rafmacalaba/datause-encoder-data huggingface dataset:
# **Table B1**

# **Summary Statistics of Tanzania’s Cotton By‐Products Industry** **<u>(Ten Companies)</u>**

||**_I_**|**_II_**|**_III_**|**_IV_**|**_V_**|**_VI_**|**_VII_**|**_VIII_**|**_IX_**|**_X_**|
|---|---|---|---|---|---|---|---|---|---|---|
|**I. Nature of operation**|||||||||||
|**Permanent employees**|35|60|30|15|3|12|8|9|57|3|
|**Temporary employees**|32|275|100|30|90|90|46|45|200|33|
|**Capacity (tons/day)**|75|90|100|70|100|60|60|30|200|50|
|**Year operations began**|1996|1967|2008|2006|2003|1999|2001|2008|1999|1997|
|**II. Crushings (000 tons of see**|**d)**||||||||||
|**2007**|40.0|16.5|4.0|6.4|—|—|—|—|—|—|
|**2008**|9.0|4.5|6.2|7.1|10.0|8.0|7.5|1.2|20.0|20.0|
|**2009**|9.0|4.2|20.0|10.0|7.5|9.0|12.0|4.5|45.0|10.0|
|**III. Prices paid for cottonsee**<br>|**d (TZ shs**|**per kg)**|||||||||
|**2006**|—|—|—|120|—|130|—|—|—|—|
|**2007**|150|—|—|200|250|250|—|150|205|—|
|**2008**|250|250|250|210|250|250|—|250|175|–|
|**2009**|190|190|180|—|150|190|—|150|—|—|
|**IV. Prices received from cott**|**on oil (000**<br>|**TZ shs per**|**20‐liter cont**|**ainer**|||||||
|**2007**|25.0|—|—|—|12.0|32.0|22.0|—|—|28.0|
|**2008**|21.5|46.8|31.9|26.0|15.0|35.0|25.0|—|—|30.0|
|**2009**|30.0|36.0|27.0|30.0|20.0|27.0|27.0|—|25.0|24.0|
|**V. Cotton meal sold (tons)**|||||||||||
|**2006**|—|6,440|—|8,500|3,150|—|—|—|—|—|
|**2007**|—|7,590|—|3,692|700|—|—|—|—|—|
|**2008**|5,000|2,070|2,000|3,200|850|4,000|450|500|10,000|—|
|**2009**|—|1,932|—|—|—|—|—|—|20,000|—|

**Notes:** Some of the 2009 figures are estimates. ‘—‘ implies not reported. Some companies did not report seed prices because most of the seed came from their own ginning operations. **Source:** Vasilaky (2009).

**_Page_ 33**
countries (Poland, however, is a notable exception—see Chart 3). Slovakia, Lithuania and Estonia have relatively low tax shares (around 17-18 percent of equivalized disposable incomes).<sup>13</sup> Next are Hungary and the Czech Republic (21 percent), followed by Slovenia (27 percent). Finally, taxes as a share of equivalized disposable incomes are highest in Poland (33 percent), close to twice the average shares in Slovakia and the Baltic countries.

**Chart 3: Taxes as a share of disposable incomes**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Share of disposable income<br>Spain Ireland Slovakia Estonia Lithuania Czech Republic Hungary Luxemberg Slovenia Austria United Kingdom Belgium Finland Poland Sweden Netherlands Denmark<br><!-- End of picture text --> _Source_ : World Bank staff calculations using 2006 EU-SILC data.

20. Chart 4 compares the size of benefits across different EU member states, expressed as a share of equivalized disposable incomes. Among EU countries, the benefit share varies from a low of 17 percent in the Netherlands to 33 percent in Hungary. The share of benefits in the EU8 countries tends, on average, to be somewhat higher than in EU15 countries—it is lowest in Estonia and Slovakia (23 percent) followed by the Czech Republic (25 percent), Poland (27 percent), Slovenia (30 percent) and Hungary (33 percent).

**Chart 4: Benefits as a share of disposable incomes**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Share of disposable income<br>Hungary Ireland Slovenia Belgium Lithuania Spain Poland Finland Sweden Czech Republic Austria Estonia Slovakia Denmark United Kingdom Luxemberg Netherlands<br><!-- End of picture text --> _Source_ : World Bank staff calculations using 2006 EU-SILC data.

