rafmacalaba/datause-encoder-data huggingface dataset:
WPS4025 <mark>B</mark> ACKGROUND <mark>P</mark> APER TO THE <mark>2007 W</mark> ORLD <mark>D</mark> EVELOPMENT <mark>R</mark> EPORT

# Economic Growth, Education, and AIDS in Kenya: _¤_ A Long-run Analysis

Clive Bell _†_ , Ramona Bruhns _‡,_ and Hans Gersbach _§_

### Abstract

The AIDS epidemic threatens Kenya with a long wave of premature adult mortality, and thus with an enduring setback to the formation of human capital and economic growth. To investigate this possibility, we develop a model with three overlapping generations, calibrate it to the demographic and economic series from 1950 until 1990, and then perform simulations for the period ending in 2050 under alternative assumptions about demographic developments, including the counterfactual in which there is no epidemic. Although AIDS does not bring about a catastrophic economic collapse, it does cause large economic costs – and very many deaths. Programs that subsidize post-primary education and combat the epidemic are both socially profitable – the latter strikingly so, due to its indirect effects on the expected returns to education – and a combination of the two interventions profits from a modest long-run synergy effect.

Keywords: HIV/AIDS, Growth, Education, Kenya, Policy Programs JEL Classification: I10, I20, O11, O41

## World Bank Policy Research Working Paper 4025, October 2006

_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 the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> _¤_ We thank Emanuel Jimenez, Mamta Murthi and participants in seminars at University of California, Berkeley, Mannheim University and the World Bank for their valuable comments and suggestions. _†_ South Asia Institute, University of Heidelberg, INF 330, 69120 Heidelberg, Germany. _‡_ South Asia Institute, University of Heidelberg, INF 330, 69120 Heidelberg, Germany.

_§_ Alfred-Weber-Institut, University of Heidelberg, Grabengasse 14, 69117 Heidelberg, Germany.
behind schedule and by some estimates 90 percent over budget—its cost approaching $5,000 per installed kilowatt (Schneider et al, 2011).

Most neutral analysts would agree that the nuclear industry has been facing persistent problems that raised doubts about its much anticipated renaissance long before Fukushima (van der Zwaan et al, 1999; Nuttall, 2011). The industry‘s future has been clouded by its ever escalating construction costs, the uncertain economics of maintaining the existing nuclear plants, the challenges of radioactive waste disposal, the risks of proliferation and the continued concerns about safety. Moreover, nuclear power lost some of its appeal because of the unanticipated surge in supply of natural gas from shale in the United States and other countries. The events in Japan are likely to cause major regulatory turbulence, thus further clouding the industry‘s already uncertain economics.

**Figure 3 Average and min/max reactor construction costs for US and France versus cumulative**

**capacity**

<!-- Start of picture text --> 10,000<br>1990<br>5,000 1985<br>1996<br>1999<br>25,000<br>20,000<br>1980 15,000<br>2,000<br>1975<br>1972 10,000<br>1990<br>1985 7,500<br>1,000<br>1980 5,000<br>1977<br>500<br>1 5 10 20 50 100<br>FF98/kW<br>Average and min/max investment costs (2004 U.S. dollars/kW)<br><!-- End of picture text --> _Source_ : Grubler (2010).

In the longer term, however, increasing concerns about CO2 emissions may imply stronger prospects for nuclear power than the near-term, post-Fukushima outlook. Coal-fired generation will have to be reduced in order to limit emissions. Hydropower is cost effective in a 5
# **5.1 The Bloom-Van Reenen Sample**

Table 5 closely replicates the original regressions of Bloom and Van Reenen for the US, Germany, the UK and France. As we are interested in a broader range of variables than they were, we lose some observations but, as we will have many more degrees of freedom in subsequent exercises, we also leave disaggregated some sets of variables such as ownership which were made composite variables. In addition to the extraordinary care in the collection of data, double checking for bias etc., Bloom and Van Reenen also used a full set of covariates to account for possible interview bias- time of day, particular interviewer etc. We do not have those controls for the entire sample. Column 1 and 2 of Table 5 replicate their specifications with and without those noise controls and they do not appear critical to establishing the key relationships. This suggests that, going forward, we are unlikely to be suffering from such biases in specifications which become more complex with number of covariates estimated.

