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- <mark>Grewal, Rajdeep, Gary L. Lilien, and Girish Mallapragada. "Location, location, location: How network embeddedness affects project success in open source systems."</mark> _<mark>Management Science</mark>_ <mark>52, no. 7 (2006): 1043-1056.</mark>

- <mark>Gulati, Ranjay. "Does familiarity breed trust? The implications of repeated ties for contractual choice in alliances."</mark> _<mark>Academy of management journal</mark>_ <mark>38, no. 1 (1995): 85-112.</mark>

- Hadfield, Gillian K. and Bozovic, Iva. 2016. Scaffolding: Using formal contracts to support informal relations in support of innovation. _Wisconsin Law Review_ , 981–1032.

- <mark>Handley, Sean M., and Corey M. Angst. "The impact of culture on the relationship between governance and opportunism in outsourcing relationships."</mark> _<mark>Strategic Management Journal</mark>_ <mark>36, no. 9 (2015): 1412-1434.</mark>

- <mark>Hart, Oliver, and John Moore. "Incomplete contracts and renegotiation."</mark> _<mark>Econometrica: Journal of the Econometric Society</mark>_ <mark>(1988): 755-785.</mark>

- <mark>Hendley, K., Murrell, P. and Ryterman, R., 2000. Law, relationships and private enforcement: Transactional strategies of Russian enterprises.</mark> _<mark>Europe-Asia Studies</mark>_ <mark>. 52(4), pp.627-656.</mark>

- Hendley, Kathryn, and Murrell, Peter. 2003. Which mechanisms support the fulfillment of sales agreements? Asking decision-makers in firms. _Economics Letters_ , 78(1), pp 49-54

- Holbrook, A., M. Green, and J. Krosnick. 2003. Telephone versus Face-to-Face Interviewing of National Probability Samples with Long Questionnaires: Comparisons of Respondent Satisficing and Social Desirability Response Bias. _The Public Opinion Quarterly_ . 67(1), pp. 79-125.

- Holbrook, Allyson, Young Ik Cho, and Timothy Johnson.  2006. "The Impact Of Question And Respondent Characteristics On Comprehension And Mapping Difficulties." _Public Opinion Quarterly_ , Vol. 70, No. 4: 565–595.

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56
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 28. Senegal has been a destination for private sector investment in West Africa**

Average annual investment as a percentage of GDP, 2008 <!-- Start of picture text --> 4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Transport ICT Electricity<br>Percentage of GDP<br><!-- End of picture text --> _Source:_ World Bank 2010a. _Note:_ GDP = gross domestic product; ICT = information and communication technology.

Private participation has been implemented in all kinds of infrastructure services with relative success: energy, water, transport, and ICT. All types of contracts have been experimented with: lease, rental, concession, BOT, and divestiture (table 24). Over the years, 14 infrastructure projects with private participation have reached financial closure (this total includes the renewals). Eleven of them are still operational, including one electricity distribution project, three electricity generation projects, one railway project, one seaport project, one toll road project, one water supply project, and three telecommunications projects.

**Table 24. Total investment commitments in infrastructure projects with private participation**

<u>Average between 2000 and 2009</u>

|**Sector**||**Number o**<br>|**farrangements and**<br>|**value (US$ milli**<br>|**ons) by type**<br>||
|---|---|---|---|---|---|---|
||Concession|BOT/BOO|_Marchand_|_Location _|Partialprivatization|_Affermage_|
|Energy|2($88)|2($146)||1($6)|||
|ICT|||2($408)||1($1,536)||
|Transport|4($720*)||||||
|WSS||||||2($20)|

_Source:_ World Bank 2010a. _Note:_ (*) 2009 preliminary data reported. BOT = build operate transfer; BOO = build operate own; ICT = information and communication technology; WSS = water supply and sanitation.