13 Latvia is excluded from these tables, as data on gross incomes are not available in the EU-SILC dataset.

11
- Verma, V., Marchant, T., and Scott, C. (1988). _Evaluation of crop-cut methods and farmer reports for estimating crop production: results of a methodological study in five African countries_ . London: Longacre Agricultural Development Centre Ltd.

- World Bank (2016). “The Uganda poverty assessment report. Farms, cities and good fortune: assessing poverty reduction in Uganda from 2006 to 2013.” Retrieved on May 11, 2017 from <u>https://goo.gl/Pvp9ew.</u>

30
Table 7: Earthquake fatalities and institutional quality

||7.1|7.2|7.3|7.4|7.5|7.6|7.7|
|---|---|---|---|---|---|---|---|
|Ln(_AIDj,t-1_)|-0.220|-0.524|-0.686|-0.275|-0.635|-1.028|-1.204**|
||(0.531)|(0.418)|(0.979)|(0.453)|(0.823)|(0.991)|(0.501)|
|Ln(_GDPj,t-1_)|-0.372|-0.093|-0.413|-0.564*|-0.739|-0.970*|-0.981***|
||(0.394)|(0.286)|(0.442)|(0.317)|(0.608)|(0.516)|(0.259)|
|_CORR CONTjt_|-1.371<br>(0.738)|||||||
|_GOV EFFjt_||-1.999***<br>(0.503)||||||
|_POL STABjt_|||-1.226<br>(0.309)|||||
|_REG QALjt_||||-1.376***<br>(0.411)||||
|_RULE LAWjt_|||||-0.550<br>(0.727)|||
|_VOICE ACCjt_||||||0.099<br>(0.490)||
|_DEMOCRACYjt_|||||||0.128***<br>(0.027)|
|Ln(_POP jt)_|-0.258|0.112|-0.849|0.462|-0.718|-0.998|<br>-0.947*|
||(0.686)|(0.462)|(0.656)|(0.703)|(1.260)|(1.084)|(0.550)|
|Ln_(POPDENSjt)_|-0.210|-0.041|0.286|-0.222|-0.021|0.004|-0.003|
||(0.565)|(0.426)|(0.681)|(0.577)|(1.031)|(0.941)|(0.489)|
|_OPENjt_|-0.036***|-0.029***|-0.028***|-0.023**|-0.034**|-0.031**|-0.020*|
||(0.011)|(0.009)|(0.010)|(0.009)|(0.011)|(0.014)|(0.011)|
|_MAGNITUDEijt_|2.174***|2.164***|2.210***|2.269***|2.272***|2.282***|2.332***|
||(0.302)|(0.307)|(0.295)|(0.300)|(0.291)|(0.281)|(0.247)|
|_NATHAZ – RISKj_|<br>0.198|<br>0.159|<br>-0.065|<br>0.633**|<br>0.095|<br>0.064|<br>-0.042|
||(0.223)|(0.169)|(0.330)|(0.248)|(0.340)|(0.314)|(0.226)|
|_LATITUDEj_|<br>0.026|<br>0.021|<br>0.046|<br>-0.020|<br>0.023|<br>0.018|<br>0.014|
||(0.064)|(0.040)|(0.058)|(0.052)|(0.094)|(0.086)|(0.040)|
|_ELEVATIONj_|<br>-0.002**|<br>-0.002***|<br>-0.001**|<br>-0.001*|<br>-0.002**|<br>-0.001|<br>-0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Obs.|367|367|367|367|367|367|367|
|Log pseudolikelihood|-1441.739|-1435.797|-1440.266|-1436.581|-1444.365|-1445.120|-1439.325|
|Prob>χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|0.000|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

