rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6 Need Factors Used for Grant Financing of Health Care in Selected Countries**

|_Country name_|_Need Factors for Health Care Grants_|
|---|---|
|_(a) Need based top-up for health_|_care in general grants_|
|Belgium|Age, gender, unemployment, disability|
|Finland (to local governments)|Age, disability, remoteness, local tax base|
|Germany|Age, gender|
|Netherlands|Age, gender, urbanization, income base|
|Switzerland|Age, gender, region, income|
|_(b) Need-based specific purpose_|_transfers for core health services_|
|Denmark|Age, children of single parents|
|England|Age, sex, mortality, unemployment, elderly living alone|
|France|Age|
|Italy (two-thirds)|Age, gender, mortality|
|Northern Ireland|Age, gender, mortality, low birth weight|
|Norway (50 percent)|Age, gender, mortality, elderly living alone|
|Portugal (15 percent)|Burden of illness: diabetes, hypertension, AIDS, tuberculosis|
|Scotland|Age, gender, mortality, rural costs|
|Spain|Cross-boundary flows|
|Sweden|Age, living alone, employment status, housing|
|Wales|Age, gender, mortality, rural costs|
|_(c) Health transfers using compo_|_site indexes based upon principal component analysis_|
|Brazil|Infant mortality, 1–64 mortality, 65+ mortality, mortality rate by<br>infectious and parasitic diseases, mortality rate for neoplasia,<br>mortality rate for cardiovascular conditions, adolescent mother<br>percentage, illiteracy percentage, percentage of homes without<br>sanitation, percentage of homes without running water,<br>percentage of homes without garbage collection.|
|South Africa|Percentage female; percentage children under 5; percentage<br>living in rural area; percentage older than 25 without schooling;<br>percentage unemployed; percentage living in traditional<br>dwelling, shack or tent; percentage without piped water in house<br>or on site; percentage without access to refuse disposal;<br>percentage without access to phone; percentage without access to<br>electricity; percentage living in household headed by a woman.|

_Source_ : World Bank (2006).

37
# **Public Procurement and the Private Business Sector: Evidence from FirmLevel Data**

## **1. Introduction**

Governments set and implement the rules that guide the economy. Governments also use resources to provide public goods such as infrastructure. Public procurement is the one area where both government spending and government rule setting come into play. The size of public procurement in most economies is significant. On average public procurement makes up about 14.5 percent of GDP, with countries such as Eritrea and Angola going up to as high as 33% and 26% respectively (Djankov et al., 2017). In developing economies, public procurement can go up to 50% or more of total government expenditure (Knack et al., 2017). The quality of public procurement can have far-reaching effects throughout the economy given its magnitude.

Poor public procurement systems can incur significant costs on the economy. Opaque systems can increase rent-seeking behavior. Favoritism in awarding contracts can increase corruption, discouraging fair competition that could otherwise drive prices down and increase quality (World Bank, 2016). A fair and transparent public procurement system can encourage greater firm participation, decrease corruption, and improve the quality of public goods such as infrastructure. The use of online procurement portals can also encourage online connectivity in the private sector. Public procurement systems can also push the boundaries of innovation in the private sector. Using rich firm-level data we explore the far-reaching effects of public procurement systems throughout the business sector. We test whether public procurement systems are correlated with firm engagement in procurement, the prevalence of corruption in the business sector, the losses faced by firms when shipping domestically, firm innovation, and online connectivity.

The public procurement literature has explored certain aspects of public procurement and linked it to a number of outcomes. Several studies have explored the role of discriminatory public procurement policy 2
_The Creative Wealth of Nations_ To address this issue, the WBG could apply the “evidence-based and selective country engagement model” to,

- Assess the relationship between weak country brands and trade, tourism, and investments.

