rafmacalaba/datause-encoder-data huggingface dataset:
electronic tender had it not been advertised electronically, has been predicted using the same two models (using matching methods) used for the main data indicators. The models are shown below along with a fixed effects regression for comparison.

TABLE 39: TREATMENT EFFECT ON NEWSPAPER ADVERTISEMENTS

|Dependent variable|Number o|f Newspaper Advertisem|ents|
|---|---|---|---|
|Model|Model (1a) –|Model (1b) –|Model (1c) –|
||Matching within<br>agencyacrossyear|Matching within year<br>across PEs|OLS with<br>Fixed Effects|
|Control: Manual Processing||||
|Treatment: E-procurement|-1.484***|-1.463***|-1.598***|
||(0.04)|(0.04)|(0.03)|
|Ln of Contract Value|0.016***|-0.007+|0.009***|
||(0.00)|(0.00)|(0.00)|
|Procurement Method: OTM|0.335***|-0.049***|0.357***|
|(LTM – Base)|(0.01)|(0.01)|(0.01)|
|PE Fixed Effects|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|
|constant|Yes|Yes|Yes|
|R<br>2|0.458|0.557|0.482|
|N|85674|34590|87824|

Note: + p<0.10 * p<0.05 ** p<0.01 *** p<0.001 We observe that the number of advertisements has gone down in all three models, corroborating the expected decrease. The rates for advertisement are regulated and revised by the Department of Films and Publication, Government of Bangladesh depending on the circulation of respective newspaper. Based on the survey of Procuring Entity Officials, the average cost of an advertisement for a tender was approximately Tk. 25,200 (approximately US$ 295). It has been assumed that the advertisement costs have increased commensurate with inflation. Based on these figures the cost savings were between Tk. 266 crore (US$ 31 million, as per model 1a) and Tk. 210 crore (US$ 24 million as per model 1b) for about 69,000 tenders.

- b. Tender Preparation Costs – The shift to electronic tendering has meant a number of costs pertaining to tender preparation are no longer incurred. There were significant printing costs, transportation costs, postal costs that are being saved due to the shift. The savings per tender is estimated on the basis of responses of the PE official’s survey. The averages are calculated by organization as the number of tenders vary significantly by organization (with LGED the majority of tenders in consideration), and the tender preparation cost vary in a statistically significant way across organizations. As the PE officials were asked the average tender preparation cost over a time period, the tender preparation costs have not been adjusted for inflation. The total tender preparation cost savings was estimated to be Tk. 53.5 crore (US$ 6.3 million).

72
could reflect greater enforcement or compliance. But interestingly, the spread across firms also often rises.

This paper is just simple empirics and gives simple, but compelling, answers. If we ask ―are people hotter in Phoenix?‖ we could plausibly answer ―No, for the most part, people stay indoors with air conditioning‖ without positing complex behavioral theories and testing their causal assertions. We are just examining the data and use regressions as data summarizing techniques, not to assert causality or test theories. But we do find that the idea that a single country number summarizes completely, or even modestly well, the policy _experiences_ of firms in those countries, even when limited to the same domain, is not tenable. We caution the reader from jumping from this to any particular conclusion about the implications of what this means, as we don‘t know. We are unsure just what these empirical findings mean but they must imply _something_ about how we should think about policy, policy implementation, and policy reform for promoting and accelerating broad based growth, and we are as anxious as they next guy and gal to find us just what it does mean.

# **II. Description of the data**

The two large-scale, multiple year, multiple country data collection efforts undertaken by the World Bank, each of which uses a different approach to understanding the constraints facing the private sector. DB provides measures of compliance with regulatory requirements and ES provides firms‘ reported experiences associated with regulations and other investment climate conditions. By using them together, this paper explores the relationship between _de jure_ and _de facto_ measures of regulations.

