Privatization, Inequality and Welfare: Evidence from Nicaragua1

Samuel Freije and

Luis A. Rivas

Abstract Our main objective in this paper is to study the effects of privatization on income distribution and welfare. We use household-level data from Nicaragua because privatization in this country occurred during a transition from a command to a market system, and it is likely that the massive privatization process that took place affected the economy as a whole, rather than a firm, a group of firms or a single economic activity. We give a detail account of the divestment strategy, and describe the fiscal consequences of the privatization process in Nicaragua. We then study to what extent the massive privatization of the first half of 1990s contributed to the rise in wage inequality observed in that period. We also study the welfare consequences of the changes in price and access to electricity occurred in the second half of the 1990s, when reforms to public utilities were undertaken and private sector participation was allowed. One of the main findings is that factors other than privatization itself were important contributors to the rise in wage inequality observed in the first part of the 1990s, but the evidence also suggests that state-ownership was able to contain dispersion through wage standardization, especially at the top of the distribution. Regarding the reforms to electricity and the inclusion of private firms in the generation of electricity, first and second-order welfare changes due to price increases suggest that welfare losses occurred at all deciles. When we account for access, individuals with access during the entire period experienced welfare losses, with richer households experiencing larger losses. In addition, for those individuals who obtained access during the reform period, welfare changes were positive, with relatively larger gains concentrated in the bottom of the distribution.

DRAFT: PRELIMINARY AND INCOMPLETE

1 Freije is at Universidad de Las Américas in Puebla, and Rivas is an Economic Advisor to the Ministry of Finance and to the Central Bank of Nicaragua. The authors are grateful to Huberto Ennis, Luis Felipe López-Calva, David Mackenzie, Dilip Mookeherrjee, Santiago Pinto, Miguel Urquiola, participant at the NIP/IADB at LACEA 2001, and participants at LACEA 2002 for helpful suggestions, especially to André Portela Souza who provided very insightful comments to an earlier draft. The authors also thank José J. Rojas and Horacio Martínez, both at the Central Bank of Nicaragua, for providing the data. The views hereby expressed do not represent the official position of Ministry of Finance nor the Central Bank of Nicaragua. The authors claim sole responsibility for any errors and omissions.

1 Introduction

In the last two decades, a large number of countries have embarked on large-scale

privatization programs. Most of the theoretical literature indicates that private

ownership should be preferred to public, particularly when innovation and cost

reduction are important.2 The empirical literature by and large supports the theory.

The evidence is conclusive: privatization has been beneficial in the sense that it has

lead to significant improvements in firms’ profitability and productivity, as

employment has been reduced and efficiency has risen.3 As Megginson and Netter

(2001) emphasize, however, most of the empirical literature focuses on the

efficiency implications of transferring ownership from the public to the private sector.

To a large extent, empirical studies ignore equity considerations, and most of them

compare firms' performance before and after privatization using firm or industry-level

data.4

In this paper, we investigate the possible effects of privatization on income

distribution and welfare. In countries that implemented massive privatization

programs, such as the transitional economies of Eastern Europe, it is likely that the

divestment process affected the economy as a whole, rather than a firm, a group of

firms, or even a single economic activity. The flow of workers from the public to the

private sector along might have affected economywide inequality, and to the extent

that it also affected labor market institutions, privatization could have had a

significant impact on private as well as public sector inequality through changes in

employment, productivity and wages.5 Privatization of public utilities, in turn, could

have lead to changes in consumer welfare through changes in access, prices and

2 For a review of the theoretical literature, see Shleifer (1998) and references therein. 3 For a comprehensive review of the empirical literature, see Megginson and Netter (2001). 4 Studies have also focused on the fiscal aspects of privatization, such as its effect on government revenue, and other macroeconomic aggregates; see for example, Davis et al. (2000), Gupta et al. (1999), Mackenzie (1997), and Heller and Schiller (1989). 5 Newbery (1995), for example, shows that reforms such as privatization (and other reforms) may affect inequality through price changes.

service quality.6 For this study we employ household-level data from Nicaragua. We

use Nicaraguan data because we believe that this country's privatization process

was unique.7 On the one hand, Nicaragua's privatization occurred under almost

identical conditions to those faced by the transitional economies of Eastern Europe:

Nicaragua was under a socialist regime, and the authorities extensively transferred

ownership to the public sector through nationalization and expropriations of private

enterprises. In 1989, two years prior to the beginning of the privatization effort, most

of the country's productive capacity was in public hands. Official figures indicate that,

excluding the banking sector, public utilities, and infrastructure services such as

airports and ports --which were also held by the state-approximately 30 percent of

GDP was produced by state-owned enterprises.8

On the other hand, contrary to the former socialist countries of Eastern

Europe, socialism in Nicaragua was relatively short-lived. The country was under the

regime from 1980 to 1990, and during most of the period, the country underwent a

protracted civil war. When the socialist regime was defeated in the urns in 1990, the

newly elected government initiated a large-scale economic liberalization aimed at

stabilizing the economy, which included the divestment of state-owned enterprises

(SOEs).9 Aside from the typical political constraints faced by transitional economies,

such as the pressure to move swiftly from a command to a market system, low

financial saving, and strong union power, the Nicaraguan authorities faced an

unusually large demand for firms whose main asset consisted of cultivable land,

especially from war veterans and demobilized army soldiers. In addition, there were

strong immigration pressures as the former elite and the professional middle and

upper-middle classes returned to the country from abroad.

We can divide the Nicaragua's privatization process in two major phases. The

first one, which took place mostly between 1991 and 1996, consists of the 6 See McKenzie and Mookherjee (2002) and references therein. 7 See also De Franco (1996) and Buitelaar (1996). 8 In terms of value-added, this consisted of 45% of manufacturing, 70% of mining, and 26% of agriculture, among other activities. For a full account of this, see CORNAP (1993). 9 See CORNAP (1993).

divestment of SOEs under the control of the so-called National Corporations of the

Public Sector, or CORNAP for its Spanish acronym. The CORNAP was created in

1990 to lead the divestment of about 350 SOEs that operated in various sectors

such as farming, fishery, industry, forestry, mining, commerce, trade, transport,

construction, and tourism. The SOEs under CORNAP were clustered in 22 share-

holding companies labeled “corporations”. CORNAP directly controlled these

corporations, along with other “non-incorporated” SOEs.

The second stage of the privatization effort begun in 1995, and up to the

latest year for which we have data, which is 1999, it was still an ongoing process.10

Between 1995 and 1998, a comprehensive reform package was implemented and

intended to lead to the full privatization of public utilities. In some areas, however,

private sector participation was allowed and some SOEs were given in concession

to the private sector. This is particularly evident in electricity and telecommunications

where private participation had been allowed since 1997 and 1995, respectively.

Therefore, given the data constraints, we hope to capture the welfare effects of

changes in access, prices or service quality brought about by the reforms.

An important parallel process was the deregulation of the banking system that

took place during most of the 1990s. A reform to the banking laws in 1991 allowed

the operation of private commercial banks alongside state-owned banks. The latter

were only gradually closed or privatized during a long and slow process, starting in

1994 and ending in 2000.

Before turning to the analysis, in Section 2 we describe the data used and

some important methodological issues and in Section 3 we give a detailed account

of the privatization process itself. Section 3 is self-contained and those acquainted

with the process or interested in the quantitative analysis can safely skip this section.

The rest of the paper is organized as follows. Section 4 briefly describes the fiscal

10 Electricity distribution and part of electricity generation were later privatized (in 2000 and 2002 respectively). The telephone services monopoly was privatized in 2001.

impact of privatization. In Section 5, we discuss the labor market effects of

privatization, with particular emphasis on the impact on employment and wage

inequality. In Section 6, we explore the consumption effects of reforming public

utilities. We do this by analyzing the impact of reforming the electricity monopoly on

consumers' welfare, through the effects of changes in prices and access. Section 7

is reserved for comments and conclusions.

2 The Data

The data employed was obtained from a variety of sources. For the most part, we

use individual data from three different surveys: two Living Standard Measurement

Surveys for the years 1993 and 1998 (EMNV-93 and EMNV-98 for their Spanish

acronym), and one Household Income and Expenditure Survey for the year 1999

(EIGH-99, for its Spanish acronym).

The EMNVs were conducted by the Nicaraguan Instituto Nacional de

Estadísticas y Censos, with the technical and financial sponsorship of multilateral

institutions such as the World Bank, the Inter-American Development Bank, the

United Nations Development Program, the United Nations Population Fund, as well

as of the Scandinavian agencies ASDI from Sweden, NORAD from Norway, and

DANIDA from Denmark. Both EMNVs are nationwide surveys based on the World

Bank's Living Standards Measurement Study methodology. The EMNV-93 was the

first attempt to have a comprehensive living standards survey in Nicaragua and

included information on household characteristics, such as education, health,

employment, migration, and household expenditures. The EMNV-98 added an

extended questionnaire, which included questions on anthropometric data, fertility,

time allocation, independent businesses and household saving. Both EMNVs have a

complex survey design using population weights, two-stage sampling and

stratification, and both surveys interviewed around 25,000 individuals in

approximately 4,500 households.

The EIGH-99 is a nation-wide urban survey that contains information on

dwelling characteristics, household characteristics, and households' income and

expenditures. It was conducted by Banco Central de Nicaragua. Like the EMNVs,

the EIGH-99 also has a complex survey design using population weights, two-stage

sampling and stratification, and it interviewed around 5,900 households in

approximately 4,800 dwellings.

For information on employment and earnings, we use the modules referring to

principal job of all individuals aged 15 and older. Given that at the individual level

there are differences in hours per day and days per week worked, as well as in

payment frequencies, earning is computed in hourly terms to facilitate comparisons.

For consumption expenditure, we gathered information on household weekly

consumption of food products, and monthly consumption of household expenditure

and biannual expenditure on clothing and home appliances. Again, the different

measures were homogenized into a single frequency unit. We transform all

expenditure data to monthly real data using the consumer price index reported by

Banco Central de Nicaragua.

For some of the descriptive statistics presented in Section 3, we use

establishment-level data that the Ministry of Labor, which uses to calculate national

labor market aggregates. We also use administrative data from public utilities and

regulatory agencies and from CORNAP and Banco Central de Nicaragua to

calculate the performance of CORNAP divestment, such as number of privatized

firms, beneficiaries of the privatization process, divestment methods, and the fiscal

impact of the divestment process.

3 The Privatization Process

3.1 The Divestment of CORNAP Enterprises

The divestment of CORNAP enterprises took place almost entirely between 1991

and 1996.11 At the beginning of the period, most of the enterprises under CORNAP

had large debts and a large portion of their assets were subject to restitution claims

from previous owners who had been confiscated in the 1980s. In addition, the capital

stock of most SOEs under CORNAP was highly depreciated or obsolete. They

presented negative cash flows and their revenue contribution to the government was

rather low in comparison with the large quasi-fiscal deficits that they generated. In

addition, CORNAP faced strong bargaining power from unionized workers, and also

faced strong demand pressures for enterprises whose main asset consisted of

cultivable land from workers, previous owners of farms, demobilized soldiers, retired

army members, and organized peasants without land.

The above constraints forced CORNAP to divest enterprises through various

methods. In particular, SOEs were divested through: liquidation, merge and

acquisition, restitution, and sale or lease. Liquidation consisted of dissolving the

enterprise, usually justified on grounds of financial non-viability, lack of interest in the

part of investors, or any other reason rendering the firm inoperable. Merge or

acquisition consisted of fusing a SOE to an existing firm, or incorporating it to a state

institution, such as a ministry or another government facility. The latter took place

whenever it was believed that the state should continue providing the good or

service in question. Restitution involved returning firms, assets or shares to their

previous owners or their relatives. Finally, firms could be divested through a sale of

the firm, assets, or shares, and through a lease, which gave temporary rights to the

assets of a firm and/or the management thereof. This arrangement gave the leasing

party the option to buy the firm. One method alone was used in the case of some

enterprises, but for others, various methods were used.12 Table 1 presents the

11 See also De Franco (1996). 12 See document by Banco Central de Nicaragua (1996).

information on the corporations under CORNAP, the number of enterprises that

each corporation controlled, the non-incorporated enterprises that were also part of

CORNAP and the privatization progress by 1993 and by 1998. Notice that by 1998,

nearly 96 percent of the 351 enterprises under CORNAP had been divested.13 Also

notice that by 1998, except for a few firms in manufacturing and construction,

virtually all of CORNAP had been divested.

{TABLE 1 HERE}

Table 2 provides a summary of the results of CORNAP divestment. It is clear

that for the period 1991-1996, restitutions corresponded to approximately 29 percent

of total divested assets, and acquisitions were close to 25 percent of total divested

assets. The remainder was divested through sales or leases. In terms of

beneficiaries, nearly 47 percent of total assets divested through CORNAP ended up

in the hands of entrepreneurs. On the other hand, roughly 30 percent of total assets

remained in the public sector through acquisitions by other government agencies,

while the remainder went to workers and war veterans. Table 2 shows that all

restitutions went to entrepreneurs and all acquisitions to other government

institutions. The distribution of divested assets among beneficiaries and the structure

and incentives that emerged from the divestment process suggest that privatization

might have consequently affected income distribution. For example, Buitelaar (1996)

explains that 47 percent of divestment firms in manufacturing became conventional

firms, 15 percent became co-managed firms, and 8 percent became worker-owned

firms, while the remaining were liquidated. Buitelaar also shows that 68 percent of

conventional firms were returned to the original owners who had been expropriated

during the 1980s. These were the only firms who had access to new capital

investment, and these were the only firms that increased efficiency and profitability.

