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# I

### POLICY **RESEARCH WORKING PAPER** 2452

## Summary fndings

Emerging trends in education show the private sector to be playing an increasingly important role in financing and providing educational services in many countries. Private sector development has not arisen primarily through public policy design but has of course been affected by the design and limitations of public policy.

Sosale traces trends in private sector development in 11 of 70 World Bank education projects in 1995-97, asking two questions: What has been the rationale for Bank lending in education? And in countries where there is both privately financed and publicly financed and provided education, how has the Bank encouraged the private sector to thrive?

The 11 country samples reveal that the Bank's interest in private sector development is basically in capacityoriented privatization to absorb excess demand for education. This is crucial to the Bank's general strategy for education lending: promoting access with equity, focusing on efficiency in resource allocation, promoting quality, and supporting capacity building. Absorbing excess demand tends to involve poorer families, usually much poorer than those that take advantage of other forms of privatized education.

The Bank emphasizes capacity-oriented privatization, especially of teacher training for primary and secondary schools, as well as institutional capacity building for tertiary and vocational education.

The underlying principle is that strengthening the private sector's role in noncompulsory education over time will release public resources for the compulsory (primary) level. The private sector is emerging as a force governments, donors, and other technical assistance agencies cannot ignore.

Often the term _private sector_ encompasses households' out-of-pocket expenses rather than describing for-profit or not-for-profit (religious or otherwise) sectors. And lumpy investments, supporting both private and public education, are the norm.

This paper-a product of the Education Team, Human Development Network-is part of a larger effort in the network to synthesize and disseminate knowledge about private sector development in education. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Shobhana Sosale, room G8-057, telephone 202-473-6490, fax 202-522-3233, email address ssosaleC@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. September 2000. (49 pages) _The Policy Research Working Paper Series disseminates the findings_ **_of_** _work in progress to encourage the exchange of ideas about development issues. An objective of tbe series is to get the findings out quickly, even if_ **_the_** _presentations are less than fully polished. Tbe papers carry the names of the authors and should be cited ac4ordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessatily represent the view of the World Bank, its Executive Directors, or the countries they represent._
range of individual and geographic characteristics and for country and year fixed effects. In the second stage, each of our outcome variables is regressed on the predicted values of the endogenous regressors obtained from the first stage, controlling for all the variables controlled in the first stage. Note that these two stages are executed simultaneously (and not separately). More precisely, we estimate the following equations: _Road × Electict_ = _λ_ 0 + _λ_ 1 _HypoRoadict_ + _λ_ 2 _FlashDensityict_ + _λ_ 3 _Elevationict_ In equations 2, 3 and 4, _HypoRoadict_ , _FlashDensityict_ , _Elevationict_ , and _Elevationict×HypoRoadict_ are respectively proximity to the hypothetical roads (also measured as the “negative” of the distance between the centroid of an individual’s cluster to the closest hypothetical road); the density of lightning strikes in the closest location of individual _i_ for which we have data; terrain elevation; and the interaction term between proximity to the hypothetical roads and terrain elevation for individual _i_ , in country _c_ , in survey year _t_ ; _X_<sup>_′_</sup> _ict_ is a vector of individual-level controls including age, gender, urban or rural residence, and education, and average temperature computed at the grid-cell level; _μc_ captures country fixed effect; and _θt_ captures survey year fixed effects. We do not control for grid-cell fixed effects because there is not a sufficient amount of variation in the instrumental variable for roads within the grid cells. We follow a similar approach used in Moneke (2020) in controlling for local characteristics. Table 3 presents the first-stage regressions. Equation 5 is the second-stage regression.

# **5 Average treatment effects of roads and electricity**

## **5.1 Employment**

We present the estimates of the“average”treatment effects of proximity to roads and electricity grids and their interaction on employment outcomes in Table 4 in the Appendix. The first two columns display the estimated effects on work status at the survey for the LSMS and DHS samples, respectively. The third column shows estimated effects on the number of hours worked during the last seven days (derived from the LSMS sample only because information on the number of hours worked in not collected in the DHS).

These estimates show positive and highly complementary effects of roads and electricity on employment outcomes. Columns 1 and 2 show that for locations close enough to an electric grid bringing road networks 10 km closer marginally increases the probability of working by 25.7 percentage points in the 20
benefit plans they have been several times larger than those reported by the Social Security Administration, because it is unable to provide the security afforded by funded benefits.