DB provides indicators in 10 regulatory areas of the number of procedures, the time and costs associated with complying with formal requirements. It uses a hypothetical ―standard‖ firm (e.g. a privately and domestically owned, limited liability company with 10-50 employees operating in the country‘s largest city) and standardized transactions that could apply to many different activities (e.g. building a warehouse; enforcing a contract worth twice the country‘s income per capita). It explicitly assumes full compliance, that no third parties are used to facilitate completing procedures and that no payments or other activities are undertaken to influence policy outcomes. The measures are compiled by one to four lawyers or accountants in 6
Table 1: Summary Statistics

||Non-Eligibl|e (Age_≤_64)|Eligible (|Age_≥_65)||
|---|---|---|---|---|---|
||1999-2000|2001-2002|1999-2000|2001-2002|Total|
|_Panel A: Demographics_||||||
|Child Age|12.5|12.6|12.1|11.8|12.4|
|Share Men (percentage)|50.3|51.8|54.9|48.2|51.0|
|# School Age Children in HH|2.6|2.6|2.3|2.8|2.6|
|# Children under 5 in HH|0.5|0.6|0.6|0.6|0.6|
|Household Size|6.0|6.1|5.8|6.3|6.1|
|# Adults 19-64|2.8|2.9|1.9|1.9|2.6|
|Father Present|0.7|0.7|0.6|0.5|0.7|
|Mother Present|0.8|0.8|0.7|0.7|0.8|
|_Panel B: Parents_||||||
|Yrs of Education Father|5.2|4.8|6.0|5.3|5.1|
|Yrs of Education Mother|3.9|4.0|5.4|5.1|4.3|
|Age Father|54.5|54.3|51.9|51.4|53.6|
|Age Mother|49.2|49.3|47.6|47.1|48.7|
|_Panel C: Income, Expenditure (in Bs. Per month)_||||||
|<br>Income per capita w/o Bono|478.1|505.7|432.5|501.6|489.1|
|<br>Expenditure per capita|445.1|396.6|495.8|451.9|431.2|
|<br>% Children with Positive expenditure in education|78.5|77.1|82.9|83.4|79.3|
|Expenditure on education per child|51.6|34.5|62.1|46.2|44.5|
|Observations|1,067|1,540|386|652|3,645|

Table 2: Impact of UCTs on Educational Expenditures

||(1)|(2)|(3)|
|---|---|---|---|
||Log (Expenditure+1)|Pr(Expenditure_>_0)|Log Expenditure if Expenditure_>_0|
|Treatment|0.732**|0.123*|0.426*|
||(0.255)|(0.059)|(0.203)|
|N|3645|3645|2889|
|_R_<sup>2</sup>|0.025|0.014|0.031|

Standard errors in parentheses, * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001 18
_Role of NGOs in HeaIth Delivery Africa Health Policy Study_

* 10

govemmen, of .hat burden and leave them to concentrate on constituencies wielding greater political clout.

27. Clearly, the prominence achieved by NGOs is very specific to their political context; governments may need to compromise between the need fbr resources and the political threat posed by NGOs. In highly pluralistic societies, for instance, NGOs may provide services to different interest groups, thus insulating government from factional conflict. This is seemingly the case in Nigeria where ethnic and religious affiliations have been cridcal in determining the distribution of NGOs (Anheler 1989). The picture emerging of the determinants of the distribution of NGOs Is complicated and research is on:y beginning on the issue.

# Problems with NGOs

28. Despite the positive features of NGOs that make them particularly suitable **for** the provision of health care, the status of NGOs Is often problematic. The following aspects of NGOs-though certainly not applicable to all-provide reason for caution in advocating Increasing reliance on NGOs to provide health services rather than strengthening public serices. The most fundamental question, perhaps, about the potential of NGOs concerns the time scale of their activities. Given NGOs' frequently weak resoure base and their dependence on foreign funding (whether secuiar or religious), long-term planning is difficult-either for governments looking to involve NGOs or for NGOs internally. What mechanisms are there, then, to ensure that when NGOs leave the area or the country or run out of funding that the government can take over their responsibilities in serving particular communities? Or, alte-rratively, if NGOs are to become a permanent component of a pluralistic health care system, where are the dividing lines between government and NGO jurisdictions? Finally, does donor emphasis on NGOs divert attention from strengthening the long-term viability and capacity of public sector services?

