rafmacalaba/datause-encoder-data huggingface dataset:
|(6)<br>1.637***<br>-0.523<br>-0.004<br>(0.003)<br>-0.070***<br>-0.025<br>12,630<br>t<br>Host, Parent<br>Bilateral<br>Year<br>Parent<br>ear observations. The<br>ntry, year, and parent<br>GDP (in log), capital<br>. Specifcations (2) to<br>s of the multinational<br>vernment expenditure<br>* indicate signifcance<br>ics including distance|
|---|
|(5)<br>1.745***<br>(0.508)<br>-0.003<br>(0.005)<br>12,630<br>0.128<br>Host, Paren<br>Bilateral<br>Year<br>Parent<br>host-parent-y<br>ude host cou<br> indicated): <br>xchange rates<br>characteristic<br>tion rate, go<br> ***, ** and <br>t characterist|
|(4)<br>-1.495***<br>(0.414)<br>2.080***<br>(0.530)<br>1.344**<br>(0.599)<br>12,630<br>0.165<br>Parent,<br>Bilateral<br>,<br>Host-Year,<br>Parent<br>all available <br> and (6) incl<br>countries (as<br>share, and e<br>he following<br>enness, infa<br>ost country. <br>al host-paren|
|(3)<br>2.764***<br>(0.659)<br>-2.607***<br>(0.631)<br>12,630<br>0.164<br>Parent,<br>Bilateral<br>Host-Year<br>Parent<br>ed panel of <br>ns (1), (5) <br>and parent <br>expenditure<br>fects, and t<br>ex, trade op<br>stered by h<br>l for bilater|
|(2)<br>2.667***<br>(0.605)<br>12,630<br>0.165<br><br>Parent,<br>Bilateral<br>Host-Year,<br>Parent<br> an unbalanc<br>. Specifcatio<br> of the host <br>government<br>untry fxed e<br>openness ind<br>eses, are clu<br>ations contro|
|(1)<br>1.630***<br>(0.571)<br>-0.004<br>(0.003)<br><br>-0.071**<br>(0.029)<br>-0.101<br>(1.477)<br>12,630<br>0.129<br>Host, Parent<br>Bilateral<br>Year<br>Parent<br> PPML using <br>or inward FAI<br>haracteristics <br>infation rate,<br>and parent co<br>pital account <br>ors, in parenth<br>y. All specifc<br>cial language.|
|Diferential<br>Host Component<br> Component<br>Host Component (EU)<br>Host Component (Non-EU)<br> KF-METR<br> Diferential _×_ Host KF-METR<br>e Diferential)2<br>rvations<br>do-_R_2<br>rols:<br>specifcations are estimated in<br>variable is the _It/Kt−_1 ratio f<br>ed efects, and the following c<br>nness index, trade openness,<br>host country-year fxed efects<br>nt country: GDP (in log), ca<br>exchange rates. Standard err<br>5% and 10% levels respectivel<br> an indicator for common of|
|Rate<br>Non-<br>Host<br>Non-<br>Non-<br>Host<br>Rate<br>(Rat<br>Obse<br>Pseu<br>Cont<br>FEs:<br>_Notes:_ All <br>dependent <br>country fx<br>account ope<br>(4) include<br>frm’s pare<br>share, and <br>at the 1%, <br>(in log) and|

51
25

# **References :**

Aizenman, Joshua (1987). "Inflation, Tariffs and Tax Enforcement Costs." <u>Journal of International Economic Integration. Vol. 2, No. 2, pp 12-28.</u> Alesina, Alberto and Roberto Perotti (1995). "The Politcal Economy of Budget Deficits." <u>IMF Staff Papers. Vol. 42, No. 1, March</u> Anderson, Gary M. (1986). "The US Federal Deficit and National Debt: A Political and Economic History." In <u>Deficits, edited by James M. Buchanan, Charles K Rowley and Robert D. Tollison,</u> Basil Blackwell Inc, pp. 9 – 46.

