rafmacalaba/datause-encoder-data huggingface dataset:
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Roads

## **Achievements**

Ghana’s road transport indicators are strong. By almost all measures, they are well ahead of those found among low-income peers and nearing the levels expected of a middle-income country (table 2). The length of the main (primary and secondary) network is more than adequate to achieve regional and national connectivity. The record on road network quality is quite reasonable, with 75 percent of the paved network in good or fair condition and, more impressive, 74 percent of the unpaved network in good or fair condition.

Underpinning these achievements has been a serious reform of road sector institutions that has resulted in the creation of a second generation road fund and road agency. Ghana meets almost all of the best practice guidelines for road sector institutions. The country has also adopted a fuel levy ($0.06 per liter) that, as of 2006, was commensurate with road maintenance needs (figures 4 and 5). In contrast to other African countries, Ghana allocates its road fund resources much more evenly across the different road networks—rural and urban roads receive 30 and 25 percent of the total, respectively. Overall, Ghana has allocated substantial resources to the road sector in recent years; it spends on average 1.5 percent of GDP on roads, one of the highest shares in West Africa.

**Table 2. Ghana’s road indicators benchmarked against Africa’s low- and middle-income countries**

||Unit|Low-income<br>countries|**Ghana**|Middle-income<br>countries|
|---|---|---|---|---|
|Paved road density|km/1000 km<sup>2</sup>of arable land|86.6|**158.1**|507.4|
|Unpaved road density|km/1000 km<sup>2</sup>of arable land|504.7|**804.0**|1,038.3|
|GIS rural accessibility|% of rural population within 2 km of all-season road|21.7|**24.0**|59.9|
|Paved road traffic|Average annual daily traffic|1,049.6|**1,314**|2,786.0|
|Unpaved road traffic|Average annual daily traffic|62.6|**40.4**|12.0|
|Paved network condition|% in good or fair condition|80.0|**75.0**|79.0|
|Unpaved network condition|% in good or fair condition|57.6|**74.0**|58.3|
|Perceived transport quality|% firms identifying roads as major business constraint|23.0|**17.6**|10.7|

Source: Gwillliam and others 2009. Derived from AICD national database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data

## **Challenges**

In recent years, the depreciation of the national currency has gradually eroded the real value of the fuel levy. Adjusting its value is essential if Ghana is to sustain its relatively good performance on road network preservation. The absolute value of the existing fuel levy is already relatively low by African standards—about half the levels found in Ethiopia, Kenya, and Tanzania—which suggests that a revision to preserve the real value of the levy should not be overly burdensome for the transportation sector.

10
**Table 2: Differences of means among groups of stakeholders on the efficiency/equity dimensions**

||||**(T-tests)**|||||
|---|---|---|---|---|---|---|---|
||Government/<br>Non-<br>government|Producers/<br>Non-<br>producers|Patients/<br>other groups|Executive|Judiciary|Legislative|Civil society/<br>non-civil society|
|_Overall quality_<br>_of the benefit plan_|-1.162***|0.365|1.204***|-0.896**|0.167|-1.037***|1.162***|
||(-4.78)|(0.99)|(3.40)|(-2.51)|(0.30)|(-2.79)|(4.78)|
|_Efficiency_|-0.954***|0.370|0.923**|-1.030***|-0.250|-0.269|0.954***|
||(-3.77)|(1.07)|(2.41)|(-3.26)|(-0.48)|(-0.65)|(3.77)|
|_Accountability of_<br>_health care_<br>_providers to patients_|<br>-0.346|0.141|0.423|-0.593|0.393|-0.180|0.346|
||(-1.22)|(0.43)|(1.09)|(-1.69)|(0.81)|(-0.49)|(1.22)|
|_Access of_<br>_marginalized groups_<br>_(equity)_|-0.855***|0.0833|1.00**|-1.051***|0.161|-0.436|0.855***|
||(-3.03)|(0.22)|(2.70)|(-2.88)|(0.29)|(-1.08)|(3.03)|
|_Access of average_<br>_patient (equity)_|-0.733**|-0.102|0.923*|-0.875*|0.704|-0.667|0.733**|
||(-2.16)|(-0.25)|(1.81)|(-2.04)|(1.18)|(-1.51)|(2.16)|
||33|33|33|33|33|33|33|
|_N_<br>t_statistics in parenth_|_eses; p* 0.10 *_|_* 0.05*** 0.0_|_1_|||||