# _Dependence on External Funding and Personnel_

29. In the current African setdng, where self-reliant local institutions are few and government coffers nearly empty, rmany NGOs must increasingly tum to international financial support for their activities. While this support is vitally necessary during a period of economic downturn, the long-term implications of such dependence may be decreased autonomy of NGOs, more Intrusion of foreign objectives into domestic activities and reliance on the benevolence of developed country constituents at a time when pressure is increasing to reduce economic assistance to developing countries. The drwbacks of dependence on external finance also manifest themselves In the project-to-project operations of many American PVOs, for example, which have difficulty squeezing out of project flnance the requisite overhead with which to build institudonal capacity
O'Donnell O., E. van Doorslaer, R.P. Rannan-Eliya, A. Somanathan, S.R. Adhikari, D.

Harbianto, C.C. Garg, P. Hanvoravongchai, M.N. Huq, A. Karan, G.M. Leung, C.W. Ng, B.R. Pande, K. Tin, K. Tisayaticom, L. Trisnantoro, Y. Zhang, and Y. Zhao. 2007. “The incidence of public spending on healthcare: comparative evidence from Asia.” _World Bank Economic Review_ 21: 93–123.

Rosenzweig, M., and K. Wolpin. 1986. “Evaluating the Effects of Optimally Distributed Public Programs: Child Health and Family Planning Interventions.” _The American Economic Review_ 76 (3): 470–82.

Schultz, P. 2004. “Health economics and applications in developing countries.” _Journal of Health Economics_ 23 (4): 637–41.

Smith, L., M. Ruel, and A. Ndiaye. 2005. “Why Is Child Malnutrition Lower in Urban Than in Rural Areas? Evidence from 36 Developing Countries.” _World Development_ 33 (8): 1285–305.

Stock, J. H. and M. Yogo. 2002. “Testing for Weak Instruments in Linear IV Regression.” NBER Technical Working Paper 284; Cambridge, MA.

Stock, J. H., J. Wright, and M. Yogo. 2002. “A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments.” _Journal of Business & Economic Statistics_ 20 (4): 518–29.

Thomas, D., J. Strauss, and M. H. Henriques. 1991. “How does mother’s education affect child height?” _The Journal of Human Resources_ 26 (2): 183–211.

Valdivia, M. 2004. “Poverty, health infrastructure and the nutrition of Peruvian children.” _Economics and Human Biology_ 2 (3): 489–510.

26
Figure 16. Shapley Decomposition of Debit Card Dissimilarity Index <!-- Start of picture text --> (Household level, LITS)<br>100% 2.2 4.6 3.8<br>90% 15.3 20.9<br>22.1<br>80% 4.7 37.3<br>70% 19.0 10.8 56.7 23.9<br>60%<br>10.2<br>50% 16.8 12.7<br>40% 10.6<br>30% 58.8 3.2 25.6<br>52.3<br>20% 38.0 15.3<br>10% 14.3 16.9<br>0% 4.1<br>Latvia Czech Republic Turkey France Croatia<br>Age Education Gender Income Urban<br><!-- End of picture text --> <!-- Start of picture text --> (Individual level, FINDEX)<br>2011 2014<br>100% 6.2 100%<br>90% 17.1 13.0 18.9 25.1 90% 21.8 15.6 19.0 22.9 26.2<br>80%70% 28.2 1.5 22.4 5.75.2 8.6 80%70% 7.0 34.2 1.3 4.5<br>60% 16.9 58.6 60% 26.4 44.5 33.7<br>50% 50% 43.6<br>40% 44.4 40% 16.1<br>70.2<br>30% 30%<br>53.1 54.5 20.9<br>20% 13.8 20% 44.8 34.0 42.1<br>10% 14.6 22.0 10% 15.6 25.7<br>0% 0%<br>Belarus United Arab Turkey Rep. of Mongolia Belarus United Arab Turkey Rep. of Mongolia<br>Emirates Korea Emirates Korea<br>Age Education Gender Income<br><!-- End of picture text --> _Notes:._ LITS is household level. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries. _Sources_ : LITS, FINDEX Turkey’s credit card usage rate places it among a group of high-income benchmark countries both at the individual and the household levels (Figure 46 and Figure 48). Turkey’s dissimilarity value at the individual level is higher when compared to countries with similar coverage rates indicating that even though credit card ownership is well-spread, the inequality level is relatively high across the measured indicators (Figure 17). The effect of gender continues to be the principal component of the d-index at 44.4 percent despite the relative reduction in comparison to the respective values for debit card and bank account ownership. The decomposition of the EACR difference shows that the scale in credit card usage of Turkey is significantly higher than the benchmark countries (Figure 44). However, the equalization effects confirm the relatively high dissimilarity level in Turkey and suggest that the equity of credit card ownership has room for improvement.