26
**Table 1: Effective direct tax progressivity and redistribution in IDA countries**

||**RS**|**Kakwani**|**ATR**||**Income deciles**|
|---|---|---|---|---|---|
|||||0.2||
|**Ethiopia (2010)**|**0.017**|**0.285**|**0.056**|0.0<br>0.1|1 2 3 4 5 6 7 8 9 10|
|||||0.2||
|**Ghana (2012)**|**0.013**|**0.294**|**0.043**|0.0<br>0.1|1 2 3 4 5 6 7 8 9 10|
|**Honduras (2011)**|**0.006**|**0.137**|**0.044**|0.0<br>0.1<br>0.2|1 2 3 4 5 6 7 8 9 10|
|||||0.2||
|**Nicaragua (2009)**|**0.020**|**0.311**|**0.059**|0.0<br>0.1|1 2 3 4 5 6 7 8 9 10|
|**Tanzania (2011)**|**0.028**|**0.520**|**0.050**|0.0<br>0.1<br>0.2|1 2 3 4 5 6 7 8 9 10|
|**Uganda (2012)**|**0.012**|**0.471**|**0.025**|0.0<br>0.1<br>0.2<br>0.4|1 2 3 4 5 6 7 8 9 10|
|**United States (2016)**|**0.065**|**0.136**|**0.325**|0.0<br>0.1<br>0.2<br>0.3|1 2 3 4 5 6 7 8 9 10|

Source: authors calculations based on CEQ Data Center on Fiscal Redistribution (2022) 13
countries for which we have city data, do we find the value of Zipf’s coefficient to be less than 1. This implies that cities in our sample are more evenly distributed relative to predictions by the rank-size rule. For urban agglomerations however, we find that for 20 of the 39 countries which report their populations in urban agglomerations, we observe a Zipf’s coefficient lower than 1, implying that urban agglomerations present greater concentration in economic activity of a country relative to that predicted by the Zipf’s law.

# _Do urban agglomerations represent higher spatial concentration?_

In the last few years, administratively defined cities have become less meaningful as most of the growth is occurring through expanding suburbs, especially in developed countries. Table A.3 presents a comparison of the estimated Zipf’s coefficient from regression in equation (3) for a subset of countries across administratively defined cities with that in their urban agglomerations. Gabaix and Ioannides (2004) note that the Zipf’s coefficient for administratively defined city is higher than urban agglomerations. This point was made first by Rosen and Resnick (1980) and has been revisited by Soo (2005) who finds that the mean value of the Pareto exponent is lower for agglomerations than for cities. Our results suggest that this pattern continues to hold for most countries in our sample. Since urban agglomerations tend to be larger relative to the core city, they tend to show greater statistical dispersion in size distribution, that is, in economic terms have a greater concentration in economic activity.

# _Participation in GVCs and Size Distribution: Some Motivating Correlations_

Although we use four indicators to measure participation in GVCs, here we show an association between participation in GVCs and spatial dispersion using the “Re-imported domestic value added (DVA)”. Figures 2a and 2b show an added variable (AV) plot of this index with the city and UA level Zipf’s coefficient, respectively, after controlling for population density, GDP per capita (linear and square terms) and an indicator for membership in the OECD group.

Figure 2a suggests that higher participation in GVCs is correlated with higher spatial dispersion across cities. However, in the corresponding plot for urban agglomerations shown in figure 2b, we see a reverse pattern. A negative slope of this plot implies that an enhanced engagement in GVCs, is associated with higher spatial concentration. The two figures together imply that participation in GVCs is associated with a de-concentration of population from the city core to urban periphery, but not greater decentralization to places far from agglomerations.

Does a higher knowledge content GVCs participation intensify spatial concentration? Figure 2c presents the AV plot between Re-imported DVA in knowledge intensive R&D services and spatial dispersion of urban agglomerations. Figure 2c shows that the magnitude of this negative relationship is larger when participation in GVCs is based on knowledge intensive sectors.

9
variation in unemployment rates, and also leads the small area estimation procedure to underestimate the variance of the municipal random effect, which in turn gives these inaccurate predictions more weight relative to the survey data in generating the estimates. Therefore, the small area estimates for unemployment suffer from high levels of relative bias and are more weakly correlated with the census values than the direct estimates.