But while Senegal is among the countries in the region to have already attracted the private sector’s investment in infrastructure, a comparative assessment of Sub-Saharan Africa shows that these private flows remain modest in comparison to countries in other regions (figure 29a, b). Increasing private sector participation in infrastructure, however, is not an easy task, especially in an environment where investment resources are scarce and private investors highly selective. In the case of Senegal, it will be important for the government, based on sector needs over the next 5 to 10 years, to carefully prioritize projects. The government could then identify a pipeline of projects in which the private sector could play a major role in either bringing in investment resources or, equally important, bringing in technical and 43
## Table V-2

## Lending Coefficients for Agriculture, Education, PHN and Urban Projects, FY1981-85

|----------------------------------------------<br>Sector|-----------<br>FY1981|FY1982|FY1983|FY1984|__-<br>FY1985|---------------<br>FY1986|FY1987|
|---|---|---|---|---|---|---|---|
|----------------------------------------------|-----------------|||||__----------||
|Agriculture|<br>14|13|13|13|12|14|13|
|Educaticn|10|11|10|11|9|10|10|
|PHN|36|27|22|20|18|24|21|
|Urban|25|18|17|16|13|11|13|

Sources: A.R. Measham, Review of PHN Sector Work and Lending in Health, 1980-85, pg. 34; and letter from Lauren Chester.

sectoral population activities, and a need for a strategic shift in older projects from quantity to quality of ervices and from supply to demand generation activities. In arfz event, there is a real need to think of innovative ways in which supervision and monitoring time and quality can be improved. An alternative might be to design projects so that more of the supervision is handled by co-financiers or other donors, by local missions, as mentioned above, or by local representatives with more local authority. The recent Indian experience with local missions is one example of innovative methods.

# C. Coordination with Other **Agencies**

There is nothing in Bank procedures to inhibit interagency coordination within the project framework.<sup>3 5</sup> However, the style and process of coordination are important determinants of success in this area. For example, a predominant role by the Bank in inter-donor coordination may produce sub-optimal solutions, as the tendency is to find a compromise between various competing positions. On the other hand, too dominant a role in the national policy process tends to hinder donor coordination and collaboration. In Malawi, an overwhelming concern with pqlicy dialogue may have precluded care in inter-donor coordination. It is not necessary that the Bank always take the lead. It might be

> 35 However, project design may at times facilitate or inhibit interagency coordination. The Kenya Population Project specifically provides for a council to facilitate coordination with other departments and voluntary organizations. This has clearly helped to Increase cocrdination with other agencies, but at the same time it has weakened coordination between the council and the MOH.

62
sample. The set of candidate variables included all area-level remote sensing indicators as well as superarea dummies, although no super-area dummies were selected in Tanzania. In each country, the lasso selected a small number of predictor variables, only 3 in Tanzania and 8 in Sri Lanka. The dependent variable is headcount poverty, expressed as a percentage from zero to one hundred. In Sri Lanka, less annual variability in rainfall as well as a positive rainfall shock in the first quarter are associated with lower poverty rates. In Tanzania, districts with larger buildings are much less poor, as are areas with a larger share of built-up area, while more rural districts tend to be poorer. Despite containing only three variables, the Tanzania model explain half of the variation in non-monetary poverty rates. Meanwhile, the eight Sri Lanka variables explain only 26 percent of the variation in headcount poverty across subdistricts. Overall, the results suggest that the Fay-Herriot estimates, despite using predictors that only vary across area, can significantly improve on the efficiency of the direct estimates, particularly in Tanzania where the model fit is especially good.

_Table 5: GLS coefficients from Fay-Herriot estimation_

|**Sri Lanka**||**Tanzania**||
|---|---|---|---|
|Mean nighttime lights in 2016|-0.003|Mean area of nearest 25 buildings.|-19.45***|
|1990 Built-up area (GHSL)|-0.006|Percent built-up area (Global Urban<br>Footprint)|-2.56*|
|Standard deviation of rainfall|0.006***|Share rural|0.83|
|Rainfall deviation from historical<br>mean, Q1|-12.16***|Constant|84.70***|
|Colombo|-4.55**|||
|Gampaha|-3.80*|||
|Matara|-1.44|||
|Ratnapura|-6.58***|||
|Constant|-7.37**|||
|R2|0.26|R2|0.50|
|Number of Observations|328|Number of Observations|159|

Notes: Coefficients based on Fay-Herriot estimation of poverty rates using the Chandra method. Predictor variables selected using the lasso plug-in method.