Under the Systematic Country Diagnostic (SCD) (see box 13), for those select countries where it is clear that their weak image is likely to constrain their competitiveness and economic growth, the WBG could,

- Tap agents like the performing arts in cultural diplomacy, cultural policy, and nation branding initiatives.<sup>211</sup> Details for this require careful articulation that goes beyond the scope of this paper. But this could be done in partnership with embassies of those respective countries, cultural organizations, educational institutions, the private sector, including airlines and others.

# **<mark>|| Box 13 ||</mark>**

According to the WBG strategy, “A Systematic Country Diagnostic (SCD) will use data and analytic methods to support country clients and WBG teams in identifying the most critical constraints to, and opportunities for, reducing poverty and promoting shared prosperity sustainably, while explicitly considering the voices of the poor and the views of the private sector.”<sup>210</sup> Source: WBG Strategy

- Build closer linkages between the World Bank, the International Finance Corporation (IFC), and the Multilateral Investment Guarantee Agency (MIGA) to close the gap between poor country brands and private sector investment.

For the assessment methodology, please see appendix D. This is provided merely as an example. The WBG could development its own country assessment method if it wishes to do so. In any case, while developing a strategy for nation branding, it is crucial to bear in mind:

- The investments made in such image building are not easy to measure and may take a long time to pay off. That said,

- The costs incurred in engaging the performing arts to build a positive image could translate into major returns even beyond pure economic gain.

- Since every country has its own unique culture and heritage (which more or less interacts with the performing arts), each and every country has an opportunity to excel in promoting its own image, fully and positively.<sup>212</sup> For countries with a poor image, “the challenge is turning [their cultural] assets into perceived strengths through clear and consistent [communication].<sup>213</sup>

- In conflict-affected countries and in countries with other extreme governance challenges, on the other hand, solving directly these key constraints to investment is a priority.

> 210 The World Bank Group. 2014. _The World Bank Group Strategy_ .

> 211 In relation to backward and forward linkages, these activities may also help stimulate voices of the poor, build social unity, and boost domestic confidence, including that of local investors.

> 212 FutureBrand, _2011-2012 Country Brand Index._

> 213 Ibid. | See also FutureBrand, _2012-2013 Country Brand Index_ .

<mark>55</mark>
Fujita, M., Krugman, P., and Venables, A.J. 1999. The Spatial Economy: Cities, Regions, and International Trade. MIT Press.

Gallup, J. L. and J. D. Sachs. 1998. ‘ Geography and Economic Growth,’ in B. Pleskovic and J. E. Stiglitz (eds.), Proceedings of the World Bank Annual Conference on Development Economics, World Bank.

Gawande, K. 1997. “Generated regressors in linear and nonlinear models,” Economics Letters, 54, pp. 119-126.

Kaufmann, D., A. Kraay, and M. Mastruzzi. 2005. “Governance Matters IV: Governance Indicators for 1996-2004,” World Bank Policy Research Working Paper Series # 3630.

Limao, N. and A. Venables .2001. “Infrastructure, Geographical Disadvantage, Transport costs, and Trade. World Bank Economic Review, 15, pp. 451-479.

Melitz, M. 2003. “The Impact of Trade on Intra-industry Reallocations and Aggregate Industry Productivity,” Econometrica, 71, pp.1695-1725.

Mengistae, T. and Catherine Pattillo. 2004. “Export Orientation and Productivity in SubSaharan Africa,” IMF Staff Papers, 51, pp.327-353.

Moulton, B. 1986. “Random Group Effects and the Precision of Regression Estimates,” Journal of Econometrics, 32, pp. 385-397.

__________ 1990. “An illustration of a pitfall in estimating the effects of aggregate variables on micro units,” Review of Economics and Statistics, 72, pp.334-338.

Nelson, C. and R. Startz. 1990. “Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator,” Econometrica, 58, pp.967-976.

Newey, W. 1987. “Efficient Estimation of Limited Dependent Variable Models with Endogenous Explanatory Variables,” Journal of Econometrics, 36, pp. 231-250.

Pagan, A. R. 1984. “Econometric issues in the analysis of regressions with generated regressors,” International Economic Review, 25, pp. 221-247.