While a non-linear estimation model might improve these estimates, a natural question is whether larger samples might improve the small area estimates for unemployment. This ties into a larger question about the potential benefits of expanding the second stage of samples when combining survey data with alternative data sources. To shed some light on this question, we repeat the small area estimation exercise after simulating a sample in which every household in selected AGEBs is included in the sample. This will reduce measurement error in the dependent variable, which substantially improves the quality of the predictions (Lobell et al., 2021, Engstrom et al., 2021). The exercise in intended to provide an upper bound estimate of the impact of expanding the second stage of the sample. But since listing exercises are common parts of household surveys in many developing countries, it may be possible to include selected questions as part of the listing exercise. The full enumeration results also shed light on the potential benefits of expanding the second stage of samples when combining survey data with alternative data sources.

<u>Table 7 - Sample of sampled AGEBs vs. full enumeration of sampled AGEBs</u>

||(1)<br>Male LFP|(2)<br>Female LFP|(3)<br>Male<br>Unemp|(4)<br>Female<br>Unemp|
|---|---|---|---|---|
||**Panel A: All m**|**unicipalities**|||
|**Average estimated mean**<br>**squared error (x1,000)**|||||
|Random sample SAE|4.512|6.887|0.248|0.131|
|Full enumeration SAE|1.306|3.450|0.147|0.049|
|**Coverage rate**|||||
|<br>Random sample SAE|0.983|0.978|0.948|0.977|
|<br>Full enumeration SAE|0.951|0.943|0.941|0.960|
|**Estimated relative bias**|||||
|**(median)**|||||
|Random sample SAE|-0.759|-0.816|9.858|56.367|
|<br>Full enumeration SAE|-0.510|-0.275|8.363|12.187|
|**Estimated mean absolute**|||||
|**error**|||||
|Random sample SAE|0.034|0.051|0.010|0.007|
|<br>Full enumeration SAE|0.026|0.042|0.008|0.004|
|**Estimated rank Correlation**|||||
|Random sample SAE|0.436|0.659|0.426|0.241|
|Full enumeration SAE|0.673|0.748|0.633|0.599|

# **<u>Panel B: In-sample municipalities</u>**

23
and then starting to use again in a future week. Usage in the current week averages 35 percent over the first six weeks, drops to 28 percent by week 14 and 24 percent by six months, and is only 16 percent after one year. Uninterested firms were not marketed the service, and we see very little usage by these firms.<sup>11</sup> Column 2 of Table 2 uses a probit to examine the correlates of taking up Agruppa amongst those firms which expressed initial interest. The strongest predictor of not taking it up is if the firm owner was going every day to Corabastos at baseline – 70 percent of those firm owners who did not go daily took it up, versus only 58 percent of daily travelers. This might reflect them valuing the selection at Corabastos more. Firm owners who sell more of the core Agruppa products, and who have children in the household are more likely to take it up. Since Agruppa started with only five core products, one might think that take-up rates would be higher when more products began to be offered. However, Figure A3 shows there is no correlation between take-up rate in a block and the number of products on offer at the time Agruppa began selling in that block. Since the ordering of which blocks to sell in first was not randomized, but was optimized for delivery routes, this might reflect other differences among firms across blocks. However, Table 2 shows that there continues to be no relationship between take-up and the number of products on offer after controlling for owner and firm characteristics. We see a weakly significant tendency for the take-up rate to fall over time, which could either reflect less Agruppa effort devoted to new customers as their base of existing customers grew, or to the blocks that Agruppa chose to work in first being better for customer acquisition.

Column 3 of Table 2 then examines the correlates of the decision to stop using Agruppa, conditional on having ever used it, while column 4 considers the correlates of an interested firm continuing to be a customer after one year. The most important determinant continues to be whether or not they were a daily shopper at Corabastos at baseline, with those who were 61.4 percent more likely to stop buying after trying it out, and 67 percent less likely to start and remain a customer after one year. Table A3 examines how these baseline daily shoppers compare to other firms and find that they are larger and more profitable firms, that are, on average, closer to Corabastos. We do see in Table 2 that firms in blocks where Agruppa started with more products

> 11 Seven uninterested firms, and four control firms used the service within the first six months of launching in their block.

12
like Brazil and USA. However with Canada, Mexico, Peru, EU and Chile the number of zeros has decreased over the years, implying an increase in the number of products exported to these partners. For growing trading partners (left hand graph), a large reduction of zeros can suggest that there is scope for further increase of trade relations. The steepest reduction of zeros is observed in trade with South Africa, Australia and China.