_Note:_ Two-sample Wilcoxon (Mann-Whitney) rank-sum test results for ordinal variables hold The table presents simple differences of means among groups of stakeholders according to perceptions of five main characteristics of the Benefit Plan (among which _efficiency_ and _equity_ are 22
Policy Research Working Paper 5124

# **Abstract**

The Philippine economy has been growing rapidly, at an annual growth rate of 5 percent over the past five years. Such decent growth in gross domestic product, however, did not translate into an increase in household income. Wage income declined in real terms. The poverty headcount increased slightly. The fruits of economic growth were not shared equally across the country. Challenges remain to create more jobs to keep pace with the rapidly growing active population.

Using the Philippines Labor Force Survey data (2003– 2007), this paper reviews the disparities in labor market performance and examines the contribution of regional and individual characteristics. The results show that real wages declined and disparities widened between the National Capital Region and other islands. The youth, less educated, and women face more challenges in finding employment with a decent salary, other things being equal. Disparities in labor market performance are largely associated with the difference in regional structure and human capital endowment. Individual characteristics account for roughly one-third of the difference in wages between the National Capital Region and other regions; regional structures and other unobservable factors account for two-thirds of the difference.

As labor market opportunities and returns to education and other individual talents significantly differ across regions, implementation of appropriate regional labor market policies is important. Human capital, however, remains a key determinant in employment and wages. Concerted efforts to improve education will also be important.

This paper—a product of the Poverty Reduction and Economic Management Department, East Asia and Pacific Region—is prepared as a background paper for the Philippines Inclusive Growth report.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at xluo@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
also why the number of tsunami districts is just 1 or 2 instead of the 6, which is all affected districts. Volcanic eruptions see a slight increase in mean, whereas the standard deviations are close to what they are for the full sample.

Some sectors with fewer observations, such as Public Law and Order, Housing and Social Protection deviate more from the norm than the more robust sectors with more observations. However, importantly, most sectors stay within a fairly tight band both for mean and standard deviation, implying that the sectors experience similar disaster impact. Overall, we believe the number of observations for floods and earthquakes is high enough to provide fairly robust results, and even volcanic eruptions can be useful as a guidance. The tsunami index however is suffering from having only one or two districts in our sample.

Table 4: Damage Indice Descriptives when Unbalanced 2 <u>years</u>

|||Flood||E|arthquak|e<sup>_a_</sup>|Volc|anic Eru|ption||Tsunami||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Sector|Obs|Mean|SD|Obs|Mean|SD|Obs|Mean|SD|Obs|Mean|SD|
|General Administration|1,691|25.86|22.01|222|1.66|3.34|44|34.87|42.58|1|0.23||
|Agriculture|1,631|25.99|22.12|220|1.81|3.54|43|35.13|43.05|2|0.15|0.11|
|Public Law and Order|738|27.41|22.80|81|2.12|3.59|33|40.10|46.52||||
|Economy|1,641|26.01|22.09|220|1.81|3.54|44|34.87|42.58|2|0.15|0.11|
|Environment|1,360|25.74|21.34|183|1.87|3.63|41|36.51|43.56|2|0.15|0.11|
|Housing and Public Facilities|568|26.54|20.97|66|2.04|4.10|16|35.07|42.57|1|0.23||
|Health|1,642|26.03|22.09|220|1.81|3.54|44|34.87|42.58|2|0.15|0.11|
|Tourism and Culture|1,244|26.59|22.49|176|1.87|3.55|39|34.08|43.78|2|0.15|0.11|
|Education|1,642|26.03|22.09|220|1.81|3.54|44|34.87|42.58|2|0.15|0.11|
|Social Protection|1,255|25.15|21.66|172|2.01|3.81|30|28.72|38.29|2|0.15|0.11|
|Infrastructure|1,638|26.01|22.10|219|1.80|3.55|44|34.87|42.58|2|0.15|0.11|
|Category|Obs|Mean|SD|Obs|Mean|SD|Obs|Mean|SD|Obs|Mean|SD|
|Capital Expenditures|1,740|27.28|21.89|207|2.15|3.73|47|33.41|41.67|2|0.15|0.11|
|Goods and Services|1,739|27.27|21.90|207|2.14|3.74|47|33.41|41.67|2|0.15|0.11|
|Other Expenditures|1,733|27.24|21.85|210|2.16|3.71|47|33.41|41.67|2|0.15|0.11|
|Personnel Expenditures|1,752|27.22|21.87|210|2.11|3.72|47|33.41|41.67|2|0.15|0.11|

