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Who Really Benefits from Export Processing Zones?
Estimating Distributional Effects Within Nicaraguan Municipalities1
Nathalie Picarelli2
London School of Economics
November, 2014
Abstract: This paper evaluates the distributional effects resulting from the establishment of
Export Processing Zones (EPZs) at the subnational level, by analysing Nicaragua’s program
from 1993 to 2009. Using a municipal-level panel, I estimate the average treatment effect on
a set of within-municipality inequality measures and quantile treatment effects across the
deciles of the real expenditure distribution of treated areas. I find that the overall impact on
disparity within treated municipalities is ambiguous, with a sustained increase in top-end
inequality that is mitigated by a milder, fluctuating decline in disparities at the lower-end of
the distribution. Primarily, I find that individuals at the 90th and 80th percentiles are the main
beneficiaries of the policy. The effect is heterogeneous in time for the rest of the segments,
diffusing at the 10th and middle-range percentiles after eight years of a firms’ establishment in
a given municipality. The time dynamics suggest that the initial inequality-enhancing effect of
the policy fades with the more years of operation, eventually reaching the middle and poorer
segments. The estimates fail to capture any spillover effects on the real expenditure levels
across the distribution of adjacent municipalities.
JEL Codes: C21, D31, D63, F13, O15, O24
Keywords: Export Processing Zones, Inequality, Distribution, Quantiles
1. Introduction
1 This is a preliminary draft not intended for citation or circulation without the author’s permission. I am indebted to Olmo Silva and Henry Overman for their guidance. I thank participants of seminars at the LSE for their helpful comments. All errors remain my own. This work was supported by the Economic and Social Research Council. 2 Email address: [email protected]
1
The use of Export Processing Zones (EPZs)3 as a policy for trade and economic development has
exponentially grown in the past decade, particularly but not solely in developing countries. In 1986, the
International Labor Organization (ILO) estimated that there were 176 zones in 47 countries. By 2008, the
number reached more than 3,000 zones in 135 countries, accounting for over 40 million direct jobs and
over US$200 billion in global exports (Farole and Akinci 2011). The attractiveness of this type of policy
lies in its wide scope: EPZs are considered a strategy to attract foreign direct investment (FDI), create
employment, support wider economic reform strategies, and as laboratories for new policies (FIAS 2008).
Despite the importance of the phenomenon, and the existence of a growing body of literature evaluating
their performance in achieving the latter objectives (Farole 2011, Engman and Onodera 2007, Aggarwal
2006, Glick and Roubaud 2006), little attention has been paid to understanding the distributional effects
resulting from the establishment of EPZs within countries, and more precisely within the municipalities
where these locate. Yet, if successful, EPZ programs are prone to have a significant influence not only at
the macro level - through growth generated by exports, FDI, or the restructuring of the business
environment –, but also on the local economies via micro channels (i.e. higher manufacturing wages,
employment generation, backward linkages with domestic suppliers, etc.). Disentangling in what way the
establishment of a zone profits the different segments of the income distribution within concerned areas is
central to apprehend the welfare dynamics of this popular tool and shed light on the ultimate beneficiaries
of the policy at the local level. It is also of great policy relevance, particularly in developing economies with
already large levels of overall income disparities.
In this sense, this analysis builds on a small literature studying the impact of EPZ policies on outcomes
measured at the subnational level in the context of developing countries. Findings in these papers are
revealing of the extent to which local economies are influenced by the establishment of an exporting zone.
For instance, using employment data and census statistics on educational outcomes at the municipal-level
for Mexico (1986-2000), Atkin (2012) finds an increase in school dropout in concerned municipalities
following the arrival of new better-paying export jobs (i.e. due to a higher opportunity cost of remaining at
school). He estimates that this negative effect could have been compensated had the firms located in
municipalities with lower levels of educational achievement. Similar in strategy to this paper, Wang (2013)
uses a Chinese municipal dataset to demonstrate that Special Economic Zones (SEZ) benefit local
economies in several ways. He finds that on average host municipalities have higher levels and growth
rates of per capita FDI and total factor productivity (TFP), as well as higher average wages that
compensate any increase in the local cost of living following the establishment of a zone. In many ways,
these findings highlight the various channels through which EPZs can have diverse distributional effects
within the areas where they locate.
This study takes advantage of the gradual establishment of EPZs in Nicaraguan municipalities during the
period 1993-2009 to assess the distributive pattern of this specific spatially-bound policy within the host
municipalities. Nicaragua provides an excellent setting to study this phenomenon. Not only is the program
considered a success in terms of generating employment and export growth, but the number of firms
operating under the regime has increased markedly since its inception across more than 20 of the 153
municipalities of the country. By 2010, EPZs jobs represented 25 percent of total formal work across the
3 When discussing EPZs, a variety of terminologies, such as Industrial Free Zones, Free Trade Zones, Special Economic Zones and Maquiladoras are used interchangeably in the literature. Although each has their own particularity, we will consider the broad definition of them being “demarcated geographic areas contained within a country’s national boundaries where the rules of business are different from those that prevail in the national territory. These differential rules principally deal with investment conditions, international trade and customs, taxation, and the regulatory environment; whereby the zone is given a business environment that is intended to be more liberal from a policy perspective and more effective from an administrative perspective than that of the national territory” (Farole and Akinci 2011:23).
2
country, with on average firms directly employing around 7.5 percent of the total labor force of the
municipalities where they are located4. Furthermore, at traditionally high levels, inequality in the country
registered an important decline throughout the period, offering an interesting setting to study the
distributional effect of EPZs at the subnational dimension.
For the analyses, I exploit an individual-level repeated cross-sectional dataset based on official household
surveys and construct a unique municipal-level panel that allows for the examination of inequality
dynamics before and after the policy implementation. By focusing on the aggregate outcomes at the
municipal level, the study captures general equilibrium impacts of EPZ establishment within a locality. The
identification strategy is straightforward. I use both the time and cross-section variation of zones’
establishment across municipalities to estimate their average effect on a set of within-municipality
inequality measures and on the levels of real expenditure per capita across all the deciles of the expenditure
distribution of treated municipalities. In the main approach, I use a difference-in-differences (DID)
strategy extended to quantiles as discussed by Athey and Imbens (2006). To increase the robustness of the
results, I also exploit the original repeated cross-sectional data to compute unconditional quantile
treatment effects (Firpo 2007) on the entire expenditure distribution of treated municipalities. An
important feature of the overall empirical strategy needs to be emphasized. The approach does not aim at
measuring the impact of the EPZ policy on inequality across Nicaragua as a whole. Rather, it looks at the
question of knowing whether within municipalities exposed to the establishment of EPZs, certain
segments of the distribution capture more or less of the resulting gains or losses.
The validity of the findings rely on the assumption that the different empirical methods manage to account
for the underlying differences in municipalities’ observable and unobservable characteristics that are likely
to explain the non-random choice of EPZs location across time and space. In the absence of a credible
instrument, I take care of unobservable confounders by allowing for time and municipalities fixed-effects
across specifications. Additionally, I use information on accessibility and socio-economic indicators to
build alternative sets of control municipalities that are likely to be more meaningful comparable groups
under the DID setting. The assumption of historical parallel trends of main outcomes holds for the key
control groups.
Additionally, the ‘experiment’ created by this setting may differ from the ideal case where the influence of
EPZs is contained within the static administrative borders of the treated municipality. As such, two other
mechanisms also pose a potential threat to the consistency of the regression estimates: the existence of
possible spillover effects to neighbouring areas and people’s mobility throughout the period. I test for the
existence of spillovers on the levels of expenditure per capita across the distribution of neighbouring areas
using varying degrees of distance to capture dynamics resulting either from firm creation/diversion or
commuting patterns. I find the effect to be close to zero, which is consistent with findings in the literature
registering small backward linkages generated by EPZ policies (ILO 2008)5. Also, data available for 2001
pinpoints to relatively low levels of commuting (3.5 percent of sample). The situation is more complex
concerning larger displacements. Information available at the individual-level allows controlling for within-
country migration in specifications using the repeated-cross sectional dataset. I add a grouped measure as
control in the municipal-level estimations. However, a potential pitfall remains. As noted by Angrist and
Piscke (2009), time-varying variables measured at the aggregated level are usually affected by treatment,
and can introduce endogeneity. Still, while concerns regarding possible population adjustments towards
4 Data obtained from the Comisión Nacional de Zona Franca (CNZF) and Nicaragua’s Central Bank (BCN). 5 Some of the main reasons for this failure are found to be related to the difficulty for domestic firms of supplying low-cost, high-quality inputs necessary for global production at EPZs, as well as to low technological spillover resulting from the unskilled labor nature of the EPZ work (ILO 2008).
3
EPZ municipality are valid, these are likely to be negligible in the Nicaraguan context. Research conducted
with U.S. data has found that regional adjustments to labor market shocks are slow and incomplete, mostly
due to the cost of mobility (Autor, Dorn and Hanson 2012). The latter is likely to be even higher in the
context of developing countries with poor infrastructure and rigid labor markets. In Nicaragua, during the
period studied, the urbanization rate remained low (increasing form 62 percent in 1995 to 69 percent in
2010), and there was an overall decline in the urban population growth rate from 3.8 in the period 1990-
1995 to 2.83 in the 2005-2010 period. Testing for an effect of EPZ establishment on within-country
migration seems to confirm this hypothesis. Overall, I do not find evidence of population adjustments
towards municipalities hosting EPZs.
The analysis reveals interesting conclusions concerning distributional dynamics of EPZ policies at the local
level. First, I find that the overall effect on within-treated municipalities’ inequality is ambiguous, with a
sustained increase in upper-end inequality that is mitigated by a smaller varying decline in bottom-end
disparities. Above all, individuals located at the 90th and 80th percentiles of the distribution in treated areas
are the main beneficiaries of the policy over the period covered. Interestingly, the effect is heterogeneous
in time for the remaining percentiles, diffusing to the first and middle-range deciles only after eight to
eleven years of an EPZ establishment in a given municipality. The time dynamics suggest that the initial
inequality-enhancing effect of the policy fades with the more years of operation, eventually reaching the
middle and poorer segments. As mentioned, the estimates fail to capture any spillover effect resulting from
EPZ establishment across the real expenditure distribution of neighbouring areas.
This paper is original for three reasons. First, it is to my knowledge the first time that the impact of EPZs
on income distribution at the subnational level is formally assessed. Second, it contributes to add empirical
evidence to the evaluation of place-based policies in the context of developing countries. Finally,
methodologically, it combines panel and individual-level analysis to estimate treatment effect on the entire
income distribution with two alternative estimators.
The remainder of the paper is organized as follows. Section 2 lays out the theoretical framework. Section 3
briefly describes the EPZ program in Nicaragua, while section 4 introduces and examines the data. Section
5 discusses the empirical strategy and section 6 presents the results. Robustness checks are shown in
section 7. The final section concludes and looks at policy implications.
2. Theoretical Grounds
To start, I look at the theoretical underpinnings that guide this empirical work. While there has been a
strand of literature that has looked at the overall welfare effects of the establishment of EPZs on host
countries using basic trade models and general equilibrium approaches6, the analyses have failed to
consider distributional issues. For this, I consider the main channels described by the rich body of research
that has studied the influence of trade openness on interpersonal inequality through similar conceptual
frameworks. I focus on the principal mechanism, namely the labor-market effects, and then briefly
summarize secondary channels (i.e. effect on asset ownership, changes in household production and
consumption decisions, and general equilibrium effects through changes in relative prices).
2.1 Labor-Market Channels
6 A review by Jenkins et al. (1998) concludes that largely, the implications of EPZs on national welfare are found to be ambiguous and depend on the complexity of the model used as well as their specific assumptions. For more details see Hamada (1974), Hamilton and Svensson (1982), Wong (1986), Chaudhuri and Adhikari (1993), and Din (1994).
4
The most straightforward effect of EPZs on income distribution is likely to happen through changes on
labor income, resulting from the impact on the relative demand for factors of production – here that is
skilled vs. unskilled labour. The basis to understand these dynamics is the standard 2x2 Heckscher-Ohlin
(HO) model and its related theorem Stolper-Samuelson (SS). Indeed, even though the theoretical and
empirical shortcomings of this basic model are widely recognized7, it has been the foundation to think
about distributional effects of trade policies over the past decades. Together, in the simplest form, they
predict that trade openness pushes countries to specialize in the production of goods where they have a
relatively-abundant factor of production. This, in turn, boosts the demand for the relatively-abundant
factor and raises its relative returns. In developing countries, the basic form of the theorem therefore
implies that unskilled-labor (the abundant factor of production) will be favoured by the resulting
distributional changes.
The dynamics alas are much more complex and depend on the consideration of additional interactions.
There are fundamental limits to this factor-endowment-based trade theory. In the context of developing
countries the most salient one is the de facto constraints to labor reallocation – one of its fundamental
mechanisms of adjustment – due to labor market rigidities, the existence of prominent informal sectors
and low labor mobility. In fact, recent trade models have shown that liberalization can reduce the wages of
unskilled labor even in labor abundant economies (Banerjee and Newman 2004). Goldberg and Pavcnik
(2007) offer a good review of alternative dynamics. These effects reverse the distributional predictions of
the basic HO model. I describe some of them next.
First, altering the simple HO model by introducing non-traded goods or additional factors of production
overturns the simple SS theorem predictions. In these cases, if the production of tradable goods requires a
higher ratio of skilled-to-unskilled workers, then trade openness will benefit skilled workers. Harrison and
Hanson (1999) have shown for instance that exporting plants in Mexico employ a higher share of white-
collar workers than non-exporting firms, thus inferring a higher ratio of skilled-to-unskilled labor than
similar firms in the domestic economy. A similar outcome occurs when adding capital mobility between
countries, with the idea that the increase in capital flows is associated with a higher demand for skilled
workers.
Adding technology to the equation also increases the relative demand for skilled-labour: Feenstra and
Hanson (1997) show that cheaper access to foreign technology allows developing countries to compete
internationally in more skill-intensive goods, which in turn allows firms in high-income countries to
minimize costs by outsourcing intermediate inputs to these countries. While outsourced goods would be
characterized as unskilled-labor-intensive from a developed country’s perspective, the authors argue that
they are actually skilled-labor-intensive when compared with existing domestic activities in developing
economies. As such, outsourcing increases the average skill intensity of production in developing
economies, thus increasing the relative demand for skilled labor and inducing an increase in the skill
premium. Similar dynamics have been shown by Verhoogen (2008), who argues that trade openness leads
to an upgrading of the average product quality in exporting plants, which in turn generates demand for a
better qualified workforce. Finally, an additional channel includes the existence of an industry wage
premium. The latter may arise from productivity improvements resulting from the higher exposure to
trade in a specific industry together with labor market rigidities that prevent labor reallocation (Heckman
and Pages 2000).
