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

# Power

## **Achievements**

Zambia is endowed with a relatively large amount of cost-effective hydropower. In terms of electricity supply, Zambia enjoys a much more favorable position than many of its neighbors (table 6). Due to the demands of its large-scale mining sector, Zambia has a relatively large generation capacity and power consumption per capita, several times higher than that of other resource-rich countries in Africa. Abundant hydro resources allow Zambia to produce electricity at around $0.08 per kilowatt-hour (kWh), about half the average cost of electricity production in Africa.

The national utility Zambian Electricity Supply Company Ltd. (ZESCO) also performs relatively well in terms of operational efficiency. In the two key indicators of operational performance, revenue collection and distribution losses, ZESCO performs well relative to the resource-rich peer group and near the level of the middle-income peer group (table 6).

**Table 6. Benchmarking** **<u>power indicators</u>**

||Unit|Resource<br>rich|**Zambia**|Middle-income<br>countries|
|---|---|---|---|---|
|Installed power-generation capacity|MW/mill. people|43.2|**154.9**|798.6|
|Power consumption|kWh/capita|205.7|**771.0**|4,479.3|
|Power outages|Day/year|14.5|**49.8**|5.9|
|Firms’ reliance on own generator|% consumption|44.9|**19.5**|10.9|
|Firms’ value lost due to power outages|% sales|7.0|**3.7**|1.6|
|Access to electricity|% population|46.1|**20.1**|59.9|
|Urban access to electricity|% population|79.4|**50.0**|85.2|
|Rural access to electricity|% population|28.0|**3.5**|31.8|
|Growth access to electricity|% population/year|2.4|**0.3**|1.5|
|Revenue collection|% billings|81.1|**96.5**|100.0|
|Distribution losses|% production|25.8|**12.0**|10.1|
|Cost recovery|% total cost|53.9|**39.1**|100.0|
|Total hidden costs as % of revenue|%|168.3|**93.3**|0.1|
|U.S. cents|**Zambia**|Predominantly<br>generatio|hydro<br>n<br>Oth|er developing regions|
|Power tariff (residential at 75 kWh)|**2.9**|10.3||5.0–10.0|
|Power tariff (commercial at 900 kWh)|**4.4**|11.7|||
|Power tariff (industrial at 50,000 kWh)|**2.9**|11.4|||

_Source:_ Eberhard and others, 2008 Derived from data downloadable at AICD on-line power utilities database http://www.infrastructureafrica.org/aicd/tools/data _Note:_ MW = megawatt.

16
aggregate were, unsurprisingly, very often positive. In the absence of data on returns on less liquid assets, this means the specification can only be run with M1.3 Since we are concerned only with the direct effect of depreciation on money demand through the interest rates, cointegration based estimation methods were not appropriate since they generate the total impact elasticity through all variables in the system.<sup>4</sup> Although the literature on estimating interest response of money demand is long and contentious, as we will see, the precision of these estimates does not appear critical to the results. The threshold value is scaled by the money multiplier, so as to make it consistent with the ratio of reserves to Ml, rather than base money.

Finally, unlike the European subsample, the Latin American countries adopted a target zone or peg at different times within our sample period. Further, in the case of Argentina, the deceleration of inflation in the early part of the stabilization plan introduced a high degree of both real exchange variance, interest rates and other variables that was unrelated to the sustainability of the peg per se. We therefore begin the sample in 1992:1 when inflation was falling to levels below 50%. The effect in both cases is to generate an unbalanced panel.

As a preliminary test of the model, table la presents the thresholds calculated first using the GARCH and then the moving average specifications of the variance, as well as the level of RIMl for the entire sample, and table lb the same information for several countries experiencing crises in both Europe and Latin America. As is immediately evident, on average, the reserve ratio 3 This also raises questions about the interpretation of the interest rate coefficient in empirical tests of monetarist models of the exchange rate that employ M2, and use these same interest rates as the opportunity cost of holding it. Estimates available on request.

4 Johansen(1995) and Lutkepol (1994) argue that the coefficient from cointegrating regressions cannot be interpreted as the necessary partial elasticities since they capture shocks transmitted through all other variables and cannot be allowed a _ceterisparibus_ interpretation.

8
wage, the replacement ratio varies from 49-45 percent while on unemployment insurance to 29 percent with social assistance/support, and these numbers become 90 percent if he can only get a minimum-wage job.

