rafmacalaba/datause-encoder-data huggingface dataset:
Figure 10 shows the evolution of _Service EXPY_ over time. Interestingly, all groups start out with more or less the same level of _Service EXPY_ in the beginning of the 1990s, diverge in late 1990s and from 2000 they keep the differences between each other and grow in parallel. EU-15 made the largest jump in late 1990s while the NMS continued an already strong trend albeit at a lower level then EU-15. An interesting pattern arises when looking at different countries within EU-15 (see Fig A12 in the Appendix). Northern EU-15 has a solid development of _Service EXPY_ , while Continental EU-15 is less successful but high levels and growth in Luxemburg push up the average. Southern EU-15 though is lagging behind with no country standing out. ACC suddenly dropped in _Service EXPY_ in late 90s and has since not been able to catch up with the rest of Europe. This graph would hence suggest that EU-15 has been most successful in latching on to the globalization of services, followed by NMS, while ACC has not benefitted from this global trend. It remains however, to analyze to what extent these groups have managed to turn higher service sophistication into economic growth.

**Figure 10: Service EXPY, 1990-2007.**

<!-- Start of picture text --> 18000<br>16000<br>14000<br>12000<br>10000<br>8000<br>6000<br>EU-15 NMS ACC India USA China<br>4000<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Source : Authors’ calculations.<br><!-- End of picture text --> Figure 11 shows log _Service EXPY_ against log GDP per capita, for 1992 and 2007 respectively. For both time periods, the scales have been kept constant as we also want to bring attention to the shifting paradigm of service exports globally. In order to look at the evolution of service export sophistication over time, we contrast the level correlation in 1992 with the correlation in 2007. The relationship is positive at each point in time, i.e. richer countries have typically a higher sophistication of services, and the three European groups are generally in line with this pattern.

14
regulatory measures performed in other countries to use internationally harmonized standards and certification procedures (Figure 11). Lagging behind in terms of use of internationally harmonized certification procedures may be partly explained by the fact that Croatia still has to fulfill its obligations with respect to the overall EU accession process. It is reasonable to expect that transplantation of the _Acquis_ shall implement provisions on multilateral recognitions of standards and certification procedures in many areas. Nevertheless, it should not be assumed that the problem will be eliminated in that way entirely. Figure 13 shows notable differences between EU countries with common history (e.g. Czech Republic vs. Slovak Republic). It also shows significantly lower barriers in non-EU countries vs. EU countries (e.g. Albania vs. Poland). Hence EU accession by itself is not a sufficient condition to believe that regulatory barriers to trade and investment shall be sufficiently reduced. This finding is of critical importance in the case of Croatia where other non-explicit barriers play far more important role than explicit barriers which are expected to be affected by implementation of EU standards.

**Figure 11:  Regulatory barriers to trade and investment**

<!-- Start of picture text --> 2.9<br>3.0<br>2.5<br>2.0<br>1.6 1.6<br>1.5<br>1.0<br>0.4<br>0.5 0.2 0.2<br>0.0 0.0 0.0 0.0 0.0<br>0.0<br>Czech Rep.HungaryBulgariaRomaniaAlbania 2007 EU15 OECD Slovak RepublicMICs PolandCroatia 2007<br><!-- End of picture text --> _Note: Other MICs are Brazil, Mexico, Turkey. Values refer to 2007 for Croatia and Albania, 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see Annex I._

# **4. Inward Oriented Policies: State Control and Barriers to Entrepreneurship**

**Inward oriented policies in the PMR system can be decomposed into two broad categories: indicators of state control and barriers to entrepreneurship** (Figure 12).