Radelet, S. and J. Sachs .1998. “Shipping costs, Manufacturing Exports, and Economic Growth. Paper Presented at the American Economic Association Meetings, Harvard University.

Redding, S. and A.J. Venables .2000.”Economic Geography and International Inequality,” Discussion Paper No. 2568, Centre for Economic Policy Research, September.

________________. 2004. “Economic geography and international inequality,” Journal of International Economics, 62, pp. 53-82.

25
_25_

# Regression 11

Dependent variable: Standard Deviation of Real Per Capita GDP Growth 1960-1995 Number of observations: 114

|Mean of dep. var.<br>Std. dev. of dep. var.|=.052993<br> =.021039|LM het.<br>Durbin-W|test=.058422 [.809]<br>atson=1.82486 [<.466]|
|---|---|---|---|
|Sum of squared residuals|=.023269|Jarque-Bera|test=7.39687 (.025]|
|Variance of residuals|=.221607E-03|<br>Ramsey's RE|SET2 = .32S584E-02 (.955]|
|Std. error of regression<br>R-squared|=.014886<br> =.534787|F (zero sl<br>Schwarz B|opes)=15.0879 [.000]<br>.I.C.=-8.12293|
|Adjusted R-squared|=.499342|Log likel|ihood=322.561|
|Estimated|Standard|||
|Variable<br>Coefficient|Error|t-statistic|<br>P-value|
|SSA<br>.045460|.524191E-02|8.67245|[.000]|
|ASIA<br>.0376574|.424625E-02|8.87235|[.000]|
|ECA<br>.028!505|.330153E-02|8.63375|[.000]|
|MENA<br>.046493|.842420E-02|5.51904|[.000]|
|LAC<br>.034650|.462190E-02|7.49686|(.000]|
|OIL<br>.012151|.580664E-02|2.09255|[.039]|
|COMMOD<br>.709771E-02|.396159E-02|1.79163|[.076]|
|MICROSTATE .020540|.518460E-02|3.96181|(.000]|
|Standard<br>.249983|.105027|2.38017|(.019]|
|Deviation Of||||
|Terms of Trade||||
|Standard Errors are heter|oskedastic-con|sistent (HCT|YPE=2).|
31 addition to the different pace in quota increases, the FCSK and Poland move ahead because of the elimination of duties on their coal imports into Germany and Spain in 1996;

# A. Copenhagen Summit Concessions

The concessions granted by the EC summit in Copenhagen in June 1993 are significant for two reasons. First, the EC explicitly recognized the aspiration of the CEE-5 countries to acquire membership status, although no specific timetable was agreed upon. Second, it granted the CEE-5 further trade concessions for industrial products. The significance of the latter is that they accelerate the transition to free access to EC markets by a year.' Specifically, these new concessions provide for abolishing tariffs: (i) on steel products after four rather than five years; (ii) on the "four-year-delayed' free trade group after two instead of four years; and (iii) on the MFA group in frve years rather than six years.' Other concessions provide for an increase in the growth rate of tariff-free quotas/ceilings by '10 percentage points over the ITAs' rates of 15 percent (Hungary) and 20 percent (others) per annum and accelerates the elimination of triffs by two years on the "five-year-delayed" free trade group.

How meaningful are these concessions? Table 13 highlights the differences between the ITA schedule and the schedule as mmofied by the Copenhagen Summit (again using te 1992 actual commodity composition of flows as weights). Within the next two years (1994-95), the net gain for former troika countries, as measured by the increase in share of products obtaining duty-free access to EC markets, amounts to 2-3 percentage points. The gains for Bulgaria and Romania are negligible except for 1997. However, one should also take into account an accelerated increase in margins of preference, esecially forthe "quota/five-year-delayed" group: although the new provisions do not have a large impact in terms of increasing the liberalized share of exports, they increase the competitive position of the countries, particularly of the Visegrid group. The accelerated schedule of reductions in triffs on textles and clotig may have a similar impact, assuming that CEE-5 quotas are not binting. The major benefits of the Copenhagen concessions are realized in the fifth year of the ITAs when the duty-free c increased

- In addition, the EC summit offered-concessions on imports of farm products (levies and dities are to be reduced by 60 percent in two and a half years).