Again, the results are again robust to the inclusion or exclusion of sectoral dummies which our full sample does not have. As in Bloom and Van Reenen, in both OLS specifications, whether or not the manager has a degree emerges and the share of employees with a college degree emerges significantly and positively. If the firm is family controlled emerges negatively although we find that the noise controls does reduce the significance of the negative effect of being founder owned. Finally, competition emerges as positively correlated in both specifications. As an additional test of robustness, we employ our other subset of the data that includes industry level dummies, but not some of our preferred variables. Running the smaller sample (Table A.1) with fewer covariates suggests that all covariates preserve their sign and significance, especially the human capital variables, when industrial dummies are included.

The unconditional quantile technique permits us to look at the correlates of these variables across the distribution. Columns 3-5 report the unconditional 10<sup>_th_</sup> , 50<sup>_th_</sup> and 90<sup>_th_</sup> quantiles. For the most part, the Bloom and Van Reenen findings hold up well, although with some important additional insights. First, the correlation of the two human capital variables with quality is most importantly an effect among the best firms. Having a managerial degree has under half the effect at the 10<sup>_th_</sup> as it does at the median and 15
During the last decades, international capital flows have grown significantly, playing an increasingly important role in the economies of both developed and developing countries alike. Many studies have analyzed the behavior of international capital flows. For instance, it is well known that investors have not been nearly as internationally diversified as their consumption and income path would imply. This literature has highlighted some factors that can explain the patterns of international cross-country investments and this lack of international diversification in particular – for example, the existence of explicit costs to international investment and the presence of implicit ones, like political or country risks and informational asymmetries.<sup>1</sup> Although somewhat successful in describing some features of international portfolio investments, only a few of these theories have been able to address the firm-level aspect of international portfolio choices. For example, there is evidence that information asymmetries play an important role in explaining differences in portfolio holdings between domestic and foreign investors within a given country.<sup>2</sup> In particular, widely documented is a preference among foreign investors for large firms if compared to the preferences of domestic investors, a pattern also known as the foreign investor bias. Kang and Stulz (1997) document these preference patterns in Japan and the results are interpreted as an indication that information asymmetries are an important explanation for this bias as a model in which foreigners know less about small firms would be able to fit their investment decisions. Furthermore, Dahlquist and Robertsson (2001)

> 1 Most arguments can be classified into two categories: deviations from the international CAPM affecting foreign asset holdings, and foreign investors facing direct or indirect barriers in foreign investments. See for example Lewis (1999) and Karolyi and Stulz (2003) for surveys of the home bias literature.

> 2 See for example, Brenan and Cao (1997), Edison and Warnock (2004), Aggarwal, Klapper, and Wysocki (2005), Bae, Stulz, and Tan (2008), Leuz, Lins and Warnock (2009), among many others.

2
5 by using a theoretically sound aggregation procedure that answers precise questions regarding the trade and welfare reductions imposed by each country’s price- and trade-distorting policies. The literature has developed considerably over the past two decades, particularly with advances by Anderson and Neary (summarized and extended beyond their 2005 book) and the partial equilibrium simplifications by Feenstra (1995).

Notwithstanding these advances, there are few series of consistently estimated indexes across countries. A prominent exception is the work of Kee, Nicita and Olarreaga (2009) who, following the approach of Feenstra, estimate a series for developing and developed countries. However, they provide estimates only for a snapshot in time (the mid-2000s), and their estimates are based only on import barriers. Other studies have been country and sector specific, such as an application to Mexican agriculture in the late 1980s (Anderson, Bannister and Neary 1995). All previous work appears to have focused on constructing index numbers of distortions for a single country; and most do not provide them for long time periods, exceptions being Irwin (2008) for U.S. import protection policy and Lloyd, Croser and Anderson (2010) for global agricultural policy (but not disaggregated by instrument).

