rafmacalaba/datause-encoder-data huggingface dataset:
14 reserves in the face of a worsening current account performance.

||||**Table**|3||
|---|---|---|---|---|---|
||Si|erra Leo<br>198|ne, sele<br>1/82- 1|cted yea<br>986/67|rs|
||1978/79<br>-1980/81<br>|<br>81/82<br>(average|83/84<br>)|85/86|1986,<br>2nd half|
|Tax Revenues as<br>Z of GDP|15.2|11.4|6.6|5.5||
|Growth rate (2):||||||
|Monetary Base*|18.0|30.3|37.5|83.4||
|Consumer Prices|<br>17.0|25.1|78.7|70.4|200+**|

* The monetary base is defined as the sum of commercial bank reserves (including cash), currency outside banks, and net claims on the government by commercial banks. Average growth rates are computed from the end of the preceding period to the end of the final period (e.g., the 78/79-80/81 average uses end-1977 and end-1981 as endpoints).

** The CPI grew 74 percent between June and December, 1986. The implied annual inflation rate is over 200 percent.

SOURCE: World Bank; IMP, International Financial Statistics Yearbook, 1987.

Tax performance deteriorated in 1981/82, and increases in the monetary base began to be a more important source of finance. Domestic inflation accelerated, and tax revenues declined further as a percentage of GDP, setting up a Tanzi-style interaction: between 1981/82 and 1985/86, the domestic inflation rate rose from 25 to 70 percent per year and tax revenues fell from 11 to 5.5 percent of GDP. By the end of the period, the country was on a 14 The terms of trade moved strongly against Sierra Leone in the late 1970s and early 1980s: the prices of coffee, cocoa, and diamonds all collapsed, and the price of oil rose.

11
Table 4. Determinants <u>of labor productivity per worker</u>

|VARIABLES|(1)<br>LP|(2)<br>LP|(3)<br>LP|(4)<br>LP|(5)<br>LP-2SLS|
|---|---|---|---|---|---|
|**Capital**||||||
|Log. Stock of capital|0.181***|0.173***|0.172**|0.167**|0.176***|
||(0.0287)|(0.0246)|(0.0442)|(0.0423)|(0.0290)|
|**Labor**||||||
|Proportion of|0.363|0.349|0.613*|0.559*|0.487**|
|temporary worker|(0.307)|(0.291)|(0.280)|(0.257)|(0.186)|
|Avg. Education of|0.0618|0.0555|0.124**|0.127**|0.213***|
|Workers|(0.0374)|(0.0384)|(0.0425)|(0.0422)|(0.0458)|
|**Size and Age**||||||
|Market share|11.04**|10.76**|8.281*|7.995*|23.38***|
||(3.997)|(3.767)|(3.650)|(3.381)|(4.107)|
|Young|-0.0500|-0.0638|0.108|0.124|0.0934|
||(0.0606)|(0.0557)|(0.113)|(0.118)|(0.126)|
|Mature|0.0481|0.0483|0.162|0.169|0.0958**|
||(0.104)|(0.104)|(0.0870)|(0.0944)|(0.0403)|
|**Access to finance**||||||
|Credit line or overdraft||0.338**|0.336**|0.252*|0.211*|
|Facility||(0.0849)|(0.119)|(0.123)|(0.110)|
|**Investment Climate (%)**||||||
|Cost of insecurity|||0.0134|0.00604|0.0063|
||||(0.0163)|(0.0154)|(0.0179)|
|Cost power outage|||-0.00795**|-0.00670**|-0.00718***|
||||(0.00221)|(0.00259)|(0.0023)|
|Bribe|||0.0249**|0.0239**|0.0718**|
||||(0.00743)|(0.00800)|(0.0316)|
|Management time|||0.00429|0.000547|-0.00329|
||||(0.00392)|(0.00515)|(0.0091)|
|**Outward-orientation**||||||
|Exporter||||0.572***<br>|0.320<br>|
|||||(0.100)|(0.591)|
|**Sector**||||||
|Manufacturing|-0.530|-0.470|-0.537**|-0.317|1.323|
||(0.558)|(0.489)|(0.197)|(0.237)|(1.102)|
|Observations|2,716|2,711|1,405|1,405|1,277|
|R-squared|0.493|0.502|0.586|0.596|0.577|
|Weak identification test|||||14.59|
|States FE|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|

Source: Authors’ estimations based on the data from the Enterprise Surveys 2007/2009 and 2014. Note: LP=labor productivity per worker. Robust standard errors clustered at the States level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All the estimates use the weights from the survey.