4In addition, the EC has pledged to improve rules concerning "outward processing."
**Figure 1: Employment rate by survey and year**

<!-- Start of picture text --> 80%<br>75% 72.2% 72.3% 73.6%<br>70.3%<br>70% 68.2% 68.7%<br>65%<br>60%<br>55%<br>51.3% 51.4%<br>49.3% 50.2%<br>50% 47.7%<br>47.2%<br>45%<br>40%<br>HIES LFS HIES LFS<br>Household heads Age 15 and above<br>2002 2006 2009<br><!-- End of picture text -->

**Table 10: 2009 imputed per capita consumption expenditures (Rs.) evaluated at means of the right hand side variables based on HIES and LFS**

||HIES|LFS|Ratio of prediction<br>(LFS/HIES)|
|---|---|---|---|
|Urban|4251|4701|1.11|
|Rural|3015|3017|1.00|
|Estate|1928|1926|1.00|

Source: Authors' estimations using the corresponding rounds of HIEs and LFS

**Table 11: Two variables explain about half of the 11 percent discrepancy in imputed  in urban areas for imputed household** **<u>expenditures per capita</u>**

||Right hand side variables(X)|Coeff.|Mean<br>HIES|of X<br>LFS|X*C<br>HIES|oeff<br>LFS|Difference<br>in prediction<br>(approx.pp)|
|---|---|---|---|---|---|---|---|
||Employed ratio|0.99|0.350|0.380|0.348|0.377|2.9|
|Urban|Head's educationpassed AL|0.82|0.190|0.228|0.156|0.187|2.5|
||Employed ratio|0.45|0.379|0.411|0.172|0.186|0.7|
|Rural|Head's educationpassed AL|0.54|0.102|0.100|0.055|0.054|0.1|
|Estate|Head's educationpassed AL|0.48|0.020|0.027|0.010|0.013|0.1|

Source: Authors' estimations using the corresponding rounds of HIES and LFS. Note: The samples of HIES and LFS data are restricted to the common survey period and geographic coverage. In the estates, "employed ratio" was not included in the estimation model.

17
||||Recurrent Heatth Exzeriditu<br>Puhtc|Table 3 cont,<br>re. by Souwce*<br>Private|||Sh||<br>|
|---|---|---|---|---|---|---|---|---|---|
||||<br>MO<br>SoclotSecurit<br>OtherPubtic|<br>Ot-f-<br>Pit|Oh||are of Out-<br>|Share of Private<br>|<br>Share of ForLt<br>|
|trv|Year|CArrency|<br> y<br> <br>-L-et<br>Sudoet<br>Sudoet|uo<br>rvae<br>Pocket<br>Insurnce|ter<br>Private|Total|of-Pocket In<br>Total|Irsuracme In<br>Total|Pbtilc_Inumrnce_<br>in Total|
|Zaire|1986<br>(|USS<br>t<br>milllaw)|O<br>O<br>Alaocovers<br>health care of goverrsnt<br>biecluddal:<br>(I) Privte-sector<br>firm.<br><br>(II)<br>Donrs gn 160s,sep|41.0<br> <br>qplaoees.<br>14.O;<br>rrate<br>breakdowi not a|154b<br>vailoble,|205.0<br>10.0.|20.0X|||
|Zmbia|1981<br>(|Kuwaha<br>millions)|<br>.s9<br>*-<br>30 <sup>.</sup>9<sup>a</sup><br>Z2ianCornolidated CoperNines (Z|199<br>CCN),thestatemini|4.5<br>ng corpor|128.2<br>ation.|15.5X|--|24.1S|
|Zilabwe|1987<br>|ZS<br> <br>(mi<br>ll ions)|_310.t8_<br>_-l_<br>99 32<sup>b</sup><br>aCentrdl Goverrment.<br>bInludos:<br>(i)N.icip.lities,<br>25.00;<br>CIncludes:(i) Church**missions,**2.56;<br>(111) Voluntary Orafniztion|63.90<br>105.00<br> (ii)<br>Foreign A"ists<br>(ii)<br>Industries,<br>NI<br>s,<br>4.50.|5781c<br>nce<br>74.3<br>nrw, Com|636.21<br>2.<br>erciat|<br>10.04X<br>Farm.50.75;|16.51X|--<br>w|