7 On the theoretical side the model relies on restrictive assumptions such as perfect mobility of factors within a country, perfect competition, and fixed technology; while on the empirical side there has been no support for the predictions in its basic form.
5
Extending these conceptual frameworks to EPZs is relatively straight-forward given that employment
generation in the zones correspond to ‘export jobs’, and thus it impacts labor income through an increase
in the relative demand for a specific factor of production or by the possible existence of a wage premium
in the exporting sector. As described for the general case of trade openness, the distributional dynamics
will depend on which factor of production relatively benefits from EPZs’ jobs. The answer is far from
obvious. The simple HO-SS predictions imply that the zones will concentrate in unskilled-labor-intensive
sectors such as manufacturing or agriculture - as is manifestly the case in Nicaragua and other developing
economies -. In its basic form, this concentration should favour unskilled labor and thus lead to a decrease
in inequality. Using the case of Madagascar, Glick and Roubaud (2006) find indeed evidence that
employment in the zones benefit workers with low-levels of schooling. However, the more subtle
mechanisms are also very much at play with EPZs (i.e. outsourcing, capital flows, export-related
productivity improvements, labor market rigidities), and they may in practice induce a skill premium that
modifies the distributional predictions of the standard model, as discussed. There is in fact more
documented evidence on the latter. Engman (2011) finds evidence of higher TFP levels in the EPZ sector
of Honduras. Atkin (2012) and Wang (2013) also provide solid evidence that the levels of wages are equal
or higher in EPZs for a similar set of skills than outside the zones. The broader literature exploring the
impact of exporting firms and multinational corporations on labor market outcomes in developing
countries provides further evidence of the existence of a sector premium8.
2.2 Secondary Channels
There are seemingly many additional mechanisms by which EPZs may impact income distribution; the
overall general equilibrium effect on inequality depending in the end on the net balance of forces. I discuss
the two most relevant next.
First, EPZs may affect income distribution in benefiting the owners of the factors of production other
than the skilled and unskilled labor (Meschi and Vivarelli 2008). An immediate example comes with land
ownership or natural resources. In this case, EPZs could contribute to income polarization. Anderson
(2005) imagines mechanisms that could affect the amount of inequality in the ownership of the factors of
production per se, either via income effects that relax credit constraints for instance, or via the reaction of
individuals to the way they adjust their holdings of assets in response to changes in the return of those
assets (i.e. land).
Second, in the medium to long term, important complementary channels are the ones linked to
households’ production and consumption decisions. The more direct one relates to the employment
choices for households’ members, which is particularly important in low-income countries. The
establishment of EPZ affect the choice between employment in the export sector and self-employment in
the informal sector, or with respect to educational choices. At the same time, consumption decisions may
be affected through general equilibrium effects reflected in local prices. For empirical evidence on these
two channels see Atkin (2012) and Wang (2013), respectively.
2.3 Spatial Considerations
An important concern here is the spatial aspect. The spatially-fixed nature of EPZs and their propensity to
hire or outsource in the area where they operate implies that the micro channels described above are likely
to have a local dimension. Limitations to factor mobility may help emphasize this dimension. These
8 Atkin refers to the findings of this literature, asserting that the most robust stylized facts that has emerged from research in this area using micro-level firm data, has been that exporting firms pay higher wages. For Mexico he cites Bernard (1995), Zhou (2003) and Verhoogen (2008).
6
characteristics justify the subnational approach taken in this paper. In this sense, results complement
findings in the strand of literature that has looked at the effect of trade liberalization on outcomes in local
and regional labor markets (Autor, Dorn and Hanson (2012), Kovak (2011), Topalova (2010))9.
3. Nicaragua’s Export Processing Zone Program
3.1 Ley de Zona Franca and Stipulations
Resembling analogous initiatives, Nicaragua’s EPZ regime (‘Zonas Francas’) was put in place with the aim
of generating employment, attracting FDI, increasing non-traditional exports, acquiring new technologies
and expanding international trade. The present program is relatively young compared to other Central
American countries as a previous initiative was abandoned for a decade during the civil conflict (1979-
1990) and the program was only reintroduced following the shift towards democracy and the market
economy in 1990. In 1991 Decree N.46-91 formally established the existing regime, with the appropriate
regulation passed in the following year (Decree N.31-92). Together, these two decrees created the legal
framework concerning the organization of EPZs, the fiscal incentives, the rights and obligations of
operators and users, as well as the regulatory body in the form of the National Commission of Free Trade
Zones (CNZF). The law was amended over the years, with its most notable reform taking place in 2005
(Decree N.50-2005). The latter significantly simplified procedures, and together with the signing of a Free
Trade Agreement with the U.S. (DR-CAFTA) in the same year, is considered instrumental in the increase
of FDI and firms’ participation in the EPZ program in the country (World Bank 2011).
Under the Nicaraguan legislation, foreign and national private firms are considered eligible for EPZ status
if they are oriented towards the production and export of goods and services. The law distinguishes three
different categories of firms: administrators (industrial parks operators), users (situated within industrial
parks) and “single-factory” EPZs. Technically, the regime institutes the entire territory as a possible host
for an EPZ, with firms applying to the status and locating themselves freely either as stand-alone plants, in
the form of industrial parks or within the latter. For the empirical analysis, I primarily define as an EPZ
single factories and industrial parks hosting one or more firms, which have been designated as such, and
are reported by the CNZF10.
Following standard practice, the program is based on attracting and generating business by offering
particular tax incentives to qualifying firms. These include the full exemption on (1) income tax, (2) taxes
on municipal property sales, (3) imports taxes of machinery, equipment and intermediate goods, as well as
of transportation and support services for the zone, (4) municipal taxes, (5) indirect taxes, taxes on
selective sales or consumption, (6) export taxes on processed products in the area; as well as the (7)
unrestricted repatriation of capital. Under Nicaraguan law, EPZs are allowed to sell intermediate goods
and inputs to other exporting firms in the country, provided they pay custom duties. However, they are
strictly forbidden from selling final goods in the local market. The legislation also provides conditions
under which EPZ developers may benefit from tax exemptions, as well as the possibility for EPZs to
subcontract to local Nicaraguan firms which are then exempted from VAT. These characteristics of the
program are important. On the one hand, the restriction to sell in the local market as well as the barriers to
sell intermediate inputs means that the larger effect of EPZs is going to be contained to the exporting
sector, without hampering local producers. On the other hand, the possibility to outsource to local
9 These have emphasized the limitations of perfect factor mobility across geographical areas, and focused on treating local labor market or subnational entities as sub-economies subject to differential trade shocks 10 From the CNZF Annual Yearbooks, I compiled information on EPZs location by the end of each year of interest, origin and sector of operation.
7
providers, which are then exempted from VAT, indicates that there are incentives in place to generate
backward linkages with local producers. Both these dynamics suggest that the impact of zone
establishment on local economies will be positive, either through business creation or employment
generation. The relative price effects on consumption decisions or local costs of living are harder to
predict.
Figure 3.1 Gradual Establishment of EPZs in Nicaraguan Municipalities (1993-2009)
1993
1998
2001
2005
2009
Notes: GIS digital maps. Municipalities are highlighted following the establishment of an industrial park or single factory under the EPZ regime. During the period considered some municipalities may contain more than one EPZ. Data was compiled from CNZF Annual Yearbooks.
8
3.2 Characteristics and Results
Since the launch of the program, the number of EPZs in the country visibly picked up. For the period
under study, the number of single factories and industrial parks under the regime increased from 1 in 1993
(only publicly-owned park) to 64 in 2009, located in 20 (of 153) different municipalities and 10 (of 18)
provinces (Figure 3.1). Approximately 90 percent of these EPZs operate in the manufacturing sector, with
a predominance of firms producing textile and apparel goods, followed by cigar production and light
electronics. Near 35 percent of the firms are from Nicaraguan origin, followed by North American (28
percent), Asian (19 percent) and other Central American countries (12.5 percent).
The policy has been successful in terms of generating employment, and has contributed significantly to
exports growth. Exports from EPZs increased from representing only 1 percent of GDP in 1994 to 15
percent of GDP in 2010. By that same year, the zones accounted for 50 percent of total exports in the
country (Figure 3.2). More precisely, mirroring their strong concentration in the manufacturing sector, they
were responsible for 93 and 95 percent of Nicaragua’s exports of apparel and machinery, respectively. The
growing contribution of manufacturing to the total productive value-added over the period confirms the
expansion of the sector: manufacturing went from representing near 16.5 percent in 1994 to around 20.5
percent in 2010, just behind agriculture which remained first throughout the period. It is important to note
that there seems to be a relatively low to moderate level of backward linkages with local producers. Their
share in EPZ’s exports is estimated to be close to 30 percent (ECLAC, 2012), putting Nicaragua far
behind leading Latin American countries such as Uruguay (61 percent)11.
Figure 3.2 EPZs’ Exports as a share of GDP and Total Exports FOB (1994-2011)
Figure 3.3 Number of EPZs and Employment (1991-2009)
More notably, EPZs have had a significant impact in terms of employment generation (Figure 3.3). In
2010, EPZs jobs represented 25 percent of total formal work in the country. The zones are estimated to
directly employ near four percent of Nicaragua’s labor force (2.2 million in 2009), and the CNZF estimates
that the impact through indirect employment is three times as high. This mechanism is probably the
principal channel through which EPZs may impact the local economy of municipalities where they locate.
On average in 2009, EPZs directly employed nearly 7.5 percent of the total labor force of concerned
11 In Central America, Nicaragua remains amongst the best performers. Honduras has the highest local contribution at 35 percent and Panama de lowest at 7 percent.
Notes: Data compiled from CNZF Annual Yearbooks, Pro Nicaragua and Nicaragua’s Central Bank; * in Figure 3.3 specifies years when household survey data is available.
9
municipalities, and reached more than 20 percent of their labor force indirectly (the numbers stay high at
the province level as well at 3.1 and 9.3 percent, respectively). Women accounted 55 percent of people
employed in EPZs in 2010, a decline from its early beginnings when they averaged above 60 percent. Still,
these numbers are well below popular cases such as Bangladesh (85 percent) or the Philippines (74
percent) and imply a wider reach of EPZs’ employment in the country.
Nicaraguan labor laws regulate work in EPZs and minimal wages have been in place since 1999 (Table
3.1). Despite the country having the lowest manufacturing wages in the region, a source of its comparative
advantage, minimum wages in EPZs have been traditionally higher than the ones in the overall
manufacturing and agricultural sectors throughout the period. Seemingly, this results from the sector
having registered the highest productivity levels in the country during the past decade12, pushed by the
expansion of EPZs’ activity in agribusiness and textiles.
Table 3.1 Average National Wage and Minimum Wages in Selected Sectors (1997-2009)
A final note regarding Nicaragua’s general labor market is warranted. Labor force has expanded rapidly
since the 1990s, at annual average rates that have outstripped population growth (3.6 percent vs. 1.7
percent, respectively). Positively, employment generation has followed a similar rate, with unemployment
remaining stable at around 6 percent for most of the 2000s. However, despite the sustained growth of the
manufacturing sector, the employment structure of the country remained largely unchanged in the last
decade, dominated by the labour-intensive low-skill agricultural sector. The latter even registered an
increase in participation in the 2000s (World Bank, 2012). These trends mirror the relatively more rural
nature of the country13 and its relatively low education level. In fact, while average educational attainment
12 In 2010, manufacturing accounted for nearly 21 percent of output and 11 percent of total employment (Central Bank of
Nicaragua). 13 Urbanization rate increased from 62 percent in 1995 to near 69 percent in 2010 (UN-Habitat), and there was an overall decline in urban population growth rate from 3.8 in the period 1990-1995 to 2.83 in the 2005-2010 period.
Minimum Wages
Agriculture Manufacturing EPZ
1997 1,617.3 300.0 500.0 .
1998 1,964.1 . . .
1999 2,282.3 450.0 600.0 800.0
2000 2,585.0 . . .
2001 2,897.1 550.0 670.0 895.0
2002 3,134.5 580.0 730.0 960.0
2003 3,388.2 615.0 825.0 1,037.0
2004 3,686.3 669.3 897.9 1,128.6
2005 4,266.2 769.4 1,032.7 1,298.4
2006 4,823.6 869.4 1,212.7 1,478.4
2007 4,957.2 1,025.9 1,431.0 1,744.5
2008 5,341.9 1,392.1 1,941.8 2,367.5
2009 6,010.3 1,573.1 2,155.5 2,556.7
Average National
Notes: Data compiled from Nicaragua’s Central Bank, Nominal Córdobas.
10
increased significantly during the past decade it remained one of the lowest in Latin America14.
Additionally, human capital accumulation in Nicaragua is unequal and inefficient with most of the
population falling short of even starting secondary education and only a small proportion completing
tertiary levels (World Bank, 2012).
4. Data and Basic Trends
4.1 Datasets
The data for this analysis were drawn from several sources. I compiled a repeated cross-sectional dataset
for the period 1993-2009 based on five LSMS Household Surveys15 (Encuesta Nacional de Hogares para la
Medición del Nivel de Vida, EMNV) collected by the Nicaraguan Institute of Statistics (Instituto Nacional
de Información de Desarrollo, INIDE). These contain detailed information on living standards of
individuals, occupation, industrial affiliation and various other household and individual characteristics.
The complete cross-sectional dataset amounts to 138,429 individual observations, from a total of 26,162
households, randomly selected according to the INIDE’s methodology to be representative at the national,
regional, urban and rural levels. I used the information from these repeated cross-sectional surveys to
construct a panel at the municipal-level and calculate inequality indices and various expenditure deciles at
this level of aggregation. These measures include a correction for the sample bias using sampling weights.
Municipalities with less than 30 observations per period were excluded to reduce measurement error.
Overall, the sample comprises observations for an unbalanced panel of between 80 to 136 municipalities
per year (Table 4.1).16 Information on Export Processing Zones is compiled from various CNZF Annual
Yearbooks, which contain information on location and sector of operation of each zone.