20
27 Malaysia adjusted by staying on, or close to, its production possibility frontier, Chile adjusted by moving inside it (see figure 2). During the first of the two post-crisis periods (1982-5) shown in table 2, Chile is the only economy with negative growth. What were the distributional consequences of this type of adjustment?

Household surveys show that it was essentially the heads of households of the lowest income quintile that bore the brunt of unemployment: in that group, 25 percent were unemployed, compared to 9 percent in the lower-middle-income quintile and 2 percent in the highest-income quintile. Also, unemployment hurt the lower-income groups the most because they had fewer income earners per family. While household surveys were not available for the other counties, so we cannot draw comparisons, it is unlikely that this pattern of adjustment would have been observed in other countries where the crisis mostly led to underemployment among the poor in the informal sector.

Another interesting piece of evidence from the Chilean adjustment episode is the impact of the real exchange rate depreciation on the purchasing power of real incomes. As pointed out in figure 4, if households have consumption patterns that differ in their tradable goods intensity, a depreciation of the real exchange rate will have a redistributive effect through the consumption side. The evidence (see Meller, table 6) suggests that this effect was quite significant because of the large change in the value of the real exchange rate during adjustment: the middle 40 percent income group lost about 4 percent less real income through the real exchange rate devaluation than the lower 40 percent income group.

In contrast, Malaysia adjusted with very little secondary adjustment costs during 1984-87 - private consumption fell by 8.4 percent in real terms and investment by about one third. But there were two fundamental differences in initial conditions compared to Chile: Malaysia's investment rate was much higher and its initial indebtedness was less (see Table 2). Both factors undoubtedly helped the Malaysian government to secure external funding at a time when other developing countries were
_Table 2: Determinants of Export Gains Under AGOA_

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Inflation (annual%)|-0.015***<br>(0.004)|-0.067<br>(0.764)|-0.068<br>(0.764)|-0.040***<br>(0.002)|-0.033***<br>(0.006)|-0.033***<br>(0.006)|-0.035***<br>(0.010)|-0.111***<br>(0.207)|
|Exchange Rate Stability|-0.186***<br>(0.000)|-0.118***<br>(0.000)|-0.117***<br>(0.000)|-0.230***<br>(0.000)|-0.205***<br>(0.000)|-0.205***<br>(0.000)|-0.206***<br>(0.000)|-0.219***<br>(0.000)|
|External Debt (% of GNI)|-0.124|0.0383|0.040|-0.151|-0.159|-0.159|-0.152|-0.081|
||(0.876)|(0.580)|(0.738)|(0.645)|(0.767)|(0.767)|(0.619)|(0.525)|
|Mobile Subscriptions (100 people)|0.284***<br>(0.009)|0.205***<br>(0.001)|0.203***<br>(0.002)|0.369**<br>(0.032)|||||
|Access to Telecom|||||0.331*|0.331*|0.297*|0.198*|
||||||(0.089)|(0.089)|(0.078)|(0.079)|
|Legal & Property Rights|-0.103**|0.036**|0.033**|-0.052*|-0.042***|-0.0421***|-0.121***|0.200***|
||(0.045)|(0.047)|(0.075)|(0.002)|(0.002)|(0.002)|(0.001)|(0.001)|
|Transparency Index|0.0134**|0.106*|0.104*|0.0140**|||||
||(0.022)|(0.063)|(0.095)|(0.018)|||||
|Political Corruption|0.083||-0.008|0.118|0.122|0.122|||
||(0.130)||(0.126)|(0.404)|(0.199)|(0.199)|||
|Political Stability|0.035|-0.103|-0.105|-0.046|-0.049|-0.049|-0.069|-0.117|
||(0.369)|(0.332)|(0.244)|(0.541)|(0.754)|(0.754)|(0.926)|(0.810)|
|Quality of Government (ICRG)||||0.0126|-0.0168|-0.0168|-0.0221|-0.112|
|||||(0.411)|(0.793)|(0.793)|(0.803)|(0.907)|
|Labor Market Regulations||||-0.149***|-0.132***|-0.132***|-0.107***|-0.050***|
|||||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Net Oil Exports per capita||||||||0.532<br>(0.330)|
|GDP||yes|yes||||||
|Year FE|yes|yes|yes|yes|yes|yes|yes|yes|
|Country FE|yes|yes|yes|yes|yes|yes|yes|yes|
|Observations|313|292|292|298|298|298|298|298|
|R-squared|0.448|0.260|0.261|0.486|0.470|0.470|0.469|0.484|
|F-Stat|4.349|4.349|4.349|4.349|4.349|4.349|4.349|4.349|