47. Similarly, in the first case, the household replacement ratio is around 70 percent when receiving unemployment insurance and 67 percent otherwise, while in the second these numbers go up to 112 percent. This implies that total household income would actually decrease as a result of the adult l's re-employment due to the loss of child allowances. Notice that this is also the reason why the replacement ratio here is actually higher than that calculated for case A.

|**Benefitregime**<br>**Income**|**UI**<br>**First3 months**<br>**(RR50%)**|**UI**<br>**Afterfirst****_3_months**<br>**(RR45%)**|**SA/SS**<br>**(Objective)**|
|---|---|---|---|
|**Adultl'sincome(AW)**|5,400<br>(0.49)|4,927<br>(0.45)|3,230<br>(0.29)|
|**HHincome(AW)**<br>**HH income**<br>**(AW)**|**10,260**<br>**~~(0.71)**|**9,787**<br>**(0.67)**|**9,670**<br>**(0.67)**|
|**Adultl'sincome(MVV)**<br>**Adult l's**<br>**income(MW)(0.**|3,230<br>**90)**|3,230<br>**(0.90)**|3,230<br>**(0.90)**|
|**HUincome(MW)**<br>_|**9,670**<br>_<br>_<br>_<br>_<br>_<br>_<br>**(1.12)**|**9,670**<br>(1.12)|**9,670**<br>(1.12)<br>**.**|

# A **couple with five children.**

48. This third example intends to reproduce the setup of a generic Roma household. For this purpose, given that the average number of children among Roma families is 4.2, compared to 1.2 for non-Roma households, we consider a large family with five young children. We assume that all five lack any means of subsistence, and that their age distribution is as follows: one of them is under 3, one more is between 3 and 6, two are between 6 and 15, and the eldest if above 15. These differences allow for variation in the amount of the child allowance corresponding to each child, and help us bring into the analysis the disincentives associated not only with the number of children, but with their age. A look at the structure of child allowance payments will make this point clear:

- Up to 6 years of age: SK640/month

- * 6-15 years: SK790/month * Above 15 years: SK840/month

49. Moreover, because education levels are very low among this group and traditionally Roma women do not work outside the house, we only consider the case where adult 1 was previously employed at the minimum wage and adult 2 is unemployed or out of the labor force.

13
and it increases juvenile catch inside closed areas after the end of closure periods (temporal spillover). These areas and time periods are not targeted by the policy; the regulator only intends to change juvenile catch inside closed areas during closure periods. Summing the direct, temporal spillover, and spatial spillover effects, I estimate that the policy increases total juvenile catch by 48% despite a second regulation which sets binding limits on total catch (juveniles and adults). The closures policy backfires—worsening its target outcome on net—because closures cause vessels to reallocate fishing to areas where the share of juveniles is higher.

Why do closures change where vessels fish? Due to contracts and other regulations in the fishery, reducing search costs is the primary margin by which fishers can increase profits. Closures might help fishers reduce search costs because closures implicitly signal high-productivity fishing locations: the regulator implements closures in response to realtime anchoveta catch data from all vessels, and there is only anchoveta catch in an area if anchoveta are sufficiently abundant. This information is potentially valuable because there is zero anchoveta catch in most areas,<sup>2</sup> but strong correlations in anchoveta catch over time and space.<sup>3</sup> For these reasons I posit that closures implicitly provide information about the value of fishing near closures (before, just outside, and after closures). I confirm that one way in which vessels respond to closures is by fishing closer to them. Then I provide evidence that absent vessels’ responses to the information, fishing near closures is more productive than fishing elsewhere.

If information is a mechanism underlying the policy’s spillover effects, then vessels that receive stronger information shocks from closure announcements should have larger treat-

> 2I observe the universe of fishing events. Zero anchoveta catch includes instances in which there is no fishing in an area as well as instances in which vessels set their nets but catch zero tons.

> 3For example, the daily probability any vessel catches anchoveta in a given .1 _◦_ grid cell ( _∼_ 11 by 11 km) during the fishing season is 0.5%. But conditional on at least one vessel catching anchoveta in a .1<sup>_◦_</sup> grid cell yesterday, the probability of positive anchoveta catch today in the same grid cell is 31% (temporal correlation). Conditional on at least one vessel catching anchoveta in a .1<sup>_◦_</sup> grid cell today, the probability of positive anchoveta catch in at least one adjacent grid cell on the same day is 92% (spatial correlation).