14 For the population 25 years and older, average educational attainment reached 6.2 years in 2011. While this was three times higher than in the 1960s, it remained well below the Latin American average of 9 years (World Bank, 2012). 15 The Living Standards Measurement Surveys were developed by the World Bank to improve the type and quality of household data collected by statistical offices in developing countries. The years of each EMNV are: 1993, 1998, 2001, 2005, and 2009. 16 Only 5 municipalities are omitted because of N<30 rule across the years. As a robustness check, I run the different specifications with a more balanced panel, omitting observations observed less than 4 times. Conclusions remain unchanged.
Sample Size 1993 1998 2001 2005 2009 Total
Municipalities 83 124 124 136 108 575
with EPZs 1 4 9 17 20 51
Individuals
Inviduals (>15 years)
Households
Table 4.1 Sample Description (Cross-Section and Panel Datasets)
Notes: Not all municipalities are observed every year. Municipalities with less than 30 individual observations
each year were dropped. On average, there are 250 individuals surveyed per municipality across the years, with
min 60 and max 5,000.
25,081 23,620 22,763 36,614
26,162
58,286
138,429
4,447 4,202 4,159 6,861 6,493
30,351
11,909 10,825 9,799 15,248 10,505
11
4.2 Conceptual Issues: Measuring Inequality
Assessing interpersonal inequality has always been a delicate issue, the ideal measure being based on
comparisons of an individuals’ wellbeing over their entire lifespan. Given the obvious constraints, an array
of different methods for analysing dispersion have been suggested and used in the empirical literature, with
most studies using cross-section regression analysis on changes or levels of synthetic or parametric
inequality measures based on income, wages or expenditure data.
In the present analysis I will use two popular measures of inequality for an overall appraisal of the effect of
EPZs establishment on the inequality of a given municipality: the Gini and Theil (T) indices17. Still,
different parts of the income distribution may have diverging dynamics and impact differently overall
disparity. This reality suggests that using a single distributional statistic may be unduly restrictive and
pinpoints to the importance of looking at the shape of the distribution more generally (Voitchovsky 2005).
In this sense, while the synthetic measures offer a starting point, I will also compute the ratios of income
percentiles on either side of the median to measure top and bottom-end inequality. More precisely, I use
the 50/10 percentile ratio to measure bottom-end inequality and the 90/50 ratio to estimate top-end
values. Additionally, following Milanovic (2002, 2008) I will focus on the pattern of change across all the
deciles shares of the distribution. This approach not only presents a more nuanced and accurate picture of
the entire distribution than any single number, but has the advantage of showing the precise beneficiaries
of the policy.
This study follows common practice in referring to the measure of living standards as income, which
should be interpreted broadly to encompass all the characteristics associated with geographical location
including climate or local public good provision, in order to assume that individuals with the same level of
income at different locations are equally well-off (Shorrocks and Wan 2004). Here, following best practice
income is proxied by consumption expenditure. The choice for using consumption expenditure as a
measure of income has been widely discussed in the literature. It is arguably the most appropriate variable
for capturing lifetime wellbeing (Deaton 1997) as it better captures intertemporal shifts of resources and
incorporates changes in purchasing power. Furthermore, expenditure data are of better quality in LSMS
Household Surveys (Deaton 2004; Banerjee and Duflo 2007), given that reporting problems are less
pronounced and consumption is less affected by the redesigning of surveys, which hampers comparability
across years.
The Gini and Theil indices, as well as obviously the percentile ratios, are hence all computed based on per
capita expenditure data. I use constant annual per capita expenditure (in Córdobas of 1999, C$)18, defined
as the household annual net expenditure divided by the number of persons in the household. All
households surveyed and all of their members are included (though consumption is adjusted depending on
members being children or adults). Missing values and zero incomes have been excluded. Additionally,
expenditure has been adjusted for difference in prices across regions. As noted by Shorrocks and Wan
(2004), adjusting for spatial price differences will tend to lower the between-group inequality, while not
17 Given the popularity of these measures I do not define them with more precision (See World Bank 2009). The Gini coefficient is derived from the Lorenz curve, and ranges from 0 (perfect equality) to 1. Theil indices belong to the family of the general entropy (GE) inequality measures, and can take values from zero to infinity (zero representing perfect distribution). The main determinant of GE measures is the alpha parameter, with higher alpha values increasing the sensibility of the measure to variations in the upper-tail of the distribution, and vice versa. Theil’s T corresponds to a GE with alpha equal to 1. I sometimes include Theil’s L (alpha equal to zero), as well as the GE measure with alpha 2, for comparison. 18 I use the Managua CPI from the Central Bank of Nicaragua.
12
altering inequality within regions, and are an important factor to take into account given the strong
correlation of prices with welfare levels.
A final note, empirically the treatment of space in the analysis of income inequality and regional income
distributions is relatively recent (Rey and Janikas 2005). This paper follows the common approach of
looking at aggregated income differentials partitioned into a set of spatial units, and analyses the data in
terms of the inequality observed within each unit, i.e. municipalities. The choice of using municipalities as
main unit of analysis stems from the fact that they are the ultimate discrete policy unit for which welfare
data is available in Nicaragua, and they remain the reference framework for delivering public services and
distributing government funds (Lall and Chakravorty 2005).
4.3 Descriptive Statistics & Basic Trends in Inequality
Tables 4.2 to 4.5 contain key summary statistics for the main variables used in this paper. They are
grouped according to municipalities’ EPZ status (or treatment status). The descriptive statistics for control
variables are presented in Table.A.1 in Appendix.
The first two tables below (4.2-4.3) offer a noticeable contrast regarding inequality and real per capita
expenditure measures at the beginning and end of the period studied. To begin with, there is a sharp
reduction in inequality between both years, independently of the method used and across all municipalities.
The fall is in line with the national trend: between 1993 and 2009 the Gini coefficient declined from 49.4
to 37.1 in the country as a whole (World Bank, 2009).
Second, one can also notice similar levels of expenditure per capita at the tails of the distribution in both
groups at the beginning of the period. Interestingly, for the same year the difference for the mean and
Table 4.2 Selected Outcome Statistics for Municipalities according to EPZ status, 1993
t-test
N=
Mean SD Mean SD Mean SD
Gini Coefficient 36.58 10.01 39.22 9.06 36.1 10.16 -1.03
Theil's T Index (GE1) 22.96 11.01 23.45 7.88 22.87 11.54 -0.18
Percentile Ratio 90th/10th 5.71 3.05 6.33 2.51 5.59 3.14 -0.8
Percentile Ratio 50th/10th 2.1 0.67 2.3 0.64 2.06 0.67 -1.16
Percentile Ratio 90th/50th 2.68 1.12 2.7 0.66 2.68 1.18 -0.07
Expenditure per capita
10th percentile 1,068.15 664.13 1,351.26 451.51 1,015.57 686.15 -1.69
50th percentile 2,155.74 1,236.90 3,078.41 1,200.08 1,984.38 1,173.63 -3.07
90th percentile 6,141.49 7,088.38 8,456.50 3,981.40 5,711.57 7,467.10 -1.28
Mean 3,108.71 1,908.33 4,403.47 1,908.33 2,868.25 2,371.87 -2.2
Entire SampleMunicipalities
Ever EPZ
Municipalities
Never EPZ
Notes: The data is for 83 municipalities in 1993, 13 ever designated EPZ. Expenditure measures are expressed in constant Cordobas
of 1999 (Exchange rate US$1=C$11.8). Theil's T index is a Generalized Entropy (GE) inequality measures, with α=1. PR90/10,
PR50/10 and PR90/50 correspond to percentile ratios of the 90th to 10th percentile, 50th to 10th, and 90th to 50th, respectively.
701383
Selected Outcome Measures
13
median are statistically significant, implying to some extent already higher level of real per capita
expenditure at the mid-percentiles of treated municipalities in 1993. By 2009, all deciles and the mean
display highly significant differences (at 1 percent levels) between treated and non-treated groups, in
favour of higher levels of expenditure per capita in EPZ municipalities. Finally, it is interesting to look at
the evolution of within-group inequality. While both groups had similar within-area disparity levels in
1993, the municipalities hosting a zone reveal statistically significant higher levels for both the Gini and
Theil indices in 2009. A look at the percentile ratios seems to indicate that these higher disparity levels
result from dynamics between both tails and at the top-end of the distribution. Largely, these trends
presuppose the existence of a heterogeneous effect of EPZ establishment across the different income
segments in treated areas, one which appears not to be distributional-neutral.
Table 4.4 below offers a more detailed assessment of the same variables. It displays the difference in
selected outcome variables between No-EPZ and EPZ municipalities up until the specific period of firm
establishment – i.e. by establishment sequence. Supporting the results of the previous tables, all four
groups display non-statistically significant differences regarding inequality indices and percentile ratios in
the period before firm establishment compared to the non-treated group. The picture is more nuanced
concerning levels of expenditure per capita. In fact, the first group shows no statistically significant
difference with respect to the non-treated group, while the subsequent groups display a similar result only
at the 90th percentile. The median and mean are the only percentile statistically significantly higher for
treated municipalities for all groups 2 to 4 prior to firm establishment, suggesting that municipalities
treated later already displayed higher levels of real per capita expenditure across the mid-range percentiles
than non-treated ones.
Above all, these results confirm that treated and non-treated municipalities had comparable within-area
inequality levels as well as similar levels of real expenditure per capita at the tails of the distribution prior to
Table 4.3 Selected Outcome Statistics for Municipalities according to EPZ status, 2009
t-test
N=
Mean SD Mean SD Mean SD
Selected Outcome Measures
Gini Coefficient 28.22 6.69 33.39 4.6 27.19 6.59 -3.8
Theil's T Index (GE1) 13.44 5.5 16.66 4.75 12.79 5.43 -2.81
Quantile Ratio 90th/10th 4.2 1.74 4.87 1.15 4.07 1.81 -1.8
Quantile Ratio 50th/10th 2.06 0.64 2.05 0.33 2.07 0.691 0.11
Quantile Ratio 90th/50th 2.05 0.56 2.4 0.57 1.98 0.53 -3.04
Expenditure per capita
10th percentile 2,921.71 906.09 3561.35 906.56 2,793.78 1,106.54 -2.76
50th percentile 5,707.41 1,905.50 7,129.75 1,305.00 5,423.01 1,883.80 -3.66
90th percentile 11,709.70 5,200.08 17,175.93 5,702.00 10,616.45 4,364.32 -5.51
Mean 6,554.00 2,228.38 8,769.49 1,637.93 6,095.62 2,057.86 -5.17
Entire SampleEPZ
Municipalities
No EPZ
Municipalities
Notes: The data is for 108 municipalities in 2009, 20 designated EPZ. Expenditure measures are expressed in constant Cordobas of 1999
(Exchange rate US$1=C$11.8). Theil's T index is a Generalized Entropy (GE) inequality measures, with α=1. PR90/10, PR50/10 and
PR90/50 correspond to percentile ratios of the 90th to 10th percentile, 50th to 10th, and 90th to 50th, respectively.
108 20 88
14
zone establishment. They reinforce the hypothesis of an inequality-enhancing effect in treated areas
resulting from EPZ institution and justify the interest of formally testing such a relationship.
In order to better understand the underlying mechanisms behind the changes in inequality within each
cluster, I look next at the average annual per capita growth rates, calculated across treated and non-treated
municipalities, at each percentile of the municipalities’ expenditure distribution. Figure 4.1 shows the
average values for the entire period (1993-2009). It is straightforward to see that expenditure at all deciles
grew over the period irrespective of zone formation. Interestingly, both groups show similar trends in that
they display higher growth rates at the bottom-end of the distribution (10th to 50th percentiles). Never-
EPZs municipalities also show higher annual growth rates at every percentile but the 90th, with the biggest
range at the 50th and 70th. When decomposing in two sub-periods (Figure 4.2) relatively similar dynamics
Timing of Treatment
A. Granting Sequence
Municipalities with newly estasblished EPZs
Total Municipalities with EPZs
B. Dispersion Measures by Group,
(for period prior to EPZ establishment)
Expenditure per capita
10th percentile
50th percentile
90th percentile
Mean 3,904
[-1.48]
4,137
[-1.84]
4,984
[-2.25]
5,650
[-2.07]
[-1.11] [-1.07] [-1.27]
8,827 7,855 8,718 9,853
35.5
[-0.16] [-0.58] [-0.24]
[-1.03] [-1.73] [-0.94]
[-0.23] [-1.32]
[-0.32] [0.04]
[-0.09] [0.33]
Group 3
[2002-2005]
Group 4
[2006-2009]
Group 1
[<1999]
Group 2
[2000-2001]
4
4 9
8
17
3
40.63 35.62
1,680
3,825
20
5
2,279
4,357
22.53
5.83
Notes: Expenditure measures are expressed in constant Cordobas of 1999 (Exchange rate US$1=C$11.8).The t-statistics
in brackets measure the statistical significance of the difference between treated and non-treated group for the selected
measure up until year of treatment, by treatment group. Values for each non-treated group are not represented. Theil's T
index is a Generalized Entropy (GE) inequality measures, with α=1. PR90/10, PR50/10 and PR90/50 correspond to
percentile ratios of the 90th to 10th percentile, 50th to 10th, and 90th to 50th, respectively.
Quantile Ratio 90th/10th
[-1.93]
[-1.52] [-2.40] [-2.19] [-1.90]
[-0.89]
Quantile Ratio 50th/10th 2.21 2.38 2.30
1,448
3,147
20.38
6.13
Theil's T Index (GE1)
1,498
3,325
34.15
17.42
4.37
[0.19]
[0.67]
[0.38]
1.93
[-1.31] [0.67]
5.44
[0.51] [0.38]Quantile Ratio 90th/50th
2.64 2.44 2.34 2.26
Table 4.4 Selected Outcome Statistics for Municipalities by Establishment Sequence
[-1.06] [-0.31] [-1.04] [-0.57]
18.12
Gini Coefficient
15
exist for the first half (1993-2001) – the period with also the higher rates of growth -, while the second half
exhibits slower rates with a lesser dominance of the No-EPZ group. In fact, during this period real per
capita expenditure grew at a higher rate for the EPZ group at the 10th and 90th percentiles.