Co-managed firms were also able to increase productivity but to a lesser extent,

while worker-owned firms were unable to do so. Worker-owned firms had a tendency

13 See also De Franco (1996).

to underinvest, which eventually led to failure, with most workers turning to

agricultural activities

(TABLE 2 HERE}

3.2 Reforming and Privatizing Public Utilities

The legal framework required to privatize public utilities begun to take shape with the

telecommunications law of 1995.14 The reforms culminated with two laws intended to

prepare energy and water and sewage to be fully privatized and given in concession

to the private sector, respectively.15 Between 1995 and 1999, public utilities

remained in public hands. It was not until 2000 that electricity distribution was

privatized. Telephones service was privatized in 2001 and part of energy generation

in 2002. Political constraints, as well as market limitations, hindered their

privatization earlier. Despite the failures, and perhaps as a consequence thereof,

private sector participation had been allowed in telecommunications since 1995 and

in electricity generation since 1997.

3.2.1. Telecommunications

Prior to 1995, the state-owned TELCOR was the telecommunication monopoly.16 In

1995, the National Assembly passed a law, creating a new enterprise, ENITEL, by

exchanging TELCOR assets for shares accruing to the government.17

In the case of ENITEL, the government was authorized to sell up to 40

percent of the telephone company's shares, including a management contract, to a

firm or group of firms. The law committed the government to sell and donate 10 and

14 See Ley 210: Incorporación de Particulares en la Operación y Ampliación de los Servicios Públicos de Telecomunicaciones (1995), and its later reform Ley de Reformas a la Ley 210: Ley de Incorporación de Particulares en la Operación y Ampliación de los Servicios Públicos de Telecomunicaciones (1998). 15 For energy and water, see the Ley de la Industria Eléctrica (1998), and the Ley General de Servicios de Agua Potable y Alcantarillado Sanitario (1998), respectively. 16 TELCOR: Telecomunicaciones y Correos de Nicaragua. 17 See previous footnote; ENITEL: Empresa Nicaragüense de Telefonía. In addition, the law created a new enterprise, Correos de Nicaragua, to provide postal service, thereby dividing it from telephone service.

1 percent of the shares, respectively, to employees, and to gradually sell the

remaining shares through public offerings (or bids), starting six months after the

initial sale. Finally, the law entitled the government to a single “control” share needed

for any substantial sale of the company's assets, change in the company's social

objectives, and to dissolve or merge the company.

The initial sale of 40 percent of the company's shares took place in 2001,

through the two-stage process stipulated in the 1995 law: a pre-selection of potential

buyers and a public bid among selected candidates. The failures to sell the assets

earlier were attributed to the burden imposed by pre-selection criteria and required

expansion plans to which the buyer would be duty-bound.18 But it is more likely that

the reluctance of having audited financial statements and the presence of a large

debt outstanding with providers were the true causes of such failures.

An important provision of the reform was that TELCOR will remain as the regulatory

agency. The main regulatory mechanism will be the signing of concession contracts

between TELCOR and ENITEL. Such contracts must state, among other things, how

services should be provided, service quality, coverage area, system charges, and

expansion plans.

3.2.2. Electricity

Prior to the reforms, the state-owned INE was engaged in generation, transmission,

and distribution of electricity.19 In 1997, private sector participation was allowed in

generation, and some state-owned generating plants were given to the private

sector in concession. The 1998 law vertically separated generation, transmission,

and distribution, by segmenting INE in 6 separate companies: 3 generation plants, 1

transmission company (ENTRENSA), and 2 distribution companies.20

18 The pre-selection criteria inlcuded lower bounds on the potential experience of the buyer, billing volume, minimum number of active subscribers, and minimum capital size. 19 INE: Instituto Nicaragüense de Energía. 20 ENTRENSA: Empresa Nicaragüense de Transmisión de Energía S.A.

In addition to INE's segmentation, the law created a national energy

commission to design policies and strategies regarding the energy sector, while INE

remained as the regulatory agency. INE, therefore, is in charge of carrying out three

major tasks: promoting competition by guarding against disloyal competition,

keeping firms from taking dominant market positions, and setting system charges. In

particular, the law requires system charges to be set according to efficiency

considerations -that final prices remain close to competitive prices-and financial

viability -that sufficient revenue is allowed to recover investment, operation,

maintenance costs, and service expansion related expenditure-. In an attempt to set

prices right, distribution companies must submit system charges to INE. The latter

would approve or disprove the proposal and would allow changes to approved

charges only in cases in which the general price level or energy costs change. In

addition, the law authorizes INE to grant tax exemptions to the imports of machines,

equipment, and other materials used in generation, transmission, distribution and

commercialization of electricity, as long as these exemptions do not conflict with

previously enacted tax laws.

Electricity distribution was privatized in 2001, and two of the generation plants

were sold to the private sector in 2002. The transmission company, on the other

hand, will remain state-owned.21 It is important to point out that in Nicaragua

electricity is provided under a national interconnected system and other independent

systems. Before the reforms, INE was distributing most of the electricity but not all.

The coexistence of the national interconnected system and independent networks

continued even after the reforms and could be the source of certain degree of

vertical integration in the future. For example, the law states that economic agents

engaged in generation cannot engage in transmission through the national

interconnected system, but they are allowed to engage in secondary transmission. In

addition, generation companies are allowed to sign contracts directly with

distributors and large users. In distribution, some integration is allowed, since the

21 The generation plants were sold to Costal Power. The two distribution enterprises were acquired by the Spanish company Unión Fenosa.

law allows agents to engage in generation, transmission, and commercialization

outside the national interconnected system.

3.2.3. Water and sewage

In the case of the water industry, little has been done. Although the water and

sewage law of 1998 and the subsequent reform in 2001 have paved the way for a

concession system, the industry has encountered difficulties implementing it, mostly

because the system is obsolete or destroyed.22

The 1998 law created a new enterprise for the provision of water services,

ENACAL, and the previously state-held company INAA was left as the regulatory

agency.23 A concession system has been designed, which consists of four major

areas: production of potable water, distribution of potable water, collection of

serviced water, and disposal of serviced water. Candidates for concession will be

proposed by INAA. In the case of private firms, the National Assembly must ratify

any concession. Concessions to public firms can be granted directly. In all cases,

concessions will last 25 years.

The regulating body, INAA, will undertake public bids for concessions,

determine the limits of the geographic areas to be given in concession, and set

system charges. However, limits to the area of expansion after a concession are

fixed at the outset.24 Under this mode, the concessions scheme will operate under a

system charges set with the provisions that economic efficiency is attained,

operative efficiency prevails, users assume the corresponding total cost, and that

financial efficiency is attained.25 The law does not eliminate cross-subsidies at once.

Temporary cross-subsidization may be allowed for system and basic service users.

22 The water and sewage system were seriously damaged by hurricane Mitch in 1998. We could not find, however, estimates to assess the extent of the damage. 23 ENACAL: Empresa Nicaragüense de Acueductos y Alcantarillados; INAA: Instituto Nicaragüense de Acueductos y Alcantarillados. 24 The limit established by law is no more than 30 percent expansion of the initial concession. 25 In the law, economic efficiency means equality in prices of additional units of the service, and financial efficiency means that enough financial resources should be generated to cover operations, management, maintenance, and expansions.

The 2001 reform makes clear the authorities' objective, which is to segment

ENACAL into independent regional enterprises. With this division, investment in

obsolete or destroyed systems can be prioritized in order to minimize the time

needed to have the enterprises ready for concession.

4. The Fiscal Effects of Privatization

Privatization of SOEs can impact a country's fiscal stance through various

channels.26 Davis et al. (2000) argue that the immediate and direct effect of

privatization on the fiscal balance depends on the share of the privatization proceeds

that accrue to the budget. In addition, how are the proceeds used and how the

process itself affects macroeconomic aggregates may in turn have further fiscal

consequences. The relative importance of these factors depends on the actions

taken by the authorities prior to privatization, the sales process, and the post-

privatization regime (Mackenzie, 1997). In this section, we give an account of the

fiscal consequences of the privatization process in Nicaragua, despite major data

limitations. We concentrate on the contemporaneous impact of privatization as well

as its effect over time by studying whether privatization proceeds were accounted for

in the national budget, how these proceeds were used, and how fiscal and quasi-

fiscal variables behaved during the active privatization period.

{TABLE 3 HERE}

Table 3 presents data on gross privatization proceeds and the fraction that

accrued to the fiscal accounts. The table shows that the privatization proceeds from

CORNAP divestment were on average 2.5 percent of GDP per year during the years

of active privatization. Strikingly, the proceeds did not accrue to the budget, nor

there was any legislature enacted for proceeds oversight. Presumably, the

authorities thought that by keeping the proceed off the budget they reduced the

26 For fiscal and macroeconomic issues in privatization of SOEs, see Davis et al. (2000), Gupta et al. (1999), Mackenzie (1997), and Heller and Schiller (1989).

possibility of misuse, notwithstanding the reduction in transparency of the

subsequent use of these funds.

Clearly, to the extent that cash proceeds amounted to a yearly average of

only 0.6 percent of GDP, gross receipts underestimate the extent of asset

divestment under CORNAP. As a matter of fact, most CORNAP privatization took

place through credit and liability transfers. Table 4 shows that the majority of

transactions consisted of sales, while the remainder constituted capital increments

from restituted assets. It should be also noted that, by definition, no transactions

were recorded in the case of acquisitions and liquidations.

{TABLE 4 HERE}

In the case of electricity distribution, privatization receipts were relatively

large, close to 5 percent of GDP, of which a large share, 80 percent, accrued to the

budget (see Table 3).27 Since Nicaragua was under an International Monetary Fund

(IMF) program at the time of privatization, the authorities were advised to register the

proceeds “below” the line, which essentially means that the receipts obtained from

the privatization of public utilities were not deficit determining. The argument for

doing this has been raised elsewhere. Mackenzie (1998), for example, argues that

spending privatization proceeds may have important inter-temporal effects, since

privatization is an exchange of assets and they should be differentiated from other

government revenue in the design of fiscal policy. Other argument is that

privatization proceeds should not be used to support the fiscal position permanently,

since they may give a misleading view of the deficit.28

It is clear then, that proceeds from the entire privatization process in

Nicaragua did not impact the balance of the government operations and did not

27 We do not consider effective telephone service privatization because of the possibility that the process may be reversed due to allegations of corruption. If it were not, gross receipts of public utilities privatization will increase to approximately 7 percent of GDP. 28 For this and other arguments, see Davis et al. (2000) and the references therein.

affect, therefore, government's net worth. In the case of the divestment of CORNAP

enterprises, contrary to the privatization of electricity distribution, the proceeds did

not even affect the government liquidity position.

The lack of transparency and oversight that characterized the privatization

process of CORNAP enterprises makes it hard to pin down the use of such

proceeds, but a large portion was presumably used to support the bureaucracy in

charge of CORNAP. As for the privatization of electricity distribution and generation,

the proceeds were used as a financial cushion, though a large fraction was used as

a mean of financing short-term gaps.

It is difficult to assess the fiscal and macroeconomic impact of privatization,

other than how proceeds were accounted for and how they were used, since

privatization in Nicaragua took place as part of a large set of reforms.29 In any event,

we present some evidence on the evolution of tax revenue and gross government

transfers to SOEs. We also provide selected information from the quasi-fiscal

balances of public utilities, such as overall balances before net transfers to the

government, net transfers and external and domestic financing of public utilities.

However, for the reasons stated above, the evidence presented should be treated

with care.

Regarding tax revenue, there is evidence that, at least in the case of large

divested firms, tax revenues increased in the post-privatization period. For instance,

three large companies that together contributed 1.1 percent of GDP in revenue in

the 1990s, increased their contribution to an average of 2 percent of GDP in the four

years following privatization.30 The same report shows that in the two fiscal years

after CORNAP privatization started, 20 percent of total revenue contribution by large

firms came from newly privatized firms. This is consistent with the evidence found by

Galal et al. (1994), Shaikh et al. (1996), and La Porta and López-de-Silanes (1999), 29 See Freije and Rivas (2002). 30 These companies were two bottling enterprises (ENSA and MILKA) and the beer producer Compañía Cervezera Nicaragüense. For details, see Banco Central de Nicaragua (1996).

who find that privatized firms become significant tax contributors after privatization.

In addition, using panel data analysis Davis et al. (2000) show that there is some

evidence that privatization leads to a positive and ongoing increase in tax revenue

as a share of GDP in non-transition economies.

In addition, Banco Central de Nicaragua (1996) reports that during the 1980s,

direct and indirect subsidies to CORNAP enterprises amounted to an average of

11.2 percent of GDP. A fraction of this amount corresponded to an implicit subsidy,

since CORNAP enterprises enjoyed non-indexed credit from the then state-owned

banks (an estimated 7.5 percent of GDP per year during the period). The other

fraction was calculated by annualizing the outstanding unpaid debt acquired by

CORNAP enterprises during the 1980s from state-owned banks and the central

bank, which was absorbed by the government in 1990 previous to divestment. This

corresponds to an annualized 3.7 percent of GDP. The elimination of these

subsidies suggests an important lessening of the fiscal burden from these

enterprises.

With regard to public utilities, it is hard to assess what will be their full fiscal

impact, merely because their privatization process has not yet come to an end. The

best we can hope for is to see the fiscal impact of reforms and of the partial

privatization of some services. Figure 1 shows total government transfers and

government transfers to public utilities as a share of GDP. Although, total transfers

show an upward trend, transfers to public utilities were stable for most of the 1990s.