5
12 _Table 4 :_ **_Subnational governments’ own source revenues as share of their total revenues (1999)_** _10 European transition countries_

||**Own-Taxes +**<br>**Non-Tax**<br>**Revenue**|<br>**General**<br>**Purpose**<br>**Grants (with**<br>**objective**<br>**criteria)**|<br>**Specific**<br>**Grant (not**<br>**conditional)**|**Total Own**<br>**Source**<br>**Revenues**|
|---|---|---|---|---|
|**Bulgaria**|13,4|28,6|0|42|
|**Czech**<br>**Republic**|40,2|0|7,5|47,7|
|**Estonia**|15,4|13,5|0|28,9|
|**Hungary**|33,3|0,3|0,9|34,5|
|**Latvia**|20,4|2,3|0|22,7|
|**Lithuania**|4,8|2,3|0|7,1|
|**Poland**|35,1|30,6|0|65,7|
|**Romania**|21|0|0|21|
|**Slovak**|||||
|**Republic**|46,6|0|9,7|56,2|
|**Slovenia**|28,1|15,9|0|44|
|**Mean**|**25,8 **|**9,4 **|**1,8 **|**37,0 **|

Source : OECD, Fiscal Design Across Levels of Government, 2001, 2002.

# _New measures of decentralization_

In order to estimate the effects of decentralization it is important to find a good measure for it. Ebel and Yilmaz have shown that expenditure or revenue share were not reliable measures of decentralization because they did not take any consideration of subnational governments’ control over their revenues. The new indicators they proposed were interesting in the way that they illustrated which element of decentralization had positive or negative effect on economic outcomes. However, they revealed nothing about the effects of overall decentralization.

To fill this gap, three new measures of decentralization are proposed in this paper. The first one, called “revenue autonomy”, is the ratio of subnational governments own source revenue over its total revenue. Own source revenue is defined in the previous section of this text. It is the sum of tax autonomy, non-tax autonomy and intergovernmental grants
|**Table 4: Base reg**|**ression results**|**for growth ra**|**te of labor pro**|**ductivity**||
|---|---|---|---|---|---|
|Dependent variable: Growth Rate of<br>Labor Productivity|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Ethnic Fractionalization|-18.785***<br>(4.464)|-13.716***<br>(4.551)|-13.624***<br>(4.601)|-13.157***<br>(4.613)|-12.101***<br>(4.294)|
|Initial Labor Productivity (logs)|-5.424***|-6.051***|-6.061***|-6.088***|-6.040***|
||(0.556)|(0.527)|(0.520)|(0.540)|(0.566)|
|GDP per capita (logs)||3.116**|3.088**|3.002**|3.788***|
|||(1.243)|(1.268)|(1.238)|(1.360)|
|Log of Number of workers (lagged)||2.925***|2.813***|2.826***|2.950***|
|||(0.502)|(0.540)|(0.546)|(0.559)|
|Age of firm (logs)|||-0.767|-0.688|-0.622|
||||(0.985)|(0.975)|(0.965)|
|Manager experience (years, logs)|||0.999|0.925|1.048|
||||(1.025)|(0.978)|(0.957)|
|Exports (proportion of sales)|||1.895|1.828|1.687|
||||(1.744)|(1.724)|(1.764)|
|Foreign Ownership Y:1 N:0|||1.374|1.301|1.344|
||||(1.446)|(1.456)|(1.475)|
|Firm is Financially Constrained Y:1|||0.512|0.367|0.422|
|<br>N:0|||(1.784)|(1.715)|(1.693)|
|Female Top Manager Y:1 N:0||||0.444|0.409|
|||||(1.186)|(1.244)|
|Firm Provides Training Y:1 N:0||||0.000|-0.002|
|||||(0.012)|(0.012)|
|Courts Obstacle Y:1 N:0||||-1.169|-1.185|
|||||(1.193)|(1.199)|
|Total hours of power outages||||-0.005|-0.003|
|||||(0.009)|(0.008)|
|Firm Purchased Fixed Assets Y:1||||0.822|0.605|
|N:0||||(1.043)|(1.046)|
|Time Tax||||-0.056|-0.058|
|||||(0.056)|(0.056)|
|Population (logs)|||||-0.392|
||||||(0.845)|
|Primary Education|||||<br>0.057|
||||||(0.087)|
|Freedom from Business Regulations|||||-0.457<br>(0.996)|
|Merchandize Trade to GDP ratio|||||<br>0.007<br>(0.028)|
|Growth rate of GDP per capita (%,<br>|||||-0.185|
|annual)|||||(0.207)|

50
_38_ problem, caused by evasion, temporary unemployment or withdrawal from the work force. They are also observed in Latin American and OECD countries.