Parallel to the trend in debit card usage, the coverage of credit card ownership also fell in Turkey between 2011 and 2014, from 45.1 to 32.8 percent. The dissimilarity remains stable at around 26 percent. Netherlands and Demark experience a similar decline in their coverage and EACR rates which shows that Turkey is not alone in undergoing a decline in credit card ownership.

18
11 -

-

Macroeconomic policies in the labor-exporting country may, however, influence the choice of the channel of transfer--official versus unofficial. Furthermore, it is argued that as the stock of migrants becomes older, the "required" component of remittances experiences a secular decline and becomes relatively les important. This means that the short-run versus long-run dichotomy between the two sets of influences is not as straightforward as suggested by Glytsos (1988). Since actual remittances data reflect both "required" and "desired" components, any empirical model that strives to have meaningful policy implications must account for the determinants of both concepts.

Except for few exceptions (Katseli and Glytsos (1986), Glytsos (1988), and Swamy (1981)), no such synthesis exists in the literature. Katseli and Glytsos explicitly consider the above two concepts and, as noted above, find remittances from Greek migrants in Germany to be positively related to income per capita and interest rate in Germany, and negatively related to income and the inflation rate in Greece. However, Katseli and Glytsos fail to account for the length of stay--a major determinant of remittances. This deficiency is partially rectified in Swamy (1981) by introducing the ratio of females to males. A further important finding of Swamy is the significant and positive effect of the stock of migrant workers. However, Swamy failed to find significance for the black market premium in her cross-country regressions.

To recapitulate, a useful empirical model for the determination of officlal remittances must include the following:

- (1) The income level in the host country or the income differential between the host and home countries; the stock of
engage in relatively more accountable frameworks. This is due to the fact that more intense real external shocks make even operational accountability more burdensome, and therefore does not necessarily imply a reluctance of country authorities to be accountable. Following this argument one can expect that higher terms of trade volatility can result in inclination towards debt management guidelines rather than strategic benchmarks for debt management. The series was acquired from the GDF & WDI Central database of the World Bank.

The estimation results for the PROBIT models of yi<sup>P</sup> and yi<sup>B</sup> are reported in Table (2). The respective log-likelihoods were again maximized using the BHHH algorithm and the inference is based on Huber-White QML standard errors.

The estimation results in Table (2) indicate that the probability of a strategy being available to the public can be from about 21 percent explained by selected economic indicators. Similarly, the probability of a strategy being expressed in terms of a strategic benchmark, rather than strategic guidelines, can be from about 47 percent explained by selected economic indicators, a percentage signi...cantly higher than in the case of public strategies. The unexplained part of the probability that yi<sup>P=1ory</sup> i<sup>B=1isattributedtoinstitutional,politicalandidiosyncraticfactors.</sup> We now proceed to a more detailed discussion of our results.