Notes: Dependent variable is the treatment effect (gap) in terms of export/trade due to AGOA (mills. US $); p-values are in parenthesis; *** significant at 1%, ** at 5% * at 10% levels. All specifications include both country and year fixed effects. All coefficients have been standardized allowing comparison as to the relative strength of each factor. A one standard deviation increase in an independent variable is associated with a rise or fall of the trade impact by _β_ standard deviation, where _β_ is the value of the coefficient.
<!-- Start of picture text --> 0.20<br>2<br>s<br>.<br>6°==o 15<br>€<br>0.10<br>Z<br><<br>0.05 Nee TI<br>2010 2012 2014 2016 2018<br>-® General Cargo -® Refrigerated -®- Dry Bulk<br><!-- End of picture text --> <!-- Start of picture text --> 0.20<br>0.15<br>2<br>e<br>S8<br>=<br>= 0.10<br>3<br>irs<br>0.05<br>en ee<br>2010 2012 2014 2016 2018<br>-® General Cargo -® Refrigerated -®- Dry Bulk<br><!-- End of picture text --> <!-- Start of picture text --> 1500<br>2<br>im<br>5S<br>eors2g 1000<br>on<br>S<br>u<br>2<br>500<br>nn a a ae<br>2010 2012 2014 2016 2018<br>—® General Cargo -® Refrigerated -®- Dry Bulk<br><!-- End of picture text -->
# **C.2**

We have focused in this paper on the sectoral split of inflows by government, central bank, banks, and corporates, and found important differences between public and private flows. Another way to examine the roles of the public and private sector is to split the data by Public and Publicly-Guaranteed Debt (PPG) vs Private Non-Guaranteed Debt (PNG). This allows us to capture flows nominally allocated to the private sector which should actually be considered liabilities of the public sector, such as borrowing by public and quasi-public corporations common in many EM.<sup>87</sup> We can do this for emerging and developing economies using the World Bank’s Debtor Reporting System (DRS) data found within the World Bank International Debt Statistics (WB-IDS). This data is annual going back to 1970 for many countries, but we use a balanced sample of 14 EM and 60 developing countries over 19812014:<sup>88</sup>

**Emerging (14):** Brazil, Bulgaria, China, Colombia, Egypt, India, Indonesia, Jordan, Malaysia, Mexico, Peru, Philippines, Thailand, Turkey

**Developing (60):** Algeria, Bangladesh, Belize, Benin, Bhutan, Bolivia, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Republic

of Congo, Costa Rica, Cote d’Ivoire, Dominica, Dominican Republic, Ecuador, El Salvador, 87The usual definitions allocate flows to the sector of the immediate borrower, not the sector who is ultimately owes the debt, which may result in effectively misattributing the debt to the wrong sector. Also, note that all of our measures are based on the residency principle, however the recent increase in offshore bond issuance can also be a source of mismeasurement of capital flows. Offshore bond issuance has received significant recent attention in Shin (2013), Avdjiev et al. (2014), Avdjiev, McCauley, and Shin (2016), and others, so we refer the interested reader to those sources for a more complete discussion of the issue.

> 88The DRS data is first split into short term, long term, and IMF credits. The long term data can be further subdivided by PNG debt and PPG debt. The PPG debt can further be split by creditor. We assume that the portion of PPG debt that is short term is negligible, and so attribute all Short Term Debt to PNG. We further combine IMF credit into PPG debt to get our split of total external debt into public and private components. This is analagous to the decomposition done in Alfaro, Kalemli- Ozcan, and Volosovych<sup> ̈</sup> (2014), who do their analysis

103
has an elasticity close to unity. However, once you include the time dimension, the relationship becomes weaker with elasticities smaller than one.

