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.
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.
3
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.
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).
7
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 shareholding 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
approximately 4,500 households.
interviewed
around
25,000
individuals
in
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
12
See also De Franco (1996).
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 postprivatization 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 quasifiscal 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).
These companies were two bottling enterprises (ENSA and MILKA) and the beer producer
Compañía Cervezera Nicaragüense. For details, see Banco Central de Nicaragua (1996).
30
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:
Var ( y t ) = α tsVarts + (1 − α ts )Vart p + α ts ( y ts − y t ) 2 + (1 − α ts )( y tp − y t ) 2
where Var ( y t ) is the overall or economy-wide variance of wages at year t; α ts is the
fraction of individuals working in the public sector; Vart j and y tj are the variance and
mean of wages in sectors j=p,s, respectively; and y t 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:
Var ( y t ) − Var ( y u ) = (α ts − α us )[Varts − Vart p + ( y ts − y t ) 2 − ( y tp − y t ) 2 ]
[
]
[
− yu )
2
+ (1 − α us ) Vart p − Varup + α us Varts − Varus
+ α us
[
( y ts
− yt )
2
− ( y us
]
[
]
+ (1 − α us ) ( y tp
− yt )
2
− ( y up
− yu )
2
]
(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).
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).
37
earning equations for each period and for both urban and rural sector separately.
That is, we estimate:
y nts = X nts β ts + Z nts δ ts + ε nts
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:
( )
[
I y f − I ( y i ) = [I ( y1) − I ( y i )] + [I ( y 2) − I ( y1)] + [I ( y3) − I ( y 2)] + I ( y f ) − I ( y3)
39
]
(2)
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).
There was also migration of unskilled labor from the rural sector to neighboring countries during the
period.
44
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).
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.
46
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 secondorder 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
∆U = −(q 0 ∆p )
(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:


∆p ∂ log q
∆U = − q 0 1 +
∆p 

 2 p 0 ∂ log q
(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.
(5)
∆U = −(∆ log p ) w0
and (4) becomes:
 ∆ log p ∂ log w 

∆U = −(∆ log p) w0 1 +
2
∂ log q 

(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 elasticity
∂ log w
∂ log q
.
We did this by estimating the Engel equation parametrically, utilizing the following
AIDS model for household h and good j:
whj = α j +
k
∑
i =1

γ ij log p i + β j  log

xh
nh


x
 +φ j  log h


nh


2

 + λ ′j Z h


(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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Figure 1:
Government Transfers
14.5
13.5
12.5
11.5
10.5
% of GDP
9.5
8.5
7.5
6.5
5.5
4.5
3.5
2.5
1.5
0.5
-0.5
1991
1992
1993
1994
1995
Total
1996
1997
To public utilities
1998
1999
2000
Figure 2:
NICARAGUA (RURAL SECTOR): Average wage by employment sector and occ
1993
1998
real hourly wages (in 1999 cordobas)
12.0
10.0
8.0
6.0
4.0
2.0
0.0
level 1
level 2
level 3
level 1
level 2
occupations
private sector
Notes:
The occupation categories are:
level 1: managers and professionals; level 2, clerical and salesmen; level 3,
craftsmen, operatives, laborers and service workers
public s
Figure 3:
NICARAGUA (URBAN SECTOR) Average wage by employment sector and occ
1993
1999
40.0
real hourly wages (in 1999 cordobas)
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
level 1
level 2
private sector