{FIGURE 1 HERE}

At the firm level, ENEL, which still controls part of electricity generation and

transmission, and ENITEL, the telecommunications company, have improved their

fiscal balance positions since reforms started in 1995. Table 5 shows that, in the

case of ENEL, net transfers to the government, which were on average negative

during the period 1991-1995, have on average broke-even during the period 1995-

2000. The table also shows that ENITEL has considerable reduced average external

and internal financing. These figures reflect the on-going effort to increase these

firms efficiency to prepare them for privatization. Notice that in the case of ENACAL,

which will not be privatized, its fiscal balance worsen on average and average net

transfers from the government increased after 1995.

{TABLE 5 HERE}

5. Privatization, Employment, and Wage Inequality

There is reason to believe that ownership patterns, between public and private,

played an important role in terms of employment patterns and other labor market

aggregates, but this alone would not constitute conclusive evidence of a clear-cut

relationship between privatization and inequality. Table 6 shows that inequality in

Nicaragua increased in certain economic activities with a large share of privatized

enterprises, such as agriculture, but it also increased in other activities with little or

no privatization, such as social services. The table also shows that inequality

decreased in certain activities with a high proportion of privatized assets, such as

mining, manufacturing, and financial services.

{TABLE 6 HERE}

What is clear, as it will be shown below, is that overall wage inequality in

Nicaragua unambiguously increased between 1993 and 1998. In this section we

attempt to answer: to what extent privatization contributed to the rise in wage

inequality? In the case of Nicaragua, this is not an easy task, because privatization

was not the only reform implemented during the period.31 We start with the

hypothesis that if CORNAP privatization had a direct impact on inequality, then

changes in the fraction of workers employed in the public sector should account for

most of the changes in overall wage inequality. But the CORNAP divestment

31 See Freije and Rivas (2002) for an account of the reforms undertaken in the 1990s and how they affected labor market performance.

process may have also affected inequality indirectly, to the extent that it affected

collective bargaining practices --coverage, scope, etc.--, and government policy

toward the labor market. If such is the case, we are required to distinguish between

sources of inequality arising from changes in workers characteristics --such as

education, type of occupation, gender, etc.--, and the observed rewards to such

characteristics, from unobservable changes that might be attributed to the

privatization process itself.

{TABLE 7 HERE}

Before presenting the analysis, Table 7 presents summary statistics regarding

the distribution of employed workers in Nicaragua from the surveys described in

Section 2. During the period analyzed, the fraction of female workers declined in the

rural areas but increased in urban region. Illiteracy among employed individuals,

which declined in both the rural and the urban regions, remained high in comparison

to Latin American and world standards.32 In both rural and urban areas, the fractions

of workers that report incomplete primary, secondary or tertiary education increased,

while the fractions of workers that have completed any of them decreased.

Regarding occupations, the fraction of clerical and sales workers in the urban sector

increased while laborers and service workers declined. The opposite patterns

occurred in the rural sector. Agriculture continued to be the most important

employment generation activity in the rural sector. In virtually all the other activities

the fraction of employed workers declined, the exception being construction.

Construction was also important in the urban areas.

Table 7 also indicates that informal activities underwent a relative expansion.

This can be verified by observing that the percentage of employed workers

contributing to social security experienced an overall decline, which is consistent

with the evidence found by Freije and Rivas (2002), who show that the activities that 32 According the the World Bank Development Report, overall illiteracy is 34 percent of the adult population in Nicaragua, compared to 11 and 18 percent in Latin America and the World, respectively.

contributed significantly to employment generation in Nicaragua also experienced a

proliferation of informal jobs and an increase in the number of workers who held a

secondary job, the latter associated with the drop in real wages and productivity that

occurred during the period. Finally, and particularly important for the analysis below,

is that in both the rural and the urban sectors, the fraction of workers in the public

sector decreased dramatically between 1993 and 1999. In the urban region, the

fraction of workers in the public sector dropped by more than 40 percent, while in the

rural region, it fell by more than 80 percent.

5.1 First-Order Assessment: A Variance Decomposition

Determining the sources of the change in inequality can provide an assessment of

the distributional impact of privatization. We now present an overall decomposition of

Nicaragua's overall wage dispersion. We believe that this decomposition gives a

first-order approximation of how privatization affected private as well as public sector

inequality. We use this decomposition to study the role of privatization in producing

differences in inequality among rural and urban workers between 1993 and 1998

and between 1993 and 1999, respectively. To explain how the decomposition was

carried out, let yjit be real labor wage (in logarithms) of individual i at year t. The

superscript j denotes whether the individual works in the private sector (j=p), or in

the public sector (j=s). Throughout this section, we refer to hourly labor wages,

which we construct by adding cash salary and in-kind compensation from all jobs per

month divided by hours worked per month. The overall dispersion can be written as:

22 ))(1()()1()( t

pt

stt

st

st

pt

st

st

stt yyyyVarVaryVar −−+−+−+= αααα

where is the overall or economy-wide variance of wages at year t; is the

fraction of individuals working in the public sector; Var and

)( tyVar stα

jt

jty are the variance and

mean of wages in sectors j=p,s, respectively; and ty is the overall or economy-wide

average of wage.33

Then for any two years, say t and u, it can be proved that the change in

overall wage dispersion is:

[ ] [ ][ ] [ ]2222

22

)()()1()()(

)1(

])()()[()()(

uput

pt

suu

sut

st

su

su

st

su

pu

pt

su

tptt

st

pt

st

su

stut

yyyyyyyy

VarVarVarVar

yyyyVarVaryVaryVar

−−−−+−−−+

−+−−+

−−−+−−=−

αα

αα

αα

(1)

That is, the overall change in earnings dispersion can be divided into five

sources:

• How the change in the fraction of people working in the public sector contributes

to the change in overall inequality (the first term in the right hand side)

• How the change in the relative wage dispersion in the public and private sectors

contributes to the change in overall inequality (the second and third term in the

right hand side, respectively)

• How the mean earnings gap in each sector affects the change in total inequality

(fourth and fifth term in the right hand side).

It should be straightforward to show that the decomposition in (1) is not

unique, and that alternative decomposition can be calculated in which the weights in

(1) may differ. But it is clear from (1), that the dispersion of wages may have been

affected through various channels. Privatization could have caused a reduction in

the share of individuals working in the public sector. The effect of this on inequality

would depend on the place in which these workers would have been in the absence

of SOEs. But the relative earning gaps in both, the private and the public sector, as

33 Freeman (1980) and Blau and Kahn (1996) use a similar decomposition to asses the role of unionism on US wages and for international comparisons, respectively. Juhn et al. (1993) employ a similar approach to assess the impact of industry on wage inequality.

well as the initial inequality within each sector, also had an important effect on total

inequality.

In computing the decomposition, we were forced to make an identifying

assumption. 34 The EMNV-93 contains questions, which allows us to identify

individuals by geographical location (urban or rural) and also by sector (public or

private). However, although the EMNV-98 still allows one to identify individuals by

location, it does not have a question that allows one to identify individual by the

sector in which they work. Conversely, in the EIGH-99 one can sort out individuals

by sector, but since it is a urban survey, no rural-urban sorting is possible. In view of

the above, we opted for the following strategy. We first estimated (1) for individuals

in the urban areas, using the EMNV-93 and the EIGH-99 surveys. We then

estimated the decomposition using the EMNV-93 and the EMNV-98 surveys for

individuals who worked in the rural areas, assuming that public sector employment

in the rural areas was negligible. We feel that this assumption is not very restrictive

in light of the evidence from establishment level data. The Survey for

Establishments, used by the Ministry of Labor to construct the official labor market

statistics, indicates that the shares of SOEs in agriculture, fishing and mining, which

were 32.8, 100, and 100 percent respectively in 1993, all dropped to 0 percent by

1998. This is shown in Table 8, which also shows that the share of SOEs, including

all economic activities, dropped from 60 to 25.4 percent.

{TABLE 8 HERE}

In Table 9, we present means and variances of log real wages. The table

shows that, in the rural areas, the mean log real wage declined in both the public

and the private sectors. However, inequality, as measured by the variance,

increased only slightly in the rural areas. On the other hand, the increase in the

average log real wage in the urban areas, in both the public and private sectors, was

34 In the relevant tables, a different decomposition is included, in which different weights for each of the same components were derived.

accompanied by a considerable increase in dispersion in the public sector only. In

both the rural and urban sectors, the unconditional average log real wage was

higher for public sector workers in 1993, and the difference continued in 1998.35

{TABLE 9 and 10 HERE}

The decomposition of change in total variance shows that, for both

decomposition methods, change in public sector variance and mean gap account for

a large share of the total change. On the other hand, the change in private sector

variance would have caused a decline, instead of a rise, in total variance. The size

of the impact due to change of employment sector share is not robust to

decomposition method. In some cases it has little negative and in some it has a

large positive impact.

Table 10 shows the same decomposition exercise using real hourly wages

(instead of its logarithm). Again, we observe that changes in variance are the main

positive factor explaining the change in inequality, whereas changes in public sector

employment have no consistent effect across sectors and decomposition methods.

The patterns presented in Tables 9 and 10 imply that factors other than the

reduction in αs are responsible for the increase in wage dispersion. Ownership

patterns, however, might have affected overall variance indirectly by affecting within

sectors dispersion. One could argue that state-ownership was containing wage

dispersion in the public sector through wage standardization, because of the

existence of uniform rates among comparable workers across SOEs and ranges of

rates for the various occupational categories within SOEs. After all, unions were

much more prevalent before privatization and their bargaining power declined

considerably during and after privatization. Collective bargaining became much more

decentralized with privatization, with single-firm agreements prevailing over

35 As it will be shown below, when one controls for workers characteristics, however, private sector workers earn higher wages than their public sector counterparts.

industrywide or economywide agreements.36 In addition, government policies, aimed

at equalizing pay among similarly skilled workers within establishments, could have

also contained wage dispersion in the public sector.37 The importance of within

private sector dispersion at the national level could be explained in part by the

extension of collective bargaining agreements for public sector workers to the private

sector.38

The above notwithstanding, we need to look further into within wage

dispersion in the public and private sectors, because the principal problem in

comparing dispersion of wages between public and private sector workers is to

differentiate between the effect of other factors correlated with privatization. We

need to distinguish, therefore, between the portion of the change in inequality that

can be attributed to changes in workers characteristics, in their distribution among

industries or occupations; what fraction is attributable to the market values of such

personal characteristics; and what fraction can be attributable to privatization (and

other reforms for that matter).

5.2 Changing Attributes and Market Values

In this sub-section, we employ a full distributional accounting framework and

decompose the change in overall wage inequality in three components: changes in

the distribution of observed characteristics (i.e., education, occupation, gender, etc.);

changes in the market value of such characteristics, and changes in unobservable

attributes, unobservable market values, along with any remaining measurement

error. In doing this, we follow Juhn et al. (1993), and we call these effects:

observable characteristics effect, observable market value effect, and residual effect.

In order to obtain this decomposition, for each year we first estimate the following

36 For more on collective bargaining and inequality, see Freeman (1980) and Blau and Kahn (1996). 37 These policies were undertaken on the basis of perceived inequality previous to “statization”, when different wages were paid to individuals based on the workers' characteristics as perceived by management, rather than based on the actual position's characteristics. 38 This is common in socialist countries, where most workers belong to unions and wages are controlled by the state. See also Rezler (1973).

earning equations for each period and for both urban and rural sector separately.

That is, we estimate:

snt

st

snt

st

snt

snt ZXy εδβ ++=

where ysit is the log of real hourly wage of individual i at year t, Xit is a vector of

individual characteristics, and εit is the unobservable component of wages.39 The

superscript s denotes whether the individual works in the formal sector (s=f), or in

the informal sector (s=i).40 With this at hand, we estimate (2) for the two years we

are comparing, say t=1,2. From these estimations, we obtain the OLS parameters

and the corresponding residual distributions. Then, following Juhn et al. (1993) we

produce three hypothetical earnings vectors. This technique allows us to decompose

the overall change in inequality in four additive components: changes in observable

quantities, changes in observable prices, changes in observable prices related to

public sector employment and changes in the distribution of unobservable.

What we basically do is to identify these components by recovering the

hypothetical wage distributions with any subset of components held fixed.

Consequently, one hypothetical earnings vector arises from fixing the market value

of observables and fixing the residual distribution from t=1 (with each individual

placed in the corresponding place of the t=2 residual distribution). Likewise, another

hypothetical distribution arises from allowing both observable market values and

observable attributes to vary over time. Hence, for any inequality index I(.), we can

report four sources of inequality. That is, for any inequality index I(.), we decompose

inequality as follows:

( ) [ ] [ ] [ ] [ ])3()()2()3()1()2()()1()( yIyIyIyIyIyIyIyIyIyI fiif −+−+−+−=− (2)

39 To be precise the observable characteristics are gender, age, marital status, years of schooling, occupation, economic activity, firm-size, geographic location (urban-rural), sector of activity (public or private), and whether or not the individual holds a secondary job. 40 The distribution of employment into formal and informal sector depends on whether the worker pays contributions to the Social Security or not.

where the four terms on the right hand side capture the four sources of changes in

inequality mentioned above.41

{TABLE 11 HERE}

Table 11 describes the data used in the regressions. More importantly, tables

12 and 13 present the wage regressions for the rural and urban areas, separated

into formal and informal sectors respectively. Each table presents two regressions,

one for the base year 1993 and one for the comparison year. Age, formal schooling,

economic activity and job position are significant across all years, sectors and areas.

For instance, in the rural sector, school premia relative to no formal education

workers were significant in the informal sector for almost all education categories in

1993 and in 1998, but the returns to schooling declined during that period across all

education categories. In the formal sector, however, returns to education were

significant only for tertiary education in 1993 and not even for this category in 1998.