Pakes and Griliches (1980), Hall et al. (1984) and Hall et al. (1986)were the first to study the relationship between patents and R&D, focusing on the time dependence between these variables at the firm level. A common result in these studies is that the impact of R&D has a "U" shape: R&D invested during the first and the last years tend to have a positive and significant impact on firm patenting, but the expenditure incurred between these years seems to have little effect. The estimation of Pakes and Griliches (1980), performed with a linear model with data from 121 U.S. companies, indicates that the elasticity of the sum of contemporaneous and lagged R&D is significant and close to 0.6. Hall et al. (1984) and Hall et al. (1986) estimate the relationship between patents and R&D using Poisson and Negative Binomial models for a broader group of U.S. firms. They find that when controlling for fixed effects the elasticity of the sum of contemporaneous and lagged R&D is smaller than that found by Pakes and Griliches (1980), with estimates between 0.3 and 0.5. Jaffe (1986), unlike previous work, used only current R&D expenditure in the knowledge production function, as well as technological spillovers. Jaffe’s estimated elasticity of patents with respect to R&D, controlling for fixed effects, is equal to 0.4, but the effect rises with the R&D of other firms.

Blundell et al. (1995) developed an alternative GMM estimator in which the unobservable fixed effect of firms is approximated by the history of patents prior to the period of the estimation sample, namely a pre-sample mean estimator. Blundell et al. (2002) used this estimator in a dynamic context finding elasticities around 0.5. This pre-sample mean estimator is the closest approach to endogenous growth models as it takes into account previous patents as a determinant of current patent counts.

At the aggregate level, Botazzi and Peri (2003), using cross-sectional data for Europe, find elasticities close to 0.8 for the relationship between patents and R&D. Moreover, they find spillovers within 300 kilometers. Bosch et al. (2005) used a panel of 49 countries, using various techniques to estimate elasticities that vary between 0.44 and 0.96. The estimates include static and dynamic specifications, as well as the pre-sample mean estimator, models of count data (with Poisson and negative binomial distributions) and linear GMM.

As mentioned, we address the possibility of multiple sources of patent spillovers. In particular, we consider the following variables: (i) stock of patents from countries of the same income group or region, and (ii) the stock of patents in the remaining income groups or regions of the world. We denote by SP the stocks of patents. In a first stage, we estimate global knowledge spillovers on national patent counts by estimating the following function, which generally corresponds to count-data estimators: where subscript i indexes countries, j represents income group and t is time (year). The variable SP 8
<u>Böhme et al. (2015).</u><sup>23</sup> The IV is not systematically correlated with school expenditures, local economic conditions as proxied by night lights <u>(Henderson et al. 2012), local infrastructure or</u> public goods as reported in the community questionnaire. Further, communities with IV values above and below median values are distributed evenly across the country (figure S.3). Summary statistics for the IV variable can be found at the bottom of table 1.

The dependent variables of our empirical analysis are the child’s school enrollment status, the three categories of private education spending - payments to teachers, tutoring expenses, transportation expenses - and the time spent by the caregiver. The reduced form estimates are reported in table 2, panel B. The lack of a selection correction results in a statistically significant correlation between the instrument and school enrollment,<sup>24</sup> which indicates better migration options for those who leave school after the end of compulsory schooling. Correlations between the instrument and payments to teachers, as well as tutoring expenses, are negative and statistically significant.<sup>25</sup> The first stage IV estimates are reported in panel C of table 2. The _Network-Growth_ -IV is a positive and highly significant predictor of the household’s migration status. The instrument’s estimated coefficient implies that a one standard deviation increase in _Network-Growth_ increases the likelihood of (at least one) household adult member migrating by approximately 14 percentage points. The Kleibergen-Paap rank test rejects underidentification at least at the 5% significance level in all the 2SLS regressions.