**Figure 13. Evolution of Number of Zeros by Partner 1990-2005 (HS 6 digit)**

<!-- Start of picture text --> Argentinean zeros per partner Argentinean zeros per partner<br>5000<br>4950 4800<br>4900<br>4300<br>4850<br>4800<br>3800<br>4750<br>4700<br>3300<br>4650<br>4600<br>2800<br>4550<br>4500<br>2300<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>AUS CHN IDN IND JPN MAR MYS BRA CAN CHL EEC15<br>THA TUN TUR ZAF JPN MEX PER USA<br>Zeroes per partner<br>Zeroes per partner<br><!-- End of picture text --> <!-- Start of picture text --> 2005<br><!-- End of picture text --> _Source:_ Comtrade.

_Note_ : A zero indicates the absence of trade in a specific product, so an increase/decrease in the number of zeros reflects a reduction/increase in exported products. Note that the scale of the number of zeros on the Y axis is different on the left and right graphics.

**Argentina is clearly following the same pattern as the LAC region, with a decrease in the number of zeros over the past years** (Figure 14). The country has experienced a steeper reduction in the 2004-2005, similarly to what has happened in the group of industrialized countries and East Asia.

**Figure 14. Evolution of the Average Number of Zeros for Argentina vs Comparators**

<!-- Start of picture text --> Average number of zeros Latin<br>4,800 American<br>4,300<br>Industrialized<br>3,800<br>3,300<br>2,800 East Asian<br>2,300<br>ARG<br>Zeros per partner<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> _Source:_ Comtrade.

**This is consistent with the findings of Brenton and Newfarmer (2007),**<sup>**10**</sup> **who assess the contribution of both intensive and extensive margins to export growth.** The contribution of the intensive margin (first three columns) and extensive margin (last three columns) to growth is similar for the 1995-2004 periods (Figure 15). However the dominant impact on growth comes from increases

> 10 Brenton,P and Newfarmer, R (2007), Watching More Than The Discovery Channel. Export Cycles and Diversification in Development. WPS4302 World Bank.

14
Tables 2a and b show that movement into self-employment is always associated with a substantial and significant _rise_ in per hour after-tax remuneration from every other sector, reaching over 25% from the fornal and informal salaried sectors. However, the joint NUES/MES panel (table 3) shows that after modest adjustments for capital costs, and much larger ones for unpaid labor,"' both contract work and self-employment share simrilar premia with respect to formal salaried and informal salaried work, and the differential between them is insignificant. Contract work appears similar to selfemployment and may therefore share the same composition of voluntary and involuntary entrants.

Salaried informal employment, however, suffers a discount relative to all other sectors. After adjusting the differentials for standard firm size effects observed in the U.S.,"<sup>2</sup> moving to an informal sector firm from a comparably-sized formal sector firm now yields a 12-15% rise in the "microenterprise" sample, comparable to those above, but only a slight rise in the "unprotected" sample. Further, the large asymmetries in both cases suggest that informal salaried workers gain far more entering salaried formal work than they lose leaving, as would be predicted if they were queuing for 11 A return of 10% was imputed. Most micro-enterprises who save in commercial accounts or cajas de ahorro received 3% real. Implicitly we are assuming 7% for depreciation. The low level of capital employed results in the overall differential being relatively insensitive to the cost of capital value chosen. The more detailed treatment of taxation in the MS allows dropping the previous assumption of complete avoidance by the self-employed and induces a slight moderation in the after-tax differential. The adjustment for unpaid labor may be overstated if Balan et al are correc **t** hat "Since in most cases these family members would not otherwise be employed outside the household, their contribution to family finances is a 'net' one..."(p.218) or if the unpaid labor was in training.

12 The wage/siz **e** lasticities were taken from Barron et al's (1987) U.S. study which estimates the impact net of unionization effects and capital-labor ratios in a society where labor laws are enforced across size. A simple regression of the log of the wage on the log of firm size yields coefficients on the order of .042 to .069 and very significant, roughly double the 2.8% found by Barron et al.. Behar (1988) and Ros and Marquez (1990) find that in Mexico, as others have found elsewhere, wages increase with firm size and urther, large firms often pay benefit **s** ubstantially above those established by law. Because we cannot control for capital-labor ratios and unionization, and because this size effect may reflect the very fact that the infornal in small firms are rationed out of larger firms by excessive wage legislation, including achange in firm size variable in the differential regressions might obscure the very effec **t** o be measured.