Consider ...rst the estimation results for yi<sup>Pinthe...rstcolumnofTable(2).We</sup> ...nd that the level of GDP per capita, the level of indebtedness and the average growth rate do not seem to be important in explaining a country’s decision to make its debt management strategy public. Further, variations in economic growth seem to negatively impact on the probability of a strategy being made public, however, this impact is not signi...cant at common levels. On the other hand, increasing volatility of domestic prices, as measured by CPI, signi...cantly a¤ects the probability of a strategy being public. This result could be related to the e¤ect of volatility of in‡ation on the uncertainty pertaining to government revenues. The government in defence of its strategy and for the sake of accountability prefers to make its debt management strategy available to the public so that the e¤ect of an unexpected shortfall in government revenues and the e¤ect of volatile prices on ...nancing premiums are 24
the same sector, d) banks’ ratings in different regions, and e) banks’ ratings for a particular instrument, etc. Thus it would be much easier to monitor and supervise.

For countries that wish to implement the CRB approach but be fully Basel II compliant according to the letter and not just the spirit of the proposals, then one possibility is to adopt the Standardized Approach for capital and use the CRB approach to define provisions. Under recent changes to the Basel II proposals, if the CRB approach is calibrated to give provisioning levels equal to the full “value at risk” minus the Standardized Approach’s capital requirement, then a country would have a fully Basel II compliant and rational system where banks’ reserving policies were roughly equal to the value at risk<sup>51</sup> . Such an approach would build very naturally on the types of public databases many countries have built up to monitor and enforce provisioning rules. This standard might be labeled Basel II-CRB.

There are then a group of developing countries that will indeed wish to implement the IRB approach - at least for a small group of larger and more sophisticated banks. These countries will of course need to dedicate substantial resources to consider carefully the IRB proposals, possibly recalibrate them for the task at hand, and monitor and supervise them effectively. There will also be a substantial cost for banks in these countries in terms of regulatory costs for the data and systems required. Only countries that score well in terms of BCP compliance will be in a position to fully reap the rewards of these more advanced approaches.

One criticism of Basel II has been that there are so many alternatives that the essence of a standard has been lost. It is hoped by defining the islands of a) Basel I, b) Basel II-a, c) Basel II-b, d) Basel II-CRN, and e) Basel II-IRB that countries may navigate more effectively through the sea of standards.

Finally, given the globalization of banking, Basel II will also focus the minds of supervisors on a set of important cross-border issues. These range from particular definitions in different countries regarding both the SA and the IRB approaches to broad issues regarding the degree of desired homogeneity of approaches in home and host countries. From the standpoint of a developing country, if a bank (whether a subsidiary or a branch depending on local legislation) is large in the local financial system and there is no broad,

> 51 The roughly comes from the use of a reserving policy for individual loans for a portfolio of credit risks – as Basel II’s IRB approach also approximates to the full Value at Risk of a loan portfolio.

46
RES imposes a substitution of fossil fuel based electricity generation with renewable electricity and would cause reduction of fossil fuel demand for power generation and thereby fossil fuel supply. Moreover, it also increases electricity prices and causes outputs of electricity intensive industries to decrease further.

Table 5 Sectoral output change in regions under ETS and ETS-cum-RES cases (%).