12
Table 4: Benchmark model parameterization and targets

|Variables / Parameters|Values|Criteria / Targets|
|---|---|---|
|_ρ_, _ωa_, _ωm_|0.96, 0.07, 0.15|Standard in literature|
|_ε_|0.002|Herrendorf et al. (2009)|
|_rt_, _zAm_|0.1131, 1.0085|GDPpc growth, Brazil, 1995-2013|
|_α_, _γ_|0.283, 0.567|Restuccia and Rogerson (2008)|
|_βa_, _βm_, _βs_|0.045, 0.230, 0.108|Own estimates|
|_θa_, _θm_, _θs_|0.023, -0.103, 0.081|Own estimates|
|_μm_, _μs_|0.73, 0.27|Brazilian input-output tables, 2014|
|_ν_|0.18|Sposi (2012)|
|_δk_, _δg_|0.0452, 0.0226|Capital formation investment, Brazil, 1971-2007|
|_Aat_, _Ast_<br>Mean<br>Average growth|Diferent values<br>0.650, 1.486<br>0.0310, 0.028|Sectoral GVA shares, Brazil, 1970-2007|
|_ξ_|0.6204|Annual cost of corruption, Brazil, 2010|
|_ψ_|1.390|Own estimates|
|_foat_, _fomt_, _fost_<br>Mean<br>Average growth|Diferent values<br>0.131, 0.702, 0.206<br>0.0100, 0.0287, 0.0495|Number of workers per frm in each sector|
|_λt_<br>Mean|Diferent values<br>-0.834|Infrastructure stock, Brazil, 1970-2007|
|Average growth|0.0038||
|_ei_, _Am_1970, _fq_|1, 1, 0.1|Normalizations|

Output Tables, 2016 release. In 2007 the share of manufacturing in fixed capital formation was about 73%. Then, we assign the remaining 27% to services. Given this information, we choose _μm_ = 0 _._ 73 and _μs_ = 0 _._ 27. The elasticity of substitution between sectors in investmentgoods production is taken from Sposi (2012), who estimates a value of 0.18 for _ν_ using data from the Republic of Korea.

On the production side, we adopt the values of Restuccia and Rogerson (2008) in a similar setting for the shares of private inputs; in particular, _α_ = 0 _._ 283 and _γ_ = 0 _._ 567. Given that there are not available estimates of the impact of electricity access to output production at the sectoral level, we estimate them. Following the similar approach to the one explained in section 5.1, we perform an econometric analysis employing variation in electrification over the period 1970-1996 using Brazil municipality panel data on sectoral production, labor use 34
Table 14 Random Effects Models <u>of Carbon and Number of Trees per Hectare (Panel by Plot)</u>

||Carbo|nper Hectare (kg)||Number|of Trees per|Hectare|
|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|
|CFdummy|-8,487.571||7,600.603|20.743||5.904|
||(0.49)||(0.49)|(0.29)||(0.09)|
|CanIDFUGyear|39,128.191|34,763.998||-35.991|-25.423||
||(2.59)***|(2.61)***||(0.45)|(0.35)||
|NDVI_1990|145,770.325|147,309.483|143,300.952|700.720|695.905|701.707|
||(2.48)**|(2.50)**|(2.39)**|(1.95)*|(1.94)*|(1.95)*|
|Totalforestarea|156.688|152.319|167.560|-0.103|-0.093|-0.113|
||(2.45)**|(2.44)**|(2.83)***|(0.84)|(0.73)|(0.97)|
|Hills|19,657.847|17,868.913|19,193.049|322.515|327.937|323.174|
||(1.23)|(1.15)|(1.17)|(3.90)***|(3.98)***|(3.93)***|
|Altitude|-12.790|-13.780|-13.888|0.010|0.012|0.010|
||(1.43)|(1.57)|(1.54)|(0.16)|(0.20)|(0.17)|
|Slope|-227.715|-259.042|-246.500|2.400|2.439|2.421|
||(0.60)|(0.69)|(0.64)|(1.42)|(1.46)|(1.44)|
|Fire|6,721.456|6,216.976|1,418.738|88.541|89.330|90.940|
||(0.59)|(0.55)|(0.13)|(1.77)*|(1.79)*|(1.87)*|
|Foresttype|-32,342.954|-33,643.059|-35,057.537|-155.173|-153.625|-154.057|
||(2.52)**|(2.70)***|(2.68)***|(1.92)*|(1.93)*|(1.91)*|
|Forestperhh|-5,095.268|-5,208.786|-5,282.483|5.910|6.172|6.202|
||(1.08)|(1.14)|(1.18)|(0.37)|(0.39)|(0.38)|
|HHsinfug|0.209|-0.253|1.786|0.075|0.076|0.074|
||(0.03)|(0.04)|(0.27)|(2.92)***|(3.00)***|(2.88)***|
|Migrationrate|-101,588.661|-104,451.74|-52,280.944|861.879|867.848|812.644|
||(0.84)|(0.86)|(0.43)|(1.22)|(1.23)|(1.19)|
|Soilcolor|13,449.747|13,181.987|13,326.861|53.434|53.697|53.368|
||(1.38)|(1.38)|(1.36)|(1.58)|(1.60)|(1.58)|
|Sal|25,749.355|26,115.502|26,117.655|110.564|110.024|110.343|
||(2.57)**|(2.66)***|(2.61)***|(2.85)***|(2.82)***|(2.84)***|
|Aspect|20,452.780|20,280.512|20,599.299|-10.592|-10.325|-10.418|
||(1.31)|(1.29)|(1.30)|(0.18)|(0.17)|(0.17)|
|Erosion|12,459.351|12,339.529|9,480.500|66.936|67.147|68.355|
||(1.08)|(1.06)|(0.82)|(1.21)|(1.21)|(1.24)|
|Soiltype|25,191.496|25,660.390|26,618.187|-56.189|-56.439|-56.520|
||(2.50)**|(2.60)***|(2.61)***|(1.66)*|(1.66)*|(1.67)*|
|Constant|-14,795.335|-11,822.53|8,336.989|80.470|75.749|62.025|
||(0.47)|(0.39)|(0.26)|(0.36)|(0.34)|(0.28)|
|_N_|<br>620|620|620|<br>620|<br>620|<br>620|
|# Groups|130|130|130|130|130|130|
|R<sup>2</sup> withingroup|0.02|0.02|0.03|0.05|0.05|0.05|
|R<sup>2</sup>between|0.36|0.36|0.33|0.23|0.23|0.23|
|groups|||||||
|R<sup>2</sup>overall|0.23|0.23|0.22|0.17|0.17|0.17|
|WaldΧ<sup>2</sup> (17)|147.30|141.36|111.93|66.38|66.10|64.64|
|Prob> Χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01; Robust Standard Errors in Parentheses