8
External shocks. Transition countries in Europe and Central Asia suffered from the collapse of the Council for Mutual Economic Assistance (CMEA) foreign trade system which had isolated the conditions of foreign trade in socialist countries from those prevailing in the world's hard currency trade. Starting in 1991, foreign trade of the former socialist countries was conducted on a hard currency basis. No payments union among former CMEA countries was established, and trading relationships among these countries collapsed.

In addition, some countries were hit by idiosyncratic external shocks. For instance, certain banks located in some countries of the former Soviet Union (for example in _Estonia_ and in the _Kyrgyz Republic)_ lost access to part of their assets held in Russia after the collapse of the Soviet empire. In _Latvia_ and _Lithuania,_ banks profitability was sharply reduced by the decline of trade financing opportunities resulting from price liberalization in Russia.<sup>5</sup> Macroeconomic conditions. The transition process and external shocks led to severe output contraction in all countries at the onset of transition). These downturns precipitated banking crises in many countries.

**Table 5. Growth**

||1990|1991|1992|1993|1994|1995|1996|1997|1998|
|---|---|---|---|---|---|---|---|---|---|
|(GDP annual per|cent change)|||||||||
|**_Central and East_**|**_ern Europe_**|||||||||
|Bulgaria|-9.1|-11.7|-7.3|-1.5|1.8|2.1|-10.9|-6.9|4.0|
|Czech Rep.|-0.4|-14.2|-3.3|0.6|2.7|5.9|4.1|1.0|-2.2|
|Hungary|-3.5|-11.9|-3.1|-0.6|2.9|1.5|1.3|4.6|5.0|
|Macedonia|-9.9|-12.1|-21.1|-9.4|-2.7|-1.6|0.9|1.5|5.0|
|Poland|**-11.6**|-7,0|2.6|3.8|5.2|7.0|6.1|6.9|4.8|
|**Average**|**-6.9**|-11.4|-6.4|-1.4|**2.0**|**3.0**|**0.3**|**1.4**|3.3|
|**_Baltics_**||||||||||
|Estonia|-8.1|-7.9|-21.6|-8.2|-1.8|4.3|4.0|11.4|4.0|
|Latvia|2.9|-11.1|-35.2|-16.1|2.1|0.3|2.8|_6.5_|3.8|
|Lithuania|_-5.0_|**-6.0**|-19.6|-17.1|-11.2|2.3|5.1|6.1|4.4|
|**Average**|-3.4|-8.3|-25.5|**-13.8**|**-3.6**|**2.3**|**4.0**|8.0|4.1|
|_cis_||||||||||
|Georgia|-12.4|-20.6|-44.8|-25.4|-11.4|2.4|10.5|11.0|4.0|
|Kazakhstan|-0.4|-11.0|-5.3|-10.6|-12.6|-8.2|0.5|2.1|-2.5|
|Kyrgyz Rep.|3.2|-7.9|-13.9|_-15.5_|-20.1|-5.4|5.6|9.9|2.0|
|Ukraine|-3.4|-10.6|-17.0|-14.2|-22.9|-12.2|-10.0|-3.0|-1.7|
|**Average**|-3.3|-12.5|-20.3|-16A4|<br>-16.8|-5.9|1.7|_5.0_|_0.5_|

_Source: International Monetary Fund "World Economic Outlook" (1998, 1999), EBRD "Transition Report" (1997. 1998)._ The implementation of macroeconomic stabilization policies also made some countries more susceptible to crises. In fact, tight monetary policies, introduced as part of stabilization programs, forced up nominal interest rate and reduced inflation, thus raising real interest rates and affecting the borrowers' ability to service their debt. This occurred in _Estonia_ in 1992 following the introduction of the currency board; _Latvia_ after 1993; the _Czech Republic_ in 1996; and _Macedonia_ in 1992.

> 5 Latvian and Lithuanian banks were instrumental in financing East/Wes **t** rade. In particular, special financing opportunities arose in 1993/94, when commodities prices in Russia were well below world market prices.

11