Members of Jamsostek have little to gain from their participation. Based on past results of the program, a member can expect to receive an annuity of about 7 percent of their final salary after making 35 years of contributions. This low replacement rate is primarily a reflection of the low rate of return on investment that Astek has been able to produce. The returns credited to members are further reduced by high administrative expenses, leaving no gains after inflation in recent years. (Each member could do better by simply putting the 5.7 percent contributions in a bank account and paying taxes on the interest, which after 35 years could be expected to provide a replacement rate of about 12 percent of the final salary.)

**_Operating Expenses_** In 1995, Pt. Astek has about 2,600 employees and 90 offices, divided into 8 regions around the country. The headquarters in Jakarta has a staff of about 426. There are 60,000 employers making monthly contributions on behalf of about 9.1 million members and about I million retirees.

As with most centralized public sector pension organizations throughout the world, Pt. Astek incurs considerable administrative expenses in its operations. In spite of recent management changes, which have resulted in some gains in efficiency, there is still a significant scope for further improvements, as shown in a cross-country comparison of administrative expenses and investment performance in the table below. Note that Taspen, which operates under a similar economic environment as Astek and is relatively costly by international standards, has nonetheless kept its expenses well below those of Astek.

# **Relative Cost and Performance of Pension Administration**

||Indonesia<br>Pt. Astek|Indonesia<br>Pt. Taspen|Malaysia<br>EPF|Singapore<br>CPF|
|---|---|---|---|---|
|Admin. Expenses as % of:|||||
|_Contributions_|11.7|7.0|2.0|0.5|
|_Pension Assets_|3.2|0.6|0.2|0.1|
|Real Investment Returns|0.0|1.3|4.8|2.9|
|(% per annum)|||||

Sources: World Bank - _Averting Old Age Crisis;_ Factbook on Jamsostek 1994; and data provided by Pt. Taspen.

**_Resource Mobilization_** With low compliance, high lapse rates and poor investment results, resource mobilization under the program has been limited. While similar programs in Chile, Singapore and Malaysia have accumulated assets of at least one-third of GDP, Jamsostek is not expected to mobilize more resources than 2.5 percent of GDP in the next 50 years even if its coverage rises to 80 percent (see figure below).
per function. This average is consistent across both general and sector-specific business functions. The distribution of _ANUMf,j_ reveals that 62.6% of functions use more than one technology, and 28.3% use at least three.

The vector of technologies used in a business function is relevant to two economic literatures. Schumpeterian models predict that when adopting a more sophisticated technology, an establishment will abandon the less sophisticated ones it was using. In models of technological leapfrogging, late adopters skip the less sophisticated technologies to directly use more sophisticated ones. Although FAT only provides cross-sectional data, it can be informative about the empirical support for these predictions. We explore the frequency of instances where establishments (i) completely skip or abandon less sophisticated technologies, (ii) use the least sophisticated technology despite having more advanced options, and (iii) create sophistication gaps by skipping some technologies in the business function.

Of the 37.4% of functions where only one technology is used, 52.8% use the least sophisticated technology. In the remaining 47.2%, the technology used is not the least sophisticated. Therefore, only in 18% (37.4% * 47.2%) of functions, establishments have fully skipped or abandoned all less sophisticated technologies. Conversely, in 70.4% of functions where multiple technologies are used, one of the technologies the establishments use is the least sophisticated technology in the grid. Finally, sophistication gaps are infrequent. Overall, they occur in 25% of business functions: 27% among GBFs and 17% among SSBFs. The GBFs where gaps are more frequent are payments (48%), business administration (34%), and sales (28%).

These observations show that establishments continue to use less sophisticated technologies even in business functions where more advanced technologies are available. FAT, therefore, does not support the predictions of Schumpeterian and leapfrogging models regarding the abandonment and skipping of less sophisticated technologies.