In the urban sector, we find that formal education is significant only for those

with more than incomplete secondary in 1993 and for all categories in 1999.

However, the size of the premium for additional education declines over the period.

On the other hand, gender, marital status, occupation and second job are

significant only in the urban areas. For example, the gender gap in the rural sector,

which was negligible and not statistically significant in 1993, became substantial and

statistically significant by 1998 (around 9%). In the urban sector, the gender gap,

which was already large and significant in 1993 (20.0%), declined by 1999, although

it remained quite high (13.1%).42

{TABLE 12 and 13 HERE} 41 See appendix 2 for a detailed explanation of the simulation procedure and the inequality change decomposition. 42 Although we do not focus on gender inequality, this evidence supports the finding that gender inequality is pervasive in countries that have undertaken similar reforms. See, for example, Brainerd (1998).

Occupational categories did not significantly contribute to individual earning in

the rural sector in 1993 or 1998, but in the urban sector, managers and

professionals earned significantly higher wages than workers in any other

occupation (clerks, salesmen, craftsmen, operatives, service workers, etc.) In

addition, the wage premia for managers and professionals increased between 1993

and 1999. Regarding public versus private sector, managers and professionals

earned significantly less if they worked in the public sector rather than in the private

sector, with the premium declining overtime. Moreover, other occupation categories

had a narrower differential with managers and professionals if they worked in the

public sector rather than in the private sector. This differential increased over the

period.

Figures 2 and 3 illustrate the argument made in the foregoing paragraph. They show

the average hourly wages for different occupations in the public and private sector

for urban and rural areas. It can be seen that the difference in hourly wages between

levels one and three in the public sector remained around one cordoba in the rural

area but increased to more than 20 cordobas in the urban area. A similar pattern is

found in the private sector. This is consistent with the story of increasing inequality in

the urban public and private sectors and declining or stagnant inequality in the public

rural sector.

{FIGURE 2 and 3 HERE}

Clearly, the demand for skilled labor increased in the urban formal sector,

particularly for individuals who possessed the skills required to operate in the “new”

market-oriented economy that emerged from privatization and deregulation of the

Nicaraguan economy, especially in activities such as finance, marketing,

management, and the like. The results presented above are also consistent with our

earlier remarks about the expansion of informal activities in the urban sector, where

the premia was significant and increasing for workers who contribute to social

security (i.e., formal workers). What is puzzling is that the education premia in the

rural sector declined for all education categories. One possible explanation is that

subsistence-level agriculture expanded as cultivable land was given to demobilized

soldiers, retired army members, and organized peasants without land. These

groups, as workers who had benefited from privatization in manufacturing by forming

workers-owned firms, opted for agricultural activities. At the same time, demand for

laborers without any formal education may have increased in labor intensive

activities, such as coffee gathering and the like.43 Another possibility is that some

migration from the urban to the rural sector occurred during the period (reversed

migration), as skilled labor became relatively more abundant in the urban sector with

the immigration of the former elite, and the middle and upper-middle classes.44

Finally, it is important to notice that the premium earned by workers employed

in public utilities in urban areas, which was high and significant in both rural and

urban areas (48.8% and 73.3% respectively), decreased and became statistically

insignificant during the period; a pattern consistent with the streamlining of public

utilities explained in Section 3.

The decomposition of inequality given by (2) is presented in Tables 14 and 15

for the urban and rural sectors respectively. The first thing to notice, which has

already been mentioned, is that inequality has almost unambiguously increased in

Nicaragua. Except for the variance of real hourly wages in rural Nicaragua which

decreased, apparently due to a decline of the fifth to first decile ratio. Inequality

increased in all other percentile differentials in both the rural and urban sector. The

increase in inequality is also robust to the index choice. The Gini coefficient and

various Generalized Enthropy and Atkinson indexes presented also indicate that

inequality increased across the board.

{TABLE 14 and 15 HERE} 43 See also Freije and Rivas (2002). 44 There was also migration of unskilled labor from the rural sector to neighboring countries during the period.

We find greater widening at the top of the distribution in both the rural and

urban sectors. Although the time span is relatively short, we observe that most of the

increase in inequality in the rural sector can be explained by changes in observed

characteristics whereas in the urban sector it is explained by changes in the prices

of such characteristics. We interpret that this is the consequence of changes in the

composition or rural labor. In fact, as it can be seen in table 11, the share of

agricultural activities rose dramatically in rural Nicaragua, while agricultural wages

stagnated over the period.45 Table 12 provides more evidence of declining human

capital premia, explaining the smaller relevance of prices upon inequality changes,

as well as increasing premia for activities other than agriculture over the period.

What is the source of this phenomenon? We hypothesize that demographic

pressures due to the return of refugees, demobilization of war veterans, and internal

population growth led to a flood of labor supply that the rural sector was not in

capacity to absorb.46

On the other hand, changes in the prices of observable characteristics are the

most important element in explaining inequality changes in the urban sector. Again,

the evidence of table 13 shows why. The human capital and job characteristic

coefficients changed significance and values over the period. It is of special

relevance that the coefficients for working in public utilities were significant in 1993,

but not anymore in 1999. The resurgence of private banking and

telecommunications in urban Nicaragua during the last decade may also add a grain

of understanding to the widening in the top of the urban distribution.

The coefficients for occupation and public/private sector employment also

changed significance and magnitude. These coefficients represent no less than one

third of the effect caused by all coefficients. If we observe carefully, the price

45 See Freije y Rivas (2002). 46 A datum that hints in that direction is that around 55% of the total new employment generated in the rural sector is composed of people aged 15 to 19. In contrast, only 26% of new urban employment was in that age over the same period.

differentials directly related to having a job in the public sector have a large

explanatory power. This is consistent with our previous argument that privatization

brings about a new set of industrial relations in both the public and private sectors,

where wages will be more in line to productivity and competitive forces rather than

political conventions, governmental coordination or union pressures.

Despite all of the above it should be noticed that unobservable characteristics

and unobservable prices (as well as measurement error) contributed to the increase

in inequality to a much lesser degree. Actually, most values in the last column of

Table 13 and 14 are negative, that is they contributed to diminish inequality. This

indicates that privatization was not the only factor explaining inequality. It seems that

it was, perhaps, the largest when compared to others that we can single out in the

urban sector and not even so in the rural sector. In the rural sector, however, it could

be argued that the demobilization of war veterans together with the small

redistribution of land held by the CORNAP are aspects of the Nicaraguan

privatization/transition process that indirectly influenced the widening of inequality in

the rural sector.

6. Welfare Effects of Utilities: Prices, Access, and Quality

As already explained, utilities remained in public hands between 1993 and 1998, but

during the same period the Nicaraguan authorities implemented a reform package to

prepare them for their eventual privatization. In addition, during the same period,

private sector participation was allowed in electricity and telecommunications. Table

5 provided some evidence that the reforms to utilities had a positive fiscal effect (see

Section 4), but in order to assess the welfare impact of the reforms and of the

participation of private firms in the provision of electricity and telecommunications,

we need to examine the impact of the reforms on the price, access, and quality of

the such services. The main objective in this subsection is to observe expenditure

decisions to infer underlying changes in indirect utility functions, which will then be

used to estimate the welfare impact. Because of data limitations, we focus our

analysis on electricity.47

6.1 The Distributional Effect of Price Changes

We first examine the effect of electricity prices on the distribution of real incomes

across different households. To do this, we use simple non-parametric techniques to

uncover the shape of the Engel curves and to estimate kernel densities.48 These

methods provide relatively simple graphical descriptions of the average welfare

effects of price changes that operate through consumption. In the subsequent

subsections, we turn to alternative analysis of welfare, by estimating consumer

surpluses and by accounting for changes in the access to electricity.

The price of electricity increased 24.2 percent in between 1993 and 1998.49

Most of the increase was concentrated in the period 1995-1998, which correspond to

the years of active reform. But how can we account for the distributional

consequences of such price increase in a simple manner? Since (almost) all

households interviewed in the surveys don't produce electricity services, one way to

do this is by describing consumption patterns for electricity in relation to consumers'

living standards. Such description provides an estimate of the first-order welfare

effect of the price increase (Deaton, 1989). We do this by taking expenditure shares

of electrical services at different points along the total expenditure per capita

distribution. The logic behind this procedure is simple: a price increase has the

greatest impact on consumers who devote a larger share of their budget to

electricity. Clearly, we are implicitly taking total expenditure per capita as our

measure of household living standards. To compute expenditure per capita, we take

total expenditure on all items consumed by the household (reported in the surveys). 47 Although private participation in telecommunications was allowed earlier than in electricity, private provision consisted exclusively of cellular services, and the household surveys lack information related to household expenditure in such services. 48 Deaton (1989) provides both a theoretical motivation for the use of non-parametric techniques for assessing the welfare effect of small price changes, as well as the actual non-parametric analysis that we adapt for our own. See also McKenzie and Mookherjee (2002). 49 In constant Nicaraguan cordobas, electricity increased from 0.95 per KwH in 1993 to 1.18 per KwH in 1998. These prices are reported according to the consumer price index elaborated by the Banco Central de Nicaragua.

We then divide by the number of persons in the household to obtain per capita

monthly expenditure.50

{FIGURE 4 HERE}

Figure 2 shows estimates of the distribution of living standards across

households in 1993 and 1998; that is, it shows the estimated density functions of the

logarithm of per capita expenditure for the two years. As explained, the densities are

estimated by kernel smoothing. Intuitively, these densities can be thought of

smoothed-out histograms, in which the height of the curve at any point is determined

by the number of observations that are close to the point.51 Notice that the relative

position of the density shifted to the left overtime. In other words, the modal 1998

household is worse off by 1993 standards.

{FIGURE 5 HERE}

In Figure 5, we present non-parametric regressions of the electricity

expenditure share on the logarithm of total expenditure per capita for the years 1993

and 1998. The estimation is also performed with kernel smoothing. At any given

value of total expenditure per capita, the graph shows the values of the electricity

expenditure share for observations nearby (within the kernel). The weights are the

same as the ones used to estimate the densities depicted in Figure 4, but are scaled

by the estimate of the densities at the point. Hence, as the sample increases in size,

the estimate converges to the conditional expectation of the electricity share given

the value of total expenditure. There is a number of interesting aspects of the graph

that are worth pondering. First, notice that the Engel curves are non-monotonic. In

1993, the share of the budget spent on electricity declines as living standard rises

only for the very poor and the very rich, but it actually increases for the middle range

50 Since expenditure in certain goods can vary from the weekly to the annual frequency in the surveys, we homogenize all expenditure to the monthly frequency. 51 For a detailed explanation of the technique, we refer the readers to Deaton (1989) and the references therein.

of the distribution, where most households are concentrated (greater mass). In

addition, among poor households in 1993, those at the bottom of the expenditure

distribution (say below expenditure of 2 in logarithmic scale), more than 10 percent

of their budget was allocated to electricity; while in 1998 households with

expenditure below 2 in logarithmic scale did not spend anything on electricity,

suggesting that very poor households may have been priced out. But it is clear from

the graph that in 1998 consumers spent less on electricity than in 1993, even when

controlling for the size of the budget. This is consistent with Figure 2; households

became poorer on average and they also allocated a lesser share of their budget to

electricity.

{FIGURE 6 and 7 HERE}

Figures 6 and 7 plot the same regression as Figure 5, but excluding the

extreme 5 percent of the observations (2.5 at each extreme of the distribution) and,

in addition, it shows the density function. The results are similar. Clearly, in both

1993 and 1998, for a large range of the expenditure distribution, the share of the

budget spent on electricity increased. In addition, at all levels of expenditure,

households spent less on electricity in 1998 than in 1993 (see figure 8).

{FIGURE 8 HERE}

Figures 9 and 10 show the same regressions of the electricity expenditure

share, separating rural from urban areas. These figures show that the increasing

section of the curve is mainly due to the behavior of the rural sector. Electricity bill

shares are strongly increasing for rural households with expenditures below 7 (in

logarithms) whereas shares are less sloped for the urban sector.

{FIGURE 9 and 10 HERE}

For the sake of completeness, in Figure 11 we plot the non-parametric

regression for kerosene, a close substitute of electricity (in what lighting is

concerned), for which we have expenditure data. The price of kerosene remained

practically constant between 1993 and 1998. In Figure 5 we present the Engel curve

using the whole sample, and in Figure 6 we exclude the extreme 5 percent of the

observations. Notice that in both years Engel's Law holds; the curves slope

downward as expenditure increases and converge to zero; sufficiently household

allocate a zero share of their budget to kerosene. In addition, for households who

spent a positive amount on kerosene, the share of the budget allocated to kerosene

was smaller in 1998 than in 1993 among the poorer households. Hence, 1998

households were not only poorer on average (the empirical kernel density shifted to

the left from 1993 to 1998), but they also allocated less of their budget to kerosene.

{FIGURE 11 HERE}

It is clear that the non-parametric approach that we just described have its

limitations, but at least it gave us considerable information of the evolution of access

to electricity between 1993 and 1998, at least to a first-order degree. The approach

also helped us bound the distributional effect of the price increase.