Figure 4.1 Average Annual Growth Rates of Expenditure per capita
at Different Points of Municipalities Distribution, by EPZ Status (1993-2009)
Figure 4.2 Average Annual Growth Rates of Expenditure per capita at
Different Points of Municipalities Distribution, by EPZ Status (subperiods)
It is hard to draw conclusions from these various pictures. The higher growth rates of expenditure per
capita in never-treated municipalities throughout the period primarily impact inequality between the
groups. In this sense, while not the main focus of this analysis, a separate note on inequality between
municipalities is granted. In fact, the ratio of mean expenditure per capita between treated and non-treated
groups’ does decrease throughout the years19. Within-group inequality trends are harder to derive,
particularly as they seem to be heterogeneous in time. In this sense, a more formal analysis is needed to
infer any causal effect of the policy on within-area disparity and across the different income segments in
host municipalities.
19 The ratio of mean real expenditure per capita between treated and never-treated municipalities decreases from 1.53 in 1993, to 1.43 in 1998-2001 and finally 1.37 in 2009.
16
5. Empirical Strategy
The main empirical analysis relies on the Difference-in-Differences (DID) framework. The interest in
using this framework stems from not being able to observe what would have happened in the treated
municipalities had there not been any EPZ firm established there. For the identification strategy, I rely on
the significant variation provided by the timing of zone creation across the sample of municipalities. To
tackle the challenge regarding unobservable factors I allow for municipality and year fixed-effects, as
discussed next.
5.1 Basic Specifications
I follow Angrist and Pischke (2009) and define equation (1) as the extension of the simple DID model for
multiple periods and groups, in order to estimate the Average Treatment Effect on the Treated (ATT)
with the panel dataset. I denote EPZ as a dummy variable that switches to one when a municipality m gets
treated in year t. The basic specification takes the following form:
𝑌𝑚𝑡 = 𝛾𝑚 + 𝜇𝑡 + 𝛿𝐸𝑃𝑍𝑚𝑡 + 𝑋′𝑚𝑡𝛽 + 휀𝑚𝑡 (1)
Where m and t index for municipality (m=1, ..., 145) and year (t=1993, 1998, 2001, 2005, and 2009)
respectively, 𝑌𝑚𝑡 is the outcome variable of interest in municipality m at year t; 𝛾𝑚 are municipality-level
fixed-effects and 𝜇𝑡 are time effects. The fixed-effects capture the permanent differences in the
municipalities’ observed and unobserved characteristics that may influence the location of EPZs as well as
any time varying shocks. X’ is a vector for time-varying individual characteristics aggregated at the
municipality level. 휀𝑚𝑡 is the error term. Following Bertrand, Duflo and Mullainathan (2004) I use robust
standard errors clustered at the municipal level to account for the fact that outcomes are likely to be
correlated at this subnational level. The parameter of interest is 𝛿, which measures the average effect of the
establishment of an EPZ on the outcome Ymt (i.e. mean real expenditure per capita, inequality indices, and
decile ratios). The effect is identified by comparing municipal outcomes among treated and non-treated
groups before and after EPZ establishment.
I measure the effect across the expenditure distribution by extending the DID method to each quantile (𝜏)
instead of the mean (Athey and Imbens 2006). The specification takes the following form:
𝑌𝑚𝑡| 𝐸𝑃𝑍𝑚𝑡,𝜌𝑚𝑡 (𝜏) = 𝛾𝑚(𝜏) + 𝜇𝑡(𝜏) + 𝛿𝐸𝑃𝑍𝑚𝑡(𝜏) + 𝑋′
𝑚𝑡(𝜏)𝛽 + 휀(𝜏,𝜌𝑚𝑡) (2)
Where notations are the same as previously defined in the simple DID cases but 𝜏 now stands for each
decile of the expenditure distribution estimated at the municipality level. The robust standard errors
remain clustered at the municipal-level. This method referred to as the QDID allows comparing
individuals across both groups and time according to their quantile. Here, the quantiles are fixed and the
DID estimator compares changes in the levels of expenditure around them, under the identifying
assumption that the growth in expenditure from pre-EPZ to post-EPZ cohorts at each particular quantile
would have been the same in both treatment and comparison groups in the absence of treatment. It relies
on the assumptions of rank-preservation and homogeneity of treatment effect across individuals (Athey
and Imbens 2006).
17
5.2 Refining the validity of the DID Strategy
5.2.1 Counterfactuals
The strength of the DID approach for causal interpretation rests on the construction of an appropriate
control group that can act as a suitable counterfactual for the treated municipalities. The key assumption
behind a valid counterfactual being that the two groups’ measured outcomes would have followed the
same trends over time in the absence of policy (i.e. common trend assumption). Given that the EPZ
regime in Nicaragua exempts the zones from taxes at all subnational levels, it is rational to assume that the
choice of locating in a particular municipality remains conditioned on other observables and unobservable
characteristics measured at this spatial level. The export-oriented nature of EPZs and its human capital
needs imply that accessibility conditions (i.e. ports, airports and roads) and basic socio-economic
characteristics are likely to play an important role in the choice of location. As such, the baseline
comparison group of all never-treated municipalities may not necessarily stand as the best counterfactual.
To address this issue, I construct a set of alternative control groups using detailed information on
municipalities’ characteristics (Figure 5.1) and test their validity.
Figure 5.1 Control Groups
I base the first one on the conditions of road infrastructure. Given the limited development of Nicaraguan
ports, airports and railways, road networks remain the main mode of transport for most domestic
movements and a large share of imports and exports (World Bank, 2013). As such, good roads are not
only likely to be one key determinant for EPZs locational choices, but they may also be a proxy for
additional infrastructure conditions. I use the meters of paved roads every 1 km2 as main measure for road
accessibility, and set the threshold at the minimum in treated municipalities (40 meters). The second group
is constructed based on a distance-buffer set on the municipality’s centroid distance to the capital city.
There are several reasons behind this choice. First, as in most developing countries, the capital city is likely
Road-Network Buffer Distance-Buffer RAAN/RAAS Exclusion
Notes: GIS digital maps and World Bank (2011)
.
18
to be a key player for exports and imports, as well as the epicentre of economic activity in the country.
EPZs may have an interest in locating in its vicinity to avoid higher transport costs and take advantage of
agglomeration economies. Second, using alternative criteria for the distance-buffer would sizeable limit the
pool of municipalities. The threshold is set to the maximum distance for an EPZ municipality (<220km).
Finally, I construct a control group excluding the two autonomous regions of the Atlantic coast (RAAN
and RAAS), which correspond to the poorest areas of the country and are thus likely to have lower
infrastructure and human capital endowments. Furthermore, these areas also have very particular ethnic
and economic characteristics that differentiate them from the rest of the country.
Table 5.1 above presents summary statistics for the relevant selected outcome variables using the different
control groups for the baseline year. First, consistently with previous tables, none of the proposed
counterfactuals has statistically significant differences with the treated municipalities in terms of the
inequality measures prior to EPZ establishment. Still, there are statistically significant discrepancies
regarding the levels of real expenditure per capita across the chosen points of the distribution for all
groups. For the empirical analysis I choose to present results with the baseline control group of all non-
treated areas and the road-network control group. Both exhibit the closer statistically significant similarities
with treated municipalities for the tested outcomes variables in 1993, and together they provide a further
robustness check for the estimations. Results with the other control groups are sometimes used for
additional support. Figure 5.2 below provides visual evidence for the existence of a common underlying
trend between treatment and control municipalities using the two chosen definitions. The figures display
the trend for municipalities treated in the later sequences (2001 and 2005) to add pre-treatment years.
Table 5.1 Selected Outcome Statistics for Municipalities according to EPZ status and Control Group, 1993
N=
Mean SD Mean SD t-test. Mean SD t-test. Mean SD t-test.
Selected Outcome
Measures
Gini Coefficient 39.22 9.06 35.8 8.27 -1.29 36.30 9.63 -0.9836.37 9.4 -0.99
Theil's T Index (GE 1) 23.45 7.88 23.36 11.7 -0.03 23.03 7.88 -0.13 23.71 11.43 -0.09
Percentile Ratio 90th/10th 6.33 2.51 5.77 3.41 -0.55 5.60 3.27 -0.74 5.68 3.18 -0.69
Percentile Ratio 50th/10th 2.3 0.64 2.07 0.97 -1.08 2.10 0.69 -0.95 2.09 0.68 -1.02
Percentile Ratio 90th/50th 2.71 0.66 2.72 1.24 0.04 2.59 1.03 -0.36 2.71 0.67 0.01
Expenditure per capita
10th percentile 1,351.26 451.51 1,006.2 774.5 -1.5 931.6 523.6 -2.6 970.9 692.0 -1.9
50th percentile 3,078.41 1,200.08 1,968.6 1,292.3 -2.7 1,873.9 9,191.4 -3.9 1,927.3 1,209.4 -3.1
90th percentile 8,456.50 3,981.40 6,038.1 8,950.1 -0.9 4,875.9 3,987.6 -3.5 5,710.8 7,964.7 -1.2
Mean 4,403.47 1,908.33 2831.97 2723.29 -1.9 2,612.5 1,374.0 -3.8 2,804.6 2,485.0 -2.2
13 47 63 61
Notes: The data is for 83 municipalities in 1993, 13 ever designated EPZ. Expenditure measures are expressed in constant Cordobas
of 1999 (Exchange rate US$1=C$11.8). The t-test measure the mean differences of each control group with respect to the treated
one. Theil's T index is a Generalized Entropy (GE) inequality measures, with α=1. PR90/10, PR50/10 and PR90/50 correspond to
percentile ratios of the 90th to 10th percentile, 50th to 10th, and 90th to 50th, respectively.
Municipalities
Ever EPZ
Different Control Groups
1. Road Network Buffer 2. Distance-Buffer 3. Excludes RAAN/RAAS
19
Figure 5.2 Parallel trends across Selected Points of Real Expenditure Distribution Between Treated and Non-Treated Municipalities
20
6 The Impact of EPZ Establishment on the Inequality Dynamics of Treated Municipalities
6.2 Average Effect of EPZ Establishment
6.2.1 Mean Levels of Expenditure per capita and Inequality Measures
I start by examining the effect of EPZs establishment on the average real expenditure per capita of treated
municipalities. Table 6.1 presents the results of different DID estimates. The first two columns report the
results from equations (1), while in columns (3-4) the same specification is applied using the individual-
level datasets20.
First, the results confirm that, all else equal, the formation of an EPZ increased the average levels of real
20 To gauge the relevance of the relationship more rigorously, I use the original individual-level repeated cross-sectional dataset to estimate ATT on the levels of real per capita expenditure. These estimations permit the inclusion of a precise set of controls measured at the individual-level that are otherwise not available (such as age, individual educational achievement, household size, gender and migration behaviour). Additionally, to control even more richly for differences across municipalities, I follow Besley and Burgess (2004) and add a municipality-specific time trend in the form of interactions between a municipality dummy and a linear time trend. The larger sample allows introducing these dummies
without compromising the degrees of freedom. The equation becomes: 𝑌 𝑖𝑚𝑡 = 𝛾0𝑚 + 𝛾1𝑚𝑡 + 𝜇𝑡 + 𝛿𝐸𝑃𝑍𝑚𝑡 +𝑋′𝑖𝑚𝑡𝛽 + 휀𝑖𝑚𝑡 , where most of the parameters are the same as in equation (1) but i now indexes for individuals in municipality m at year t. Standard errors remain clustered at the municipal-level. The proximity of the results in terms of size and significance in columns (1) and (3) provide visual confirmation of the validity of using the municipal-level aggregations despite not being able to include individual controls related to income.
(1) (2) (3) (4)
All Municipalities
601.52* 576.33* 517.51** 834.70**
(1.71) (1.67) (2.05) (2.06)
Observations 575 575 107,725 107,725
R-sq. 0.64 0.18 0.27 0.28
Road Network Buffer
498.79 427.75 459.5* 738.95**
(1.33) (1.15) (1.65) (2.05)
Observations 417 417 78,385 78,385
R-sq. 0.64 0.1 0.26 0.27
Year FE Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes
Municipality-Time Trend No No No Yes
Province-Time Trend No Yes No No
Covariates Yes Yes Yes Yes
Table 6.1 DID Estimates of the Effect of EPZs on Average Real Expenditure
per capita
Notes: Robust t-statistics in parentheses, Clustered SE ( municipalities). Covariates for
columns (3 & 4) include dummies for time-varying individual characteristics: educational level,
age square, gender, urban, sector of work, relationship with head of household and having
migrated within the past five years. In columns (1 & 2) eq. 1 uses dummies for percent of
population by educational achievement, share of urban population & migrants, at the
municipality level.
*** p<0.01, ** p<0.05, * p<0.1
EPZ
EPZ
21
expenditure per capita of individuals in treated areas over the period under consideration. The size of the
effect is relatively large in the range of C$450-840, and corresponds to around 15 to 30 percent of the
average expenditure level at the beginning of the period. It is reassuring to see that despite varying in
significance level, the results have a similar size in columns (1) and (3) across both control groups. The fact
that the coefficients are no longer significant in column (1 & 2) when using the road-network buffer to
delimit the counterfactual, suggests however that the effect might be less important when considering
similar municipalities. It might also imply a time-varying dynamic or a heterogeneous effect across the
different segments of the distribution that fails to be captured by the average estimates. Finally, it is
interesting to note that the inclusion of municipality-specific time trends in column (4) increases the size of
the coefficients, while keeping the statistical significance across both control groups. The fact that results
are robust to the inclusion of the trends is encouraging, as it suggests that the expenditure difference is not
resulting from individuals being in municipalities where expenditure levels were increasing anyway,
irrespective of the treatment21. To test whether municipal-level estimations do not suffer from any
potential negative omitted variable bias (OVB) without the inclusion of the trends, I estimate equation (1)
adding flexible province-time trends (column 2). I find results to be unchanged in terms of significance and
smaller in size, while losing significantly the number of degrees of freedom.
Table 6.2 below presents the results of the DID estimates (equation 1) on the synthetic inequality
measures constructed at the municipal-level.
While positive, there is no effect visible on the Gini coefficients irrespective of the control groups. Theil’s
T conversely does register higher inequality levels at 10 percent significance for treated municipalities
against the road-network control group (column 5). Given the particularity of the Theil index, I also run
regressions using the index in its generalized form (Generalized Entropy) with varying levels of alpha22.