level 3
level 1
occupations
Notes:
The occupation categories are:
level 1: managers and professionals; level 2, clerical and salesmen; level 3, craftsmen, operatives,
laborers and service workers
level 2
public
Figure 4:
Distribution of household expenditure, Nicaragua 1993
.5
1998
density function, 1993
1993
.25
0
0
3
6
9
real household expenditure per head, in logs
Figure 5:
Electricity bill within total real expenditures, Nicaragua
.15
ratio
.1
1993
.05
.025
0
2
4
6
8
10
total real expenditure per head (in logs)
Figure 6:
Electricity bill within total expenditures, Nicaragua 1993
electricity bill share (ratio)
.04
.03
.02
.01
0
0
3
6
9
real household expenditure per head, in logs
12
Figure 7:
Electricity bill within total expenditures, Nicaragua 199
electricity bill share (ratio)
.04
.03
.02
.01
0
0
3
6
9
real household expenditure per head, in logs
Figure 8:
Electricity bill within total expenditures, Nicaragua 199
electricity bill share (ratio)
.04
.03
1993
.02
199
.01
0
0
3
6
9
real household expenditure per head, in lo
Figure 9:
Electricity bill share, rural and urban Nicaragua 1993
.04
electricity bill share (ratio)
Urban
.03
.02
Total
Rural
.01
0
0
3
6
9
real household expenditure per head, in lo
Figure 10:
Electricity bill share, rural and urban Nicaragua 1998
electricity bill share (ratio)
.04
.03
urban
.02
total
.01
rural
0
0
3
6
9
real household expenditure per head, in lo
Figure 11:
Kerosene within total real expenditures, Nicaragua
.4
ratio
.3
.2
.1
0
2
4
6
8
total real expenditure per head (in logs
Figure 12:
Nicaragua: virtual prices for electricity
10000.0
2163.0
1000.0
real cordobas
480.4
104.4
100.0
35.3
11.3
10.0
9.1
4.3
4.4
Decile 6
Decile 7
1.0
Bottom decile
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Table 1. Privatization under CORNAP
Corporations and enterprises under CORNAP
Farming public sector corporations
Poultry (CAN)
Agroindustry (CONAZUCAR)
Tobacco (TABANIC)
Meat (CNC)
Banana production (BANANIC)
Rice (NICARROZ)
Promotion of agroexport products (AGROEXCO)
Coffee (CAFENIC)
Livestock (HATONIC)
Milk (CONILAC)
People's industry corporations (COIP)
Corporations linked to the primary sector of the economy
Forestry (CORFOP)
Fishery (INPESCA)
Minery (INMINE)
Corporations linked to internal and external trade
Imports and farming services (IMSA)
Construction supply (CATCO)
Commerce (CORCOP)
Foreign trade enterprises (CONIECE)
Pharmaceuticals (COFARMA)
Corporations linked to transportation and construction
Construction enterprises (COENCO)
Transportation (COTRAP)
Nicaraguan turism corporations (COTUR)
Autonomous enterprises 2/
Grains (ENABAS)
Engineers (PROA)
ENDEPARA african palm El Castillo
ENDEPARA african palm Kucra Hill
Agroindustry (YUCASA)
Agroindustrial enterprise of Sébaco
Agroindustry (IFRUGALASA)
Construction (SOVIPE-ENFARA)
SOVIPE investments
Enterprise Arlen Siu
Source: Central Bank of Nicaragua.
1/ Spanish acronyms whenever available.
2/ Enterprises whose shares were not held by corporations under CORNAP.
Number of
enterprises
351
80
5
9
5
11
2
8
7
14
16
3
89
30
13
7
10
83
11
21
34
7
10
28
6
22
30
11
1
1
1
1
1
1
2
1
1
1
Privatized
by
1993
1998
289
343
76
80
5
5
8
9
5
5
9
11
2
2
8
8
7
7
13
14
16
16
3
3
57
83
23
30
10
13
7
7
6
10
73
81
9
11
14
19
33
34
7
7
10
10
23
28
6
6
17
22
27
30
10
11
0
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
Table 2. CORNAP: Divestment procedures and beneficiaries
As percentage of total asset value under CORNAP
Business people
Workers
War veterans
Others 2/
1991-1996 period
47.4
12.9
1.5
27.9
Acquisitions
0
0
0
25
Restitution
28.4
0
0
0
Sales and leases 1/
19
12.9
1.5
2.9
Cash and credit sales
12.1
6.1
0.5
0
Transferred liabilities
6.9
1.9
0
2.9
Lease with option to buy
0
4.9
1
0
1997-2000 period
7.7
1.4
0.2
1
Total
55.1
14.3
1.7
28.9
Source: Banco Central de Nicaragua
1/ Transferred liabilities refers to tranfers of outstanding debts.