There are several reasons why scalar index numbers are superior to aggregates of NRAs and CTEs for comparing contributions of different policy instruments. With respect to welfare losses, the IWRI, always positive because it is a mean of order two measure (see below), has the desirable attribute that contributions of different instruments sum to 100 percent of a country’s welfare loss from its agricultural policies. Further, the IWRI correctly takes into account that the welfare effect of a policy is related to the square of the price distortion. As for the ITRI, it correctly assesses the positive and negative impacts on trade volume of different measures (e.g., a positive production subsidy in both an import-competing and exportable sector would have offsetting effects on the volume of trade), whereas they could be masked in NRA and CTE aggregates. Furthermore, the theory of the ITRI and IWRI allows for the differential responses of different products when faced with the same ad valorem rate of policy distortion, because elasticity terms appear in the indexes’ formulae.
averaged between 2006 and 2008 – are regressed on a set of 7 explanatory variables that broadly proxy for the themes outlined in the diagnostic in Figure 3. Transport and logistics quality is captured by the Logistics Performance Index (LPI, 2010); 2.) A measure of backbone services is proxied by the ICT price basket (ITU, 2009); 3.) Micro regulatory environment is captured by the Ease of Doing Business Index (2010); 4.) Innovation is captured by a sub‐indicator of the Knowledge Economy Index 2009; 5.) General productivity is captured by estimates of total factor productivity (UNIDO, 2009); 6.) Tariff restrictions, disaggregated for agricultural and non‐agricultural exports, imposed by foreign markets is captured by the MA‐TTRI (WTI, 2010); and 7.) Tariff restrictions at home are captured by TTRI (WTI, 2010)<sup>12</sup> . Added to the list are controls such as income per capita, and population.

Merely used to gauge correlation, and not a causal relationship, this incomplete exercise does, however, give some useful direction for how the diagnostic may be approached. Both manufactured and agricultural exports are affected by the quality of trade‐related infrastructure. Manufacturing exports are significantly influenced by the micro‐regulatory environment and backbone services, while for agricultural exports, trade policy appears to matter more. While not discounting any component of the diagnostic, sector specific empirical analysis may provide some focus or a set of hypothesis for which factors may be most important; ultimately, country‐specific situations may confirm this or re‐focus the diagnostic on other issues.

||**Manufactured**<br>**Exports/GDP**|**Agricultural**<br>**Exports/GDP**|
|---|---|---|
|**Log of GDP per capita**|26.61|13.71*|
||[24.33]|[7.12]|
|**Log of GDP per capita squared**|‐2.34*|‐1.09***|
||[1.39]|[0.40]|
|**Log of population**|‐2.43*|‐1.74***|
||[1.43]|[0.44]|
|**Logistics Performance Index**|‐0.23**|‐0.05*|
||[0.09]|[0.03]|
|**Ease of Doing Business**|‐0.09*|0.01|
||[0.05]|[0.02]|
|**Market Access**|0.04|‐0.03|
|**Trade Tariff Restrictiveness Index**|||
||[0.06]|[0.02]|
|**Trade Tariff Restrictiveness Index**|‐0.01|‐0.03*|
||[0.09]|[0.02]|
|**ICT price basket**|‐0.35**|0.00|
||[0.17]|[0.05]|
|**Total Factor Productivity**|‐0.02|‐0.03|
||[0.19]|[0.06]|
|**Innovation**|0.04|‐0.07|
||[0.17]|[0.05]|
|**Constant**|53.95|14.26|
||[127.39]|[38.63]|
|**Observations**|74|74|
|**R‐squared**|0.42|0.42|

12 The indices were normalized by being converted into ranks. The interpretation of coefficients is therefore in ordinal, not cardinal terms: one rank improvement (i.e., decrease in rank) is associated with a change in the share of exports in GDP.

13
Jun and Singh (1996), using polled panel data, note the relationship between political risk, business conditions, macroeconomic variables and FDI. Busse and Hefeker (2005) explore the linkages between political risk, institutions and FDI, concluding that political stability and basic democratic rights are highly significant determinants of FDI.

As mentioned, the literature on infrastructure investments and risk is thinner. In the case of PPI transactions, Hammami et al. (2006), using the World Bank PPI Database concludes that lower levels of corruption and more effective rule of law are associated with more Public-Private Partnership projects. This study captures only the effect on the number of projects committed, not the investment levels per se, leaving room for further study, especially if we consider that bigger projects (committing more resources) may be more sensitive to the risk of the country.