21 Nicaragua has 18 counties, 2 of them being the autonomous regions. 22 In General Entropy (GE) measures, like Theil indices, this parameter represents the weight given to distances between incomes at different parts of the income distribution. They can take any real value but the most common values of α used are 0, 1, and 2, with the first two corresponding to Theil’s L and Theil’s T.
Gini Coefficient Theil Index (T) GE (2) Gini Coefficient Theil Index (T) GE (2)
(1) (2) (3) (4) (5) (6)
EPZ 1.032 1.756 3.410* 1.08 2.02* 3.894*
(0.66) (1.504) (1.864) (0.678) (1.686) (1.977)
Year FE Yes Yes Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes Yes Yes
Covariates Yes Yes Yes Yes Yes Yes
Observations 575 575 575 417 417 417
R-sq. 0.21 0.20 0.20 0.21 0.22 0.21
Number of Muni. 145 145 145 107 107 107
All Municipalities
Table 6.2 DID Estimates of the Effect of EPZs on Within-Municipality Inequality – Indices
Road Network Buffer
Notes: Robust t-statistics in parentheses, Clustered SE ( municipalities). Covariates include illiteracy rate, primary and
tertiary education, as well as share of urban population and migrants at municipal-level. Theil's T and GE (2) are
generalized entropy (GE) inequality measures with α=1 adn 2, respectively. The higher the alpha parameter the
greater the sensitivity of the index to variations in the upper-tail of the distribution.
*** p<0.01, ** p<0.05, * p<0.1
22
Interestingly, with alpha equal to 223 - which gives higher weight to changes that affect the upper tail of the
distribution - the index registers statistically significant higher inequality levels on EPZ municipalities
regardless of the choice of control group. The difference on disparity levels is relatively high, at well-above
1 point for both measures (i.e. 5 percent of 1993 level). A first observation, and one to which I return
below, is thus that following EPZ establishment, treated municipalities seem to display higher inequality
levels against non-treated ones, when captured by measures sensible to dynamics in the upper tail of the
distribution.
The results of the DID estimates on the percentile ratios in table 6.3 below support this primary
conclusion. The impact on the 90/50 ratio is consistently significant at 5 percent level and positive across
specifications (columns 3 & 6). It indicates a 0.36 to 0.38 points higher ratio for upper-end inequality in
treated municipalities across the period (i.e. near 10 percent of baseline value). Interestingly, EPZ-
municipalities also register a decline on bottom-end inequality over the period, with the 50/10 percentile
ratio negative and statistically significant. The latter displays nonetheless a lower significance, at 10 percent
level, and a smaller size (-0.2 points) with both control groups. These two opposite tendencies are notably
driving the positive but not significant 90/10 ratio, and might explain the lack of significance of the Gini
coefficient with both dynamics cancelling each other out.
The results of these first specifications suggest an ambiguous effect of EPZ establishment on overall
inequality within host municipalities. The estimates that capture a statistically significant impact seem to
indicate that a rise on inequality is driven by dynamics at the upper tail of the distribution. Upper-end
inequality is evidently higher in treated municipalities over the period considered. The trend on bottom-
end disparity is less clear with negative but lower significant values across specifications that fail to
translate into a reduction of inequality in any of the overall measures. These developments imply a larger
impact of EPZ formation on the upper-tail of the distribution. To explore formally this hypothesis, I
estimate next the treatment effect across all deciles of the real expenditure distribution of treated areas.
23 Theil’s L (α=0), like the Gini, presents a positive but not statistical significant coefficient. It has been omitted throughout to avoid redundancy.
PR90/10 PR50/10 PR90/50 PR90/10 PR50/10 PR90/50
(1) (2) (3) (4) (5) (6)
EPZ 0.249 -0.204* 0.38** 0.21 -0.215* 0.365**
(0.617) (-1.919) (2.205) (0.518) (-1.889) (2.114)
Year FE Yes Yes Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes Yes Yes
Covariates Yes Yes Yes Yes Yes Yes
Observations 575 575 575 417 417 417
R-sq. 0.10 0.05 0.10 0.11 0.05 0.13
Number of Muni. 145 145 145 107 107 107
Notes: Robust t-statistics in parentheses, Clustered SE ( municipalities) Covariates include illiteracy rate, primary and tertiary
education, as well as share of urban population and migrants at municipal-level. PR90/10, PR50/10 and PR90/50 correspond
to percentile ratios of the 90th to 10th percentile, 50th to 10th, and 90th to 50th, respectively.
*** p<0.01, ** p<0.05, * p<0.1
Table 6.3 DID Estimates of the Effect of EPZs on Within-Municipality Inequality – Percentile Ratios
All Municipalities Road Network Buffer
23
This strategy is useful to directly evaluate the heterogeneity of the effect across income segments, and
decompose the results found with the aggregated indices and ratios.
6.2.2 Percentiles of the Real Expenditure Distribution.
Table 6.4 below presents the results of DID estimates across all deciles of the real per capita expenditure
distribution of treated municipalities (QDID, equation 2). The first column is estimated using all
municipalities in the sample (i.e. the never treated municipalities as comparison group), and the second
column uses the road-network buffer to define the control group. In interpreting these results, it should be
kept in mind that I estimate treatment effect on each decile of the expenditure distribution of treated
municipalities strictly with respect to its respective decile in the non-treated group.
All Municipalities Road Network Buffer
(1) (2)
263.4* 225.8
(1.845) (1.497)
160.9 100.5
(0.997) (0.593)
175.5 109.7
(0.825) (0.503)
305.0 235.7
(1.391) (1.042)
310.0 234.4
(1.266) (0.934)
360.2 316.0
(1.162) (0.954)
512.7 428.0
(1.509) (1.208)
1,085** 991.5**
(2.347) (2.046)
2,648** 2,539**
(2.352) (2.135)
Yes Yes
Yes Yes
Yes Yes
575 417
145 107
Real Expenditure
per capita
10th Percentile
20th Percentile
30th Percentile
40th Percentile
Table 6.4 QDID Estimates of the Effect of EPZs across the
Expenditure Distribution in Treated Municipalities
Notes: Robust t-statistics in parentheses, Clustered SE ( municipalities).
Covariates include illiteracy rate, primary and tertiary education, as well as share of
urban population and migrants at municipal-level. Rsq. for both columns ranged
between 0.35-0.71. Regression in column (2) limits observations using the road-
network buffer.
*** p<0.01, ** p<0.05, * p<0.1
Municipality FE
Covariates
Observations
Number of Muni.
50th Percentile
60th Percentile
70th Percentile
80th Percentile
90th Percentile
Year FE
24
These estimates provide supportive evidence to the hypothesis of inequality dynamics being directly
related to the impact of the policy on the highest percentiles of the distribution. The most salient feature in
table 6.4 is the containment of the treatment effect at the top-end of the distribution (80th and 90th
percentiles). These are consistent across all specifications, with both deciles particularly keeping a 5 percent
significance level in both columns. The size of the impact is high at around one fourth to one third of the
level of expenditure per capita at the respective decile for all municipalities in 1993. It remains high when
comparing with the average values across the period, at 15 percent of the level in treated-municipalities
and about one quarter for both groups of control municipalities. Above all, these results suggest that the
establishment of a zone profits the top deciles of the distribution in host municipalities, which are better-
off, compared to their respective percentiles in non-treated areas.
Additionally, the coefficient at the 10th percentile also displays a positive and significant value with the
baseline control group (at 10 percent level) (column 1). The effect is lost with the most restrictive road-
network buffer24 but remains positive and close to significance. The lack of consistency in terms of
significance level across the control groups is suggestive and implies a relatively smaller effect than on the
levels of expenditure at the other two percentiles. It should be considered carefully. One interpretation of
this finding could be that EPZ benefit the poorer segments not through direct job creation but indirect
informal ones, which might manifest with lower intensity across the sample or take longer to materialize.
In contrast, while the coefficients are positive, the estimates do not register any significant effect at the 20th
to 70th percentiles. These outcomes support the ambiguous effect of EPZ establishment found on overall
inequality measures, with the mid-percentiles contributing to smooth any impact of treatment registered at
the tails.
Taking everything into account, the primary conclusions from estimates in tables 6.2 to 6.4 remain that
contrarily to what might be naively expected from this type of policies, the establishment of an EPZ in a
given municipality primarily increases the levels of real expenditure per capita at the upper-end of the
distribution. Specifically, compared to their peers in non-treated areas, the high-income segments are
relatively better-off in treated municipalities, making them the first beneficiaries of the policy. Interestingly,
this effect is not translated into a definite increase of overall within-municipality inequality in treated areas.
As seen, the coefficients for the Gini and Theil indices are positive but with no (or little) statistical
significance. Decomposing inequality helps to shed light on a more nuanced picture. Mainly, as a direct
consequence of the treatment effect at the upper-end percentiles, top-end inequality clearly increases in
treated areas over the period. Additionally, the estimates with the percentile ratio 50/10 are statistically
significant and indicate a reduction on the bottom-end inequality of treated municipalities. Still, this effect
is smaller and less consistent throughout control groups, surely explaining the lack of an impact on overall
inequality dynamics, and resulting from the smaller, if significant, increase at the poorest decile of the
distribution in treated areas.
These first conclusions are interesting in that they provide an empirical underpinning for the expectations
of the more advanced HO models. In fact, two general explanations could be behind these findings. First,
the concentration of the effect at the upper-end of the expenditure distribution may indicate that the
owners of the factors of production are the ones actually benefitting from zone establishment, either
through direct investment or land ownership. This is easy to imagine in a country with already high levels
of inequality. Second, the results could also be the consequence of the existence of a skill premium for
white-collar workers in the EPZ sector. In line with some of the theoretical predictions, the skill premium
24 As a robustness check I also run the QDID with the distance-buffer and RAAN/RAAS exclusion control groups. Both estimations display similar coefficients in terms of size and sign. The top deciles show similar statistical significance, while the lowest one is only significant against the RAAN/RAAS exclusion group. See Appendix 2.
25
might stem from a higher skilled-to-unskilled labor ratio, as well as from higher productivity levels in the
exporting sector together with limited labor mobility. These hypotheses fit remarkably well the Nicaraguan
case. As previously described, the labor market is characterized by low levels of educational achievement, a
de facto constraint to labor mobility. At the same time, productivity levels have increased markedly in the
manufacturing sector over the past years, notably because of the exporting EPZ sector. These dynamics
would imply that the skill intensity in the zones is higher than in the rest of the manufacturing sector,
interestingly supporting the predictions of Harrison and Hanson (1999) and Verhoogen (2006). Strangely,
one would expect that mid-range percentiles could also benefit from this skill premium. The fact that they
do not seem to register any significant treatment effect could result from a segmentation of the high-skill
labor sector. A final consideration should be given to the effect captured at the 10th percentile of the
distribution. If indeed positive and significant, it is reasonable to assume that the effect is driven from
indirect spillovers or trickle down effects, possibly in the informal sector from services provided to EPZs.
These findings would be supportive of polarization theories. Still, more information on the skill
composition of the EPZ job market in Nicaragua would be needed to reach any further definite
conclusion.
6.3 Spillover Dynamics & Commuting
In applying the DID framework to the data, it is important to consider carefully the ‘experiment’ created
by the establishment of a zone in a given municipality. In the ideal case, the decisions would be
independent random events that varied in timing and had no spillover effects to non-treated areas.
Additionally, there would be no interaction between treated and non-treated areas. While the empirical
strategy already controls for the major sources of endogeneity (i.e. time-varying shocks, selection-bias) the
present analysis may still differ from the ideal case. This section addresses the concerns related to possible
interactions taking place between an EPZ municipality and its neighbouring ones, either through spillover
dynamics or commuting patterns. If existent, these mechanisms are expected to have a reasonable impact
on the expenditure per capita of neighbouring areas. Not taking them into consideration would lead to a
bias estimation of the previous EPZ effects.
To begin with, I define spillovers as any form of business diversion or creation that could take place
because of agglomeration forces between treated and non-treated municipalities adjacent to each other.
Backward linkages between EPZs and local producers are probably the main mechanism through which
spillovers can be expected to happen. In this sense, I conduct a preliminary test using the individual-level
repeated cross-sectional dataset to measure the impact of EPZ establishment on the likelihood of being
self-employed (as a proxy for business start-ups25) amongst the working age population. I find no evidence
of a statistically significant effect26 with both logit and OLS estimators. While marginally positive the
coefficients are almost no different from zero, which is consistent with findings in the literature (Farole
and Akinci 2011). Even though these results are relatively reassuring for the quasi-experimental setting
created here, spillovers to adjacent municipalities could still be taking place in alternative ways and the
intensity could be higher when considering the spatial dimension.
Additionally, I consider commuting behaviour as form of spillover to adjacent areas, given that individuals
working in a treated municipality but residing in a neighbouring area are likely to have a similar impact in
terms of consumption (or simply in accounting for expenditure levels), than business creation/diversion in
the locality where they reside. Commuting is hard to measure. Information is only available for 2001, when
25 This is in line with the related literature. 26 See Appendix 2.
26
it represented 3.56 percent of the total sample (5.05 and 3.02 percent in treated and never-treated
municipalities, respectively). Reassuringly, the number seems sufficiently small not to alter the results.
Next, I use a distance-definition to consider them in the formal analysis.
In effect, in order to account for the possibility of the above-mentioned dynamics, I measure treatment
effect across the real expenditure distribution of municipalities adjacent to a treated area, using different
levels of proximity. For this, I compute a modified version of equations (1 & 2) adding a set of dummy
variables that switch to one for municipalities neighbouring a treated area, at two different levels of
proximity. Because of the particular small size of Nicaraguan municipalities in the west part of the country,
considering the shared border as criteria for proximity would imply denoting the vast majority of non-
treated municipalities in the region as neighbors. Instead, I use a municipalities’ centroid distance to the
centroid of the nearest treated one with two levels of distance, one set at between 5 to 15km and the other
at 15 to 35km27. This differentiation additionally allows accounting for both dimensions, commuting with
the closer group, and more formal spillover effects with the one further away.
Table 6.5 provides the QDID estimates augmented with the neighbor dummies across the deciles of the
real per capita expenditure distribution. For simplicity, I show the tables with the road-network control
group only28. It is encouraging to find a close similarity in the size, sign and statistical significance of the
EPZ coefficients to the ones in previous estimations. Above all, these results further emphasize that
individuals at the top-end of the distribution benefit the most from the establishment of an EPZ in the
municipality where they reside throughout the period.
