discrete time transition model to estimate the impact of policy on the number of reported days in customs. Since the policy data we employ as independent variables are missing observations in a non-random manner, we use a multiple imputation technique in order to maximize the use of available data.

Our estimates identify two policy bundles that are statistically significant predictors of reported

> 44The preponderance of oil exporters on the export side highlights the way in which the welfare gains on the export side may be overstated. The TFA may not have a large impact on the cost of exporting oil, but the calculations treat all exports equally. On the import side the implied gains from reform in Italy may also overstate the total gains. Italy’s imports from the EU are not likely to be overly affected by the TFA. The TAB data measure outcomes for overseas shipments, and the time burden of clearing Italian customs should be considerably lower for shipments from the EU.

35
23 variable z in equation 13 is one that causes variation in savings and investment without causing any change in A, one would expec **t** o estimate a coefficient c5 that was negative.

# 5 Results from estimation

Table 1 gives definitions of variables used in what follows. Table 2 gives summary statistics for these variables. The set of variables that cover the largest set of countries are those for basic national income accounts concepts of GDP, the share i = I/Y of investment (both private and government) in CDP, and the share of noninvestment spending by the government. Data for these variables for 112 countries from 1960 to 1985 are taken from Summers and Reston (1988). These data have the advantage that they are corrected for deviations from purchasing parity, so that comparisons of per capita income are more meaningful than comparisons made in terms of official exchange rates. Trade data on exports and ipports covering this same period are available from the World Bank for 90 of these original 112 countries. Most of the subsequent analysis is done with this set of countries. Finally, data on numbers of scientists and engineers are available for a subset of 22 of these countries.

Table 3 shows the basic finding for these data that has been confirmed in many different data sets by many different authors. If variables are measured over long time periods, the share of investment inGDP is closely related to the rate of growth of GDP. By itself, it explains 34% of the cross country variance in growth rates. This finding suggests that the result for the developed countries (noted for example in Romer 1987) carries over to a broad sample of countries weighted towards less developed countries. Growth in output and in capital are both
# **_FIGURES_**

|Figure 1.1. Patterns of urbanization and growth in the transition economies and middle income count<br>1960-1990|ries,<br>2|
|---|---|
|Figure 3.1. Real GDP level in 2001 relative to 1989|12|
|Figure 3.2. Sectoral output growth between 1990 and 2000|13|
|Figure 3.3. GDP per capita ppp in ECA countries compared to other regions 2001|14|
|Figure 3.4. National poverty level in ECA countries and comparators|15|
|Figure 3.5. National poverty and GDP per capita growth|15|
|Figure 3.6. Urban poverty and GDP per capita growth|16|
|Figure 4.1. Share of poor in capital cities, other urban and rural areas in ECA countries|21|
|Figure 4.2. Percent of uneducated household heads who are poor|22|
|Figure 4.3. Percent of large families with 5 or more members who are poor|22|
|Figure 4.4. Percent of unemployed household heads by country groups|25|
|Figure 4.5. Poor unemployed household heads by settlement area|25|
|Figure 4.6. Percentage of poor household heads who are unemployed, by settlement type|26|
|Figure 4.7. Poor unemployed household heads – Distribution by country groups|26|
|Figure 4.8. Access to infrastructure and energy services in ECA in the early 2000s by location|29|
|Figure 4.9. Reliability of infrastructure and energy services in ECA in early 2000s|30|
|Figure 4.10. Water connection comparison in capital and other urban areas in ECA countries|30|
|Figure 4.11. Water reliability|31|
|Figure 4.12. Water connection versus reliability in ECA countries (capital)|31|
|Figure 4.13. Water connection versus reliability in ECA countries (other urban)|32|
|Figure 4.14. Payment for infrastructure and energy services|34|
|Figure 4.15. Payment rates for water and reliability of service in ECA capitals|34|
|Figure 4.16. Incidence of poor sanitation and environmental conditions|35|
|Figure 4.17. Distribution of basic infrastructure connections by quintiles in capital cities|36|
|Figure 4.18. Distribution of basic infrastructure connections by quintiles in other urban cities|37|
|Figure 4.19. Lack of access to inside toilets and use of dirty fuels by quintiles for ECA countries|37|
|Figure 4.20. Reliability of water and electricity by quintiles in capital cities and other urban areas|38|
|Figure 4.21. Reliability of water for richest and poorest quintiles in the capital cities|39|
|Figure 4.22. Reliability of water for richest and poorest quintiles in the secondary cities|39|
|Figure 4.23. Payment incidence by quintiles in capital cities|40|
|Figure 4.24. Payment incidence by quintiles in other urban cities|40|
|Figure 4.25. Space per capita in ECA capital cities and comparator countries|42|
|Figure 4.26. Comparison of residential density in Moscow and Paris by distance from city center|43|