2/ Composed mainly of governments institutions.
Total
89.7
25
28.4
36.3
18.7
11.7
5.9
10.3
100
Table 3. Gross and net proceeds of privatization that accrue to the budget 1/
Gross proceeds
Net proceeds accruing to the
during active privatization
budget during active privatization
Years of active
Million of US
Percentage
Million of US
Percentage
privatization
dollars 4/
of GDP 5/
dollars 4/
of GDP 5/
CORNAP enterprises 2/
Public Utilities 3/
Telecommunications
Electricity
Generation
Distribution
Water and Sewage
1991-1996
1995-on going
2002
2000
242.9
167.9
167.9
52.9
115
-
2.5
7.2
7.2
2.3
4.9
-
0
108.4
108.4
19.4
89
-
0
4.6
4.6
0.8
3.8
-
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/
4/
5/
Net proceeds from the privatization of electricity distribution refers to gross proceeds minus ENEL's commercial debt (US$26 millions).
Annual data in national currency converted to U.S. dollars using annual average exchange rates.
Average of annual ratios of privatization proceeds to GDP during active privatization for CORNAP; percentage of GDP in the year 2000
for public utilities.
Table 4. Divestiture procedures and form of payment
Form of payment as a share of total transactions
Cash
Credit
Liability transfer
Total
Liquidation
0.0
0.0
0.0
0.0
Restitution
0.1
2.1
8.2
10.5
Merge or acquisition
0.0
0.0
0.0
0.0
Sale or lease
22.3
38.3
28.9
89.5
Total
22.4
40.5
37.1
100.0
Source: CORNAP
Table 5. Public Utilities: Fiscal balances and net transfers
(percent of GDP)
Electricity (ENEL)
Fiscal balance 1/
Net transfers 2/
External and domestic financing 3/
Telephone services (ENITEL)
Fiscal balance 1/
Net transfers 2/
External and domestic financing 3/
Water and Sewage (ENACAL)
Fiscal balance 1/
Net transfers 2/
External and domestic financing 3/
1991-1995
1995-2000
-0.99
-0.16
0.58
-0.78
0.00
0.57
1.55
0.43
0.47
1.23
0.21
-0.20
-0.41
-0.14
0.20
-1.35
-0.17
0.23
Source: Banco Central de Nicaragua
1/ Average overall deficit before net transfers to the government.
2/
3/
Average net transfers to the government.
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/
1993
1998
Change
Percentage
change
Gini
0.5165
0.5418
0.0253
4.9
Generalized Entropy (2)
1.0837
1.3645
0.2808
25.9
Between
Within
0.059
1.025
0.052
1.313
-0.007
0.288
-11.8
28.1
By industry
Agriculture, hunting, forestry and fishing
Mining and Quarrying
Manufacturing
Electricity, Gas and Water
Construction
Wholesale, retail trade, restaurants and hotels
Transport, Storage and Communication
Financing, Insurance, Real State and Business Services
Community, Social and Personal Services
1.448
0.830
0.672
0.327
0.491
1.104
0.648
0.555
1.003
1.842
0.635
0.473
0.462
1.454
1.017
1.202
0.546
1.414