Regarding the coefficients for neighboring areas, they seem to ratify the previous suspicion that spillover
effects are in fact limited. None of the coefficients for neighboring municipalities display statistical
significance, implying there are indeed no spillovers from EPZ establishment on the levels of expenditure
per capita across the deciles of the distribution in adjacent municipalities. Truthfully, it could also mean
that they take longer to materialize and have not yet done so in the period studied. The pattern deserves a
special mention. Particularly, while not significant, the effect is negative at the bottom deciles in
municipalities situated at 5 to 15km from a treated area. This suggests that the poorer segments in small
municipalities adjacent to a treated area are worse-off than those in host municipalities as well as in zones
further away. One possible explanation could be related to business diversion in the informal sector
towards treated areas together with a higher cost of commuting for the poor. Conversely, coefficients for
the upper-deciles are consistently positive and the largest for all degrees of proximity. They decline in size
with distance, but none is statistically significant.
Overall, I find little evidence of spillover effects on the levels of expenditure per capita in neighbouring
areas as a result of EPZ establishment in a given municipality. While interesting for themselves, these
outcomes support previous findings and confirm that bias from these phenomena is likely close to zero.
27 The measure is of course imperfect. While using the centroid’s distance offers the advantage of reducing the noise from larger municipalities where a large proportion would not be in contact with treated areas, it might also exclude some bordering zones. Smaller units of analysis and geocoding would be needed for a more precise measure. For simplicity, I refer to the measure as the direct distance between municipalities, but it always registers the distance between the centroids of two municipalities. 28 The results are consistent across different control groups. The only exception being a positive and significant coefficient at the 10 percent level for the 1st decile in treated municipalities using all never-treated municipalities as control group. Coefficients share a similar size; see Appendix 4.
27
10th
percentile
20th
percentile
30th
percentile
40th
percentile
50th
percentile
60th
percentile
70th
percentile
80th
percentile
90th
percentile
(1) (2) (3) (4) (5) (6) (7) (8) (9)
EPZ 216.8 77.22 79.95 244.2 237.7 361.7 465.3 1,052** 2,562**
(1.374) (0.446) (0.368) (1.058) (0.929) (1.008) (1.231) (2.103) (2.109)
Neighboring Municipalities1 -157 -337 -313.3 135.3 26.71 138.8 326.5 733.6 532.7
(5 to 15km) (-0.550) (-1.247) (-0.931) (0.411) (0.091) (0.422) (0.743) (1.364) (0.884)
Neighboring Municipalities2 28.88 6.396 2.038 59.2 31.82 211.5 111.2 240.6 336.9
(15 to 35km) (0.185) (0.032) (0.007) (0.207) (0.105) (0.616) (0.319) (0.552) (0.462)
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 417 417 417 417 417 417 417 417 417
R-sq. 0.635 0.686 0.675 0.708 0.707 0.605 0.602 0.587 0.344
Number of Muni. 107 107 107 107 107 107 107 107 107
Notes: Robust t-statistics in parentheses, Clustered SE ( municipalities). Covariates include dummies for illiteracy rate, primary and tertiary education. as well as share of urban
population at municipality level. The variables Neighbor Municipalities are dummy variables that equal one if a municipality's centroid is situated at 5 to 15km, and 15km to 35km
distance from a treated municipality, and zero otherwise. I use the centroid's distance to avoid noise from larger municipalities. Observations are limited by Road-Network buffer.
*** p<0.01, ** p<0.05, * p<0.1
Table 6.5 The Effect of EPZs Establishment & Spillover Dynamics
Road Network Buffer
28
6.3 Extended Results. The Heterogeneous Effect of EPZ Establishment
The subsequent empirical section of this paper explores whether the effect of EPZs on local
distributional dynamics is heterogeneous in time. It has actually been extensively documented in the
literature that the impact may take time to materialize. In some cases it has also been found to diminish
with the years, as low labor costs vanish. Evaluating the causal effects of EPZ establishment as they
grow or fade in time across the different segments of the expenditure distribution provides an
opportunity to capture with more detail the mechanisms through which the policy affects income
distribution.
Hence, to explore the time pattern of EPZs establishment on within-municipality inequality, I compute
a modified version of equation (1) on municipal-level outcomes Ymt, adding a set of dummy variables
for 3 leads or post-treatment years (Autor, 2003) in the following form:
𝑌𝑚𝑡 = 𝛾𝑚 + 𝜇𝑡 + 𝐸𝑃𝑍𝑚𝑡0+ ∑ 𝛿+𝜋
3𝜋=1 . 𝐸𝑃𝑍𝑚,𝑡+𝜋 + 𝑋′𝑚𝑡𝛽 + 휀𝑚𝑡 (3)
Where the sum in the right-hand side allows for 3 leads (𝛿+4+, … , +𝛿+𝜋) or post-treatment years. The
dummies are equal to one in only one year each, per treated municipality29. The treatment dummy
𝐸𝑃𝑍𝑚𝑡0 equals one only on the year of EPZ establishment, or year zero. All other variables are as
previously specified. QDID Equation (2) is augmented in the same fashion.
Table 6.6 provides the estimates augmented with the leads, using the various inequality measures as left-
hand side variables. For simplicity, I show the tables with the road-network control group only30. The
first three columns exhibit the results for the different within-municipality inequality indices. The
coefficients are all positive and show consistent increment with post-treatment years. Particularly, it is
noticeable a high-jump in inequality after 11 years of EPZ operation in a host municipality. Still, the
incremental time dimension registered here should be considered carefully. The fact that the Gini
coefficient or Theil index are not significant, or that the GE index is only significant for one period,
supports the hypothesis of nuanced dynamics on the different segments of the distribution throughout
the years.
Estimates with the percentile ratios display similar, if more discernable, trends (columns 4 to 6). In fact,
coefficients for the 90/50 percentile ratio are significant and positive throughout almost all post-
treatment years, supporting previous conclusions, and suggesting a growing effect on upper-end
inequality throughout the period. Indeed, here too I observe a constant increment with a larger size after
11 years of EPZ establishment. Interestingly, the coefficient is already significant at 5 percent level for
year zero. Just as with average estimations, the pattern on bottom-end inequality displays a slightly more
nuanced behavior. First, consistent with previous findings, there is a decrease in bottom-end inequality
of treated areas, which persist over time. Yet, while all coefficients are negative, the pattern of reduction
is not harmoniously incremental. In fact, the decline is higher for years 4 and 11, and only the first
period registers statistical significance. This suggests a varying effect of EPZ establishment at the lower-
end of the distribution. A final observation is granted regarding the fluctuation registered by the 90/10
29 I compute leads for +4 years, +8 years, and +11 years of EPZ establishment. +4 and +8 years also include municipalities with +3 and +7 years, respectively, in order to avoid noise from a lead with a few municipalities. Each lead contains 4 to 16 treated municipalities. 30 Results using the alternative control group show similar coefficients in terms of significance and size; see Appendix 5.
29
percentile ratio. While never statistically significant, the signs of the coefficients alternate every other
period of post-treatment years. They are negative for post-treatment years 4 and 11, and positive
otherwise. Undoubtedly, this reflects the heterogeneous time dynamics of the policy across the lower-
end of the distribution in treated areas, and provides further evidence of the nuanced impact of EPZ
formation on overall within-group inequality.
Table 6.7 below shows the results of the QDID estimates on all the percentiles of the expenditure
distribution of treated municipalities by post-treatment years. Four remarks deserve mention. First, the
positive sign and relatively large size of most of the coefficient seem to indicate a consistent incremental
effect on the level of real expenditure per capita at all percentiles of the distribution with the more years
of EPZ operation. Yet, not all coefficients display statistically significance. Particularly, no coefficient is
statistically significant for year zero. This is consistent with findings in the literature reporting a delay on
the repercussion of this type of policy, which is likely to be more pronounced when measured on real
per capita expenditure levels.
Second, the higher effect happens at the very upper-tail of the distribution, notably at the 80th to 90th
percentiles. These segments are the only ones that benefit from the establishment of a zone in the near-
term (after 4 years of establishment). The constant statistically significant impact starting from year four
implies a growing absolute effect on the expenditure per capita at the end of the upper-tail. Evidently,
this trend explains the higher upper-end inequality levels, even despite statistically significant increases in
the expenditure per capita at the 50th to 70th percentiles after 8 years of EPZ operation. This dynamic is
also the main driver of the average effect registered earlier, displaying statistical significance for the end
Gini CoefficientTheil Index (T) GE (2) PR90/10 PR50/10 PR90/50
(1) (2) (3) (4) (6) (5)
EPZ establishment, t0 1.51 1.27 2.38 0.34 -0.16 0.35*
(1.00) (1.141) (1.332) (0.879) (-1.353) (1.928)
EPZ establishment, t+4 0.57 2.33 4.89* -0.05 -0.30** 0.33*
(0.293) (1.475) (1.949) (-0.111) (-2.289) (1.783)
EPZ establishment, t+8 1.584 2.17 3.84 0.190 -0.12 0.32
(1.089) (1.155) (1.295) (0.261) (-0.708) (1.493)
EPZ establishment, t+11 2.39 5.31 9.75 -0.30 -0.40 0.37**
(0.653) (1.310) (1.572) (-0.450) (-1.577) (2.115)
Year FE Yes Yes Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes Yes Yes
Covariates Yes Yes Yes Yes Yes Yes
Observations 417 417 417 417 417 417
R-sq. 0.207 0.222 0.211 0.105 0.062 0.132
Number of Muni. 107 107 107 107 107 107
Table 6.6 The Time-Effect of EPZs Establishment on Within-Municipality Inequality Measures
Notes: Robust t-statistics in parentheses, Clustered SE ( municipalities). Covariates include illiteracy rate, primary and tertiary education
at municipality level. GE and Theil's T indexes are generalized entropy (GE) inequality measures. They respectively have α=1 and α=2.
The higher the alpha parameter the greater the sensitivity of the index to variations in the upper-tail of the distribution. PR90/10,
PR50/10 and PR90/50 correspond to percentile ratios of the 90th to 10th percentile, 50th to 10th, and 90th to 50th, respectively. All
leads are equal to one in only a year each per treated municipality. EPZ dummy equals one only for year of establishment. EPZ t+4
includes t+3, and EPZ t+8 includes t+7, to have balanced dummies. Include observations with Road-Network Buffer.
*** p<0.01, ** p<0.05, * p<0.1
30
of the upper-tail only.
Third, and one of the most interesting revelations of this time decomposition, is that the lower-end of
the distribution (10th) and mid-range percentiles (40th to 60th) start benefiting from EPZ establishment
after 8 years of EPZ operation. Different explanations are conceivable here. First, the trend could result
from the existence of backward linkages with local communities in the form of outsourcing to small and
medium-size firms. The 8 year delay is consistent with the time it would take to materialize this type of
relationship. Caution is warranted given that backward linkages have usually been found to be small in
EPZ evaluations, and I have found no evidence of an EPZ effect on the likelihood of being self-
employed. Still, the time lag necessary to measure this type of relationships, particularly on real
expenditure levels, may explain the difficulty in registering any average effect in previous estimations or
across the timeline considered in this study. It is interesting to note the local dimension of the possible
backward linkages, as I found no evidence of spillovers to neighbouring municipalities. Additionally,
more classic trickle down effects might also be taking place, resulting from both medium-term
consumption and production dynamics after the expansion of the zones or increases in productivity
over the years.
Finally, the significance registered at the 10th percentile after 8, and particularly 11 years of EPZ
establishment deserves a special mention. It is the only percentile at the very bottom of the distribution
that registers a significant positive and accumulative effect of EPZ formation for the last two periods.
This suggests that EPZ policies need time before any positive effect reaches the poorer segments. The
fact that a significant impact is registered at the same time than for middle range percentiles could also
imply it arises from indirect trickle down effects. Interestingly, I do not find any significant effect at the
20th and 30th percentiles, implying that only the very bottom of the distribution ultimately benefits from
EPZ establishment. Further disentangling the drivers behind these heterogeneous time dynamics should
be considered for further exploration but are beyond the scope of this work.
While findings of this section are consistent and reinforce previous results, they also shed light on the
more complex inequality dynamics of the EPZ policy. On the one hand, they highlight the
predominance of the effect at the upper-end of the distribution, particularly the very top percentiles.
This salient feature is reflected in the significant and accumulative increase of upper-end inequality
throughout the years. Yet, the time decomposition also reveals a diffusion of the treatment effect at the
middle of the distribution and 10th percentile, after 8 years of zone establishment. These heterogeneous
time dynamics of EPZ establishment for the bottom and mid percentiles are certainly behind the
ambiguous overall impact of the zones on within-municipalities inequality. Particularly, they explain the
changing significance level for bottom-end inequality. These findings are interesting in that they provide
a time nuance to the local distributional impact of EPZ establishment, which evolve to be less inequality
reinforcing with the more years of operation.
31
10th
percentile
20th
percentile
30th
percentile
40th
percentile
50th
percentile
60th
percentile
70th
percentile
80th
percentile
90th
percentile
(1) (2) (3) (4) (5) (6) (7) (8) (9)
EPZ establishment, t0 108.6 -40.91 40.37 108.2 51.98 196.2 408.5 671.3 2,210
(0.736) (-0.269) (0.271) (0.560) (0.219) (0.642) (0.958) (1.362) (1.604)
EPZ establishment, t+4 374.9 243.2 142.9 325.0 310.5 206.6 170.1 972.0* 2,354*
(1.426) (0.828) (0.383) (0.943) (0.916) (0.477) (0.374) (1.717) (1.808)
EPZ establishment, t+8 333.4* 411.7 362.5 601.2** 987.3** 1,171*** 1,102*** 2,228*** 3,860***
(1.948) (1.538) (1.236) (2.358) (2.436) (2.660) (2.771) (2.832) (3.813)
EPZ establishment, t+11 876.2*** 470.9 679.9 850.7* 1,174*** 1,150*** 1,235** 1,851*** 5,088***
(3.006) (1.223) (1.264) (1.721) (3.013) (2.725) (2.224) (2.893) (4.863)
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 417 417 417 417 417 417 417 417 417
R-sq. 0.639 0.688 0.677 0.711 0.712 0.608 0.604 0.592 0.352
Number of Muni. 107 107 107 107 107 107 107 107 107
Notes: Uses Road-Network Buffer to delimit control group. Robust t-statistics in parentheses, Clustered SE ( municipalities). Covariates include illiteracy rate, primary and tertiary
education, as well as the share of urban population and migrants at the municipality level. All leads are equal to one in only year each per adopting state. EPZ dummy equals one only
for year of establishment. EPZ t+4 includes t+3, and EPZ t+8 includes t+7, to have balanced dummies.