Barro, Robert J. (1974). "Are Government Bonds Net Wealth ?" Journal of Political Economy. Vol. 82, No. 6, pp 1095-1117.

Barro, Robert J. (1979). "On the Determination of the Public Debt." Journal of Political Economy. Vol. 87, No. 5, pp 940-971.

Barro (1991) : "Comments on McCubbins" in Politics and Economics in the Eighties. ed by Alberto Alesina and Geoffrey Carliner. University of Chicago Press and National Bureau of Economic Research.

Berg, Andrew and Jeffrey Sachs (1988). "The Debt Crisis : Structural Explanations of Country Performance." Journal of Development Economics. Vol. 29, No. 3, pp 271-306.

Bloom, David E. and Jeffrey G. Williamson (1997). <u>Demographic Transitions and Economic Miracles in Emerging Asia. Working Paper, Harvard Institute for International Development,</u> Harvard University.

Buchanan, James M. and Richard E. Wager (1977). Democracy in Deficit : The Political Legacy <u>of Lord Keynes. Academic Press, New York.</u> Buchanan, James M. and Jennifer Roback (1987). The Incidence and Effects of Public Debt in the Absence of Fiscal Illusion.” Public Finance Quarterly. Vol. 15, pp. 5-25.

Clingermayer, James C. (1991). “An Intergenerational Transfer Model of State Debt Financing.” <u>Public Choice. Vol. 72, No. 1, pp. 13-21.</u> Cukierman, Alex, Sebastian Edwards and Guido Tabellini (1992). "Seigniorage and Political Instability." American Economic Review. June, Vol. 82, No. 3, pp 537-555.

Cukierman, Alex and Allan H. Meltzer (1989). "A Political Theory of Government Debt and Deficits in a Neo-Ricardian Framework." American Economic Review. September 1989, Vol. 79, No. 4, pp 713-732.

Culter, David M., James M. Poterba, Louise M. Sheiner and Lawrence H. Summers (1990). “An Aging Society : Opportunity or Challenge ?” Brookings Papers on Economic Activity. No. 1, pp. 1-73.

Diamond, Peter A. (1965). “National Debt in a Neoclassical Growth Model.” American Economic <u>Review. December, Vol. 55, pp. 1126-1150.</u> Edwards, Sebastian and Guido Tabellini (1991). "Explaining Fiscal Policies and Inflation in Developing Countries." Journal of International Money and Finance. Vol. 10 (Supplement), pp. S16-S48.
sovereign debt markets, non-bank financial institutions, the migration of credit intermediation outside the regular banking system, and digital financial services.

# _<u>Banks</u>_

In contrast to the regulators, local banks are roughly split between positive and moderately negative spillover effects (Table 5). This assessment applies to the net impact on the overall development and deepening of the domestic financial sector, and to whether domestic banks will be put at a disadvantage as compared to foreign banks.

# **Table 5 - Local bank respondents: Net spillovers of the combined reforms**

_In conclusion, regarding the overall impact of reforms, what do you think is, or is expected to be, the net impact of their adoption in other jurisdictions on the overall development and deepening of the domestic financial sector?_

|**N = 20**|**Highly**|**Moderately**|**No**|**Moderately**|<br>**Highly**|
|---|---|---|---|---|---|
||**negative**|**negative**|**impact**|**positive**|**positive**|
||||**at all**|||
||2|6|0|11|1|

A few local banks commented that the reforms will lead to better financial development and stability outcomes in their jurisdictions, for example: _“Better regulation and practices will contribute to a more solid and resilient financial system.”_ _“[The reforms will generally lead to] increased resilience of credit institution[s] and less contagion risk. Increased customer confidence in financial institutions which they perceive to be more regulated than before, thus safer.”_ Some local bank respondents noted specifically that they may benefit from a retrenchment of foreign banks: 21
# **5.1.2 Variants on the Fixed Effects Generation Capacity Model**

A large number of variants, static and dynamic, are reported in full in Cubbin and Stern (2004). Here, we summarise the key results.