0.394
-0.195
-0.199
0.135
0.963
-0.087
0.554
-0.009
0.411
27.2
-23.5
-29.6
41.3
196.3
-7.8
85.5
-1.6
41.0
Source: 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 a
1993
ru ra l
(% )
G ender
m a le
7 2 .6 0
fe m a le
2 7 .4 0
1998
ru ra l
(% )
1993
u rb a n
(% )
1999
u rb a n
(% )
7 8 .4 0
2 1 .6 0
5 6 .7 0
4 3 .3 0
5 3 .7 0
4 6 .3 0
A g e ( in y e a r s )
3 4 .4 0
3 1 .8 0
3 6 .1 0
3 6 .0 0
M a r ita l S ta tu s
m a r r ie d
U n m a r r ie d
6 8 .2 0
3 1 .8 0
5 3 .5 0
4 6 .5 0
6 3 .4 0
3 6 .6 0
6 1 .1 0
3 8 .9 0
F o r m a l S c h o o lin g
illit e r a t e
lit e r a t e
n o fo r m a l e d u c a t io n
p r im a r y in c o m p le t e
p r im a r y c o m p le t e
s e c o n d a r y in c o m p le t e
s e c o n d a r y c o m p le t e
t e r t ia r y in c o m p le t e
t e r t ia r y c o m p le t e
3 4 .3 0
6 5 .7 0
4 3 .1 0
3 1 .2 0
1 2 .4 0
6 .3 0
5 .3 0
0 .3 0
1 .2 0
3 1 .1 0
6 8 .9 0
3 5 .6 0
3 9 .8 0
1 0 .6 0
8 .3 0
4 .0 0
1 .0 0
0 .7 0
8 .2 0
9 1 .8 0
1 2 .5 0
2 5 .0 0
1 6 .0 0
1 5 .4 0
2 2 .1 0
3 .1 0
6 .0 0
6 .9 0
9 3 .1 0
0 .0 0
3 5 .8 0
1 1 .9 0
2 4 .1 0
1 4 .2 0
2 .7 0
1 1 .4 0
O c c u p a tio n 1 /
fir s t
second
t h ir d
2 .5 0
1 1 .0 0
8 6 .5 0
2 .2 0
6 .5 0
9 4 .3 0
1 3 .4 0
1 9 .2 0
6 7 .4 0
1 4 .6 0
3 8 .2 0
4 7 .2 0
S e c to r
p r iv a t e
p u b lic
8 9 .1 0
1 0 .9 0
9 8 .7 0
1 .3 0
7 6 .5 0
2 3 .5 0
8 6 .4 0
1 3 .6 0
E c o n o m ic A c tiv ity :
a g r ic u lt u r e
m in in g
m a n u fa c t u r in g
p u b lic u t ilit ie s
c o n s t r u c t io n
t r a d e , r e s t a u r a n t s & h o t e ls
t r n a s p o r t a t io n & c o m m .
fin a n c e a n d in s u r a n c e
s e r v ic e s
g o ve rn m e n t
5 4 .0 0
0 .2 0
6 .7 0
0 .5 0
2 .1 0
1 4 .1 0
2 .3 0
0 .2 0
1 8 .2 0
1 .7 0
6 6 .5 0
0 .6 0
5 .7 0
0 .4 0
3 .1 0
1 0 .4 0
1 .7 0
0 .1 0
1 0 .3 0
1 .3 0
5 .6 0
0 .2 0
1 6 .9 0
1 .5 0
4 .9 0
2 7 .2 0
5 .6 0
1 .4 0
3 0 .8 0
6 .0 0
3 .1 0
0 .1 0
1 6 .8 0
0 .9 0
5 .9 0
2 9 .1 0
6 .0 0
1 .9 0
2 9 .8 0
6 .3 0
S o c ia l S e c u r ity
c o n t r ib u t o r
n o n - c o n t r ib u t o r
1 2 .3 0
8 7 .7 0
7 .4 0
9 2 .6 0
3 5 .0 0
6 5 .0 0
2 5 .8 0
7 4 .2 0
S e c o n d jo b
yes
no
4 .3 0
9 5 .7 0
9 .4 0
9 0 .6 0
3 .3 0
9 6 .7 0
1 2 .2 0
8 7 .8 0
P o s itio n
e m p lo y e e
h o u s e a id
s e lf- e m p lo y e d
p r o fe s s io n a l s e lf- e m p lo y e d
u n p a id fa m ily w o r k e r
e m p lo y e r
5 4 .6 0
7 .1 0
3 7 .6 0
0 .0 0
0 .4 0
0 .3 0
4 5 .6 0
2 8 .1 0
0 .0 0
2 2 .9 0
3 .4 0
6 1 .1 0
6 .8 0
3 0 .3 0
0 .7 0
0 .2 0
0 .9 0
5 8 .7 0
5 .6 0
3 0 .1 0
1 .1 0
0 .1 0
4 .4 0
S o urc e : A utho rs ' c a lc ula tio ns u s in g 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 n a 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 .