*** p<0.01, ** p<0.05, * p<0.1
Table 6.7 The Heterogeneous Time-Effect of EPZ Establishment
Road Network Buffer
32
7. Robustness Checks
The final empirical section contains some alternative specifications and identification strategies that
examine the robustness of the conclusions and address remaining concerns of people’s mobility across
areas. In general, the results from the various checks are quite similar to the main estimates.
7.1 Quantile Treatment Effect (QTE)
The main results in this paper regarding the effect of EPZ establishment across the expenditure
distribution of treated municipalities are obtained by applying the DID to each quantile instead of the
mean (QDID). As discussed by Athey and Imbens (2006), this method permits measuring the treatment
effect by comparing individuals across both groups and time according to their quantile, under the
assumptions of rank-preservation and homogeneity of treatment across individuals.
To probe further these results, I next use the repeated cross-sectional individual level dataset to estimate
unconditional quantile treatment effects on the treated following Firpo (2007, et al. 2009), and controlling for
unobserved time and group effects. The two methods are similar but rely on different dimensions of the
distributions in the identifying assumptions31. Additionally, compared to commonly-estimated conditional
QTE, the added value of this method is that it does not rely on any parametric functional form
assumption and results do not change dependent on covariates (unconditional)32. In the absence of a valid
instrument, I use selection on observables but include time and province-level fixed effects to take care as
much as possible of unobservable characteristics.
I define the unconditional QTE on the treated for quantile 𝜏 by:
∆𝜏= 𝑄𝑌1𝜏 − 𝑄𝑌0
𝜏 (4)
Where in the presence of a binary treatment D ∈ {1, 0}, 𝑌𝑖1 would be realised if individual i were to receive
treatment 1 (i.e. EPZ establishment in the municipality where she resides), and 𝑌𝑖0 would be realised
otherwise. The method proposed by Firpo (2007) uses a quantile regressor estimator with weights in two
steps: first, the probability of treatment is estimated nonparametrically33 and second, estimators are
calculated as solutions of two separate minimization problems from observable data. The final estimators
are equal to the differences between respective quantiles for treated and non-treated municipalities
adjusting for participation probability, and expressed as solutions of minimization problems, where the
minimands are sums of check functions34 (Firpo, 2007). The method is based on the assumptions of
selection on observables and common support.
31 The QDID holds the quantile fixed and compares changes in the levels of expenditure around this quantile, under the identifying assumption that the growth in expenditure from pre-EPZ to post-EPZ cohorts at this particular quantile would have been the same in both treatment and comparison groups in the absence of treatment. UQTE compares changes in the distribution around a quantile, holding the level of earnings fixed. The identifying assumption coming down to the expenditure growth from pre-EPZ to post-EPZ cohorts around a particular expenditure level being the same in the treatment group as in the comparison group in the absence of policy. See Havnes and Mogstad (2010, 2014) for detailed discussion. 32 For a detailed discussion see Firpo (2007), Firpo et al. (2009) and Frolich and Melly (2010). 33 The first step is a nonparametric estimation of a propensity score to obtain the weighting33 estimator related to the conditional probability for a municipality of hosting an EPZ. The weights are introduced to correct for differences in the distribution of observable characteristics between treated and control groups, with the aim of comparing strictly similar municipalities. 34 As defined by Angrist and Pischke (2009) by: 𝑄𝜏(𝑌𝑖|𝑋𝑖) = arg min 𝐸[𝜌𝜏(𝑌𝑖 − 𝑞(𝑋𝑖)) ], where 𝜌𝜏(𝑢) =
(𝜏 − 1(𝑢 ≤ 0))𝑢 is called the check function. Quantile regression achieves the minimization by substituting a linear
model for q (Xi).
33
The first step weighting estimator of Firpo (2007) uses D as its own instrument and is therefore expressed
as:
(�̂�, ∆̂𝜏) = arg min
𝛼, ∆∑ 𝑊1
𝐹 × 𝜌𝜏(𝑌𝑖 − 𝛼 − 𝐷𝑖∆) (5)
𝑊𝑖𝐹 =
𝐷𝑖
Pr (𝐷 = 1|𝑋𝑖)+
1 − 𝐷𝑖
1 − Pr (𝐷 = 1|𝑋𝑖)
I use the method developed by Frolich and Melly (2010) that uses local logit estimator as preliminary
estimator Pr (D=1|Xi) for implementing (7), and allows standard errors to be robust for
heteroskedasticity. As mentioned, I include time and province-level fixed effects to take care as much as
possible of unobservable characteristics. Table 7.1 below presents the results across the real expenditure
distribution of treated municipalities.
50th Percentile
60th Percentile
70th Percentile
80th Percentile
90th Percentile
Real Expenditure per capita
10th Percentile
20th Percentile
30th Percentile
(-0.385)
104.6
(0.742)
Notes: z-statistics in parentheses. Propensity score and variance estimated by local
logit regression with h=0.2, 0.5, 1 and lambda=1. Estimations include year and
county fixed effect, as well as the following control variables: age sq., sector of
employment, number of household members; dummies for urban residence,
gender, educational level (illiteracy to terciary level), and migrant status over last 5
years. It also includes dummies for municipal-level infrastructural conditions (paved
roads for every 1km2 at threshold, distance to capital city at threshold, landlocked).
*** p<0.01, ** p<0.05, * p<0.1
(1.631)
146.9
(1.083)
219.0
(1.493)
261.0
40th Percentile
Observations 90,413
Table 7.1 Unconditional QTE Estimates of the Effect of EPZs across
the Expenditure Distribution of Treated Municipalities
1,273***
906.5***
(3.847)
(3.544)
(2.261)
448.7**
(1)
-100.6
(-0.889)
-58.66
34
Reassuringly, the results of the unconditional QTE are relatively close to the main QDID estimates.
Mostly, they suggest fairly robust findings regarding the concentration of the main effect of EPZ
establishment at the very upper-tail of the distribution. There is now a smaller (i.e. half of the previous
estimate at the last decile) but more significant positive effect on the level of real expenditure per capita for
individuals at the 80th and 90th percentiles. Additionally, the coefficient at the 70th percentile is now
significant at 5 percent level. Coefficients for the 30th to 60th percentiles remain positive and not
significant, as before. A major difference nonetheless with respect to the previous results, concerns the
bottom-end of the distribution. While not significant, coefficients for the 10th and 20th percentile have now
a negative sign, implying that the poorer deciles would be worst-off in treated municipalities compared to
their peers in non-treated ones. This dynamic would imply a bigger impact on overall inequality, with a
larger difference between the tails. One explanation for this difference is that Firpo’s estimator computes
marginal quantiles using a weighting and matching estimator in the first step regression. As such, the latter
might be putting a higher weight at the difference generated by the strong positive treatment effect at the
upper-tail of the distribution (Firpo et al. 2009).
Overall, these estimations confirm the robustness of the main findings through the QDID. While the size
of the effect is discussable, there is little doubt about a major effect happening at the very upper-end of the
distribution.
7.2 Balanced Panel & Weight of Capital City
Given the unbalanced nature of the panel and the fact that the identification strategy relies on the timing
and cross-section variation of EPZ establishment across municipalities, I next address the possibility that
my results are driven by the inclusion of a particular observation as treated or non-treated. Largely,
conclusions remain robust to the different changes in the sample size.
For this, I start by limiting the sample to municipalities observed for at least 4 of the 5 periods considered.
For brevity I only report results of treatment effect across the deciles of the expenditure distribution of
treated municipalities (table 7.2). Reassuringly, the change does not alter previous conclusions. There is a
marginal enlarging effect with a relatively higher size on the significant coefficients, and estimations against
all never-treated municipalities capture a significant treatment effect at the 70th percentile. Results on
inequality measures, as well as in specifications checking for spillover dynamics and time heterogeneity
remain unchanged.
Next, I test that the capital city (and municipality) of Managua is not pooling the results. The
agglomeration remains by far the largest in the country in terms of economic output and population, with
its average level of real expenditure per capita being twice the average of all other municipalities in the
country. The range is almost as high across all the decile of the expenditure distribution. Managua is also
the only municipality treated during the entire period. Table 7.3 reports the QDID estimates. Again, I find
that results are not altered, providing support to the main findings of this paper. Conclusions also hold for
the inequality measures, as well as in specifications checking for spillover dynamics and time heterogeneity.
Given the special weight the longer treated municipality may have on the different time leads, I report
results of the heterogeneous EPZ time effect across the expenditure distribution on table 7.435. As
mentioned, overall conclusions remain unchanged. It is interesting to note the positive and significant
effect now captured for individuals at the 90th percentile on year zero, as well as the fact that the
coefficient at the 1st decile is now only significant strictly after 11 years of EPZ establishment. There seems
to be a marginal attenuating effect in most of the coefficients, particularly below the 80th percentile.
35 See Appendix 6 for results with alternative control group.
35
All
Municipalities
Road Network
Buffer
All
Municipalities
Road Network
Buffer
(1) (2) (1) (2)
302.6** 250.0 266.4* 229.5
(2.087) (1.645) (1.852) (1.510)
216.0 146.8 160.4 106.8
(1.376) (0.901) (0.985) (0.625)
227.2 142.4 169.9 112.3
(1.093) (0.67) (0.789) (0.508)
364.7 291.8 299.4 238.4
(1.651) (1.285) (1.343) (1.034)
392.5 301.2 315.0 246.3
(1.601) (1.206) (1.273) (0.969)
490.4 437.7 363.0 334.8
(1.624) (1.356) (1.15) (0.993)
623.8* 523.0 518.3 447.6
(1.863) (1.486) (1.513) (1.246)
1,163** 1,050** 1,075** 985.5**
(2.474) (2.139) (2.322) (2.029)
2,767** 2,522** 2,614** 2,505**
(2.387) (2.055) (2.338) (2.124)
Yes Yes Yes Yes
Yes Yes Yes Yes
Yes Yes Yes Yes
486 359 570 412
107 80 144 106
Observations
Number of Muni.
Notes: Robust t-statistics in parentheses, Clustered SE
(municipalities). Covariates include illiteracy rate, primary and tertiary
education, as well as share of urban population and migrants at
municipal-level. Rsq. for both columns ranged between 0.35-0.71.
Regression in column (2) delimits observations using the road-
network buffer.
*** p<0.01, ** p<0.05, * p<0.1
70th Percentile
80th Percentile
90th Percentile
Year FE
Municipality FE
Covariates
Table 7.3 The Effect of EPZs across the Expenditure
Distribution in Treated Municipalities - Excl. Managua
Real Expenditure per
capita
10th Percentile
20th Percentile
30th Percentile
40th Percentile
50th Percentile
60th Percentile
Municipality FE
Covariates
Observations
Number of Muni.
Notes: Robust t-statistics in parentheses, Clustered SE
(municipalities). Covariates include illiteracy rate, primary and tertiary
education, as well as share of urban population and migrants at
municipal-level. Rsq. for both columns ranged between 0.38-0.71.
Regression in column (2) delimits observations using the road-
network buffer.
*** p<0.01, ** p<0.05, * p<0.1
Year FE
Table 7.2 The Effect of EPZs across the Expenditure
Distribution in Treated Municipalities - Balanced Panel
Real Expenditure per
capita
10th Percentile
20th Percentile
30th Percentile
40th Percentile
50th Percentile
60th Percentile
70th Percentile
80th Percentile
90th Percentile
36
10th
percentile
20th
percentile
30th
percentile
40th
percentile
50th
percentile
60th
percentile
70th
percentile
80th
percentile
90th
percentile
(1) (2) (3) (4) (5) (6) (7) (8) (9)
EPZ establishment, t0 147.9 -11.91 87.17 163.2 113.3 287.3 525.7 821.4 2,533*
(0.961) (-0.07) (0.571) (0.809) (0.461) (0.897) (1.190) (1.607) (1.783)
EPZ establishment, t+4 396.4 268.4 174.3 367.0 378.3 286.5 231.3 1,093* 2,565*
(1.411) (0.851) (0.434) (0.997) (1.053) (0.628) (0.474) (1.834) (1.871)
EPZ establishment, t+8 267.2 314.0 219.7 422.7* 868.9* 1,083** 1,030** 2,291** 3,652***
(1.412) (1.078) (0.714) (1.754) (1.886) (2.120) (2.156) (2.499) (3.297)
EPZ establishment, t+11 845.0** 310.7 517.0 601.3 1,004* 975.1* 922.1 1,662** 5,371***
(2.083) (0.608) (0.688) (0.909) (1.890) (1.774) (1.267) (2.030) (4.230)
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 412 412 412 412 412 412 412 412 412
R-sq. 0.639 0.688 0.677 0.711 0.712 0.608 0.604 0.592 0.352
Number of Muni. 106 106 106 106 106 106 106 106 106
Notes: Uses Road-Network Buffer to delimit control group. Robust t-statistics in parentheses, Clustered SE (municipalities). Covariates include illiteracy rate, primary and tertiary
education, as well as the share of urban population and migrants at the municipality level. All leads are equal to one in only year each per adopting state. EPZ dummy equals one only
for year of establishment. EPZ t+4 includes t+3, and EPZ t+8 includes t+7, to have balanced dummies.
*** p<0.01, ** p<0.05, * p<0.1
Table 7.4 The Heterogeneous Time-Effect of EPZ Establishment - Excl. Managua
Road Network Buffer
37
7.3 Mobility Issues
A final concern that remains with regards to the ‘experiment’ created in this paper relates to possible bias
from people’s mobility across areas during the period studied. Information available at the individual-level
for some of the household surveys would allow controlling for some degree of within-country migration in
specifications using the repeated cross-sectional dataset. As mentioned, I add an aggregated measure as
control for municipal-level estimations. However, as noted by Angrist and Piscke (2009), time-varying
variables measured at the aggregated level are usually affected by treatment and can introduce endogeneity.
A potential pitfall thus remains. To understand the magnitude, I use the information available in the
repeated-cross sectional dataset on individual migration across municipalities.