Other important dimensions of quality of service are correct answers to personal queries, the volume and design of information provided, the reliability of payment of pensions, the investment risk that must be borne by the worker-pensioner, the expected loses due to embezzlement by service providers, etceteras. To make up for the paucity of data on quality, we attempt a description of the services offered by each one of the pension systems discussed here. The variations in the portfolio of services offered and their quality are extremely large, and have substantial beanng on average administrative charges.

This paper devotes substantial space to the Chilean pension system, since it appears as an attractive option in current discussions of pension reform. Its attractiveness can be thought of as a high perceived quality, including substantial insulation from political pressures, high ability to reach the rate of return available in the country, high degree of funding and outstanding quality of service. Although we do not attempt to measure these qualities, we ask whether the administrative charges are also higher than in pension systems that seem to offer less quality. If they were not, we would be in the presence of an unusually rare combination. The evidence in this paper suggests that administrative charges in the new Chilean system are in an intermediate range of costs. On the other hand, Chilean costs appear to be higher than in the state-run system in Malaysia, which offers lower quality services but still much better than Zambia.

Our study does not consider the costs incurred by employers, who are used as collection agencies for the government. One study that attempted to measure employers' costs through a survey was Beveridge's for England'. He found that these costs were around 20% of explicit administrative costs, but his survey was restricted to firms with more than 2,200 employees. It is not surprising that over history, consideration of administrative cost for employers affected the evolution of the public mandatory pension system. In the US, coverage under the social security retirement system was restricted to industrial wage earners until the 1950's because the administration cost of payroll tax collection and related record-keeping in small establishments and among the self-employed was felt to be prohibitive (Parsons, 1992). For the same reason, firms with less than 30 workers in Bolivia are currently exempted from contributing.

**1** Beveridge, W. (1942). Appendix E, pages 284 **and** **_285_** and Table **XL.**
10 <!-- Start of picture text --> Fi c'iie I iPor T rr e<br>xs ;~SF + + D2<br>POV'ERTY LINE<br>AS -5 OF W'AGE<br>GR+<br>IRL + us<br>UK +<br>3 0 3.5 4-0 4 5 5-0 5-5<br><!-- End of picture text --> PER CAPiTA CONSUMPTION AT INTERNAT.PRICES

# Notes;

Poverty line is a per capita poverty line for four-member urban household. Both axis are in logs.
|**Table 4.1: Time**<br>**People's Republ**<br>(SPC: State Plann<br>|**table: Container Transport Project: World Bank –**<br>**ic of China Container Transport Project**<br>ing Commission; SETC: State Economic and Trade Commission)<br>|
|---|---|
|1996|Project listed as a candidate project by the SPC|
|November 1996|Bank project pre-identification mission|
|May 1997|Bank project identification mission|
|November 1997|First project preparation mission|
|1997|Environmental impact analyst selected|
|February 1998|Second project preparation mission|
|March 1998|SETC approved the investment proposals from four provincial<br>economic and trade commissions|
|April 1998|Bank pre-appraisal mission|
|July 1998|Appraisal|
|1999|SPC approved SETC proposal to use World Bank loan to finance|
|January 1999|Loan agreement negotiated|
|March 1999|WB Board Approval|
|September 1999|Loan agreement signed|
|June 2001|One province withdrew – difficulty in connection with acquisition<br>of land.|
|November 2005|Implementation completion report|

Source: World Bank, Implementation Completion Report (SCL-44440) on a Loan in the Amount of US$71 Million to The People’s Republic of China for a Container Transport Project. Report No: 33620, November 18, 2005 15
or because they are net importers of commodities. Larger countries, in contrast, are more affected by the capital outflows and higher borrowing costs as they tend to be more exposed to the international financial markets, or lacking appropriate insurance mechanisms, and/or tend to be net exporters of commodities.

<!-- Start of picture text --> Figure 2. Impulse Responses of GDP to Various Shocks: Large countries, by population<br>reer tby rrate_us<br>.0015 .0005 .001<br>.001 0 0<br>−.0005<br>.0005 −.001<br>−.001<br>0 −.002<br>0 5 10<br>gdp govexp<br>.01 .004<br>.003<br>.005 .002<br>.001<br>0 0<br>rrate gdp_us<br>.0005 .002<br>0 .0015<br>.001<br>−.0005<br>.0005<br>−.001<br>0<br>0 5 10 0 5 10<br>step<br>95% CI Orthogonalized IRF<br><!-- End of picture text --> Next, consider the effects of the shocks to world demand conditions, proxied by the US GDP. A positive 1 unit shock to the US GDP is followed by a rise in domestic output in both small and large economies. The response, however, is larger in small economies, which is not surprising given their smaller scale.