Additionally, the infrequency of sophistication gaps, combined with the regular use of the least sophisticated technology, implies that we can approximate the entire vector of technologies used in a business function just by the most sophisticated technology it uses. In other words, _MAXf,j_ approximately captures the entire adoption history of the establishment in a business function.

To further explore the role of MAX in the technology upgrading process, we estimate the following regression: where _αj_ and _αf_ are establishment and business function fixed effects. The point estimate of _β_ , presented in column 2 of Table 4, is 0.84. The close to one-to-one movement of MAX and NUM suggests that the technologies that are introduced by an establishment in 20
<!-- Start of picture text -->
-<br>Policy, Research, and External Affairs|<br>Iord Development Report A<br>WPS 507<br><!-- End of picture text -->

This paper- a product of the Socialist Economics Reform Unit, Country Economics Department, and the - first such Bank study done for Eastem Europe was prepared as a background paper for the World Development Repon on poverty. It is part of a larger PRE effort to understand the factors that contribute to poveny. Copies of t-he paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Angelica Bretana, room N6-025, extension 37176 (30 pages with tables).

The deep economic crisis in Eastern Europe between 1978 and 1987 greatly affected average incomes and increased the proportion of people living below the poverty line.

The situation deteriorated most sharply in Poland, where declining incomes caused the percentage of poor people to increase from less than 10 percent of the population (before the crisis) to more than 20 percent. In Yugoslavia the proportion of poor people increased from 17 percent to 25 percent. In Hungary poverty remained at about the same level as before the crisis (less than 15 percent).

The distribution of poverty changed in all three countries. Urban poverty became dominant, as the economic condition of state sector workers - manual and nonmanual - deteriorated much more than that of agricultural and mixed (rural-urban) households. Before the crisis, most of the poor lived in rural areas. Now most of the poor (as many as 70 percent in Poland) live in cities.

Increases in poverty are explained entirely by declining incomes; overall, income distribution did not change and in some cases even "improved." But when such redistribution did occur, it was insufficient to offset the impact of declining incomes.

The PRE Working Paper Series disseminates **dhe** Findings of work under way in thc Bank's Policy, Research. and Extemal AffairsComplex. Anobjcctiveofthcscrics is to gel these findings out quickly, even ifpresentations are less than fullypolished. The findings, interprctations, and conclusions in these papers do not nccessarily represent official Bank policy.

Produced by **thc** IRE Disscmination Centcr
public spending from administrative services to infrastructure and human development, while improving or at least maintaining current government efficiency.

- It is important that the government balance spending on infrastructure and human development as they complement each other in efforts to transform and diversify Liberia’s economy. Spending on infrastructure tends to have relative strong and immediate growth and poverty reduction effects, whereas spending more directly on human development has a stronger positive impact on non-poverty MDG indicators at the cost of lowered economic growth. Both infrastructure and human development policies can be used as tools toward accelerating poverty reduction.

- Strong growth driven by rapid mining expansion entails drawbacks and risks, including the persistence of an enclave economy that will not benefit the majority of the population and increased vulnerability to fluctuations in iron ore world prices. The development of the oil industry (not considered in these simulations) would bring similar opportunities and challenges.

The paper is organized into five sections including this introduction. Section 2 presents the basic features of MAMS.<sup>5</sup> The simulation analysis, which is the focus of the paper, is covered in the next two sections: the base scenario in Section 3 and a set of alternative scenarios, which are contrasted with the base scenario, in Section 4. The final section summarizes the main findings and conclusion. Appendices 1 and 2 include a set of figures with selected simulation results and a brief discussion of the Liberian database for MAMS, respectively.

# **2. STRUCTURE OF MAMS**

MAMS includes three core institutions: households, government, and the rest of the world. Payment flows of any given year are summarized in Figure 2.1 (see Appendix).

<u>Activities produce, selling their output at home or abroad, and use their revenues to</u> cover their costs (of intermediate inputs, factor hiring, and taxes). Their decisions to pursue particular activities with certain levels of factor use are driven by profit maximization. The shares exported and sold domestically depend on the relative prices of their output in world and domestic markets.

> 5  In order to meet the needs of readers who are not interested in the details of the workings of MAMS, Sections 3 and 4 are designed to be self-contained.  For more on MAMS, see Lofgren et al. (2012) and www.worldbank.org/mams.

2