# B. Household-level model

Next, we turn to the unit level model, and examine the results of the post-lasso regression used to estimate the model parameters in Table 6. The first striking result is the overall predictive power of the models. The R<sup>2</sup> from the model is 0.32 in Tanzania and 0.25 in Sri Lana, which is notable given that the explanatory variable, non-monetary welfare, is measured at the household level while all the independent variables vary only at the subarea level. A Shapley decomposition indicates that about half of the model’s explanatory power in Sri Lanka and about a third in Tanzania are derived from super-area and sector dummies, which by construction cannot explain any variation in non-monetary areas across areas within super-areas. This illustrates one of the limitations of relying solely on R<sup>2</sup> as a measure of the ability of the model to discern poor areas within the super-areas for which credible survey estimates are already available. Below, we consider an alternative, more informative metric, which is the extent to which the small area estimation procedure increases the precision of area estimates relative to direct survey-based estimates.

20
(3) The HCI is the product of three easily‐interpretable components, each measuring productivity <u>�</u> relative to the benchmark of full health and complete education. The first term, ~~,~~ captures forgone �<sup>∗</sup> future productivity due to child mortality, since children who do not survive never become productive adults. As a result, the average productivity as a future worker of a child born today is reduced by a factor equal to the survival rate, relative to the benchmark where all children survive. The second term, e<sup>�������∗�</sup> , reflects foregone future productivity due to children completing less than a full 14 years of school. The third term, e<sup>�������∗�</sup> , reflects the reduction in future worker productivity due to poor health. Multiplying these three terms together gives the overall productivity of a worker relative to the benchmark of complete education and full health.

This approach is closely linked to standard measures of the average human capital per worker of the current workforce that have been widely used in the development accounting literature: where h�� represents the average human capital of the current workforce, and s�� and z�� represent the average levels of education and health in the current workforce. The only difference between this measure and the expected human capital of the next generation in Equation (2) is that the term reflecting the probability of survival is not required. This is because the measure of human capital of the current workforce measures the average human capital of workers who are currently living.

While measures of the human capital stock like those in Equation (4) are standard in the development accounting literature (see for example Weil (2008)), they are less well suited to the communications and advocacy purpose of the HCI. This is because measures of the human capital of the existing workforce – and most particularly the education component, reflect the educational opportunities that were available to current workers in the past when they were school‐aged children, and so now are largely beyond the influence of current and future policy interventions. Instead, the HCI measures how current health and education outcomes – that are amenable to improvement through current and future policy efforts – shape the likely future human capital of children born today.

23
Appendix I: Gender gaps in educational enrollment and labor force participation in Vietnam _Figure 13: Ratio of female to male enrollment in primary education (2015)._ <!-- Start of picture text --> Cambodia<br>Lao PDR<br>Myanmar<br>Indonesia<br>Mongolia<br>Vietnam<br>Timor‐Leste<br>EAP (excl. high income)<br>China<br>Malaysia<br>Thailand<br>0 0.2 0.4 0.6 0.8 1 1.2<br><!-- End of picture text --> _Figure 14: Ratio of female to male enrollment in tertiary education (2015)._ <!-- Start of picture text --> Lao PDR<br>Vietnam<br>Indonesia<br>EAP (excl. high income)<br>China<br>Philippines<br>Thailand<br>Mongolia<br>Malaysia<br>0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6<br><!-- End of picture text --> Source (Fig 13, 14): United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.