# _5.1.2.1 Static Model:  Individual Governance Elements_

The first set of variants was the estimation of the static model of per capita generation capacity, as in Table 5 above, but including in 4 separate equations each of the individual governance elements in our regulatory index. The main results were as follows:

- The largest estimated regulatory effect of the index components was, perhaps surprisingly, from having an electricity law (18% with a t-value of 5.1) rather than from having an autonomous regulator (10% with a t-value of 2.3). However, these must be interpreted in the light of the correlation between them of 0.78.

- Licence funding of the regulator also had a positive estimated effect (13.5% with a t-value of 3.4)

- The  estimated sign on non-mandatory civil service pay scales was negative (and significant at the 1% level) ie the opposite of that predicted by regulatory governance theory.

The strong effect of a regulatory law – and of age of regulator variables derived from the date of the law – appears to reflect a signalling and commitment effect from having a legal framework which makes even Ministry regulators significantly more accountable for how they carry out their functions. It may be that the effect of an autonomous regulator would be higher in a sample where more autonomous regulators had been in operation for more than 5 years.

Given the high degree of collinearity between the regulatory variables, we used principal components to help better identify the effects of the individual governance elements. We first computed the principal components of the four governance element. We then included the first 2 principal components (accounting for over 90% of the total index variance) in a static fixed effects regression of per capita generation capacity with per capita GDP as the other explanatory variable. The estimated impact of each of the 2 principal components was positive but only the coefficient estimate of the first principal component was statistically significant at the 5% level, with a t- value of 3.8.<sup>26</sup> Interestingly, the loadings of the individual components in the first principal component (accounting for 76% of the total index variance) were broadly similar. Nevertheless, the loading from the electricity law element was the highest, providing

> 26 Including the first 2 principal components in a dynamic equation with a lagged dependent variable produced very similar results.

27
The main data source we use is firm-level survey data collected by the World Bank’s Enterprise Surveys (ES). We complement the ES with other data sets such as the World Development Indictors (WDI, World Bank), Worldwide Governance Indicators (WGI, World Bank), Doing Business (World Bank), and Economic Freedom of the World (Fraser Institute).

The ES are nationally representative surveys of the non-agricultural and non-financial private economy. The survey includes only the formal or registered firms with 5 or more employees. Thus, informal or unregistered firms and micro firms (registered with fewer than 5 employees) are not covered by the survey. A common sampling methodology – stratified random sampling – is followed in all the surveys along with a common questionnaire.<sup>1</sup> The sample for each country is stratified by industry, firm-size, and location within the country. Weights are provided in the survey and used in all our regressions so that the sample is representative of the target population.

The sample that we use consists of SMEs in the manufacturing sector of mostly developing countries. Based on the definition used by the ES for stratification purposes, we define SMEs as all firms with fewer than 100 full-time permanent workers employed at the firm three fiscal years ago (lagged employment).<sup>2</sup> From this sample, we exclude firms with 10 or fewer employees. We do so because R&D activity involves substantial fixed costs and is therefore of not much relevance for the very small firms. Large firms (100 or more workers) are excluded because these firms operate in very different product markets than the informal firms. Thus, spillovers from informal sector to the large formal sector firms are unlikely. Results for large firms are shown in separate

> 1 Details of the sampling methodology and other related information are available at www.enterprisesurveys.org.

> 2 Information is available in the ES on the number of workers at the end of the last fiscal year and 3 fiscal years ago (or 2 fiscal years ago in the case of some countries). We use the latter for defining SMEs in order to avoid simultaneity concerns with the current (last fiscal year end) employment. See for example, Aterido et al. (2011) on the use of lagged employment to avoid the simultaneity problem.