6.1 First and Second-Order Approximations to Consumer Surplus

A possible drawback of the approach outlined by Deaton (1989) is that his

methodology is best suited for small price changes, and does not account for

quantity adjustment. In fact, Banks et al. (1996) provide evidence that first-order

approximations like the one described above may display systematic bias. They

actually show that second-order approximations work relatively better. The catch,

nevertheless, is that second-order approximations require much more information

than do first-order ones. Standard micro theory shows that first-order approximations

to welfare measures do not require knowledge of substitution effects as second-

order approximations do. In fact, second-order approximations depend on the

distribution of substitution elasticities, and require, therefore, estimates of the

derivatives of the demand functions (see McKenzie and Mookherjee, 2002).52

We will assume at the outset that households receive equal social weights,

and for the time being we ignore issues related to access. Under this assumption,

the first-order approximation of the change in utility for an individual, ∆U=(U1–U0), is

given by:53

)( 0 pqU ∆−=∆ (3)

where q0 is the initial quantity of electricity consumed and ∆p=(p1-p0) is the change in

the price in electricity.

Clearly, this overestimates the welfare effect of the price increase, since it

doesn't allow consumers to change the quantity consumed in response to the price

change. Banks et al. (1996) show that it is better to use the second-order approach:

∆

∂∂∆

+−=∆ pqq

ppqU

loglog

21

00 (4)

where the partial derivative is evaluated at the 1993 prices. Equation (4) shows that

the second order correction depends on the own price elasticity. Since the own price

elasticity is in general negative, the second-order approximation allows for some

quantity response to the price change.

Since we want to estimate the first and the second-order approximations

empirically, it is convenient to work with log prices and budget shares, w0 , instead of

level prices and quantities. In this case (3) becomes: 52 A more standard approach to examine the distributional impact of price changes is to construct cost-of-living indexes and observe whether the living costs of poor households is disproportionately affected. This is the approach followed by Levinsohn et al. (1999). 53 Banks et al. (1996) show that when there are no income effects, the change in utility is equal to a money metric measure of the change in welfare.

0)log( wpU ∆−=∆ (5)

and (4) becomes:

∂∂∆

+∆−=∆qwpwpU

loglog

2log

1)log( 0 (6)

Notice that we have assumed that households are not producers and only

consume electricity, which we think is not a very restrictive assumption. What this

assumption buys is that total income does not change with variations in the price of

electricity. Once the first and second-order approximations have been cast in terms

of log prices and expenditures shares, it is relatively straightforward to see that the

electricity share regressions of the previous subsection provide an estimation of the

distributional impact of the price change that is equivalent to the first-order

approximation in (5).

In order to compute (6), however, we had to first estimate the elasticityqw

loglog

∂∂ .

We did this by estimating the Engel equation parametrically, utilizing the following

AIDS model for household h and good j:

hjh

hj

h

hj

k

iiijjhj Z

nx

nx

pw λφβγα ′+

+

++= ∑

=

2

1

logloglog (7)

where nh is the number of household h members, Xh is total real expenditure of

household h and Zh contains other characteristics of the household. We present the

results of estimating (9) in Tables 13 and 14. For robustness we estimated it by OLS

including all the data as well as excluding the extreme observations and households

with zero electricity expenditure. Moreover, we also fit a model correcting for access

to electricity. Therefore, we re-estimate the Engel equation using a Heckman two-

stage selection correction, which is implemented as follows: we first use a probit to

estimate the probability of access, and then we add the Mills ratio obtained from this

step to (7) to get the selection correction. We do not include the price of kerosene,

the only approximate substitute, because it was not significant in every case. Data

on quality was not available and was therefore not included.54 On the other hand, we

do include an interaction between electricity price and having a refrigerator.

{TABLE 16 and 17 HERE}

The coefficients for electricity price are robust across estimation methods.

The average coefficient is around -0.03 in the urban sector, ranging from -0.01 for

those without refrigerator and –0.08 for those with one. The coefficient for the rural

sector is –0.01, ranging from 0.0 to -0.06. These coefficients allow us to estimate

expected elasticity for every single household which, in turn, makes possible the

computation of welfare changes as expressed in (6). This means that households

with electrical appliances are more price elastic than households without them, as

expected. Table 18 shows the average price elasticity by expenditure deciles in

Nicaragua. Elasticity is increasing in expenditure so that households up to the sixth

deciles are inelastic to price changes whereas households in the top three deciles

have elastic demand for electricity.

{TABLE 18 HERE}

Table 20 presents the welfare impact of the change in electricity prices

between 1993 and 1998 using (5) and (6), once (7) was estimated. Top panel

presents the first-order and second-order approximations in real cordobas, while the

bottom panel presents the approximations as a percentage of real household

expenditure per capita. We compute the mean change in utility by expenditure

deciles, assuming equal social utility weights within groups, and distinguishing total,

54 We have data on service quality only after 1998, when the regulatory agency begun operations and it consists of the number of blackouts, its duration and its source.

rural and urban areas. The results in Table 20 ignore any change in access to

electricity services, an issue that we incorporate below.

Although the first-order approximation shows that the increase in the price of

electricity reduced welfare at all expenditure deciles, the estimation shows that the

impact was stronger at the top of the distribution. This suggests that the price effect

was at least not regressive. Both in monetary and percentage terms, the loss is

smaller in the in the bottom deciles than in the top deciles (with the exception of the

bottom first decile in percentage terms). When adjustments in the quantity of

electricity are allowed (second order approximations), the negative welfare effect is

slightly less severe, the non-regressiveness is still present in monetary terms but

less so in percentage terms.

{TABLE 19 HERE}

6.2 Incorporating Changes in Access

An issue that we ignored, which constitute a potential problem in the calculations of

welfare changes that appear in Table 19, is that certain households who did not

have access to electricity prior to the reforms, could have obtained access to such

services by 1998. Fortunately, we can modify the approach used in the previous

subsection in order to incorporate changes in access in the calculations of the

welfare effects that resulted from the increase in the price of electricity. To do this,

though, an assumption is necessary: those households who had access to electricity

in 1993 continued to have access in 1998. Such an assumption does not preclude

the possibility that some households who had access in 1993, were “priced out” by

1998. It is conceivable that very poor households with access to electricity and who

actually demanded electricity services in 1993 demanded zero electricity by 1998

because of the increase in electricity prices.

In computing welfare changes, we need to divide households in three groups:

those with access in 1993 and in 1998, those without access in 1993 and in 1998,

and those who did not have access in 1993 but did have access in 1998.55 For the

first group, those with access throughout, we proceed as in the previous subsection.

For the second group, those without access in 1993 and in 1998, and for whom

welfare changes could have only arisen through induced variations in the price of

electricity substitutes, we can also employ the methods outlined in the previous

subsection. We cannot handle the third group (those without access in 1993 but with

access by 1998) in the same manner as we do for the first and the second group.

The problem with this group boils down to how one handles the price of electricity

that they faced in 1993. For these consumers, the 1993 price can be thought of as

the lowest price such that the consumers demand zero electricity if it was offered to

them at that price. This “virtual” price is calculated as the lowest price such that the

expenditure share on electricity is zero. In practice, we recover this price from the

estimated Engel equation (7), by finding the price such that the estimated

expenditure share, wh, is zero.56

{TABLE 21 HERE}

Tables 21-23 show the results for each of the household groups. The group of

households with access in 1993 and in 1998, shown in table 21, experienced a loss

in welfare across all deciles. In monetary terms, the losses were monotonically

increasing from bottom to top deciles but, in contrast with the results obtained when

the issue of access was ignored, the welfare losses in percentage terms were

almost monotonically decreasing in the urban area and U-shaped in the rural area.

Namely, percentage welfare losses were larger for poorer households than for richer

households. This is because, first, poorer households are less elastic to electricity

price changes and, second, they have a larger share of household expenditure

55 There may be some complications with the first group that we ignore: The surveys used give information regarding individual usage of electricity, but do not provide information on whether consumers have the option of access in 1993. Secondly, the surveys do not ask consumers how long they have had access for. 56 Notice that this “virtual” price differs by household, according to total expenditure and demographic information. For more on this, see McKenzie and Mookherjee (2002).

allocated to electricity, particularly among urban households (see table 18 and

figures 9 and 10).

{TABLE 22 HERE}

As it should be expected, households without access in 1993 and in 1998

obtained welfare gains, but such gains were negligible. For all practical purposes,

these households' welfare remained unchanged (Table 22). Recall that the potential

gains or losses that households in the second group could have obtained, depended

on the induced changes in the price of substitutes. But, as it was already mentioned,

the price of kerosene remained almost constant between 1993 and 1998.

{TABLE 23 HERE}

At the same time, households without access in 1993, but who obtained

access by 1998 experienced substantial gains in welfare. Among these households,

the poorest households are the ones who obtained the largest welfare gains in

percentage terms (see Table 23). For these same households, allowing quantity

adjustments (second-order approximations), enhanced the welfare gain obtained

from gaining access to electricity. Again, households at the bottom of the

expenditure distribution benefited most. This result is the consequence of two

opposing factors. On the one hand, households in the poorer deciles had a lower

expansion of electricity access when compared to richer households. Table 22

shows that growth in electricity access was higher in the urban area than in the rural

area as well as faster in the top five deciles than in the five bottom deciles. However,

virtual prices were much higher in the poorer deciles than in the richer deciles (see

figure 12). Therefore, poorer households had larger gains in welfare not just

because they gained access to electricity but because they priced that access a lot

more than other households.

{TABLE 24 HERE}

Finally, we add up all the households and compute the total welfare effect.

Table 24 shows that the welfare effect was negative in monetary terms for most

deciles in the urban areas but it was positive for the bottom six deciles in rural areas.

In both the rural and urban areas as well as the whole nation, the welfare change

was progressive in the sense that poorer deciles had either welfare gains or smaller

losses than the losses experienced by the richer deciles. When considering welfare

changes in percentage terms no gains are registered for first order approximations in

the rural area.

The conclusion from monetary and percentage welfare gains is the same: the

welfare changes of electricity partial privatization in Nicaragua were progressive.

Namely, welfare changes were larger for the bottom deciles than for the top deciles.

However, it is necessary to gauge what the impact of these changes has been upon

total welfare, poverty and inequality in Nicaragua.

What is the impact of these welfare changes on total welfare in Nicaragua? We

estimate such effect using the money-metric welfare changes reported above and

assuming a class of social welfare functions.57 Tables 24 and 25 show total welfare

changes for different areas, databases and approximation methods. In almost all

cases, a small welfare loss is reported. The estimates range from a maximum

welfare gain of 0.01% in rural areas to a welfare loss of 0.43% in urban areas.

{TABLE 25 and 26 HERE}

What is the impact of these welfare changes on inequality and poverty in

Nicaragua? To answer this question we use the estimated money metric welfare

changes and add them to the 1998 household expenditure. Then we compute

poverty and inequality indexes with and without the welfare change so we can

evaluate changes in the distribution of expenditures. Tables 26 and 27 show the

57 See appendix 3.

results of these simulations for different areas, databases and approximation

methods. There is no effect upon poverty and inequality. This is not surprising, given

the very small size of the monetary changes reported above, and the limited scope

of the privatization considered (i.e., partial privatization of electricity generation).

{TABLE 27 and 28 HERE}

7. Conclusions and Comments

This paper makes an assessment of the distributional impact of privatization in

Nicaragua. It is part of a wider research project that aims to evaluate the distributive

effects of privatization in several Latin American countries. However, the nature of

privatization in Nicaragua was different because it was not a mere transfer of

ownership or management to the private sector of several stated-owned enterprises.

Instead, it was a transition from a socialist to a market economy. Notwithstanding

this difference, and for sake of comparability with other studies, the distributive

impact is traced through its consequences upon fiscal balance, labor markets and

consumer prices.

The paper starts with a historical description of the privatization process in

Nicaragua. Two main stages are identified: first, the privatization of state-owned

enterprises under CORNAP from 1993 to 1998 and, second, the privatization of

public utilities from 1998 onwards. The first stage was characterized by lack of

transparency and the allocation of proceeds from privatization is not fully recorded in

fiscal records. Consequently, it is very difficult to ascertain the distributional impact

of this process. Given its characteristics, i.e. large asset restitutions and a few

transfers to workers and war-veterans, suggest non-progressive distributive effects.

The second stage has a more accurate record of proceeds. In this stage a better

fiscal stance, as well as smaller transfers to state-owned enterprises, may give to

The distributional impact of privatization upon labor markets is evaluated through

changes in employment and wage dispersion. In the case of Nicaragua, a large

reallocation of labor occurred as a consequence of the transition. This accounted for

nearly 15% of the labor force. This was accompanied by an increasing dispersion of

wages within the public sector and stable dispersion in the private sector, while the

mean gap between public and private sectors did not change. Consequently, wage

dispersion in Nicaragua increased for the period under study. The increasing

dispersion in public sector wages is not the consequence of a simple transfer of

assets but must be the consequence of increasing market pressures that a transition

economy overcomes. We thus acknowledge that privatization alone is not the solely

source of inequality change but it can be a large source of it. Depending on the

inequality measure, we find that changes in the compensated wage differentials

between public and private sector occupations can account up to a third of total

inequality change.

Finally, the welfare effect is measured through estimations of second order

approximations to changes in indirect utility functions. We find that the change in

electricity prices observed during the period 1993-1998, which was characterized by

some new private generation plants and restructuring of the existing public ones,

produced welfare losses for all households in all deciles of the expenditures

distribution. However, losses were larger for richer households. When accounting

changes in access to electricity, the gains concentrated among households in

deciles two to six. Adding up all the effects, i.e. prices and access, the welfare effect

of the reforms in the Nicaraguan electricity sector were slightly progressive for the

period under study. However, simulations of the impact of this welfare effect, in

money terms, upon indexes of poverty and inequality was negligible.

There are several limitations that need to be overcome in future research.