The second stage indicates no statistically significant effect of migration on the enrollment probability (column 1) as a result of parental migration. Instead, the results indicate a strong reduction in the likelihood to pay teachers conditional on individual characteristics that is even more pronounced than the negative correlation in panel A (column 3). For tutoring, we see a similar negative effect whereas transport expenditure remains unchanged (columns 5 and 7). Interestingly, the determinants of tutoring are similar to those of paying bribes, supporting the

> 23. Alternatively using only one control for all migrant shares does not yield different results, but we prefer keeping to the more conservative ability to control also for different border effects as in that earlier paper. 24. A one standard deviation increase in the instrument implies a 2.5 percentage point reduction in enrollment. 25. See tables S.3, S.4, and S.5 for the point estimates of the control variables for panels B and C.

12
_Table 8 Effects of the “training and grant” vs “training, but no grant” on main socio‐economic outcomes_

|||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|---|
|||TOT<br>(no<br>controls)<br>Main<br>|TOT<br>(controls)<br>outcomes –<br>|TOT<br>(controls<br>+<br>geography<br>controls)<br>Socio‐econom<br>|LATE<br>(no<br>controls)<br>ic<br>|LATE<br>(controls)<br>|LATE<br>(controls<br>+<br>geography<br>controls)<br>|
|Employment|Training,|0.087|0.086|0.081|‐0.069|‐0.050|0.064|
|index|no grant|(0.149)|(0.134)|(0.424)|(0.766)|(0.818)|(0.833)|
|||[0.238]|[0.215]|[0.679]|[0.968]|[0.988]|[0.989]|
||Training|0.057|0.062|0.044|0.369|0.338|0.082|
||and grant|(0.580)|(0.554)|(0.752)|(0.384)|(0.391)|(0.820)|
|||[0.595]|[0.676]|[0.813]|[0.655]|[0.626]|[0.989]|
|Consumption|Training,|0.046|0.037|0.026|‐0.389**|‐0.350**|‐0.136|
|index|no grant|(0.489)|(0.591)|(0.810)|(0.019)|(0.029)|(0.659)|
|||[0.595]|[0.676]|[0.813]|[0.071]|[0.077]|[0.989]|
||Training|0.178**|0.157**|0.169|1.042**|0.933**|1.049**|
||and grant|(0.023)|(0.048)|(0.166)|(0.027)|(0.027)|(0.015)|
|||[0.046]|[0.096]|[0.332]|[0.071]|[0.077]|[0.060]|
|Savings,|Training,|0.221***|0.205***|0.127|‐0.166|‐0.171|‐0.200|
|investment|no grant|(0.000)|(0.000)|(0.147)|(0.275)|(0.278)|(0.483)|
|and<br>debt||[0.001]|[0.001]|[0.332]|[0.572]|[0.556]|[0.989]|
|index|Training|0.434***|0.420***|0.327***|1.282***|1.270***|0.992***|
||and grant|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.007)|
|||[0.001]|[0.001]|[0.008]|[0.006]|[0.005]|[0.054]|
|Business|Training,|‐0.031|‐0.022|0.024|‐0.113|0.003|0.024|
|skills index|no grant|(0.594)|(0.727)|(0.813)|(0.520)|(0.988)|(0.929)|
|||[0.595]|[0.728]|[0.813]|[0.974]|[0.988]|[0.989]|
||Training|0.240***|0.220***|0.299**|0.267|0.046|‐0.005|
||and grant|(0.004)|(0.010)|(0.017)|(0.442)|(0.903)|(0.988)|
|||[0.012]|[0.027]|[0.069]|[0.968]|[0.988]|[0.989]|
|Observations||1,523|1,495|1,474|1,500|1,474|1,474|
|F‐stat|||||23.88|21.61|36.61|

_Note: P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level. Adjusted Benjamini‐Hochberg p‐values are reported in square brackets. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, number of buildings at baseline, and exposure to conflict events between baseline and endline. Column (3) also controls for geographic features since the estimation strategy relies on distance to the closest KCB bank branch which might correlate with other geographic characteristics. Geography controls include distance to the closest city, distance to the closest road, average land gradient and their respective interactions with selection to the original treatment group._ 31
limits the value of those preferences as a mechanism for integrating developing countries into the world economy.