8
27 Table 3: Contribution to net incomes of cotton farmers that would result from removing cotton subsidies and tariffs,<sup>a</sup> by region and by policy instrument

||(per|cent cha|ngeinva|lue added|)||||
|---|---|---|---|---|---|---|---|---|
|||Byr|eformingr<br>All other|egion:<br>||By po|licyinstru|ment:|
|Benefitingregion:|US|EU25|high-<br>income<br>countries|Develop-<br>ing<br>countries|TOTAL,<br>WORLD|Tariff|Export<br>subsidy|Domestic<br>support|
|**High-income countries**|**-12.9**|**-3.2**|**-0.4**|**1.2**|**-15.4**|**0.4**|**0.1**|**-15.9**|
|Australia|22.4|2.5|-4.2|1.5|22.2|0.2|0.3|21.6|
|United States|-20.1|1.0|0.3|0.9|-17.9|0.3|0.1|-18.3|
|EU25|8.6|-66.2|0.4|3.9|-53.3|2.3|0.5|-56.1|
|Japan|2.9|0.2|-1.9|0.3|1.5|0.1|0.0|1.4|
|Korea-Taiwan|7.2|0.6|-1.1|0.2|6.9|-0.3|0.0|7.2|
|**Developing countries**|**3.8**|**1.3**|**0.1**|**-0.9**|**4.3**|**-0.2**|**-0.1**|**4.6**|
|**_E. Europe & C. Asia_**|**_3.3_**|**_2.3_**|**_0.1_**|**_-2.4_**|**_3.3_**|**_0.0_**|**_0.1_**|**_3.1_**|
|Turkey|2.8|1.3|0.1|-2.0|2.1|0.0|0.1|2.0|
|Other ECA|4.3|4.0|0.2|-3.3|5.2|0.0|0.2|5.1|
|**_East Asia_**|**_1.4_**|**_0.5_**|**_0.1_**|**_-0.1_**|**_1.9_**|**_-0.2_**|**_0.0_**|**_2.0_**|
|China|1.1|0.5|0.0|-0.1|1.5|-0.2|0.0|1.7|
|**_South Asia_**|**_1.9_**|**_0.3_**|**_0.1_**|**_-1.6_**|**_0.7_**|**_-0.7_**|**_0.0_**|**_1.4_**|
|Bangladesh|2.9|0.5|0.2|1.4|5.0|0.7|0.1|4.2|
|India|1.7|0.2|0.0|-2.4|-0.4|-1.1|0.0|0.7|
|Pakistan|2.2|0.3|0.2|0.2|3.0|0.0|0.1|2.8|
|**_M. East &North Africa_**|**_5.7_**|**_3.4_**|**_0.3_**|**_-3.2_**|**_6.1_**|**_-0.8_**|**_-1.9_**|**_8.8_**|
|**_Sub-Saharan Africa_**|**_16.0_**|**_6.6_**|**_0.8_**|**_7.1_**|**_30.6_**|**_5.1_**|**_0.5_**|**_24.8_**|
|South Africa|21.2|3.4|1.2|-5.1|20.6|-6.4|0.4|26.6|
|Mozambique|10.2|12.7|0.4|-5.8|17.5|0.3|0.7|16.4|
|Zambia|3.1|2.2|0.4|-2.0|3.6|-2.7|0.2|6.1|
|Uganda|14.8|8.5|0.5|1.7|25.6|-0.3|0.5|25.4|
|Other Sthn & E. Africa|11.6|5.0|0.6|2.5|19.7|1.0|0.4|18.3|
|Nigeria|15.7|2.7|0.5|2.3|21.3|-0.2|0.3|21.2|
|Other Sub-Sah. Africa|18.3|7.6|1.0|9.9|36.8|7.9|0.5|28.4|
|**_Latin America & Car._**|**_9.6_**|**_1.3_**|**_0.2_**|**_-1.8_**|**_9.4_**|**_-1.1_**|**_0.1_**|**_10.4_**|
|Argentina|7.6|1.2|0.2|1.7|10.7|1.7|0.1|9.0|
|<br>Brazil|8.0|2.5|0.3|-0.5|10.3|0.3|0.2|9.9|
|Mexico|12.8|0.4|0.1|-2.8|10.5|-0.3|0.0|10.9|
|**World**|**-1.4**|**-0.1**|**0.0**|**-0.2**|**-1.8**|**0.0**|**0.0**|**-1.8**|