During the entire period, only 10.7 percent of the sample reports having moved in the previous five years
to another municipality, with a higher proportion in never-treated municipalities (12.3 percent). The flow
however seems to have been under-measured in 1998 and 2009, both years displaying very low
percentages (2-5 percent). Excluding them, 16.7 percent of the remaining sample reports having migrated
in the 5 years prior to the survey, and more relevant, only 9.1 percent of the working age population (9.6,
7.3 and 7.7 in never-treated, treated and road-network buffer municipalities, respectively). The numbers on
overall within-country migration are relatively small, and mirror the mentioned characteristics of
Nicaragua’s society throughout the period (i.e. relatively low levels of urban population, declining
urbanization rates, and labour market segmentation). There is limited information regarding origin and
destination of the flows, with data available only for 1998. For that year, 7.8 percent of the migrants
moved to a municipality hosting an EPZ. This partial information seems to indicate limited internal
migration from non-treated areas towards treated ones, with most flows taking place among non-treated
areas. To test formally for displacement resulting from EPZ establishment I estimate the likelihood of
migrating for the working age population following the establishment of a zone in a given area using both
OLS and logit estimators36. I find no evidence of a statistically significant impact. Furthermore, the sign on
the coefficients are consistently negative, implying than if anything, EPZs would deter emigration flows.
These results together with the relatively small number of migrants in the sample provide support to
discard major bias from displacement. As noted by Topalova (2010) in a similar context, the very findings
of disparate effects (of EPZ establishment) across space suggest absence of perfect factor mobility across
the country.
8. Conclusion
The present analysis has exploited a rich dataset at both individual and municipal levels to estimate the
distributional effects of EPZ establishment within their host municipalities in Nicaragua for the period
1993-2009. Evidence amassed is robust to the different specifications and identification strategies used.
Above all, I find robust evidence that EPZ establishment in a given municipality increases the average
level of real expenditure per capita at the very top-end of the distribution, with the 80th and 90th percentiles
consistently better-off compared to their peers in non-treated areas across the years. Furthermore, I find
this effect to be incremental with the more years of EPZ operation. The impact on overall within
municipality inequality is difficult to determine, which partly results from diverging dynamics between both
ends of the distribution. I find a sustained increase in top-end inequality across the period, but also a
smaller reduction in bottom-end disparities that fluctuates across the years. The slower diffusion of
treatment effect at the 10th and mid-range percentiles, only significant after 8 to 11 years of EPZ
36 See Appendix 3.
38
establishment, seems to be behind this trend. Interestingly, the heterogeneous time dynamic across the
different segments of the expenditure distribution suggests that the initial inequality-enhancing effect of
the policy fades with the more years of operation, eventually reaching the middle and poorer deciles.
Spillover effects to neighboring areas appear to be small in the timeframe studied and in no way
compromise previous findings. I find no evidence of larger displacements taking place as a result of the
policy.
The findings in this study are important from a policy perspective. They contribute to elucidate the
complex inequality dynamics of a popular policy tool. In a sense, they are supportive of the theoretical
predicaments of the more advanced HO models and polarization models. They contradict popular belief
that EPZs are directly beneficial for the poorer income segments in the municipalities where they locate.
The time lag registered for the policy to have a more equal effect across the distribution leaves a door open
for policy intervention. There is a need for further research in the area in order to identify with more
precision the different mechanisms at play.
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World Bank (2009) Nicaragua Poverty Assessment, Poverty Reduction and Economic Management Sector, Latin America and the Caribbean Region Report No. 39736-NI, Washington DC.
World Bank (2011), Lopez H.J., Shankar R. (eds). “Getting the Most Out of Free Trade Agreements in Central America”, Directions in Development (Trade). The World Bank Group, Washington DC.
World Bank (2012) “Better Jobs in Nicaragua: the role of human capital”. Human Development Department, Latin America and the Caribbean Region Report No. 72923-NI, Washington DC
41
APPENDIX 1. DESCRIPTIVE STATISTICS FOR SELECTED CONTROL VARIABLES
TableA.1 Key Descriptive Statistics of Municipality Characteristics according to EPZ status
Percentages Mean SD Mean SD Mean SD Mean SD
Primary Education 29.15 3.6 24.28 7.33 28.7 2.6 29.02 5.06
Secondary Education 22.9 6.24 11.66 6.97 31.64 6.8 18.68 8.5
Terciary Education 3.7 2.3 0.82 0.9 8.26 4.00 2.72 2.52
Illiteracy rate 18.29 8.03 34.7 12.9 14.32 5.56 27.3 10.3
Unemployment 19.6 3.19 15.4 7.5 4.09 1.57 4.33 4.55
Urban Population 63.77 20.5 30.58 22.1 66.05 21.45 34.5 21.83
1990s 2000s
Municipalities
Ever EPZ
Municipalities
Ever EPZ
Municipalities
Never EPZ
Notes: The data is for 83 municipalities in 1993, 13 ever designated EPZ. Expenditure measures are expressed in constant
Cordobas of 1999 (Exchange rate US$1=C$11.8)
Municipalities
Never EPZ
42
APPENDIX 2. QDID ESTIMATES WITH ALTERNATIVE CONTROL GROUPS
Distance-Buffer RAAN/RAAS Exclusion
(1) (2)
188.8 273.8*
(1.307) (1.875)
82.34 155.5
(0.493) (0.941)
105.8 179.6
(0.481) (0.833)
224.9 311.9
(1.015) (1.41)
242.0 328.6
(0.99) (1.336')
205.0 375.8
(0.693) (1.181)
370.5 530.6
(1.102) (1.542)
863.8* 1,071**
(1.887) (2.284)
2,185** 2,648**
(1.994) (2.298)
Yes Yes
Yes Yes
Yes Yes
432 500
107 126
Real expenditure per
capita
10th Percentile
20th Percentile
30th Percentile
40th Percentile
Table A.2 QDID Estimates of the Effect of EPZs across the Expenditure
Distribution in Treated Municipalities - Additional Control Groups
Notes: Robust t-statistics in parentheses, Clustered SE ( municipalities). Covariates
include illiteracy rate, primary and tertiary education, as well as share of urban population
and migrants measured at municpality level. Rsq. for both columns ranged between 0.52-
0.74. Regression in column (1) limits observations with respect to the distance-buffer
and in column (2) exclude municipalities in RAAN/RAAS.
*** p<0.01, ** p<0.05, * p<0.1
Municipality FE
Covariates
Observations
Number of Muni.
50th Percentile
60th Percentile
70th Percentile
80th Percentile
90th Percentile
Year FE
43
APPENDIX 3.A EFFECT OF EPZS ON BUSINESS CREATION
APPENDIX 3.B EFFECT OF EPZS ON INTERNAL MIGRATION
(1) (2) (3) (4)
0.010 0.008 0.0418 0.035
(1.12) (0.85) (0.66) (0.47)
Year FE Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes
Covariates No Yes No Yes
Observations 78,166 76,668 78,168 76,668
R sq. 0.03 0.09 0.03 0.11
Number of Muni. 145 145 145 145
EPZ
Notes: Robust t and z-statistics in parentheses, Clustered SE ( municipalities)
Covariates includes dummy variables for level of educational achievement, illiteracy,
gender, urban residence, age square, number of household members, relationship
to household head. Observations are limited to working age popluation (15-65
years).Logit outputs are log-odds coefficients.
*** p<0.01, ** p<0.05, * p<0.1
Table A.3 (a) Effect of EPZs on Business Start-ups
(Self-employed) 1993-2009
OLS LOGIT
OLS LOGIT OLS LOGIT
(1) (2) (3) (4)
-0.016 -0.192 -0.041 -0.197
(-1.01) (-0.70) (-0.50) (-0.67)
Year FE Yes Yes Yes Yes
Municipality FEYes Yes Yes Yes
Covariates Yes Yes Yes Yes
Observations 45,227 45,227 30,349 30,349
R sq./Pseudo Rsq. 0.17 0.28 0.19 0.19
Number of Muni. 145 145 145 145
Table A.3(b) The Effect of EPZs on Migration
1993-2009 Excl. 1998, 2009
Notes: Robust z and t-statistics in parentheses, Clustered SE ( municipalities).
Covariates includes dummy variables for level of educational achievement,
illiteracy, gender, urban residence, age square, number of household members,
relationship to household head. Observations are limited to working age
popluation (15-65 years).The left hand side measure for migration is a dummy
variable indicating if the person migrated within the past 5 years. Logit outputs are
log-odds coefficients.
*** p<0.01, ** p<0.05, * p<0.1
EPZ
44
APPENDIX 4. SPILLOVER DYNAMICS – ALTERNATIVE CONTROL GROUP
10th
percentile
20th
percentile
30th
percentile
40th
percentile
50th
percentile
60th
percentile
70th
percentile
80th
percentile
90th
percentile
(1) (2) (3) (4) (5) (6) (7) (8) (9)
EPZ 258.5* 147.3 157.8 320.3 327.6 386.9 551.2 1,165** 2,707**
(1.757) (0.897) (0.74) (1.438) (1.313) (1.191) (1.568) (2.488) (2.38)
Neighboring Municipalities1 -117.8 -275 -237.9 204.5 122.1 168 403.5 845.7 778.6
(5 to 15km) (-0.422) (-1.039) (-0.707) (0.587) (0.387) (0.531) (0.973) (1.653) (1.625)
Neighboring Municipalities2 24.18 46.53 58.67 142.8 126.4 202.5 208.3 453.8 545.2
(15 to 35km) (0.182) (0.276) (0.234) (0.59) (0.483) (0.707) (0.698) (1.161) (0.899)
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 575 575 575 575 575 575 575 575 575
R-sq. 0.625 0.673 0.672 0.704 0.701 0.619 0.619 0.594 0.356
Number of Muni.145 145 145 145 145 145 145 145 145
Table A.4 The Effect of EPZs Establishment & Spillover Dynamics
All Municipalities
Notes: Robust t-statistics in parentheses, Clustered SE ( municipalities) Covariates include dummies for illiteracy rate, primary and tertiary education, as well as share of urban
population, at municipality level. The variables Neighbour Municipalities are dummy variables that equal one if a municipality's centroid is situated at 5 to 15km, and 15km to 35km
distance from a treated municipality, and zero otherwise. I use the centroid's distance to avoid noise from larger municipalities.
*** p<0.01, ** p<0.05, * p<0.1
45
APPENDIX 5. HETEROGENEOUS TIME EFFECTS – ALTERNATIVE CONTROL GROUP
10th
percentile
20th
percentile
30th
percentile
40th
percentile
50th
percentile
60th
percentile
70th
percentile
80th
percentile
90th
percentile
(1) (2) (3) (4) (5) (6) (7) (8) (9)
EPZ establishment, t0 124.2 -10.12 72.46 145.7 86.34 214.5 451.5 694.5 2,214*
(0.868) (-0.069) (0.486) (0.759) (0.369) (0.740) (1.063) (1.464) (1.665)
EPZ establishment, t+4 412.8 300.6 206.0 386.5 377.6 236.4 247.4 1,070* 2,484*
(1.580) (1.033) (0.554) (1.126) (1.108) (0.567) (0.561) (1.947) (1.968)
EPZ establishment, t+8 393.9** 508.9* 474.4* 718.1*** 1,108*** 1,250*** 1,267*** 2,447*** 4,128***
(2.407) (1.949) (1.696) (3.006) (2.740) (2.892) (3.253) (3.069) (4.209)
EPZ establishment, t+11 905.2*** 562.4 787.0 936.0* 1,246*** 1,170*** 1,371*** 2,051*** 5,381***
(3.182) (1.443) (1.462) (1.900) (3.247) (3.035) (2.617) (3.548) (5.099)
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 575 575 575 575 575 575 575 575 575
R-sq. 0.629 0.676 0.677 0.708 0.706 0.623 0.622 0.598 0.364
Number of Muni. 145 145 145 145 145 145 145 145 145
Table A.5 The Heterogeneous Time-Effect of EPZ Establishment
All Municipalities
Notes: Robust t-statistics in parentheses, Clustered SE ( municipalities) Covariates include illiteracy rate, primary and tertiary education, as well as the share of urban population and
migrants at the municipality level.. All leads are equal to one in only one year each per adopting state. EPZ dummy (t0) equals one only for year of establishment. EPZ t+4 includes
t+3, and EPZ t+8 includes t+7, to have balanced dummies.
*** p<0.01, ** p<0.05, * p<0.1
46
APPENDIX 6. HETEROGENEOUS TIME EFFECTS – ALTERNATIVE CONTROL GROUP, EXCL. MANAGUA
10th
percentile
20th
percentile
30th
percentile
40th
percentile
50th
percentile
60th
percentile
70th
percentile
80th
percentile
90th
percentile
(1) (2) (3) (4) (5) (6) (7) (8) (9)
EPZ establishment, t0 162.3 16.81 107.2 186.0 142.8 288.9 555.9 804.7* 2,437*
(1.102) (0.109) (0.708) (0.943) (0.596) (0.960) (1.285) (1.680) (1.793)
EPZ establishment, t+4 438.2 335.5 248.6 438.5 458.8 320.1 325.0 1,207** 2,723**
(1.574) (1.081) (0.624) (1.199) (1.283) (0.730) (0.695) (2.102) (2.063)
EPZ establishment, t+8 325.8* 422.4 326.0 525.6** 985.8** 1,144** 1,177** 2,454*** 3,804***
(1.829) (1.470) (1.110) (2.404) (2.169) (2.312) (2.570) (2.680) (3.537)
EPZ establishment, t+11 851.3** 400.2 603.0 640.5 1,060** 937.7* 1,036 1,774** 5,485***
(2.237) (0.801) (0.826) (0.995) (2.018) (1.864) (1.565) (2.431) (4.016)
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Municipality FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Covariates Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 570 570 570 570 570 570 570 570 570
R-sq. 0.629 0.676 0.677 0.708 0.706 0.623 0.622 0.598 0.364
Number of Muni. 144 144 144 144 144 144 144 144 144
Table A.6 The Heterogeneous Time-Effect of EPZ Establishment - Excl. Managua
All Municipalities
Notes: Robust t-statistics in parentheses, Clustered SE ( municipalities) Covariates include illiteracy rate, primary and tertiary education, as well as the share of urban population and
migrants at the municipality level.. All leads are equal to one in only one year each per adopting state. EPZ dummy (t0) equals one only for year of establishment. EPZ t+4 includes
t+3, and EPZ t+8 includes t+7, to have balanced dummies.
*** p<0.01, ** p<0.05, * p<0.1