First, the privatization of public utilities is still ongoing and the data available for this

paper do not register the ultimate consequences of the process. The privatization of

telecommunications, electricity distribution and generation in years 2001 and 2002,

and the eventual transfer of water and sewerage services, will undoubtedly have

effects in subsequent years. The evaluation of this process will require household

data yet to be collected. Second, the impacts upon ownership have been mentioned

in just a cursory manner. Privatization is mainly a process about ownership changes

which, of course, have strong distributional consequences. No formal evaluation of

the distribution of assets and wealth has been done. Data on the distribution of

agricultural land, housing and corporate assets will be needed for such a study.

Third, intergenerational distributive issues, mainly related to the environmental

impacts of privatization are also absent in this study. The study of these will require

an evaluation of changes in farming methods, public expenditures in conservation

and in regulations over polluting industries.

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Macroeconomic Impact of Privatization”, Occasional Paper 194, International Monetary Fund, Washington, 2000.

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Serie de Reformas de Política Pública, 44, Santiago de Chile, 1996. Duryea, S. and M. Szekely, “Labor Markets in Latin America: A Supply Side Story,

Inter-American Development Bank”, Working Paper 374, Washington, D.C, 1998. Freeman, R., “Unionism and The Dispersion of Wages”, Industrial and Labor

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Selling Public Enterprises: An Empirical Analysis, Oxford University Press for the World Bank, New York, 1994.

Gupta, S., C. Schiller, and H. Ma, “Privatization, Social Impact, and Social Safety

Nets”, International Monetary Fund Working Paper 99/68, Washington, 1999. Heller, P. and C. Schiller, “The Fiscal Impact of Privatization, with Some Examples

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from Mexico”, The Quarterly Journal of Economics, November 1999, pp. 1193-1242.

Levinsohn, J., S. Berry, and J. Friedman, “Impacts of the Indonesian Economic

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12 (4), Fall 1998, pp. 133-150.

Figure 1:

Government Transfers

-0.5

0.5

1.5

2.5

3.5

4.5

5.5

6.5

7.5

8.5

9.5

10.5

11.5

12.5

13.5

14.5

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

% o

f GD

P

Total To public utilities

Figure 2:NICARAGUA (RURAL SECTOR): Average wage by employment sector and occ

0.0

2.0

4.0

6.0

8.0

10.0

12.0

level 1 level 2 level 3 level 1 level 2

occupations

real

hou

rly w

ages

(in

1999

cor

doba

s)

1993 1998

private sector public s

Notes: The occupation categories are: level 1: managers and professionals; level 2, clerical and salesmen; level 3, craftsmen, operatives, laborers and service workers

Figure 3:NICARAGUA (URBAN SECTOR) Average wage by employment sector and occ

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

level 1 level 2 level 3 level 1 level 2

occupations

real

hou

rly w

ages

(in

1999

cor

doba

s)

1993 1999

private sector public

Notes: The occupation categories are: level 1: managers and professionals; level 2, clerical and salesmen; level 3, craftsmen, operatives, laborers and service workers

Figure 4: Distribution of household expenditure, Nicaragua 1993

densi

ty funct

ion, 1993

real household expenditure per head, in logs0 3 6 9

0

.25

.5

1998

1993

Electricity bill within t

ratio

total re2 4

0

.025

.05

.1

.15

1993

Figure 5:

otal real expenditures, Nicaragua

al expenditure per head (in logs)6 8 10

Figure 6: Electricity bill within total expenditures, Nicaragua 1993

ele

ctrici

ty b

ill s

hare

(ra

tio)

real household expenditure per head, in logs0 3 6 9 12

0

.01

.02

.03

.04

Electricity bill withele

ctrici

ty b

ill s

hare

(ra

tio)

real househ0 3

0

.01

.02

.03

.04

Figure 7: in total expenditures, Nicaragua 199

old expenditure per head, in logs6 9

Electricity bill withinele

ctrici

ty b

ill s

hare

(ra

tio)

real ho0 3

0

.01

.02

.03

.04

Figure 8: total expenditures, Nicaragua 199

usehold expenditure per head, in lo6 9

1993

199

Electricity bill shareele

ctrici

ty b

ill s

hare

(ra

tio)

real hou0 3

0

.01

.02

.03

.04

Figure 9: , rural and urban Nicaragua 1993

sehold expenditure per head, in lo6 9

Urban

Total

Rural

Electricity bill share, ele

ctrici

ty b

ill s

hare

(ra

tio)

real hous0 3

0

.01

.02

.03

.04

Figure 10: rural and urban Nicaragua 1998

ehold expenditure per head, in lo6 9

urban

total

rural

Figure 11:

Kerosene within total real expenditures, Nicaraguara

tio

total real expenditure per head (in logs2 4 6 8

0

.1

.2

.3

.4

Figure 12:Nicaragua: virtual prices for electricity

2163.0

480.4

104.4

35.3

11.39.1

4.3 4.4

1.0

10.0

100.0

1000.0

10000.0

Bottom decile Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 6 Decile 7

real

cor

doba

s

Table 1. Privatization under CORNAP

Number ofenterprises 1993 1998

Corporations and enterprises under CORNAP 351 289 343 Farming public sector corporations 80 76 80 Poultry (CAN) 5 5 5 Agroindustry (CONAZUCAR) 9 8 9 Tobacco (TABANIC) 5 5 5 Meat (CNC) 11 9 11 Banana production (BANANIC) 2 2 2 Rice (NICARROZ) 8 8 8 Promotion of agroexport products (AGROEXCO) 7 7 7 Coffee (CAFENIC) 14 13 14 Livestock (HATONIC) 16 16 16 Milk (CONILAC) 3 3 3 People's industry corporations (COIP) 89 57 83 Corporations linked to the primary sector of the economy 30 23 30 Forestry (CORFOP) 13 10 13 Fishery (INPESCA) 7 7 7 Minery (INMINE) 10 6 10 Corporations linked to internal and external trade 83 73 81 Imports and farming services (IMSA) 11 9 11 Construction supply (CATCO) 21 14 19 Commerce (CORCOP) 34 33 34 Foreign trade enterprises (CONIECE) 7 7 7 Pharmaceuticals (COFARMA) 10 10 10 Corporations linked to transportation and construction 28 23 28 Construction enterprises (COENCO) 6 6 6 Transportation (COTRAP) 22 17 22 Nicaraguan turism corporations (COTUR) 30 27 30 Autonomous enterprises 2/ 11 10 11 Grains (ENABAS) 1 0 1 Engineers (PROA) 1 1 1 ENDEPARA african palm El Castillo 1 1 1 ENDEPARA african palm Kucra Hill 1 1 1 Agroindustry (YUCASA) 1 1 1 Agroindustrial enterprise of Sébaco 1 1 1 Agroindustry (IFRUGALASA) 2 2 2 Construction (SOVIPE-ENFARA) 1 1 1 SOVIPE investments 1 1 1 Enterprise Arlen Siu 1 1 1Source: Central Bank of Nicaragua.1/ Spanish acronyms whenever available.2/ Enterprises whose shares were not held by corporations under CORNAP.

Privatizedby

Table 2. CORNAP: Divestment procedures and beneficiaries

Business people Workers War veterans Others 2/ Total1991-1996 period 47.4 12.9 1.5 27.9 89.7 Acquisitions 0 0 0 25 25 Restitution 28.4 0 0 0 28.4 Sales and leases 1/ 19 12.9 1.5 2.9 36.3 Cash and credit sales 12.1 6.1 0.5 0 18.7 Transferred liabilities 6.9 1.9 0 2.9 11.7 Lease with option to buy 0 4.9 1 0 5.91997-2000 period 7.7 1.4 0.2 1 10.3Total 55.1 14.3 1.7 28.9 100Source: Banco Central de Nicaragua1/ Transferred liabilities refers to tranfers of outstanding debts.2/ Composed mainly of governments institutions.

As percentage of total asset value under CORNAP

Table 3. Gross and net proceeds of privatization that accrue to the budget 1/

Years of active Million of US Percentage Million of US Percentageprivatization dollars 4/ of GDP 5/ dollars 4/ of GDP 5/

CORNAP enterprises 2/ 1991-1996 242.9 2.5 0 0Public Utilities 3/ 1995-on going 167.9 7.2 108.4 4.6 Telecommunications - - - - Electricity 167.9 7.2 108.4 4.6 Generation 2002 52.9 2.3 19.4 0.8 Distribution 2000 115 4.9 89 3.8 Water and Sewage - - - -Source: Banco Central de Nicaragua and authors' calculations.1/ Proceeds from CORNAP privatization were not registered in the fiscal balances. Electricity distribution and generation were accounted below the line as privatization proceeds. Although in 2001 telephone services were privatized, we do not include these proceeds because the privatization process may be reversed due to corruption claims. If the process is not resersed, gross proceeds will amount to US$83.9 millions, and net proceeds in 2001 will amount to US$33.9 millions.2/ The proceeds from CORNAP privatization were never registered in the fiscal balances.3/ Net proceeds from the privatization of electricity distribution refers to gross proceeds minus ENEL's commercial debt (US$26 millions).4/ Annual data in national currency converted to U.S. dollars using annual average exchange rates.5/ Average of annual ratios of privatization proceeds to GDP during active privatization for CORNAP; percentage of GDP in the year 2000 for public utilities.

Gross proceeds Net proceeds accruing to theduring active privatization budget during active privatization

Table 4. Divestiture procedures and form of payment

Cash Credit Liability transfer TotalLiquidation 0.0 0.0 0.0 0.0Restitution 0.1 2.1 8.2 10.5Merge or acquisition 0.0 0.0 0.0 0.0Sale or lease 22.3 38.3 28.9 89.5Total 22.4 40.5 37.1 100.0Source: CORNAP

Form of payment as a share of total transactions

Table 5. Public Utilities: Fiscal balances and net transfers

(percent of GDP)1991-1995 1995-2000

Electricity (ENEL) Fiscal balance 1/ -0.99 -0.78 Net transfers 2/ -0.16 0.00 External and domestic financing 3/ 0.58 0.57Telephone services (ENITEL) Fiscal balance 1/ 1.55 1.23 Net transfers 2/ 0.43 0.21 External and domestic financing 3/ 0.47 -0.20Water and Sewage (ENACAL) Fiscal balance 1/ -0.41 -1.35 Net transfers 2/ -0.14 -0.17 External and domestic financing 3/ 0.20 0.23Source: Banco Central de Nicaragua1/ Average overall deficit before net transfers to the government.2/ Average net transfers to the government.3/ Includes external loans net of payments and net financing from the central bank, the financial system, and providers.

Table 6. Nicaragua: Inequality indexes for real monthly wages 1/

Percentage 1993 1998 Change change

Gini 0.5165 0.5418 0.0253 4.9

Generalized Entropy (2) 1.0837 1.3645 0.2808 25.9

Between 0.059 0.052 -0.007 -11.8Within 1.025 1.313 0.288 28.1

By industryAgriculture, hunting, forestry and fishing 1.448 1.842 0.394 27.2Mining and Quarrying 0.830 0.635 -0.195 -23.5Manufacturing 0.672 0.473 -0.199 -29.6Electricity, Gas and Water 0.327 0.462 0.135 41.3Construction 0.491 1.454 0.963 196.3Wholesale, retail trade, restaurants and hotels 1.104 1.017 -0.087 -7.8Transport, Storage and Communication 0.648 1.202 0.554 85.5Financing, Insurance, Real State and Business Services 0.555 0.546 -0.009 -1.6Community, Social and Personal Services 1.003 1.414 0.411 41.0Source: EMNV-93 and EMNV-98 and authors' calculations.

T a b le 7 . D is tr ib u tio n o f e m p lo y e d w o r k e r s in N ic a r a g u a1 9 9 3 1 9 9 8 1 9 9 3 1 9 9 9r u r a l r u r a l u r b a n u r b a n(% ) (% ) (% ) (% )

G e n d e rm a le 7 2 .6 0 7 8 .4 0 5 6 .7 0 5 3 .7 0fe m a le 2 7 .4 0 2 1 .6 0 4 3 .3 0 4 6 .3 0

A g e ( in y e a rs ) 3 4 .4 0 3 1 .8 0 3 6 .1 0 3 6 .0 0

M a r i ta l S ta tu sm a rr ie d 6 8 .2 0 5 3 .5 0 6 3 .4 0 6 1 .1 0U n m a rr ie d 3 1 .8 0 4 6 .5 0 3 6 .6 0 3 8 .9 0

F o r m a l S c h o o lin gi llite ra te 3 4 .3 0 3 1 .1 0 8 .2 0 6 .9 0lite ra te 6 5 .7 0 6 8 .9 0 9 1 .8 0 9 3 .1 0n o fo rm a l e d u c a t io n 4 3 .1 0 3 5 .6 0 1 2 .5 0 0 .0 0p r im a ry in c o m p le te 3 1 .2 0 3 9 .8 0 2 5 .0 0 3 5 .8 0p r im a ry c o m p le te 1 2 .4 0 1 0 .6 0 1 6 .0 0 1 1 .9 0s e c o n d a ry in c o m p le te 6 .3 0 8 .3 0 1 5 .4 0 2 4 .1 0s e c o n d a ry c o m p le te 5 .3 0 4 .0 0 2 2 .1 0 1 4 .2 0te r t ia ry in c o m p le te 0 .3 0 1 .0 0 3 .1 0 2 .7 0te r t ia ry c o m p le te 1 .2 0 0 .7 0 6 .0 0 1 1 .4 0

O c c u p a tio n 1 /firs t 2 .5 0 2 .2 0 1 3 .4 0 1 4 .6 0s e c o n d 1 1 .0 0 6 .5 0 1 9 .2 0 3 8 .2 0th ird 8 6 .5 0 9 4 .3 0 6 7 .4 0 4 7 .2 0