Turning to domestic variables, the effects of these variables on domestic GDP are quite intuitive and generally symmetric for small and large economies. The only exception is the trade balance to which we return below. For instance, a positive unanticipated shock to government expenditures is followed by an increase in domestic GDP in both small and large countries. Similarly, a rise in the domestic interest rate has contractionary consequence in both small and large economies, although the effect is more short-lived in smaller countries. In response to an unanticipated domestic output shock, GDP itself increases and then gradually returns toward its steady-state level, in both groups of countries, although the convergence back to the steady state is slower in larger economies, indicating higher persistence of output shocks in this group of countries.

An increase in the real exchange rate (real appreciation) leads to an expansion in GDP in both small and large countries. The effect is quite long-lived in larger economies, but dissipates after 14
**Table 1: Production functions using B&E data.**

|Variable|Model I|Modeln|Model**HII**|
|---|---|---|---|
|Intercept|5.098**<br>(0.557)|4.252**<br>(0.600)|6.341**<br>(0.768)|
|Log of Area|0.458**|0.440**|0.426**|
||(0.045)|(0.041)|(0.041)|
|Log of family size|0.276**<br>(0.077)|0.198**<br>(0.070)|0.174*<br>(0.068)|
|Log of hired labor|0.032**<br>(0.008)|0.021**<br>(0.008)|0.020**<br>(0.007)|
|Log of cash inputs|0.035*|0.001|-0.009|
||(0.018)|(0.017)|(0.017)|
|Log of fixed farm capital|-0.028|0.030|0.021|
||(0.057)|(0.053)|(0.052)|
|Log of extension staff-farm<br>ratio (Sfratio)|0.142*<br>(0.071)|-0.003<br>(0.083)||
|Sfratio x Dummy variable|||0.717**|
|for Machakos|||(0.217)|
|Sfratio x Dummy variable|||1.009**|
|for Murang'a|||(0.256)|
|Sfratio x Dummy variable|||-0.340*|
|for Kisumu|||(0.138)|
|Sfratio x Dummy variable|||-0.267|
|for Kericho|||(0.241)|
|Sfratio x Dummy variable|||-0.242|
|for Taita Taveta|||(0.248)|
|Sfratio x Dummy variable|||-0.629|
|for Trans Nzoia|||(0.385)|
|Sfratio x Dummy variable|||-0.211|
|for Bungoma|||(0.197)|
|Farm output,**1982**|0.989**|_1.055**_|1.255**|
|(000 Ksh)|(0.230)|(0.218)|(0.217)|
|Farm area,**1982**|-0.029|-0.007|0.001|
|(acres)|(0.024)|(0.022)|(0.022)|
|Age of head|-0.003|-0.003|-0.003|
|(years)|(0.003)|(0.003)|(0.003)|
|Sex of head|0.001|0.091|0.043|
|(Female=1)|(0.092)|(0.084)|(0.081)|
|Education of head|-0.029|-0.017|-0.008|
|(above primary=1)|(0.044)|(0.041)|(0.040)|
|Normal crop|0.440**|0.528**|0.557**|
|<br>(Normal=l)|(0.145)|(0.140)|(0.143)|
|Failed crop|0.184|0.667**|0.538*|
|<br>(Failed=1)|(0.244)|(0.238)|(0.238)|
|**1990**Cluster mean for|0.298*|0.356**|0.340**|
|normal crop|(0.129)|(0.121)|(0.117)|
|**1990**Cluster mean for|-0.536|-0.726**|-0.805**|
|failed crop|(0.292)|(0.265)|(0.258)|

|_Continued_|
|---|
## **Box 1  Measurement of employment: household vs. firm based surveys**

The magnitude of measured employment decline in transition economies depends on the statistical source. It tends to be much larger if data come from firm based Employment surveys, and relatively smaller if data comes from the household based Labor Force Survey (LFS). The discrepancy stems from the fact that firm based surveys cover only registered, formal sector firms, while the LFS in principle provides data on all employed individuals. Thus, the difference in employment between those two sources is an approximate measure of informal sector employment, employment in small private firms, which often are under-represented in employer based-surveys, and self-employment. This difference also reflects the changing nature of jobs in ECA. There has been a partial shift from regular full-time jobs toward casual jobs, often in the informal sector, as well as a partial shift form wage and salary employment towards self-employment. The firm based employment surveys do not account for irregular jobs and overestimate the actual job loses, however they show quite accurately the decline in the number of traditional, regular jobs.