Table 8. Survey of Establishments by Economic Activity
state-owned
1993
private
total
state-owned
share
state-owned
1998
private
total
state-owned
share
Total number of Establishments
135
90
225
0.60
66
194
260
0.25
Agriculture and livestock
Fishing
Mining
Manufacturing
Electricity, Gas and Water
Construction
Commerce, restaurants and hotels
Transport, storage and communications
Finance and Insurance
Comunal, social and personal services
Central Governement
19
1
3
24
2
7
12
16
7
16
28
39
0
0
41
0
0
8
2
0
0
0
58
1
3
65
2
7
20
18
7
16
28
0.33
1.00
1.00
0.37
1.00
1.00
0.60
0.89
1.00
1.00
1.00
0
0
0
5
1
3
2
8
6
16
25
53
2
4
71
2
6
23
7
14
10
2
53
2
4
76
3
9
25
15
20
26
27
0.00
0.00
0.00
0.07
0.33
0.33
0.08
0.53
0.30
0.62
0.93
Source: Ministerio del Trabajo, Nicaragua. Establishment Surveys.
Table 9. Variance decomposition (in 1999 log real hourly wages) (1)
URBAN SECTOR
RURAL SECTOR
1999
1998
Employment 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
1993
private sector
public sector
77.1%
22.9%
89.0%
11.0%
public sector
private sector
total
0.501
0.914
0.817
0.394
0.796
0.773
public sector
private sector
total
2.139
2.071
2.087
0.050
100%
1.857
1.382
1.436
0.013
100%
0.008
0.054
-0.028
16%
108%
-57%
-0.007
0.008
-0.014
-56%
60%
-102%
Employment shares:
Variances:
Means:
CHANGE IN TOTAL VARIANCE
Decomposition I (1)
change in public sector employment share
change in public sector variance
change in private sector variance
change in public sector mean gap
change in private sector mean gap
0.013
0.001
25%
2%
0.027
-0.002
206%
-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
1999
Employment shares:
private sector
86.4%
public sector
13.6%
Variances:
public sector
618.9
private sector
417.8
total
446.5
Means:
public sector
16.8
private sector
13.4
total
13.9
RURAL SECTOR
1998
95.9%
4.1%
16.4
38.1
37.3
5.4
3.4
3.5
1993
1993
private sector
public sector
77.1%
22.9%
89.0%
11.0%
public sector
private sector
total
117.1
380.5
318.9
80.2
74.7
75.7
public sector
private sector
total
11.2
13.3
12.8
127.6
100%
8.1
6.3
6.5
-38.4
100%
-19.5
115.0
28.7
-15%
90%
23%
1.2
-7.0
-32.7
-3%
18%
85%
Employment shares:
Variances:
Means:
CHANGE IN TOTAL VARIANCE
Decomposition I (1)
change in public sector employment share
change in public sector variance
change in private sector variance
change in public sector mean gap
change in private sector mean gap
1.4
0.0
1%
0%
0.1
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
(in 1999 real hourly wages)
Measured
characteristics
effect
(1)
0.15
1142%
Wage equation
all coefficients
effect
(1)
0.04
280%
Public sector
coefficients
effect (3)
Wage equation
residuals
effect
(1)
-0.18 -1323%
Variance of logarithms
1993
0.773
1998
0.786
Difference
0.013
Variance
75.70
37.30
-38.40
35.01
-91%
-71.40
186%
-12.65
18%
-2.02
5%
Ninth to first decile ratio
Ninth to fifth decile ratio
Fifth to first decile ratio
10.29
3.04
3.39
10.79
3.42
3.15
0.50
0.38
-0.24
0.98
2.40
-1.32
194%
627%
561%
0.08
-0.17
0.08
15%
-45%
-36%
0.90
0.19
0.10
1199%
-111%
115%
-0.55
-1.85
1.00
-109%
-482%
-425%
Generalized Entropy(-1)
Generalized Entropy(0)
Generalized Entropy(1)
Generalized Entropy(2)
1.15
0.49
0.50
0.90
3.08
0.53
0.54
0.97
1.93
0.05
0.04
0.07
-0.37
0.09
0.17
0.43
-19%
189%
437%
634%
0.01
0.01
-0.02
-0.16
0%
11%
-62%
-229%
0.05
0.02
-0.01
-0.08
855%
289%
24%
53%
2.29
-0.05
-0.11
-0.21
119%
-100%
-275%
-305%
Gini
0.51
0.52
0.02
0.07
399%
0.00
5%
0.00
558%
-0.05
-304%
0.05
145%
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
number of observations
R-squared
F(k-1, n-k-1)
Electricity price
real Household Expenditure per head
real Household Expenditure per head, squared
refrigerator
refrigerator x electricity price
shanty dwelling
access to piped water
constant
number of observations
R-squared
F(k-1, n-k-1)
All observations
4702
0.1567
127.91
coefficient
s.e.