**32 |** P a g e
# **Social Assistance and Poverty Reduction in Moldova, 2001-2004 An Impact Evaluation**

_Paolo Verme_<sup>1</sup> JEL: H53; I32; I38; P35 Keywords: Social assistance, Poverty, Evaluation, Moldova

> 1Department of Economics "S. Cognetti de Martiis", University of Torino and School of Management SDA-Bocconi, Milan. The work developed out of my contribution to the World Bank Report No. 37933 - MD "Improving Public Expenditure Efficiency for Growth and Poverty Reduction: A Public Expenditure Review for the Republic of Moldova", 2007.
|`Domestic content (DC)`|`417041.87`|_`74.40`_|
|---|---|---|
|`Domestic Value-Added (DVA)`|`409968.49`|_`73.14`_|
|`VAX -> DVA absorbed abroad`|`391624.68`|_`69.86`_|
|`Reflection`|`18343.80`|_`3.27`_|
|`Domestic double counting`|`7073.39`|_`1.26`_|
|`Foreign content (FC)`|`143511.01`|_`25.60`_|
|`Foreign Value-Added (FVA)`|`141076.94`|_`25.17`_|
|`Foreign double counting`|`2434.07`|_`0.43`_|

While the output confirms that domestic and foreign contents are not affected by changing perspective, the value-added terms are higher and, as a consequence, double counting items are smaller. This is not surprising since the sectoral-exporter perspective features a more restrictive definition of double counting (see Appendix E). Which perspective should be used? It depends on the specific empirical question. If the goal is to measure to what extent the GDP of a country could be exposed to a certain shock on the exports of a sector, a sectoral-exporter perspective might be appropriate. Indeed, in this case, the relevant border to trace value-added is the exporting country/sector’s one. Instead, the _default_ perspective (the “exporting country” one) is the most appropriate if the goal is to compute GVC-related trade indices - since to this end the relevant border is always the exporting country’s one - and is suited to obtain measures of value-added trade traced in disaggregated trade flows that are consistent with the aggregate figures. Notice that this additivity property is a feature of the “exporting country” perspective only.<sup>16</sup> It can be easily verified by showing that value-added components and GVC-related trade in the aggregate Chinese exports - as computed before using the synatx `icio, exporter(chn)` - equal the sum of the very same measures obtained for each sector. A possible implementation of this check is the following ``` .quiicio,exporter(chn,all)output(gtrade) ```