# **Despite the substantial job loses the employment ratio is still relatively high**

The substantial job loses that have occurred during the transition are reflected in the lower degree of the utilization of labor resources. The employment ratio – which shows the percentage of the population of working age that is employed –is presently much lower than before the transition in virtually all transition economies. On average it dropped by close to 20 percentage points in CEE, around 15 percentage points in Belarus, Russia and Ukraine, and some 10 percentage points in other CIS (Figure 1). This means that 10 to 20 percent of the working age population which would have been employed under the old regime, is currently without work. Apparently, this is a large decline in the utilization of labor. However, despite this big decline, the employment ratio in most transition economies is still within the OECD range.

# **Figure 1  Transition has been associated with a substantial fall in the employment-topopulation ratio**

### **Employment/Population Ratio, 2002**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Geo Aze Kaz Cze Slv Rus Est BRU Bel Lat Ukr LitOECDCIS-7 + K CEEEU15 Mol Uzb Kyr Rom Hun Bul Pol Slk Cro Arm Taj Alb SEE Mac SAM<br><!-- End of picture text --> Source: UNICEF TRansMONEE database; OECD Employment Outlook, 2003 4
# **Geographic Location and Tax Revenue Linkage**

Figure 2 exhibits that there are established patterns of tax collection by region and that such patterns have not changed much over the past decade. Countries in the same geographical region are likely to share certain commonalities in their tax structures due to their similar economic and social factors as well as their imitation in the choice of tax regimes. In some cases, this similarity also reflects tax coordination policies and trade integration. However, that does not mean that the compliance with the statutory tax provisions is enforced in the same way in every country and therefore the actual tax collection may vary considerably within a region. In addition, diversity within the same regional group appears quite striking as countries endow different natural resources and might not necessarily follow the same process of development and world integration.<sup>4</sup>

**Figure 2:  Central Tax Revenue as Percentage of GDP by Region (1994-2003)***

<!-- Start of picture text --> 1994<br>1998<br><!-- End of picture text --> <!-- Start of picture text --> 30 % GDP<br>1994<br>25 1998<br>2003<br>20<br>15<br>10<br>5<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text --> <!-- Start of picture text --> SAR<br><!-- End of picture text --> Source: IMF GFS; WDI.

* Note:  The region classification follows the World Bank approach:  Sub-Saharan Africa (AFR), East Asia and Pacific (EAP), Eastern Europe and Central Asia (ECA), Latin America and Caribbean (LAC), Middle East and North Africa (MNA), and South Asia region (SAR).

The regional pattern of tax collection (Figure 2) shows that the ECA region tax share has outperformed other regions.<sup>5</sup> However, the share declined from the peak of 27 percent of GDP in 1994 to 25 percent in 2003 reflecting the “transition” economy era when central governments reorganized and, some, devolved fiscal roles. Collection effort in AFR was similar to the one in LAC in 1998 and higher than in any other regions, except for ECA. The overall tax collection in AFR has increased over the years (from 20 percent in 1994 to 23 percent in 2003). EAP, a high growth region, has relatively lower tax intake compared with

> 4 For example, some African countries, such as South Africa, Botswana, and Lesotho, have reached tax revenue ratios similar to those in middle/high income countries, while others like Congo or Guinea Bissau have maintained the lowest ratios in the world.

> 5 It is interesting to make some comparison of tax collection between regions and countries in the OECD. Since the late 1990s, the OECD on average consistently collects more taxes as a share of GDP than any regional averages.  The collection has been consistently high with the average of 32 percent in 1998 and 2003.  A close look at the high income OECD countries reveals that the highest tax intake can be found in the so-called welfare states such as Finland, Sweden, and Norway with the tax-GDP ratios respectively reaching 35, 34, and 37 percent in 2003.

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