|**Table 6: Share of Exports Eligible for**|**Preferences in the E.U. (200**<br>U.S.|**1) and the U.S. (2002)**<br>E.U.|
|---|---|---|
|Benin|0|33.9|
|Guinea-Bissau|0|41.5|
|Seychelles|0.3|86.2|
|Guinea|0.5|11.7|
|Chad|0.7|2.5|
|Uganda|1.2|52.7|
|Zambia|2.1|40.2|
|Rwanda|2.2|15.8|
|SaoTome andPrincipe|3.8|33.1|
|Ethiopia|10.7|30.4|
|Tanzania|11.0|48.1|
|Central African Rep.|12.3|0.4|
|Namibia|14.0|50.7|
|Níger|17.0|2.5|
|Djibouti|18.0|6.7|
|Mali|19.3|18.0|
|SierraLeone|23.0|21.9|
|Cote d'Ivore|25.5|37.1|
|Botswana|27.4|9.1|
|Senegal|28.3|71.7|
|Ghana|33.6|44.2|
|South Africa|36.8||
|Madagascar|43.6|82.3|
|Eritrea|55.0|46.3|
|Mauritania|63.5|33.5|
|Gambia|64.0|46.0|
|Mozambique|74.4|90.0|
|Kenya|76.3|52.6|
|Dem. Congo|81.3|3.9|
|Cameroon|82.3|20.5|
|Malawi|82.8|83.3|
|Rep. Congo|83.2||
|Swaziland|85.8|25.0|
|Mauritius|94.5|66.6|
|Gabon|94.9|6.2|
|Cape Verde|97.7|79.5|
|Nigeria|98.4|4.6|
|Lesotho|100.0|27.9|

# **6. Current Utilization of AGOA Preferences**

The economic impact of available preferences depends on the magnitude of those preferences and the extent to which they are actually utilized. The magnitude of the preferences depends on the size of the MFN tariff, while the utilization rate will be 20
Clotfelter, Charles T., 1983, “Tax Evasion and Tax Rates: An Analysis of Individual Returns,” _Review of Economics and Statistics_ , Vol. 65 (3) (August): 363-73.

Evans, Carolyn L., 2003. “The Economic Significance of National Border Effects,” _American Economic Review_ , Vol. 93 (September): 1291-1312.

Feenstra, Robert, Markusen, James R. and Rose, Andrew K., 2001. “Using the Gravity Equation to Differentiate Among alternative Theories of Trade,” _Canadian Journal of Economics_ , Vol. 34 (May): 430-447.

Feinstein, Martin, 1991. “An Econometric Analysis of Income Tax Evasion and its Detection,” _RAND Journal of Economics_ , Vol. 62(1) (Spring): 14-35.

Fink, Carsten, Mattoo, Aaditya and Neagu, Cristina, 2005. "Assessing the Impact of Communication Costs on International Trade," _Journal of International Economics,_ Vol. 67(2) (December): 428-445.

Fisman, Raymond and Wei, Shang-Jin, 2004. "Tax Rates and Tax Evasion: Evidence from "Missing Imports" in China," _Journal of Political Economy_ , Vol. 112(2) (April): 471-500.

Little, Ian, Scitovsky, Tibor and Scott, Maurice, 1970. _Industry and Trade in Some Developing Countries. A Comparative Study._ Published by Oxford University Press for the OECD.

Panagariya, Arvind and Dani Rodrik. 1993. “Political-Economy Arguments for a Uniform Tariff,” _International Economic Review,_ Vol. 34(3): 685-703.

_Polish News Bulletin_ , 2000. “Ineffective Customs Control Threatens Domestic Producers, Says NIK Report,” September 19.

Pritchett, Lant and Sethi, Geeta, 1994. “Tariff Rates, Tariff Revenue, and Tariff Reform: Some New Facts,” _World Bank Economic Review._ Vol. 8, No. 1 (January): 1-16.

Rauch, James E., 1999. "Networks Versus Markets in International Trade," _Journal of International Economics_ , Vol. 48 (June): 7-35.

Rauch, James E and Casella, Alessandra, 2003. “Overcoming Informational Barriers to International Resource allocation: Prices and Ties,” _Economic Journal_ . Vol. 113 (January): 2142.

Shleifer, Andrei and Vishny, Robert W., 1993. “Corruption,” _Quarterly Journal of Economics._ Vol. 110 (August): 681-712.

Slemrod, Joel and Yitzhaki, Shlomo, 2000. "Tax Avoidance, Evasion, and Administration," NBER Working Papers 7473, National Bureau of Economic Research, Inc.

25