> _a_ Mean and standard deviation numbers multiplied by 1,000

For the economic categories, the overall data look better, as seen in the lower panel of Table 4. The flood and earthquake districts are well covered, and for volcanic eruptions it is the same districts that have reported for all categories. Finally, the tsunami data are covered 15
Our results also echo the point made by Acemoglu et al (2012) that in the presence of a nonconvexity in the economy, efficient pricing of the marginal carbon externality (our co-state variable ξ) may not in itself be sufficient to drive the economy to the maximally efficient growth path. In Acemoglu et al the barrier is a limit on the malleability and mobility of high-skill human capital for technological innovation in low-carbon versus higher-carbon production. The policy implication there is that high-skilled workers may need to have salary premiums to be attracted to innovate in the nascent “green sector.” In our framework, the barrier can arise from that or other specialized factor immobility. In e.g. Rodrik (2008), industrial policy is advocated as a way to address coordination problems in the face of economies of scale that need to be reaped for effective development. In our setting, a lower set-up cost can overcome the effect of the nonconvexity and can be welfare-improving. This leads us to speculate that subsidies of start-up cost may be welfare-improving, as well. However, further work is needed to evaluate this possibility, especially given the caveats related to the practical difficulties governments face in prudently and effectively undertaking such efforts.

The analytical framework we have used is a very simplified one that can be (and is being) generalized in several ways in ongoing work. Especially useful extensions include developing a more dynamic representation of the set-up problem (e.g. through adjustment costs that limit the speed at which the new sector can be built up), and extending the model to deal with both environmental uncertainties (affecting investment returns in the old sector) and technological uncertainties (affecting investment returns in the new sector).

# REFERENCES

Acemoglu, D., Ph. Aghion, L. Bursztyn, and D. Hemous (2012), The environment and directed technical change. _American Economic Review_ 102 (1): 131–166.

Antoci, A., Galeotti, M., & Russu, P. (2011). Poverty trap and global indeterminacy in a growth model with open-access natural resources. _Journal of Economic Theory, 146_ (2), 569-591.

Azaradis, C. (1996). The economics of poverty traps, Part 1: complete markets. _Journal of Economic growth, 1,_ 449-486.

Azariadis, C., and Stachurski, J. (2005). Poverty traps. In Aghion, P., Durlauf, S. N, editors, _Handbook of Economic Growth_ . Amsterdam: North-Holland.

Barbier, E.B. (2010), Poverty, development, and environment. _Environment and Development Economics_ , 15(6), 635-660.

Barbier, E.B. (2006), _Natural Resources and Economic Development._ Cambridge: Cambridge University Press.

Bassetti, T., Benos, N., & Karagiannis, S. (2013). CO2 emissions and income dynamics: What does the global evidence tell us? _Environmental and Resource Economics, 54_ (1), 101-125.

Brock, W.A. and M. S. Taylor (2005). Economic growth and the environment: A review of theory and empirics. In Aghion, P., Durlauf, S. N, editors, _Handbook of Economic Growth_ . Amsterdam: North-Holland.