# **3. Data**

In order to obtain PPI levels, we utilized the World Bank PPI Database. The PPI database offers detailed information by year, country, sector and form of public-private partnership. Within sectoral categories, it distinguishes among primary and secondary sectors by investment.<sup>6</sup> It also provides the form of private investments, so we can distinguish between greenfield projects and concessions of existing assets among other types of partnerships and investments.

The database, however, captures both public contributions to the infrastructure investments as well as private contributions. That is, the database notes total project size in commitments— later adjusted to actual disbursements, investments or transfers, where information is available. Those commitments combine private and, in many cases, public sources. The threshold for consideration is that the project involves a private service provider building Greenfield assets for its own operation, or—in the case of existing assets—purchasing, concessioning or leasing assets, or otherwise contracting for provision of the infrastructure services. Only projects that have come to financial closure are included in the database. If a purely public investment is carried out in tandem with a private operator or a private management contractor, the database does not include a value for those public investments. All project figures are noted in the year that the project comes to financial closure.

> 6 As examples, Energy and Transport are “primary sectors” whereas Electricity Distribution and Airports are “secondary sectors.”

5
Table 2: OLS results for Nigeria, Cameroon, Chad

||Agriculture|Manufacturing|Services|Not working|
|---|---|---|---|---|
|Paved road|-0.0202|0.0211<sup>~~∗∗~~</sup>|0.00165|0.0200<sup>~~∗∗~~</sup>|
||(-1.48)|(3.48)|(0.16)|(3.26)|
|Internet|-0.0462<sup>∗</sup>|-0.00558|0.0480<sup>∗∗</sup>|-0.00397|
||(-2.44)|(-0.74)|(3.00)|(-0.44)|
|Road + Electricity|-0.0668<sup>∗∗</sup>|0.0294<sup>∗∗</sup>|0.0380<sup>∗∗</sup>|-0.00942|
||(-4.39)|(4.30)|(2.99)|(-1.31)|
|Internet + Electricity|0.00476|-0.00273|0.00576|-0.0158|
||(0.21)|(-0.27)|(0.29)|(-1.47)|
|Year + Country FE|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|
|R-squared|0.447|0.364|0.447|0.142|
|N. of observations|3041|3041|3041|3041|

_t_ statistics in parentheses + _p_ < 0 _._ 10, ∗ _p_ < 0 _._ 05, ∗∗ _p_ < 0 _._ 01 Table 3: OLS results for Nigeria, Cameroon, Chad using the electricity grid

||Agriculture|Manufacturing|Services|Not working|
|---|---|---|---|---|
|Paved road|-0.0406<sup>~~∗~~</sup>|0.0262<sup>~~∗∗~~</sup>|0.0119|0.0184<sup>~~∗~~</sup>|
||(-2.12)|(3.36)|(0.81)|(2.26)|
|Internet|-0.130<sup>∗∗</sup>|0.0196|0.114<sup>∗∗</sup>|-0.0317<sup>∗∗</sup>|
||(-3.40)|(1.33)|(3.82)|(-2.59)|
|Road + Electricity grid|-0.0842<sup>∗∗</sup>|0.0309<sup>∗∗</sup>|0.0600<sup>∗∗</sup>|-0.0135<sup>+</sup>|
||(-5.11)|(4.70)|(4.37)|(-1.89)|
|Internet + Electricity grid|0.0233|-0.00907|-0.0184|0.0198|
||(0.58)|(-0.59)|(-0.59)|(1.50)|
|Year + Country FE|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|
|R-squared|0.203|0.240|0.285|0.050|
|N. of observations|3041|3041|3041|3041|

_t_ statistics in parentheses + _p_ < 0 _._ 10, ∗ _p_ < 0 _._ 05, ∗∗ _p_ < 0 _._ 01 Table 4 shows the results of the analysis when including uses as reported by households and ownership of durable goods associated with road, electricity, and ICT infrastructure investments. The presence of infrastructure allows but does not ensure its use by households and represents therefore a lower bound of the impacts of infrastructure. Estimates based on uses permitted by access to services and ownership of durable goods can be interpreted as a higher bound. Estimated coefficients are therefore expected to be higher when considering uses rather then access to hard infrastructures. Variables related to use of infrastructure in Table 4 include the percentage of respondents that own a certain good or use the services allowed by certain infrastructures. Use of electricity by the household requires the presence of the national grid 14
subsidies may exist for reasons of political economy where consumer unrest could be reflected in popular opposition to price increases.