S e c to rp r iva te 8 9 .1 0 9 8 .7 0 7 6 .5 0 8 6 .4 0p u b lic 1 0 .9 0 1 .3 0 2 3 .5 0 1 3 .6 0

E c o n o m ic A c tiv i ty :a g r ic u ltu re 5 4 .0 0 6 6 .5 0 5 .6 0 3 .1 0m in in g 0 .2 0 0 .6 0 0 .2 0 0 .1 0m a n u fa c tu r in g 6 .7 0 5 .7 0 1 6 .9 0 1 6 .8 0p u b lic u t il it ie s 0 .5 0 0 .4 0 1 .5 0 0 .9 0c o n s tru c t io n 2 .1 0 3 .1 0 4 .9 0 5 .9 0t ra d e , re s ta u ra n ts & h o te ls 1 4 .1 0 1 0 .4 0 2 7 .2 0 2 9 .1 0t rn a s p o rta t io n & c o m m . 2 .3 0 1 .7 0 5 .6 0 6 .0 0fin a n c e a n d in s u ra n c e 0 .2 0 0 .1 0 1 .4 0 1 .9 0s e rv ic e s 1 8 .2 0 1 0 .3 0 3 0 .8 0 2 9 .8 0g o ve rn m e n t 1 .7 0 1 .3 0 6 .0 0 6 .3 0

S o c ia l S e c u r i tyc o n tr ib u to r 1 2 .3 0 7 .4 0 3 5 .0 0 2 5 .8 0n o n -c o n tr ib u to r 8 7 .7 0 9 2 .6 0 6 5 .0 0 7 4 .2 0

S e c o n d jo by e s 4 .3 0 9 .4 0 3 .3 0 1 2 .2 0n o 9 5 .7 0 9 0 .6 0 9 6 .7 0 8 7 .8 0

P o s it io ne m p lo y e e 5 4 .6 0 4 5 .6 0 6 1 .1 0 5 8 .7 0h o u s e a id 7 .1 0 - 6 .8 0 5 .6 0s e lf-e m p lo y e d 3 7 .6 0 2 8 .1 0 3 0 .3 0 3 0 .1 0p ro fe s s io n a l s e lf-e m p lo y e d 0 .0 0 0 .0 0 0 .7 0 1 .1 0u n p a id fa m ily w o rk e r 0 .4 0 2 2 .9 0 0 .2 0 0 .1 0e m p lo y e r 0 .3 0 3 .4 0 0 .9 0 4 .4 0S o urc e : A utho rs ' c a lc ula tio ns us ing E M N V -9 3 , E M N V -9 8 , a nd E IG H -9 9 .1 / T he o c c up a tio n c a te g o rie s a re : f irs t, m a na g e rs a nd p ro f e s s io na ls ; s e c o nd , c le rks a nd s a le s m e n; a nd , third , c ra f ts m e n, o p e ra tiv e s , la b o re rs a nd s e rv ic e w o rke rs .

f t

Table 8. Survey of Establishments by Economic Activitystate-owned state-owned

state-owned private total share state-owned private total share

Total number o Establishments 135 90 225 0.60 66 194 260 0.25

Agriculture and livestock 19 39 58 0.33 0 53 53 0.00Fishing 1 0 1 1.00 0 2 2 0.00Mining 3 0 3 1.00 0 4 4 0.00Manufacturing 24 41 65 0.37 5 71 76 0.07Electricity, Gas and Water 2 0 2 1.00 1 2 3 0.33Construction 7 0 7 1.00 3 6 9 0.33Commerce, res aurants and hotels 12 8 20 0.60 2 23 25 0.08Transport, storage and communications 16 2 18 0.89 8 7 15 0.53Finance and Insurance 7 0 7 1.00 6 14 20 0.30Comunal, social and personal services 16 0 16 1.00 16 10 26 0.62Central Governement 28 0 28 1.00 25 2 27 0.93Source: Ministerio del Trabajo, Nicaragua. Establishment Surveys.

1993 1998

Table 9. Variance decomposition (in 1999 log real hourly wages) (1) URBAN SECTOR RURAL SECTOR

1999 1998Employment shares:

private sector 86.4% 95.7% public sector 13.6% 4.3%

Variances:public sector 0.736 0.467

private sector 0.877 0.781 total 0.867 0.786

Means: public sector 2.360 1.478

private sector 2.082 0.795 total 2.120 0.824

1993 1993Employment shares:

private sector 77.1% 89.0% public sector 22.9% 11.0%

Variances:public sector 0.501 0.394

private sector 0.914 0.796 total 0.817 0.773

Means: public sector 2.139 1.857

private sector 2.071 1.382 total 2.087 1.436

CHANGE IN TOTAL VARIANCE 0.050 100% 0.013 100%

Decomposition I (1) change in public sector employment share 0.008 16% -0.007 -56% change in public sector variance 0.054 108% 0.008 60% change in private sector variance -0.028 -57% -0.014 -102%

change in public sector mean gap 0.013 25% 0.027 206% change in private sector mean gap

0.001 2% -0.002

-14%

Decomposition II (1) change in public sector employment share 0.038 76% 0.015 113% change in public sector variance 0.032 64% 0.003 24% change in private sector variance -0.032 -64% -0.015 -109% change in public sector mean gap 0.007 15% 0.011 81% change in private sector mean gap 0.001 2% -0.002 -15% Source: Own calculations using I.N.E.C.’s “Encuesta nacional de Hogares sobre Medición de Nivel de Vida, 1993” ; “Encuesta Nacional de Hogares sobre Medición de Nivel de Vida, 1998”; and “Encuesta Nacional de Ingresos y Gastos de los Hogares, 1998-1999” Notes: (1) a formal explanation of the decomposition is available in the appendix #1

Table 10. Variance decomposition (in 1999 real hourly wages) (1) URBAN SECTOR RURAL SECTOR

1999 1998Employment shares:

private sector 86.4% 95.9% public sector 13.6% 4.1%

Variances:public sector 618.9 16.4

private sector 417.8 38.1 total 446.5 37.3

Means: public sector 16.8 5.4

private sector 13.4 3.4 total 13.9 3.5

1993 1993Employment shares:

private sector 77.1% 89.0% public sector 22.9% 11.0%

Variances:public sector 117.1 80.2

private sector 380.5 74.7 total 318.9 75.7

Means: public sector 11.2 8.1

private sector 13.3 6.3 total 12.8 6.5

CHANGE IN TOTAL VARIANCE 127.6 100% -38.4 100%

Decomposition I (1) change in public sector employment share -19.5 -15% 1.2 -3% change in public sector variance 115.0 90% -7.0 18% change in private sector variance 28.7 23% -32.7 85%

change in public sector mean gap 1.4 1% 0.1 0% change in private sector mean gap

0.0 0% 0.0 0%

Decomposition II (1) change in public sector employment share 24.3 19% -0.5 1% change in public sector variance 68.2 53% -2.6 7% change in private sector variance 32.2 25% -35.2 92% change in public sector mean gap 0.8 1% 0.1 0% change in private sector mean gap 0.0 0% 0.0 0% Source: Own calculations using I.N.E.C.’s “Encuesta nacional de Hogares sobre Medición de Nivel de Vida, 1993” ; “Encuesta Nacional de Hogares sobre Medición de Nivel de Vida, 1998”; and “Encuesta Nacional de Ingresos y Gastos de los Hogares, 1998-1999” Notes: (1) a formal explanation of the decomposition is available in the appendix #1

Table 15. NICARAGUA: Juhn-Murphy-Pierce Decomposition for various inequality indexes Rural Sector Measured Wage equation Public sector Wage equation (in 1999 real hourly wages)

characteristics

all coefficients coefficients residuals

effect effect effect (3) effect 1993 1998 Difference (1) (1) (1) Variance of logarithms 0.773 0.786 0.013 0.15 1142% 0.04 280% 0.05 145% -0.18 -1323%

Variance

75.70

37.30

-38.40

35.01

-91%

-71.40

186%

-12.65

18% -2.02

5%

Ninth to first decile ratio 10.29 10.79 0.50 0.98 194% 0.08 15% 0.90 1199% -0.55 -109%Ninth to fifth decile ratio 3.04 3.42 0.38 2.40 627% -0.17 -45% 0.19 -111% -1.85 -482%Fifth to first decile ratio

3.39 3.15 -0.24

-1.32

561%

0.08 -36%

0.10 115%

1.00 -425%

Generalized Entropy(-1) 1.15 3.08 1.93 -0.37 -19% 0.01 0% 0.05 855% 2.29 119%Generalized Entropy(0) 0.49 0.53 0.05 0.09 189% 0.01 11% 0.02 289% -0.05 -100%Generalized Entropy(1) 0.50 0.54 0.04 0.17 437% -0.02 -62% -0.01 24% -0.11 -275%Generalized Entropy(2)

0.90 0.97 0.07 0.43 634%

-0.16

-229%

-0.08

53% -0.21

-305%

Gini 0.51 0.52 0.02 0.07 399%

0.00 5% 0.00 558%

-0.05

-304%

Atkinson's (1/2) 0.22 0.23 0.02 0.06 354% 0.00 -14% 0.00 -117% -0.04 -239%Atkinson's (1) 0.38 0.41 0.03 0.06 184% 0.00 10% 0.01 290% -0.03 -94%Atkinson's (2) 0.70 0.86 0.16 -0.09 -55% 0.00 1% 0.01 882% 0.25 154%Notes: (1) Percentage of total difference (2) Percentage of all coefficients effect (3) It refers to the effect solely due to changes in the coefficient of public sector employment (as well as interactions with occupation) in the wage equation. See appendix.

Table 16. Engel equation estimations with ordinary-least-squares URBAN SECTOR All observations Excluding households (1) Excluding households (1) number of observations 4702 3567 3567 R-squared 0.1567 0.1355 0.1455F(k-1, n-k-1) 127.91 72.76 63.6 coefficient s.e. coefficient s.e. coefficient s.e. Electricity price -0.034 ** 0.004 -0.033 ** 0.005 -0.011 * 0.006real Household Expenditure per head 0.002 0.003 -0.020 ** 0.009 -0.031 ** 0.009real Household Expenditure per head, squared -0.001 ** 0.000 0.001 0.001 0.002 ** 0.001refrigerator 0.025 ** 0.001 0.024 ** 0.001 0.027 ** 0.002refrigerator x electricity price -0.069 ** 0.011shanty dwelling -0.014 ** 0.002 -0.009 ** 0.003 -0.009 ** 0.003access to piped water 0.011 ** 0.001 0.007 ** 0.001 0.006 ** 0.001constant 0.024 * 0.1140.012 0.148** 0.029 ** 0.030 RURAL SECTOR All observations Excluding households (1) Excluding households (1) number of observations 3709 926 926 R-squared 0.2005 0.104 0.1101F(k-1, n-k-1) 66.79 15.14 13.25 coefficient s.e. coefficient s.e. coefficient s.e. Electricity price -0.013 ** 0.003 -0.011 0.009 -0.001 0.009real Household Expenditure per head 0.006 ** 0.001 -0.013 0.010 -0.017 * 0.009real Household Expenditure per head, squared 0.000 ** 0.000 0.001 0.001 0.001 0.001refrigerator 0.032 ** 0.003 0.025 ** 0.003 0.028 ** 0.004refrigerator x electricity price -0.059 ** 0.028shanty dwelling -0.005 ** 0.000 -0.008 ** 0.003 -0.008 ** 0.003access to piped water 0.009 ** 0.001 0.001 0.002 0.001 0.002Constant -0.011 ** 0.004 0.086 ** 0.030 0.098 ** 0.029Notes: (1) It excludes households without access and households with access but electricity bill equal to zero. It also excludes households in the top and bottom 2.5% household expenditure per head

Table 17. Engel equation estimations with selection-correction

URBAN SECTOR RURAL SECTOR All observations Excluding households (1) All observations Excluding households (1) total observations 4702 4539 3709 3471 censored 541 972 2536 2545uncensored 4161 3567 1173 926Pseudo R-squared 0.039 0.053 0.111 0.154 Wald x2 603.7 587.87 150.53 111.52 coefficient s.e. coefficient s.e. coefficient s.e. coefficient s.e. Dependent variable: electricity budget share Electricity price -0.032 ** 0.005 -0.011 * 0.006 -0.021 ** 0.008 0.000 0.010real Household Expenditure per head -0.011 ** 0.004 -0.032 ** 0.008 -0.009 0.006 -0.018 0.012 real Household Expenditure per head, squared 0.000 0.000 0.002 ** 0.001 0.000 0.000 0.001 0.001 refrigerator 0.025 ** 0.001 0.028 ** 0.001 0.026 ** 0.002 0.028 ** 0.003refrigerator x electricity price -0.070 ** 0.011 -0.058 ** 0.023 constant 0.085 ** 0.014 0.158 ** 0.025 0.070 ** 0.019 0.104 ** 0.038 Dependent variable: access to electricity service real Household Expenditure per head 0.352 ** 0.033 0.321 ** 0.030 0.231 ** 0.022 0.409 ** 0.034 shanty dwelling -0.690 ** 0.115 -1.013 ** 0.109 -0.815 ** 0.079 -0.870 ** 0.094access to piped water 1.244 ** 0.056 0.790 ** 0.050 1.207 ** 0.056 1.047 ** 0.057constant -1.710 ** 0.201 -1.715 ** 0.180 -1.940 ** 0.126 -3.076 ** 0.190rho -0.164 ** 0.045 -0.160 ** 0.047 -0.115 ** 0.062 -0.078 ** 0.080Mill's ratio -0.005 ** 0.001 -0.005 ** 0.002 -0.003 ** 0.002 -0.002 ** 0.002Notes: (1) It excludes households without access and households with access but electricity bill equal to zero. It also excludes households in the top and bottom 2.5% household expenditure per head (*) Significantly different from zero with 90% of confidence; (**) with 95% of confidence