15
Figure 27. Savings at a Financial Institution across Age Groups <!-- Start of picture text --> 2011 2014<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Turkey BRIC Rest of High Rest of ECA Turkey BRIC Rest of High Income Rest of ECA<br>Developing Income Developing<br>World World<br>15‐24 25‐64 65+<br><!-- End of picture text --> Figure 28. Savings EACR (household level, LITS) <!-- Start of picture text --> 30 27.4<br>23.7<br>25<br>21.4<br>20<br>15 12.4<br>10.1 10.4 10.5 10.8<br>9.0 9.0 9.2<br>10 8.2<br>6.8 6.5<br>5.3<br>5<br>0<br>Azerbaijan Bulgaria Turkey Tajikistan Moldova<br>Coverage Dissimilarity EACR<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 35
# FIGURE 4. TRIPS Structure<sup>and Information Flows</sup>

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Figure 2: Number Affected by Droughts in Developing Countries, 1960-2002 (Log Scale) <!-- Start of picture text --> 1,000,000,000<br>100,000,000<br>10,000,000<br>1,000,000<br>100,000<br>10,000<br>1,000<br>100<br>10<br>1<br>1960 1970 1980 1990 2000<br><!-- End of picture text --> Source: CRED: EM-DAT 39
_Table 1: Externality costs of passenger transport by private vehicle [$/1000 vehicle-km]_

|**Scope**|**Congestion**|**Air**<br>**pollution**|**GHG**<br>**emissions**|**Crashes**|**Noise**|**Total**|
|---|---|---|---|---|---|---|
|All|244.65|17.10|9.74|26.87|9.13|307.49|
|_Spatial_<sup>1</sup>|||||||
|Urban|457.81|31.56|17.12|51.38|17.87|575.75|
|Rural|69.79|8.13|13.51|19.26|0.32|111.01|
|_Temporal_<sup>1</sup>|||||||
|Peak|275.45|21.40|15.04|32.85|5.11|349.84|
|Off-peak|53.42|12.07|16.26|29.57|13.76|125.09|

1 Estimates that do not match the specified scope have been excluded from the spatial and temporal breakdowns.

Several patterns emerge comparing these data. Notably, congestion costs make up the bulk of the total externality costs associated with vehicle use at 80% of total mean costs. On the other hand, GHG emissions, which has received increasing attention recent years, contribute only 3% of the external costs. This value may be on the low end, since several studies in the catalogue of literature we reviewed are somewhat dated and the impacts of climate change have become clearer in recent years. Nonetheless, the highest cost estimate for GHG emissions in our catalogue ($44.44 / 1,000 vehicle-kilometers) still only represents a relatively small fraction of the total external cost of private vehicle use. That said, the point here is not to downplay the importance of decarbonization. On the contrary, climate change is one of the major challenges of this generation. Rather, the point is that managing demand for private vehicles should be a priority for numerous reasons, and it contributes benefits along multiple dimensions that are important to justify policies and investments. The potential imbalance of attention has been noted in the literature previously (Lindsey & Santos, 2020).

Breaking down the estimates by their spatial and temporal scope reveals some of the underlying variation. As expected, externality costs are higher in urban compared to rural areas because the external costs naturally apply to more people in dense areas. For the same reason, externality costs are more severe during peak hours than during the off-peak. Noise pollution is a notable exception to this trend. This is likely a result of higher sensitivity to noise at night.