> `. mata : st_matrix("sum_gtrade", colsum(st_matrix("r(gtrade)")))`

> `. di "aggregate Gross exports " %14.2f sum_gtrade[1,1] aggregate Gross exports 2425406.15`

``` .
``` ``` .quiicio,exporter(chn,all)output(dva) ``` ``` .mata:st_matrix("sum_dva",colsum(st_matrix("r(dva)"))) ``` ``` .di"aggregateDomesticValue-Added"%14.2fsum_dva[1,1] aggregateDomesticValue-Added2016712.86 ``` ``` .
``` ``` .quiicio,exporter(chn,all)output(fva) ``` ``` .mata:st_matrix("sum_fva",colsum(st_matrix("r(fva)"))) ```

> 16Instead, all the other perspectives are non-additive, i.e. measures at a more aggregate level cannot be obtained summing disaggregate results.

20
# **Table A3.1 Cross Sectional Robustness Checks and Additional Variables**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_Dependent variable:_|_PEFA_|_PEFA_|_PEFA_|_PEFA_|_PEFA_|_PEFA_|_PEFA_|_PEFA_|_PEFA_|_PEFA_|_PEFA_|_CPIA_|
|**GDP per capita (PPP, log)**||0.1923***|0.2331***|0.3448***|0.2584***|0.1801***|0.1952***|0.3193***|0.2048***|0.2184***|0.1910***|0.3515***|
|||(0.0502)|(0.0444)|(0.0727)|(0.0539)|(0.0485)|(0.0675)|(0.0657)|(0.0461)|(0.0450)|(0.0450)|(0.0573)|
|**Growth (per capita)**|0.0341***|0.0451***|0.0362***|0.0286***|0.0307***|0.0307***|0.0323**|0.0272|0.0352***|0.0299***|0.0306***|0.0068|
||(0.0109)|(0.0147)|(0.0107)|(0.0099)|(0.0106)|(0.0104)|(0.0123)|(0.0205)|(0.0109)|(0.0100)|(0.0100)|(0.0229)|
|**Population (log)**|0.0536**|0.0495*|0.0441|0.0965***|0.0688**|0.0462*|0.0538|0.0390|0.0664**|0.0540*|0.0452|0.0802**|
||(0.0268)|(0.0286)|(0.0291)|(0.0335)|(0.0329)|(0.0277)|(0.0343)|(0.0347)|(0.0290)|(0.0277)|(0.0279)|(0.0328)|
|**SIDS**|-0.3649**|-0.2986**|-0.2833*|(0.4117)|(0.4166)|-0.2687*|-0.3270*|-0.4238**|(0.2369)|-0.2728*|-0.3122**|(0.0164)|
||(0.1487)|(0.1411)|(0.1584)|(0.2948)|(0.2869)|(0.1370)|(0.1811)|(0.1986)|(0.1456)|(0.1435)|(0.1414)|(0.1513)|
|**Resource**|-0.3786***|-0.3168***|-0.3539***|-0.3957***|-0.4118***|-0.3602***|-0.4332***|-0.4076**|-0.3970***|-0.3676***|-0.3808***|-0.4720***|
||(0.0945)|(0.0955)|(0.0950)|(0.1018)|(0.1058)|(0.0909)|(0.1087)|(0.1746)|(0.0889)|(0.0893)|(0.0892)|(0.1307)|
|**GDP per capita (cons, log)**|0.1915***<br>(0.0402)||||||||||||
|**Revenue**||0.0075|||||||||||
|||(0.0049)|||||||||||
|**Regime (Polity IV)**|||0.0283*||||||||||
||||(0.0155)||||||||||
|**Aid (Technical Cooperation)**||||0.0887<br>(0.0650)|||||||||
|**Aid (CPA)**|||||(0.0003)<br>(0.0011)||||||||
|**Fragility (dummy)**||||||-0.1778*<br>(0.1000)|||||||
|**FS Index (Foreign Policy)**|||||||(0.0047)<br>(0.0050)||||||
|**Fiscal Shocks**||||||||(0.0303)<br>(0.0454)|||||
|**Growth Shock (4 year lag)**|||||||||0.2297*<br>(0.1371)||||
|**Growth Shock (5 year lag)**||||||||||-0.3962**|||
|||||||||||(0.1798)|||
|**HIPC**|||||||||||-0.4001***<br>(0.1262)||
|**Observations**|115|96|98|80|80|112|80|49|112|112|112|126|
|**R-squared**|0.398|0.409|0.443|0.423|0.407|0.434|0.416|0.558|0.415|0.411|0.429|0.321|

**Notes** : Robust standard errors in parentheses; statistical significance is indicated as: *** p<0.01, ** p<0.05, *p<0.1

46
**Table 18. Significance of legal and regulatory obstacles: Expatriate staff restrictions**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.114*|0.112*|||0.196*|0.196*|||
||(0.0610)|(0.0593)|||(0.108)|(0.110)|||
|Export % of sales|0.000827|0.000498|0.000699|0.000419|0.00148|0.000662|0.00131|0.000693|
||(0.000810)|(0.000807)|(0.000823)|(0.000816)|(0.00145)|(0.00152)|(0.00152)|(0.00157)|
|Share of sourcing via imports|0.000964|-0.000266|0.00168*|0.000347|0.00164|-0.000633|0.00296*|0.000583|
||(0.000879)|(0.000902)|(0.000888)|(0.000901)|(0.00154)|(0.00166)|(0.00160)|(0.00171)|
|High-income source country|-0.151|-0.0326|-0.154*|-0.0317|-0.239|-0.0331|-0.253|-0.0332|
||(0.0952)|(0.0923)|(0.0935)|(0.0910)|(0.169)|(0.173)|(0.171)|(0.173)|