-0.034 **
0.004
0.002
0.003
-0.001 **
0.000
0.025 **
0.001
-0.014 **
0.011 **
0.024 *
0.002
0.001
0.012
All observations
3709
0.2005
66.79
coefficient
s.e.
-0.013 **
0.003
0.006 **
0.001
0.000 **
0.000
0.032 **
0.003
URBAN SECTOR
Excluding households (1)
3567
0.1355
72.76
coefficient
s.e.
-0.033 **
0.005
-0.020 **
0.009
0.001
0.001
0.024 **
0.001
-0.009 **
0.003
0.007 **
0.001
0.114 **
0.029
RURAL SECTOR
Excluding households (1)
926
0.104
15.14
coefficient
s.e.
-0.011
0.009
-0.013
0.010
0.001
0.001
0.025 **
0.003
Excluding households (1)
3567
0.1455
63.6
coefficient
s.e.
-0.011 *
0.006
-0.031 **
0.009
0.002 **
0.001
0.027 **
0.002
-0.069 **
0.011
-0.009 **
0.003
0.006 **
0.001
0.148 **
0.030
Excluding households (1)
926
0.1101
13.25
coefficient
s.e.
-0.001
0.009
-0.017 *
0.009
0.001
0.001
0.028 **
0.004
-0.059 **
0.028
-0.008 **
0.003
0.001
0.002
0.098 **
0.029
Electricity price
real Household Expenditure per head
real Household Expenditure per head, squared
refrigerator
refrigerator x electricity price
shanty dwelling
-0.005 **
0.000
-0.008 **
0.003
access to piped water
0.009 **
0.001
0.001
0.002
Constant
-0.011 **
0.004
0.086 **
0.030
Notes:
(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
total observations
censored
uncensored
Pseudo R-squared
Wald x2
Dependent variable: electricity budget share
Electricity price
real Household Expenditure per head
real Household Expenditure per head, squared
refrigerator
refrigerator x electricity price
constant
URBAN SECTOR
All observations
Excluding households (1)
4702
4539
541
972
4161
3567
0.039
0.053
603.7
587.87
coefficient
s.e.
coefficient
s.e.
-0.032 **
-0.011 **
0.000
0.025 **
0.005
0.004
0.000
0.001
0.085 **
0.014
-0.011 *
-0.032 **
0.002 **
0.028 **
-0.070 **
0.158 **
0.006
0.008
0.001
0.001
0.011
0.025
RURAL SECTOR
All observations
Excluding households (1)
3709
3471
2536
2545
1173
926
0.111
0.154
150.53
111.52
coefficient
s.e.
coefficient
s.e.