5
67 _Table 5.3: Zimbabwe: Index of Industrial Production (1980 - I00)_

|Sector|Weight|1981|1982|1983|1984|
|---|---|---|---|---|---|
|_Minerals_||||||
|Chrome ore|45|96.9|78.0|78.0|86.1|
|Nickel|136|86.4|88.3|67.3|68.0|
|Iron Ore|36|67.5|51.6|57.0|57.0|
|Cobalt|7|81.9|86.0|63.4|67.0|
|Aggregate|1000|95.9|96.4|92.8|97.0|
|_Manufacuing_||||||
|Food|135|108.4|123.7|126.9|119.4|
|Soft drinks and tobacco|104|89.4|91.7|90.1|86.8|
|Textiles|101|111.6|118.8|108.8|124.1|
|Clothing & footwear|72|128.4|118.6|109.2|99.9|
|Wood & urnitue|44|103.4|85.8|82.3|81.6|
|Paper & prindng|61|112.4|112.3|106.2|95.0|
|Chemical & petro.|125|116.6|118.2|121.4|112.2|
|Non metallic minerals|37|118.0|109.7|105.4|99.0|
|Metals|288|104.8|96.4|94.8|89.4|
|Transport equipment<br>|21<br>|_155.0_<br>|178.4<br>|145.6<br>|114.7<br>|
|Other manufacturing|12|95.6|80.0|76.6|50.9|
|Aggregate|1000|100.4|108.7|105.8|100.7|

_Source:_ Government of Zimbabwe (1989).

Table 5.3 shows tha minerl output declined during 1982-83 as did manufacturing output during 1983-84. The decline in the metals subsector, which accounts for over 80 percent of the electricity consumption ofthe indusial sector, was significant This occurred despite the fact that the metal industry enjoys lower tariffs and lower th average increases in electiciy prices. The decline in output is expected, both due to limited interfuel substitution and the large electricity cost share in these industries. The competitive nature of the intemational metal markets and the general fall in demand due to the recession in the OECD were also the factors that furter limited the possibilities for passing through increases in costs to inal consumers. This fail in output also affected the raw material mining sector. Fail in output of food and agro-based manufacturs was most likely caused by severe drought during this period.

What was the impact of electricity price increase on demand for electricity in the mining and manufacturing subsectors during tiis period? In line with output losses, there was a decline in electricity demnd in tese sector Table 5.4 shows the effect on electricity demand during the period 1982 to 1984.
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23
10 Table 4 Matched and Non-matched Vocational School Leavers in Private Sector Wage Employment (Numbers, Mean monthly eamings and sds.)

||All V<br>Scho<br>|ocational<br>ol Leavers<br>|Strict|<br>Match<br>|ina<br>|Wide|r<br>Matc<br>|hina<br>|
|---|---|---|---|---|---|---|---|---|
|Field of<br>study|No.|Mean<br>Eamings|%<br>Matc|Mean E<br>Match<br>h|arninas<br>Non-<br>match|%<br>Matc|Mean Ea<br>Match<br>h|minas<br>Non-<br>Match|
|Industrial|82|28,469<br>(20,115)|57.3|27,907<br>(18,111)|29,223<br>(22,784)|69.5|28,567<br>(19,130)|28,268<br>(22,618)|
|Accounting|257|23,673<br>(23,482)|16.3|25,507<br>(18,759)|23,315<br>(24,319)|32.7|35,395*<br>(30,537)|17,981<br>(16,463)|
|Other Vocational|68|17,967<br>(12,471)|20.6|20,074<br>(13,323)|17,420<br>(12,313)|30.9|16,290<br>(10,139)|18,716<br>(13,417)|
|All Vocational|407|23,686<br>(21,5M)|25.3|25,864*<br>(17,849)|22,948<br>(22,640)|40.0|30,512*<br>(39,512)|19,172<br>(16,877)|

> * Significant at the .001 level. Differences between means were tested employing t-tests.

# Eaminis Functions Results for Vocational School Leavers

Earnings functions were estimated for the sample of vocational school leavers with the purpose of examining whether work in a study related field makes a significant contribution to their eamings. Table 5 presents the results. Column 1 shows the model with the strict match between field of study and work, and Column 2 shows the same model using the wider matching criteria.

The table shows clear labor market eamings advantages accruing to vocationally educated workers that are employed in occupations related to courses of study followed. For the strictmatch model, the earnings differential Is some 16 percent, while for the wider-match model the dffferential exceeds 27 percent. The stronger effect for the wider-match definition reflects subsequent career development, in a broader range of (better-payed) jobs.