Table 18. Elasticity and Virtual price for approximations to consumer surplus price elasticity virtual of electricity price budget share (in real cordobas) Bottom decile -0.013 2163.0Decile 2 -0.042 480.4Decile 3 -0.102 104.4Decile 4 -0.237 35.3Decile 5 -0.475 11.3Decile 6 -0.601 9.1Decile 6 -0.831 4.3Decile 7 -1.400 4.4Decile 9 -2.726 3.1Top decile -3.921 3.4

Table 19. Percentage of households in each

decile which gain access to electricity

between 1993 and 1998

total urban rural Bottom decile 0.2% 0.0% 0.2%Decile 2 4.3% 3.2% 4.6%Decile 3 4.1% 5.0% 3.7%Decile 4 5.0% 6.5% 3.8%Decile 5 7.6% 10.7% 3.9%Decile 6 8.7% 10.9% 5.6%Decile 7 10.0% 12.7% 4.0%Decile 8 9.7% 11.3% 4.4%Decile 9 11.2% 12.2% 6.1%Top decile 6.9% 7.5% 4.5%Source: Author’s calculations using EMNV-98

Table 20 Impact of electricity price change on welfare (not accounting for change in access) In real (1998=100) Cordobas

First order approximation Second Order Approximation Total Rural Urban Total Rural Urban

Bottom decile -0.079 -0.041 -0.375 -0.077 -0.040 -0.368 Decile 2 -0.260 -0.178 -0.537 -0.254 -0.176 -0.519 Decile 3 -0.532 -0.469 -0.658 -0.521 -0.467 -0.627 Decile 4 -0.793 -0.468 -1.192 -0.759 -0.463 -1.120 Decile 5 -1.190 -0.678 -1.618 -1.112 -0.664 -1.486 Decile 6 -1.909 -1.216 -2.395 -1.772 -1.156 -2.203 Decile 6 -2.207 -1.290 -2.635 -1.994 -1.225 -2.353 Decile 7 -3.530 -1.533 -4.132 -3.050 -1.332 -3.568 Decile 9 -5.234 -3.789 -5.526 -4.223 -3.437 -4.382 Top decile -13.935 -4.706 -16.323 -7.332 -2.069 -8.694 As a percentage of real household expenditure per head

First order approximation Second Order Approximation Total Rural Urban Total Rural Urban Bottom decile -0.09% -0.04% -0.43% -0.08% -0.04% -0.42% Decile 2 -0.16% -0.11% -0.33% -0.16% -0.11% -0.32% Decile 3 -0.24% -0.21% -0.30% -0.23% -0.21% -0.28% Decile 4 -0.27% -0.16% -0.41% -0.26% -0.16% -0.38% Decile 5 -0.32% -0.18% -0.43% -0.30% -0.18% -0.39% Decile 6 -0.41% -0.26% -0.51% -0.38% -0.25% -0.47% Decile 6 -0.37% -0.22% -0.44% -0.33% -0.21% -0.39% Decile 7 -0.45% -0.19% -0.53% -0.39% -0.17% -0.46% Decile 9 -0.45% -0.30% -0.48% -0.36% -0.27% -0.38% Top decile -0.40% -0.19% -0.46% -0.29% -0.16% -0.33% Source: Own calculations using EMNV-93 y EMNV-98

Table 21 Impact of electricity price change on welfare (accounting for change in access: households with access in 1993 and 1998) In real (1998=100) Cordobas

First order approximation Second Order Approximation Total Rural Urban Total Rural Urban Bottom decile -0.70 -0.61 -0.81 -0.69 -0.59 -0.80 Decile 2 -0.88 -0.77 -1.02 -0.86 -0.76 -0.98 Decile 3 -1.31 -1.70 -1.02 -1.28 -1.69 -0.97 Decile 4 -1.40 -1.17 -1.54 -1.34 -1.16 -1.45 Decile 5 -1.60 -1.29 -1.75 -1.49 -1.26 -1.61 Decile 6 -2.48 -1.95 -2.72 -2.30 -1.85 -2.50 Decile 6 -2.56 -1.79 -2.87 -2.33 -1.70 -2.57 Decile 7 -3.89 -2.20 -4.30 -3.36 -1.90 -3.71 Decile 9 -5.68 -6.11 -5.63 -4.56 -5.53 -4.43 Top decile -17.22 -13.88 -17.55 -9.02 -6.20 -9.29 As a percentage of real household expenditure per head

First order approximation Second Order Approximation Total Rural Urban Total Rural Urban Bottom decile -0.78% -0.63% -0.93% -0.76% -0.62% -0.92% Decile 2 -0.55% -0.49% -0.63% -0.54% -0.48% -0.61% Decile 3 -0.59% -0.76% -0.46% -0.58% -0.76% -0.44% Decile 4 -0.48% -0.40% -0.53% -0.46% -0.39% -0.50% Decile 5 -0.43% -0.35% -0.46% -0.40% -0.34% -0.43% Decile 6 -0.53% -0.43% -0.58% -0.49% -0.41% -0.53% Decile 6 -0.43% -0.31% -0.48% -0.39% -0.29% -0.43% Decile 7 -0.50% -0.28% -0.55% -0.43% -0.24% -0.48% Decile 9 -0.49% -0.48% -0.49% -0.39% -0.43% -0.38% Top decile -0.49% -0.55% -0.48% -0.36% -0.48% -0.35% Source: Own calculations using EMNV-93 y EMNV-98

Table 22 Impact of electricity price change on welfare (accounting for change in access: households without access in 1993 and 1998) In real (1998=100) Cordobas As a percentage of household expenditure First order Second Order First order Second Order approximation approximation approximation approximation Total Total Total Total Bottom decile 0.010 0.010 0.01% 0.01%Decile 2 0.013 0.013 0.01% 0.01%Decile 3 0.015 0.015 0.01% 0.01%Decile 4 0.015 0.015 0.01% 0.01%Decile 5 0.025 0.025 0.01% 0.01%Decile 6 0.022 0.022 0.00% 0.00%Decile 6 0.020 0.020 0.00% 0.00%Decile 7 0.039 0.039 0.00% 0.00%Decile 9 0.080 0.080 0.01% 0.01%Top decile 0.025 0.025 0.00% 0.00% Source: Own calculations using EMNV-93 y EMNV-98

Table 23 Impact of electricity price change on welfare (accounting for change in access: households with access only in 1998) In real (1998=100) Cordobas

First order approximation Second Order Approximation Total Rural Urban Total Rural Urban Bottom decile 15.0 15.0 14.6 14.6 Decile 2 23.7 25.1 16.8 24.5 24.8 23.1 Decile 3 33.6 51.6 7.3 35.0 51.2 11.5 Decile 4 14.7 24.0 8.1 17.4 23.7 12.9 Decile 5 19.8 39.2 14.0 23.2 38.9 18.5 Decile 6 17.4 29.0 13.2 21.0 29.3 18.0 Decile 6 10.4 7.9 10.8 14.9 8.3 15.8 Decile 7 15.3 21.1 14.6 19.7 20.5 19.6 Decile 9 15.9 20.2 15.5 21.1 20.4 21.1 Top decile 17.9 12.0 18.8 52.6 -3.1 61.1 As a percentage of real household expenditure per head

First order approximation Second Order Approximation Total Urban Rural Total Urban Rural

Bottom decile 12.99% 12.99% 12.66% 12.66% Decile 2 15.98% 16.90% 11.53% 16.55% 16.69% 15.84% Decile 3 15.61% 24.12% 3.24% 16.25% 23.95% 5.04% Decile 4 5.38% 9.01% 2.78% 6.29% 8.90% 4.42% Decile 5 5.38% 11.12% 3.65% 6.27% 11.02% 4.84% Decile 6 3.57% 5.93% 2.71% 4.30% 5.99% 3.69% Decile 6 1.69% 1.32% 1.74% 2.41% 1.38% 2.57% Decile 7 2.02% 2.66% 1.95% 2.59% 2.58% 2.60% Decile 9 1.38% 1.83% 1.33% 1.84% 1.85% 1.83% Top decile 0.74% 0.13% 0.83% 1.25% 0.23% 1.41% Source: Own calculations using EMNV-93 y EMNV-98

Table 24 Impact of electricity price change on welfare (accounting for change in access: all households) In real (1998=100) Cordobas

First order approximation Second Order Approximation Total Rural Urban Total Rural Urban Bottom decile -0.038 0.004 -0.371 -0.037 0.004 -0.364 Decile 2 0.804 1.027 0.050 0.844 1.014 0.269 Decile 3 0.925 1.517 -0.248 0.998 1.506 -0.009 Decile 4 -0.008 0.482 -0.608 0.160 0.476 -0.228 Decile 5 0.483 0.915 0.122 0.805 0.915 0.713 Decile 6 -0.179 0.631 -0.747 0.258 0.698 -0.051 Decile 6 -0.972 -0.933 -0.990 -0.345 -0.858 -0.106 Decile 7 -1.693 -0.550 -2.038 -0.837 -0.376 -0.976 Decile 9 -2.870 -2.366 -2.972 -1.372 -2.013 -1.243 Top decile -12.199 -4.124 -14.288 -3.405 -2.209 -3.715 As a percentage of real household expenditure per head

First order approximation Second Order Approximation Total Rural Urban Total Rural Urban Bottom decile -0.09% -0.04% -0.43% -0.05% 0.00% -0.42% Decile 2 -0.16% -0.11% -0.33% 0.58% 0.69% 0.22% Decile 3 -0.24% -0.21% -0.30% 0.47% 0.71% -0.01% Decile 4 -0.27% -0.16% -0.41% 0.07% 0.19% -0.08% Decile 5 -0.32% -0.18% -0.43% 0.22% 0.27% 0.18% Decile 6 -0.41% -0.26% -0.51% 0.04% 0.13% -0.02% Decile 6 -0.37% -0.22% -0.44% -0.07% -0.15% -0.03% Decile 7 -0.45% -0.19% -0.53% -0.10% -0.05% -0.12% Decile 9 -0.45% -0.30% -0.48% -0.11% -0.14% -0.11% Top decile -0.40% -0.19% -0.46% -0.19% -0.15% -0.20%

Table 25 Percentage change in welfare due changes in electricity prices

(ignoring changes in access)

first order approximation second order approximation total urban rural total urban rural

σ = 0.5 -0.22% -0.19% -0.03% -0.20% -0.16% -0.03%σ = 1 -0.07% -0.06% -0.01% -0.07% -0.05% -0.01%σ = 2 * 0.52% 0.43% 0.09% 0.50% 0.41% 0.09%Note: (*) A positive percentage change for this Social Welfare function means a more negative social welfare (i.e., the society is worse off). See appendix #3. Table 26 Percentage change in welfare due changes in electricity prices

(including changes in access)

first order approximation second order approximation total urban rural total urban rural

σ = 0.5 -0.04% -0.05% 0.01% -0.01% -0.03% 0.01%σ = 1 0.00% -0.01% 0.01% 0.00% -0.01% 0.01%σ = 2 * -0.04% 0.02% -0.07% -0.06% 0.01% -0.07%Note: (*) A negative percentage change for this Social Welfare function means a less negative social welfare (i.e., the society is better off). See appendix #3.

Table 27 Poverty and Inequality with and without welfare effect (ignoring changes in access)

year 1998 first order approximation (1) second order approximation (2) total urban rural total urban rural total urban rural

Poverty FGT(0) 0.359 0.193 0.578 0.358 0.192 0.577 0.358 0.189 0.577FGT(1) 0.148 0.060 0.263 0.148 0.060 0.263 0.148 0.059 0.263FGT(2) 0.085 0.028 0.155 0.082 0.028 0.155 0.082 0.027 0.155

Inequality Gini 0.556 0.496 0.589 0.556 0.496 0.589 0.556 0.496 0.589Atkinson(1/2) 0.265 0.203 0.320 0.265 0.203 0.320 0.265 0.203 0.320Atkinson(1) 0.428 0.346 0.470 0.428 0.346 0.470 0.428 0.346 0.470Atkinson(2) 0.634 0.531 0.537 0.634 0.532 0.537 0.634 0.532 0.538Notes: (1) indexes computed using consumption in 1998 minus first order approximation of welfare change (2) indexes computed using consumption in 1998 minus second order approximation of welfare change Table 28 Poverty and Inequality with and without welfare effect

(including changes in access) year 1998 first order approximation (1) second order approximation (2) total urban rural total urban rural total urban rural

Poverty FGT(0) 0.359 0.193 0.578 0.358 0.192 0.578 0.352 0.193 0.578FGT(1) 0.148 0.060 0.263 0.149 0.060 0.265 0.146 0.061 0.265FGT(2) 0.085 0.028 0.155 0.083 0.028 0.156 0.082 0.028 0.156

Inequality Gini 0.556 0.496 0.589 0.557 0.496 0.590 0.557 0.497 0.590Atkinson(1/2) 0.265 0.203 0.320 0.266 0.208 0.321 0.266 0.208 0.321Atkinson(1) 0.428 0.346 0.470 0.430 0.346 0.471 0.430 0.347 0.471Atkinson(2) 0.634 0.531 0.537 0.636 0.532 0.539 0.636 0.532 0.539Notes: (1) indexes computed using consumption in 1998 minus first order approximation of welfare change (2) indexes computed using consumption in 1998 minus second order approximation of welfare change

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