33
|**Variable**|**Description**|**Data Source/ Method**|
|---|---|---|
|||the labor force from 2008‐2012 from WDI and<br>update this using the projected growth rate.|
|Excess capital<br>costs<br>for<br>informal firms<br>(ζ)|Informal firms pay a capital rental<br>rate which is a multiple, R , of the<br>market determined net capital<br>rental rate paid by formal firms.|Based on an estimate by Scheinkman and de Paula<br>of the additional cost of capital for informal firms<br>in Brazil, we assume a constant value of 1.3 for all<br>countries. For the persistent informality<br>parameter options, we set ζ to decrease 0.25%<br>percent annually.|
|Cost of Living<br>Adjustment (μ)|The wage adjustment between the<br>rudimentary<br>and<br>modern<br>economies, which accounts for<br>differences in the cost of living or<br>in the cost of labor participation<br>between the two economies.|We find the ratio between the average monthly<br>wage of all employees and of employees in<br>elementary occupations, available for a subset of<br>countries from the ILO. We use the mean, after<br>excluding outliers, 1.81. For the persistent<br>informality parameter options, we set the growth<br>rate of μ to be 1 percent annually.|
|Ratio of formal<br>to<br>modern<br>informal<br>productivity<br>(a)|Total factor productivity in the<br>informal sector is a fraction,_a_, of<br>productivity in the formal sector<br>(A).|We use an estimate from Perry (2007) that,<br>controlling for firm characteristics, informal firms<br>are 71% as productive as formal firms. For the<br>persistent informality parameter options, we set<br>the growth rate of_a_to be 0.25% percent annually.|
|Ratio of formal<br>to rudimentary<br>informal<br>productivity<br>(b)|Labor<br>productivity<br>in<br>the<br>rudimentary<br>economy<br>is<br>a<br>fraction,<br>_b_,<br>of<br>total<br>factor<br>productivity in the formal sector.|Country‐specific estimates are obtained through<br>calibration of the model. For the persistent<br>informality parameter options, we set the growth<br>rate of_b_to be 0.25% percent annually.|
|Formal Labor<br>(LF)|The total number of workers<br>employed in formal firms, which<br>are subject to labor legislation.|We use the latest available estimates of the rate of<br>contribution to mandatory pensions systems<br>around the world (World Bank; ILO), combined<br>with average employment and labor force<br>estimates from 2008‐2012 from WDI. We assume<br>that unemployed and informal workers contribute<br>to pensions at a fraction,_d,_set to 2%, of the rate<br>among formal employees, and adjust our estimates<br>accordingly.|
|Informal<br>Rudimentary<br>Labor (LR)|The total number of workers in<br>the rudimentary economy, using<br>primary production technologies<br>with<br>little<br>capital<br>and<br>low<br>productivity|We use ILO estimates of the share of ‘vulnerable<br>employment’, defined as the sum of un‐paid family<br>workers and own‐account workers, i.e. self‐<br>employed workers without employees, combined<br>with labor force and employment data from WDI.|

30
country using the constructed measure of doing business and the business freedom index (Table B.2 in the Online Appendix).

One interesting observation from the Table 2 is that higher GDP per capita (PPP) is associated with a higher number of visits by tax officials and a more common perception of tax administration being an obstacle to business operations and growth. This signals that richer developing countries also have a more cumbersome tax administration bureaucracy. We also find that more productive and larger firms and those that export face a higher tax compliance burden.

# **_4.2. Competitiveness of public procurement and E-procurement_**

We next analyze the impact of e-procurement implementation on competitiveness of public procurement and the probability that firms had to pay bribes to secure a government contract. Table 2, Columns V and VI present the results. Although we expect the adoption of e-procurement to increase the likelihood that firms participate in public tenders and to limit corruption, our estimates demonstrate this is not the case. We obtain insignificant coefficients on both informational and transactional e-procurement systems. When we control for a country’s overall business environment the results remain insignificant (and we therefore we do not report them). The results also show that firms with higher productivity, state ownership, larger size, publicly traded, or web-presence tend to participate more often in government tenders. Firms with lower productivity, smaller size, higher export shares, or which are not publicly traded are more likely to pay bribes to secure a government contract.

Our results suggest that automation may be less effective in public procurement. By its nature, public procurement requires more discretion from government officials than tax administration, particularly for complex activities where evaluating bids for technical compliance or value for money requires considerable judgment. Public procurement also involves decisions over potentially much larger sums of money than tax returns, thereby increasing the opportunities and incentives for corruption. Given that far fewer firms bid for government contracts than file taxes, it is more susceptible to collusive relationships between bureaucrats and firms. In addition, more transparent information about government contracts may itself encourage collusion (Center for Global Development, 2014). Another explanation is that implementation of relatively unsophisticated informational or transactional e- procurement systems might not be enough to improve public procurement competitiveness. Only four high-income countries have adopted connected e-procurement systems, but they are not considered in our firm-level analysis given lack of data for our outcome variables. Finally, it is possible that firms’ reports of securing or attempting to secure a government contract from the WBES database may not adequately capture public procurement competitiveness. More research is therefore needed to assess the impact of e-government on public procurement competitiveness.