*Inciude. only recurrent _expnditures_ end _exclucs_ ependitures on traditional mdicine for reasons givYn In text.
The three sets of economic outcomes examined are aggregate growth, the reduction of poverty and inequality, and the development of basic human capabilities. Each of the three sections uses cases from the informal legal system of Indonesia to illustrate the differences between formal and informal legal systems, and to demonstrate the plausibility of the points made. The illustrative cases are largely taken from a recent study of non-state justice institutions in Indonesia, _Forging the Middle Ground: Engaging Non-State Justice in Indonesia_ (World Bank 2008).

A few caveats. This paper distinguishes local-level legal institutions from local political and social authority. It attempts to isolate informal dispute resolution at the local-level and to assess the ways in which it advances and hinders development objectives. In reality, however, local-level legal institutions are interwoven with (and sometimes identical to) local political authority, which by definition is crucial for social order. It would not be difficult to show that in the absence of social order altogether few economic objectives can be obtained; rather, this article makes the somewhat stylized distinction between the legal and socio-political functions of local authorities, and focuses on the former. Second, and related, the paper emphasizes the economic effects of local-level legal institutions. Although they perform other crucial functions in many places, such as absorbing complaints and preventing low-level conflicts from escalating into mob or collective violence, and although security obviously facilitates economic activities, the focus here is for the most part on the direct economic effects.

Third, it has been noted repeatedly that, in every society, informal institutions, organizations, and norms are as important for the promotion of economic activities as courts and other legal and quasi-legal institutions. Examples include stable trade relationships constructed on the basis of kinship or ethnic affinities, norms of reciprocity that help close-knit communities 3
8 that individuals in Ghana will form migration networks on the basis of ethnicity and religion. On the basis of this assumption, in order to link the two Ghana household surveys, we partitioned the data from the 2005/06 Ghana GLSS 5 Survey (sub-sample) into fifteen ethno-religious groups. We defined these ethno-religious groups by classifying households according to five religious and 3 ethnographic groups. The five religious groups are: (1) Catholic and Anglican; (2) Presbyterian and Methodist; (3) Pentecostal, Spiritualist and other Christian; (4) Muslim; and (5) all others.<sup>7</sup> The three ethnographic groups include: (1) households belonging to ethnic groups representing 17% or more of the population in 1998/99 (e.g., Asante and other Akan); (2) households belonging to ethnic groups representing between 10% and 15% of the population in 1998/99 (e.g., Fanti, Ga-Adangbe, Ewe); and (3) all other households.<sup>8</sup> Second, using these ethno-religious groups, we created seven instrumental variables. Some of these instrumental variables are used in our selection model, while others are used in the estimation of marginal budget shares, as described later in the paper.<sup>9</sup> The first two instrumental variables come from the 1998/99 Ghana GLSS 4 Survey and include: (1) the fraction of females receiving internal remittances (from Ghana) in the ethno-religious group; and (2) the fraction of females receiving international remittances (from African and other countries) in the ethnoreligious group. The intuition for including these variables is that ethno-religious groups share information on the costs of sending and receiving remittances, consequently those groups which receive more remittances will have lower costs. The assumption is that these variables observed in 1998/99 are correlated with the decision to receive remittances in 2005/06 but not with observed household income in 2005/06.