|>250 employees|-0.0309|0.00702|-0.0249|0.0144|-0.0405|0.0356|-0.0271|0.0605|
||(0.0761)|(0.0735)|(0.0755)|(0.0733)|(0.134)|(0.136)|(0.136)|(0.140)|
|Cumulative inv. >US$10M|0.0775|0.0650|0.0760|0.0619|0.128|0.113|0.131|0.112|
||(0.0741)|(0.0731)|(0.0727)|(0.0711)|(0.130)|(0.133)|(0.132)|(0.135)|
|Years in country|-0.000919|-0.00337|-0.000433|-0.00313|-0.000887|-0.00631|-0.000372|-0.00650|
||(0.00228)|(0.00222)|(0.00223)|(0.00219)|(0.00389)|(0.00398)|(0.00395)|(0.00404)|
|Level of foreign ownership (%)|-0.000919|0.00169|-0.000946|0.00179|-0.00144|0.00357|-0.00179|0.00354|
||(0.00127)|(0.00137)|(0.00124)|(0.00132)|(0.00229)|(0.00264)|(0.00232)|(0.00262)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.008|0.060|0.034|0.084|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Restrictions on hiring and bringing in expatriate staff]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

35
**Figure 1. Imputation Accuracy for Different Imputation Models, Poverty Indicators**

<!-- Start of picture text --> Headcount poverty rate Near poverty rate<br>m1 m1<br>m2 m2<br>m3 m3<br>m4 m4<br>m5 m5<br>m6 m6<br>m7 m7<br>m8 m8<br>m9 m9<br>m2+dist_facilities m2+dist_facilities<br>m2+soil_quality m2+soil_quality<br>m9+dist_facilities m9+dist_facilities<br>m9+soil_quality m9+soil_quality<br>m3+dist_facilities m3+dist_facilities<br>m3+soil_quality m3+soil_quality<br>m3+nightlight m3+nightlight<br>m2+nightlight m2+nightlight<br>m9+nightlight m9+nightlight<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Imputation accuracy (%) Imputation accuracy (%)<br>Extreme poverty rate Poverty gap<br>m1 m1<br>m2 m2<br>m3 m3<br>m4 m4<br>m5 m5<br>m6 m6<br>m7 m7<br>m8 m8<br>m9 m9<br>m2+dist_facilities m2+dist_facilities<br>m2+soil_quality m2+soil_quality<br>m9+dist_facilities m9+dist_facilities<br>m9+soil_quality m9+soil_quality<br>m3+dist_facilities m3+dist_facilities<br>m3+soil_quality m3+soil_quality<br>m3+nightlight m3+nightlight<br>m2+nightlight m2+nightlight<br>m9+nightlight m9+nightlight<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Imputation accuracy (%) Imputation accuracy (%)<br>USAID poverty gap<br>m1<br>m2<br>m3<br>m4<br>m5<br>m6<br>m7<br>m8<br>m9<br>m2+dist_facilities<br>m2+soil_quality<br>m9+dist_facilities<br>m9+soil_quality<br>m3+dist_facilities<br>m3+soil_quality<br>m3+nightlight<br>m2+nightlight<br>m9+nightlight<br>0 10 20 30 40 50 60 70 80 90 100<br>Imputation accuracy (%)<br><!-- End of picture text -->

**Note:** Imputation accuracy is the share of the estimates that are statistically insignificantly different from the true poverty rates for all countries and years. Red dashed line indicates mean accuracy.

40
disparity, the study concludes that differences in industry accounted for some of the disparity, with female-dominated industries exhibiting negative returns to capital and male-dominated industries having had high returns.

The de Mel et al. (2008) study also challenges the notion that female-owned businesses were poorer and more credit-constrained and shows instead, at least in the context of Sri Lanka, that female entrepreneurs were on average more likely to come from dual earner families and to have had more assets. A somewhat puzzling finding is that the females had on average lower ability, as measured by a digit span recall test that was administered to respondents, which is surprising given that they had on average more years of education. When returns to capital are allowed to vary by ability level of the entrepreneur, it is observed that those with higher measured ability experienced higher returns.