a Removal of those distortions left after the eventual phase-out of the quotas under the Multifibre Agreement at the end of 2004 Source: Authors’ GTAP model simulation results
**Table 1: Basic characteristics of household included In the sample**

||Total|Guizhou|**Province**<br>Hunan|Yunnan|
|---|---|---|---|---|
|Head'sage(years)|45 27|4596|4650|42.40|
|Head'seducation (years completed)|609|6.20|6.10|5.80|
|Noofpersonsagel4-60|3.04|3.14|292|2.90|
|No.ofpersonsage<14|093|0.96|0.74|107|
|No.ofpersonsage>60|0.35|0.35|0.50|0.25|
|Total earnedincome (Y)|515652|4832.53|591100|526733|
|Percapitaearnedincome(Y)|1235.89|1116.38|146807|131982|
|ofwhichagriculturalincome(%)|70%|69%|71%|74%|
|ofwhichnon-agnculturalincome|30%|31%|29%|26%|
|Sharereceivingremuttancesfrom fam.Members|44%|42%|60%|33%|
|Meanamountofremuttancesreceived(Y)|117992|1189.76|1220.56|108020|
|Shareofhouseholdspayingforoff-spnng|64%|63%|68%|61%|
|Meanamountofsupportgiven(Y)|674.56|593.46|884.00|66856|

Source: Own computation from SSB/CCERJWB 2001 Household Survey 25
**Table S5.2: Probability of being a non-poor household with poor members**

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|All|80% richest hh –<br>non-poor|<br>20% poorest hh –<br>non-poor|
|Dakar|11.71***|2.221***||
||(4.371)|(0.675)||
|Other urban area|2.496***|1.620|7.814|
||(0.799)|(0.546)|(11.29)|
|nb of children 0 - 4 y.o.|0.873|0.903|0.767|
||(0.0950)|(0.0932)|(0.303)|
|nb of children 5 - 14 y.o.|1.003|1.147|0.479**|
||(0.0880)|(0.0995)|(0.155)|
|nb of elderly over 66 y.o.|0.807|0.713|1.521|
||(0.167)|(0.154)|(1.011)|
|hh head is polygamous|1.056|1.151|0.408|
||(0.278)|(0.308)|(0.325)|
|Hh size|1.001|1.020|1.440|
||(0.0534)|(0.0501)|(0.337)|
|hh head has no formal schooling|1.253|2.218***|1.022|
||(0.319)|(0.560)|(0.797)|
|Nb of cells|1.283|1.093|2.079|
||(0.231)|(0.198)|(1.680)|
|2-cell hh, head + other|0.807|0.759|2.256|
||(0.329)|(0.338)|(2.563)|
|3+-cell hh: head and wives.|1.700|1.133|0.749|
||(0.792)|(0.555)|(0.948)|
|3+-cell hh: head, wife(wives), + other|2.906***|3.008***|1.193|
||(1.004)|(1.028)|(1.297)|
|3+-cell hh: head + others (no wife)|1.020|0.793|0.106|
||(0.521)|(0.403)|(0.204)|
|All meals taken together|0.288***|0.389***|0.381|
||(0.0829)|(0.101)|(0.384)|
|Log hh per adult eq consumption|0.00551***<br>(0.00283)|||
|Dakar = o,|||-|
|Constant|7.12e+27***|0.0538***|0.204|
||(4.595e+28)|(0.0255)|(0.337)|
|Observations|876|793|83|

Source « Pauvreté et Structure Familiale » (PSF) survey, author’s calculations.