||Ag|ri|Co|al|Co|il|Mi|ne|Fpa|p|Pe|tro|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||S1|S2|S1|S2|S1|S2|S1|S2|S1|S2|S1|S2|
|Total|-0.20|-0.21|-8.96|-8.90|-2.98|-3.09|-5.49|-5.48|0.64|0.61|-2.88|-2.92|
|BJ|-0.08|-0.19|-0.50|-0.78|-9.43|-9.18|-0.58|-0.90|0.05|-0.06|-5.89|-6.28|
|TJ|0.05|0.02|1.99|1.58|-1.83|-1.95|-4.28|-4.39|0.71|0.65|-1.71|-1.82|
|HB|-0.68|-0.75|-17.48|-17.25|-3.24|-3.44|-16.90|-16.75|-1.05|-1.18|-5.96|-6.15|
|SX|-15.36|-14.99|-9.60|-9.14|-5.71|-4.78|-62.52|-61.86|-7.83|-7.53|-0.36|-0.14|
|IM|1.31|1.34|-5.59|-5.42|1.14|1.02|2.20|2.24|0.99|1.08|-4.85|-4.73|
|LN|-0.20|-0.25|-12.71|-12.59|-1.76|-2.05|-10.16|-10.33|0.48|0.43|0.34|<br>0.03|
|JL|-0.05|-0.08|-9.63|-9.81|-3.33|-3.46|-2.54|-2.87|0.86|0.69|-11.14|-10.81|
|HLJ|-0.59|-0.62|-13.17|-13.60|-3.67|-3.84|-5.43|-5.37|-0.61|-0.65|-3.92|-4.10|
|SH|-0.11|-0.12|0.00|0.00|-1.80|-1.69|0.00|0.00|1.10|1.11|-3.73|-3.66|
|JS|0.27|0.28|-5.00|-4.94|-3.26|-3.35|-1.18|-1.18|0.52|0.53|-1.33|-1.43|
|ZJ|0.05|-0.02|-1.33|-1.60|0.00|0.00|-2.49|-2.83|0.41|0.27|-4.21|-3.89|
|AH|-0.35|-0.34|-7.30|-7.17|0.00|0.00|-4.76|-4.55|-0.28|-0.25|-4.03|-4.00|
|FJ|0.29|0.30|-5.18|-5.22|0.00|0.00|-1.44|-1.34|0.45|0.46|-0.22|-0.29|
|JX|0.28|0.23|-8.47|-8.72|0.00|0.00|-2.52|-2.87|1.07|0.97|-12.27|-12.34|
|SD|1.08|1.07|-9.06|-9.13|-4.88|-5.06|-5.35|-5.43|2.72|2.69|-4.61|-4.67|
|HN|-0.43|-0.42|-8.92|-8.85|-2.41|-2.52|-1.17|-1.10|-0.52|-0.49|-3.27|-3.31|
|HB|-0.94|-0.91|2.73|2.25|-26.76|-26.10|-7.89|-7.62|0.12|0.12|-3.81|-3.89|
|HN|-0.70|-0.67|-8.84|-8.75|0.00|0.00|-3.19|-3.09|-0.71|-0.67|-2.16|-2.19|
|GD|0.42|0.41|0.00|0.00|0.42|0.47|0.12|0.15|1.02|1.00|-0.16|-0.14|
|GX|-0.02|-0.02|-1.57|-1.94|0.00|0.00|-2.93|-2.94|-0.12|-0.11|4.55|<br>4.12|
|HAN|0.41|0.47|0.00|0.00|0.00|0.00|-1.88|-1.64|0.87|1.02|-3.21|-2.97|
|CQ|-0.14|-0.13|-9.43|-9.21|-30.19|-29.73|-10.21|-9.78|-0.36|-0.32|-11.16|-10.82|
|SC|-0.17|-0.15|-10.26|-10.11|-5.07|-5.03|-5.97|-5.69|0.21|0.25|-21.41|-20.89|
|GZ|-0.35|-0.67|-10.46|-11.32|0.00|0.00|0.89|-0.28|0.78|0.92|-23.46|-22.77|
|YN|-0.09|-0.06|-9.95|-9.75|0.00|0.00|-1.54|-1.42|0.01|0.09|-9.22|-8.94|
|SAX|-0.22|-0.21|-4.35|-4.36|-0.37|-0.42|0.05|0.03|0.16|0.15|-0.85|-0.91|
|GS|0.64|0.65|-3.65|-3.65|-1.03|-1.06|-1.32|-1.32|0.75|0.78|-0.80|-0.76|
|QH|-0.03|-0.02|-0.55|-0.81|-1.02|-1.11|-3.56|-3.50|-0.37|-0.35|-0.04|-0.17|
|NX|0.05|-0.02|-8.19|-8.32|-2.88|-3.32|0.00|-0.47|0.12|0.08|-2.82|-3.03|
|XJ|-0.95|-0.98|-18.46|-18.48|-3.90|-3.94|-6.72|-6.59|-0.70|-0.77|-3.02|-3.05|

24