In contrast to PK‟s reported findings, the de Mel et al. (2008) study finds no effect of the randomized grants on school-going for children, nor on health or education expenditures. In general, their findings call into question the efficiency of targeting credit programs to women rather than to entrepreneurs operating the most productive businesses. They suggest that the benefits of targeting credit toward women need to be weighed against the possible costs of not targeting to the highest return activities or to the poorest households.

# **4.1.3  Microcredit Programs in Pakistan**

Hussein and Hussein (2003) survey a large number of microcredit programs that have been implemented in Pakistan and provide an overview of the results of evaluations of these programs along various dimensions. There is mixed evidence on whether microcredit programs have reached the poorest families and whether the programs have substantially alleviated poverty. Some programs were explicitly targeted at the poor and did have a large proportion of poor borrowers, but most programs did not explicitly target the poor and oftentimes did not keep detailed records on the poverty status of borrowers. Programs in Pakistan tended to target households rather than explicitly targeting women, although a few gave loans only to women. Even for programs with explicit targeting toward women, the household was usually seen as the borrowing unit because it was not uncommon for women to pass the loan on to male relatives.

In an assessment of the gender impact of microcredit programs, a study called the _Rural Financial Markets Study_ finds that of borrowing individuals, 78 percent were male. Also, males tended to borrow larger loan amounts, with the average loan size received by women being less 32
4 Figure **i** Exports **toAU,** I **986-93** ratios will be influenced by valuation effects that affect GDP calculations (e.g., exchange rate levels), they are nonetheless suggestive. The Czech and Slovak Republics and Bulgaria appear to be the most 'open', significantly more so than the other CEECs. This in tum has implications for the potential of these countries to absorb imports of investment goods which, in tum, will strengthen the restructuring process. A comparison with the long-run openness ratios predicted by a gravity model (Hamilton and Winters, 1992) suggests that the CEECs appear to have come close to attaining the future (Figure 3). The main exception is Hungary, in part due to its (less undervalued?) exchange rate.

<!-- Start of picture text --> 0<br>- , -<br>0. 0<br>6 , Aolmd ,<br>- - &SR<br>/ ,'<br>5 -" /<br>, o<br>, .4<br>/ it ,, - li' uniya;'<br>3<br>, - ,<br>2 N<br>1 . 1<br>1909 1990 1991 1992 1993<br><!-- End of picture text --> Figure 3: Actual and 'predicted' openness ratios (exports/GDP) <!-- Start of picture text --> 40<br>_~~~~~~~~~~~~~~~3<br>30<br>25<br>Is<br>10<br>s<br>0<br>bitguia * CP&SR 2.450 Humggly utiaad a 2.270 Riumk 1.090 FW1.ooe<br>F njiaMPII<br>*, WwrlBI At<br><!-- End of picture text --> well explained by 'gravity' models where trade between partner countries is determined by the GDPs of the two partners, their populations, and the geographic distance between them. They can be used as one benchmark against which the trade performance of the CEECs can be compared. If the actual value of exports in 1993 are compared with a gravity model-based 'prediction' of longer-run 'potential' exports (Baldwin, 1994), the CEECs have already moved substantially towards realizing their potential (Table 4).

The sanme conclusions emerge if actual EU market shares and export-to-GDP ratios are compared with gravity model predictions (Appendix Table 1). The 'openmess' of CEEC econoniies as indicated by their export-to-GDP ratios varies from 16 percent for Poland to 35 percent for Bulgaria (Figure 3). Although these Trade performance of the last 4 years suggests that the long run will be achieved much more rapidly than most observers expected in 1989, and the composition of exports substantially different than what was predicted.