# **_4.3. Development and E-government_**

11
|**#**|**Code in SUST-**|**NACE**|**Description**|
|---|---|---|---|
||**RUS**|**classification**||
|31|M|Section M|Education|
|32|N|Section N|Health and social work|

_Source:_ SUST-RUS. 2012(a).

Table A3. Regions of the SUST-RUS model

|**Region number**|**Federal Districts of the Russian Federation**|
|---|---|
|Reg1|Central Federal District|
|Reg2|North-Western Federal District|
|Reg3|Southern Federal District|
|Reg4|Volga Federal District|
|Reg5|Urals Federal District|
|Reg6|Siberian Federal District|
|Reg7|Far Eastern Federal District|

_Source:_ SUST-RUS. 2012(a).

93
Table 6.2—Regression results of number of repetitions in high school, for full sample and trimmed and weighted sample

|||Full S|ample||Trim|med and|Weighted Sam|ple|
|---|---|---|---|---|---|---|---|---|
||Model 1||Model 2||Model3||Model 4||
|TEEP cohort*TEEP province|0.118|(0.61)|0.0804|(0.40)|0.290|(1.13)|0.241|(0.95)|
|Female|-0.455***|(-3.47)|-0.453***|(-3.49)|-0.383***|(-2.85)|-0.380***|(-2.86)|
|Female*TEEP cohort|0.0572|(1.25)|0.0582|(1.27)|0.0359|(0.75)|0.0376|(0.78)|
|Female*TEEP cohort*TEEP province|-0.122**|(-2.27)|-0.121**|(-2.23)|-0.143**|(-2.32)|-0.143**|(-2.30)|
|Log(consumer durables)|-0.0159|(-1.42)|-0.0204*|(-1.77)|-0.00563|(-0.42)|-0.00873|(-0.63)|
|TEEP cohort|0.205|(1.14)|0.157|(0.85)|0.275|(1.28)|0.253|(1.14)|
|TEEP cohort*Log(consumer durables)|0.00305|(0.20)|0.00812|(0.51)|0.00534|(0.29)|0.00886|(0.47)|
|Female*Log(consumer durables)|0.0235**|(2.08)|0.0232**|(2.07)|0.0204*|(1.66)|0.0200*|(1.65)|
|TEEP cohort*TEEP province*Log(consumer|||||||||
|durables)|-0.00452|(-0.26)|0.0000134|(0.00)|-0.0174|(-0.79)|-0.0127|(-0.58)|
|Age|0.0391|(0.44)|0.0524|(0.59)|-0.0218|(-0.20)|-0.00405|(-0.03)|
|Age squared|0.0000289|(0.01)|-0.000315|(-0.14)|0.00193|(0.67)|0.00147|(0.49)|
|Mother’s years of schooling|-0.00139|(-0.40)|-0.00155|(-0.44)|0.00140|(0.26)|0.00134|(0.25)|
|Mother’s age|-0.000491|(-0.35)|-0.000538|(-0.38)|0.000217|(0.13)|0.000190|(0.11)|
|Number of siblings|0.00290|(0.65)|0.00278|(0.63)|0.00570|(0.82)|0.00559|(0.81)|
|Poverty incidence rate|||0.00527*|(1.97)|||0.00634**|(2.15)|
|Infrastructure index|||0.0573|(0.45)|||-0.0133|(-0.08)|
|Number of students per classroom in high<br>school|||-0.0000552|(-0.11)|||-0.000189|(-0.39)|
|If phone interview|||0.00434|(0.15)|||0.00127|(0.04)|
|Othervariables and school fixed effects|yes||yes||yes||yes||
|Number of observations|2,776||2,776||2,585||2,585||

Source: TEEP Tracking Survey. Notes: Other variables include house value in 2000, residential land value in 2000, commercial land value in 2000, and agricultural land value in 2000.

t values are in parentheses.

* significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

38