The five other instrumental variables come from the 2005/06 Ghana GLSS 5 Survey (sub-sample) and include: (1) the fraction of households receiving internal remittances (from
|Area of residence: metropolitan|0.64|0.63|0.64|-0.01|
|---|---|---|---|---|
|Urban/semiurban|0.19|0.19|0.19|0.00|
|Rural|0.17|0.18|0.17|0.01|
|Living in the same area where they were born|0.63|0.64|0.61|0.03|
|Number of members in the household|5.25|5.26|5.25|0.01|
|Years of education by the applicant’s father|7.16|7.10|7.21|-0.12|
|Years of education by the applicant’s mother|5.65|5.54|5.75|-0.21|
|Not married|0.69|0.69|0.68|0.01|

_Source:_ Original table for this publication.

The applicants are predominantly youths with some work experience and modest educational background. The applicants’ average age is around 25 years, and more than two-thirds are unmarried. More than 80 percent have at least some work experience. Females are a minority. Only a third of the applicants are female. Only around 40 percent have higher secondary or degree qualifications. Nearly 40 percent have attended at least one vocational training—mostly short-courses—which is more than the national average, although only around 20 percent have a skills certificate. Recognition of prior learning (RPL) applicants mainly come from urban areas, most likely because of the assessment centers’ locations and available occupations for assessment. More than 80 percent of the applicants are living in either metropolitan areas or urban areas.

The average level of education in the sample translates roughly into 10 years of schooling. This education level could be because the RPL applicants must have the ability of basic literacy and numeracy. This requirement may have precluded some more disadvantaged workers from the assessment opportunities. According to Human Development Index estimate, Bangladesh’s average years of schooling in 2018 are 11.4 years (UNDP 2020). The RPL program still caters to the relatively less well-educated segment of the population.

Most occupations have skewed gender balances among the applicants (see figure 2.1), aligned with societal norms about occupational gender segregation. The vast majority of female applicants are in sewing machine operation and tailoring and dressmaking, while graphic design and it support attracted some female applicants. Very few or no females are in Electrical Installation, Plumbing, And Welding. Most of the male applicants are in electrical installation, plumbing, graphic design, and IT support. Only a handful are in sewing machine operation and tailoring and dressmaking.

8
- 19 -

School ComRositional Factors '!8. A second hypothesis was that differences between schools was accounted for by peer effects: the composition of the peer group differed between the types of schools. Three school compositional variables in addition to average student SES were examined: percent female students, average number of hours students worked, and average school achievement in mathematics or Portuguese (for social class differentiation only). The results of these analyses are presented in Table 8.

Table 8: School composition, organization and quality effects on Secondary achievement and social class differentiation, Brazil 1988

||Mathem|atics|Portug|uese|
|---|---|---|---|---|
|Independent<br>variable|Coeff.|t-stat|Coeff.|t-stat|
|Fixed|||||
|Female|-1.48***|-7.05|0.55**|2.86|
|Age|-0.38***|-5.95|-0.49|-8.31|
|Working hours|-0.03***|-4.60|-0.02**|-3.58|
|Mean achievement|||||
|Intercept|7.15*|2.17|17.30***|<br>11.74|
|Average SES|5.05***|<br>C 66|3.06***|<br>6.20|
|Federal Technical|7.64***|<br>4.24|2.55*|2.19|
|Organizational complexity|<br>-|-|0.56*|2.10|
|Class size|0.17**|3.69|-|-|
|Hours of math/Port.|1.67*|2.69|0.55|1.57|
|Percent female|-3.18|-1.71|-1.31|-1.1?|
|Average working hours|-0.09|-1.94|-0.09**|-3.07|
|SES achievement gap|||||
|Base|-1.62|-1.40|1.10|0.80|
|Salary|0.04|1.36|-|-|
|Percent female|1.06|1.39|0.40|0.77|
|Average SES|-0.33|-0.93|0.50|1.58|
|Average working hours|-0.00|-0.03|-0.01|-0.32|
|Average math/Port. test|0.04|0.93|-0.06|-0.93|

* p < .05, ** p < .01, *** p < .001