Note: Sample of Non-poor households. Dependent variable: dummy equals 1 if the household contains at least one poor cell. Logit estimates, odd ratios reported; coefficient standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 23
**<u>Table 4: Wheat and bread consumption in Mali for different household groups, 2006</u>**

||Percentag|e of households co<br>Auto|nsuming|Average c|onsumption for all<br>Auto|households|Average c|onsumption for househ<br>consumption<br>Auto|olds with positive|
|---|---|---|---|---|---|---|---|---|---|
||Purchase|consumption|Total|Purchase|consumption|Total|Purchase|consumption|Total|
|**All**|72.90%|6.40%|74.00%|23,114.22|921.54|24,035.76|31,695.91|14,334.69|32,493.31|
|**Residence area**||||||||||
|Urban|82.90%|4.80%|83.60%|36,533.92|1,194.34|37,728.26|44,053.31|24,972.86|45,137.62|
|Rural|67.00%|7.40%|68.20%|15,104.58|758.72|15,863.30|22,560.10|10,237.35|23,248.55|
|**Region**||||||||||
|Kayes|75.80%|4.80%|77.00%|34,309.36|997.85|35,307.22|45,273.90|20,933.59|45,842.73|
|Koulikoro|76.30%|9.90%|77.40%|18,550.55|1,008.97|19,559.51|24,322.94|10,225.70|25,270.15|
|Sikasso|81.30%|6.20%|82.00%|18,345.41|443.23|18,788.64|22,558.31|7,194.62|22,907.07|
|Ségou|61.40%|8.60%|63.20%|16,919.90|567.06|17,486.96|27,537.75|6,566.75|27,657.89|
|Mopti|72.40%|3.00%|72.90%|12,482.85|159.57|12,642.42|17,235.26|5,256.15|17,330.43|
|Tombouctou|60.60%|13.10%|63.40%|20,545.55|3,039.99|23,585.55|33,884.62|23,132.59|37,184.97|
|Gao|50.30%|1.60%|50.30%|22,242.48|217.86|22,460.34|44,184.46|13,323.13|44,617.23|
|Kidal|93.90%|5.40%|96.20%|118,776.22|553.87|119,330.08|126,556.77|10,213.64|124,016.15|
|Bamako|88.40%|2.80%|88.40%|42,512.07|1,835.95|44,348.02|48,100.76|65,150.48|50,178.07|
|**Quintile**||||||||||
|Q1|58.00%|4.80%|58.60%|7,404.80|440.82|7,845.61|12,768.96|9,173.68|13,398.24|
|Q2|64.40%|6.00%|65.80%|11,438.34|513.66|11,952.00|17,757.23|8,499.00|18,171.29|
|Q3|66.50%|8.20%|68.00%|16,233.94|634.08|16,868.01|24,401.63|7,688.97|24,823.89|
|Q4|74.80%|8.10%|76.10%|24,609.11|1,194.38|25,803.50|32,895.10|14,719.66|33,908.27|
|Q5|88.90%|4.90%|89.50%|42,123.95|1,401.56|43,525.51|47,395.67|28,784.88|48,622.97|

Source: Authors’ estimation using ELIM 2006.
Note that this yields a few, basic expectations. First, bribe extraction will increase with a business’s revenues—that is, bribery tracks with a business’s ability to pay (e.g. Kaufmann and Wei, 1999). Secondly, the greater the cost to formalize, so too, the increase in bribes. That is, if a business operator has opportunities outside informality, they will have little incentive to deal with corrupt officials. The associated costs also vary by location, as opportunity costs are likely to be localized. So, too, the likelihood of detection by a corrupt official will, of course, increase the chances of facing a bribe, but this is also contingent on the tolerance for corruption and local circumstances.