Table 2: Breakdown of hidden costs in Middle East and North Africa (% of GDP)

|**Country**|**Bill collection**|**T&D losses**|**Overstaffing**|**Underpricing**|**Total QFD**|
|---|---|---|---|---|---|
|Algeria|0.10|0.37|0.32|1.46|2.25|
|Bahrain|0.02|0.02|0.13|7.86|8.03|
|Djibouti|5.24|1.08|0.88|0.98|8.18|
|Egypt, Arab Republic|0.06|0.42|0.28|5.61|6.37|
|Iraq|0.13|0.83|0.21|2.44|3.61|
|Jordan*|0.75|0.84|0.21|5.96|7.76|
|Lebanon|0.21|0.41|0.03|8.20|8.85|
|Morocco*|0.20|0.33|-0.21|0.65|0.97|
|Oman*|0.18|0.22|0.10|2.70|3.20|
|Qatar|0.10|0.02|0.01|1.47|1.60|
|Saudi Arabia|0.17|0.11|0.07|4.81|5.16|
|Tunisia|0.54|0.39|0.15|0.34|1.42|
|West Bank|0.30|0.30|0.13|-0.84|-0.11|
|Yemen, Republic|0.08|0.81|0.11|3.16|4.16|
|**_Median_**|**_0.18_**|**_0.38_**|**_0.13_**|**_2.57_**|**_3.89_**|

_Source_ : Camos et al. 2017. * indicates long-standing private sector involvement.

These tables also indicate that excess T&D losses, partial bill collection, and overstaffing amounted to a substantial fraction of GDP in many developing countries so that, as well as the tariff setting issue, there were substantial inefficiencies in operation.

9
functional domains (seeing, hearing, walking, cognition, communication, and self-care), or a lot of difficulty in (or unable to do) one or more of the domains.<sup>1</sup> The 2006 VHLSS shows that the fraction of pupils aged from 6 to 17 who have at least a mother or a father with disabilities is around 5.8 percent (Table 1). Older children are more likely to have parents with disabilities. This is because older children tend to have older parents and people are more likely to become disabled as they get older. Table 1 also shows there is a negative correlation between disability and household welfare. People who have higher education and consumption are less likely to have disabilities.

Table 1 also presents the schooling rate of children with parents having a disability and children with parents not having disability. There is a large difference in the schooling rate between these two groups of children. Children with disabled parents have a lower school enrollment rate (73.1 percent) than other children (85.6 percent).

Interestingly, girls have a higher proportion of education enrollment than boys. Among children whose parents have disabilities, the schooling proportion for girl was 77.5 percent, which is much higher than the proportion of 68.4 percent for boys.

The gap in education between children with and without a disabled parent is largest for children aged from 15 to 17, which is the age for upper-secondary school. This gap in education tends to be larger for disadvantaged groups such as rural children, ethnic minority children, children living with a household head of low education degree, and poor children. For example, in the lowest expenditure quintiles, children whose parents have disabilities have an enrollment rate 14 percentage points lower than children whose parents do not have disabilities.

Among children in school, those with disabled parents also have worse educational performance. However, this difference is not very large. Table 2 shows that the fraction of pupils achieving excellent academic performance in the last school year is 14 percent for children without disabled parents, while this figure is around 11 percent for

> 1 In the 2006 VHLSS, people are asked about their difficulties in different functions including seeing, hearing, walking, cognition, communication, and self-care, and their responses are classified into 4 categories:

> a. No - no difficulty

> b. Yes – some difficulty

> c. Yes – a lot of difficulty

> d. Cannot do at all.

6