-0.021 **
-0.009
0.000
0.026 **
0.008
0.006
0.000
0.002
0.070 **
Dependent variable: access to electricity service
real Household Expenditure per head
0.352 ** 0.033
0.321 **
0.030
0.231 **
shanty dwelling
-0.690 ** 0.115
-1.013 **
0.109
-0.815 **
access to piped water
1.244 ** 0.056
0.790 **
0.050
1.207 **
constant
-1.710 ** 0.201
-1.715 **
0.180
-1.940 **
rho
-0.164 ** 0.045
-0.160 **
0.047
-0.115 **
Mill's ratio
-0.005 ** 0.001
-0.005 **
0.002
-0.003 **
Notes:
(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
0.019
0.000
-0.018
0.001
0.028 **
-0.058 **
0.104 **
0.010
0.012
0.001
0.003
0.023
0.038
0.022
0.079
0.056
0.126
0.062
0.002
0.409 **
-0.870 **
1.047 **
-3.076 **
-0.078 **
-0.002 **
0.034
0.094
0.057
0.190
0.080
0.002
Table 18. Elasticity and Virtual price for
approximations to consumer surplus
price elasticity
virtual
of electricity
price
budget share (in real cordobas)
Bottom decile
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Decile 6
Decile 7
Decile 9
Top decile
-0.013
-0.042
-0.102
-0.237
-0.475
-0.601
-0.831
-1.400
-2.726
-3.921
2163.0
480.4
104.4
35.3
11.3
9.1
4.3
4.4
3.1
3.4
Table 19. Percentage of households in each
decile which gain access to electricity
between 1993 and 1998
total
urban
rural
0.2%
0.0% 0.2%
4.3%
3.2% 4.6%
4.1%
5.0% 3.7%
5.0%
6.5% 3.8%
7.6% 10.7% 3.9%
8.7% 10.9% 5.6%
10.0% 12.7% 4.0%
9.7% 11.3% 4.4%
11.2% 12.2% 6.1%
6.9%
7.5% 4.5%
Bottom decile
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Decile 7
Decile 8
Decile 9
Top decile
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
-0.09% -0.04%
-0.43% -0.08% -0.04%
-0.42%
-0.16% -0.11%
-0.33% -0.16% -0.11%
-0.32%
-0.24% -0.21%
-0.30% -0.23% -0.21%
-0.28%
-0.27% -0.16%
-0.41% -0.26% -0.16%
-0.38%
-0.32% -0.18%
-0.43% -0.30% -0.18%
-0.39%
-0.41% -0.26%
-0.51% -0.38% -0.25%
-0.47%
-0.37% -0.22%
-0.44% -0.33% -0.21%
-0.39%
-0.45% -0.19%
-0.53% -0.39% -0.17%
-0.46%
-0.45% -0.30%
-0.48% -0.36% -0.27%
-0.38%
-0.40% -0.19%
-0.46% -0.29% -0.16%
-0.33%
Bottom decile
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Decile 6
Decile 7
Decile 9
Top decile
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
-0.78%
-0.63%
-0.93%
-0.76%
-0.62%
-0.92%
-0.55%
-0.49%
-0.63%
-0.54%
-0.48%
-0.61%
-0.59%
-0.76%
-0.46%
-0.58%
-0.76%
-0.44%
-0.48%
-0.40%
-0.53%
-0.46%
-0.39%
-0.50%
-0.43%
-0.35%
-0.46%
-0.40%
-0.34%
-0.43%
-0.53%
-0.43%
-0.58%
-0.49%
-0.41%
-0.53%
-0.43%
-0.31%
-0.48%
-0.39%
-0.29%
-0.43%
-0.50%
-0.28%
-0.55%
-0.43%
-0.24%
-0.48%
-0.49%
-0.48%
-0.49%
-0.39%
-0.43%
-0.38%
-0.49%
-0.55%
-0.48%
-0.36%
-0.48%
-0.35%
Bottom decile
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Decile 6
Decile 7
Decile 9
Top decile
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
12.99% 12.99%
12.66% 12.66%
15.98% 16.90% 11.53% 16.55% 16.69% 15.84%
15.61% 24.12%
3.24% 16.25% 23.95%
5.04%
5.38%
9.01%
2.78%
6.29%
8.90%
4.42%
5.38% 11.12%
3.65%
6.27% 11.02%
4.84%