# V. Data: Collection and Methodology

One limitation to studying informal sector businesses is that reliable data are sparse. By most operating definitions, informal businesses remain outside the bounds of many data sources. Informal businesses do not appear on business registries nor other administrative data. Adequate sampling frames for a robust survey methodology are, thus, nearly impossible to come by. Methods linked to household and labor-force surveys (for instance, what is known as the 1-2-3 method) have offered more systematic measurement of informal business activity (such as the data used by Lavallée and Roubaud, 2018), however such data are based on initial sampling designed to be representative of residential enumeration areas, and ultimately households. These methods are implemented over multiple waves and are thus much more expensive and can lack a depth of information.

The analysis here uses a data set that was developed by the World Bank, which implemented a survey in Harare, Zimbabwe over April–May 2017. The survey methodology uses a method called “adaptive cluster sampling”, with stratification (SACS), which was developed in bio-statistics (Thompson 1990, 1991) to efficiently measure elusive and clustered populations: the former characteristic being mostly self-evident and the latter—that informality tends to be highly clustered— being well-established in the literature (see, e.g., World Bank 2018, Harris, 2014, Gulyani and Talukdar, 2010).

SACS is implemented through the selection of primary sampling units (PSUs) of square-block areas. In the case of the Harare survey, the administrative area of the city was divided into 200-by200-meter squares, stratified by the classification of informal business activity (low, medium, high), with a simple random sample of PSUs in a fourth stratum made up of known markets. The idea of SACS is that the discovery of some target units (i.e., informal businesses) is useful and can make fieldwork more efficient. Implementers set a threshold for expansion, where enumerators will move (16)
1998 and Mesalles, 1998), and promoting foreign investment by means of incentives characterized by its fiscal nature.<sup>2</sup>

# **_1.c The Response of Costa Rica’s Economy to Economic Liberalization: A Long-Term Vision_**

Although several studies individually show positive outcomes for Costa Rica’s economy during the economic liberalization period,<sup>3</sup> a comprehensive vision of the performance of that economy in the period under analysis is not available. This section addresses this topic, describing the behavior of Costa Rica’s economy during the last two decades (1983-2003) with a focus on trade, employment and poverty, to provide a long-term view of the results achieved through the liberalization of the economy.

First of all, it should be noted that even though Costa Rica eliminated most of its tariff and non-tariff protection in industrial and agricultural activities, the process was carried out gradually. As shown in Figure 1.1, since 1986, when the first tariff reduction and simplification of the protectionist system took place, there was a rapid response in terms of trade flows. In fact, imports began increasing in a significant and sustained manner as did exports, the latter as a response of the export sector to the reduction of the anti-export bias generated by the import-substitution model (liberalization of imports and subsidies to exports).

<!-- Start of picture text --> Figure 1.1 Costa Rica: Exports, Imports and Average Tariff<br>1982-2003<br>9,000 50<br>8,000 45<br>7,000 40<br>35<br>6,000<br>30<br>5,000<br>25<br>4,000<br>20<br>3,000 15<br>2,000 10<br>1,000 5<br>0 0<br> 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Exports Imports Average tariff<br>Intel’s Arrival<br>Export Promotion<br>Import Liberalization FTZ Law Reform<br>Percentage<br> US$ millions<br>Capital Account Liberalization<br><!-- End of picture text --> Source: Prepared by the authors based on figures from Banco Central de Costa Rica.

It is worth noting that in 1984, along with economic liberalization, Costa Rica’s export products began enjoying free access to the US market (zero tariffs on almost all manufactured and agricultural products), thanks to the Caribbean Basin Initiative (CBI). Not surprisingly, therefore, the US market has grown in terms of its relative importance

> 2 Applying Lerner’s theorem (1936), Monge (1994) estimated that 0.66 out of each percentage point of protection granted to Costa Rica’s manufacturing sector during the import-substitution period was transferred as an implicit tax to the country’s export efforts.

> 3 See Corrales and Monge (1990), Lizano (1992), Céspedes and Jiménez (1997), and Monge and Lizano (1997).

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