3.57%
5.93%
2.71%
4.30%
5.99%
3.69%
1.69%
1.32%
1.74%
2.41%
1.38%
2.57%
2.02%
2.66%
1.95%
2.59%
2.58%
2.60%
1.38%
1.83%
1.33%
1.84%
1.85%
1.83%
0.74%
0.13%
0.83%
1.25%
0.23%
1.41%
Bottom decile
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Decile 6
Decile 7
Decile 9
Top decile
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
Bottom decile
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Decile 6
Decile 7
Decile 9
Top decile
As a percentage of real household expenditure per head
First order approximation
Second Order Approximation
Total
Rural
Urban
Total
Rural
Urban
-0.09%
-0.04%
-0.43%
-0.05%
0.00%
-0.42%
-0.16%
-0.11%
-0.33%
0.58%
0.69%
0.22%
-0.24%
-0.21%
-0.30%
0.47%
0.71%
-0.01%
-0.27%
-0.16%
-0.41%
0.07%
0.19%
-0.08%
-0.32%
-0.18%
-0.43%
0.22%
0.27%
0.18%
-0.41%
-0.26%
-0.51%
0.04%
0.13%
-0.02%
-0.37%
-0.22%
-0.44%
-0.07%
-0.15%
-0.03%
-0.45%
-0.19%
-0.53%
-0.10%
-0.05%
-0.12%
-0.45%
-0.30%
-0.48%
-0.11%
-0.14%
-0.11%
-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
total
urban
rural
-0.22%
-0.19%
-0.03%
-0.07%
-0.06%
-0.01%
0.52%
0.43%
0.09%
second order approximation
total
urban
rural
-0.20%
-0.16%
-0.03%
-0.07%
-0.05%
-0.01%
0.50%
0.41%
0.09%
σ = 0.5
σ=1
σ=2*
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
total
urban
rural
-0.04%
-0.05%
0.01%
0.00%
-0.01%
0.01%
-0.04%
0.02%
-0.07%
second order approximation
total
urban
rural
-0.01%
-0.03%
0.01%
0.00%
-0.01%
0.01%
-0.06%
0.01%
-0.07%
σ = 0.5
σ=1
σ=2*
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)
total
year 1998
urban
rural
first order approximation (1)
total
urban
rural
second order approximation (2)
total
urban
rural
Poverty
FGT(0)
FGT(1)
FGT(2)
0.359
0.148
0.085
0.193
0.060
0.028
0.578
0.263
0.155
0.358
0.148
0.082
0.192
0.060
0.028
0.577
0.263
0.155
0.358
0.148
0.082
0.189
0.059
0.027
0.577
0.263
0.155
Gini
0.556
0.496
0.589
0.556
0.496
0.589
0.556
0.496
Atkinson(1/2)
0.265
0.203
0.320
0.265
0.203
0.320
0.265
0.203
Atkinson(1)
0.428
0.346
0.470
0.428
0.346
0.470
0.428
0.346
Atkinson(2)
0.634
0.531
0.537
0.634
0.532
0.537
0.634
0.532
Notes:
(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
0.589
0.320
0.470
0.538
Inequality
Table 28
Poverty and Inequality with and without welfare effect
(including changes in access)
total
year 1998
urban
rural
first order approximation (1)
total
urban
rural
second order approximation (2)
total
urban
rural
Poverty
FGT(0)
FGT(1)
FGT(2)
0.359
0.148
0.085
0.193
0.060
0.028
0.578
0.263
0.155
0.358
0.149
0.083
0.192
0.060
0.028
0.578
0.265
0.156
0.352
0.146
0.082
0.193
0.061
0.028
0.578
0.265
0.156
Gini
0.556
0.496
0.589
0.557
0.496
0.590
0.557
0.497
Atkinson(1/2)
0.265
0.203
0.320
0.266
0.208
0.321
0.266
0.208
Atkinson(1)
0.428
0.346
0.470
0.430
0.346
0.471
0.430
0.347
Atkinson(2)
0.634
0.531
0.537
0.636
0.532
0.539
0.636
0.532
Notes:
(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
0.590
0.321
0.471
0.539
Inequality
80