rafmacalaba/datause-encoder-data huggingface dataset:
54 spells longer than six months about their possibilities in the labour market. As registered unemployment will rise in Russia, so will long-term unemployment. Rather than be too ambitious with the more conventional programmes, like retraining and public employment, Russian ECs should first develop a good infrastructure of consulting services for all the unemployed and especially for problem groups among the unemployed like e.g. the long-term unemployed. This would not only improve the matching process but would also go a long way to improve the poor image which government run ECs have among Russian workers (Standing and Chetvernina, op. cit.).

# **VI Conclusions**

Transition economies have introduced a wide range of OECD ALMPs to combat unemployment. Many of these measures are, however, rather on paper than employed in any substantial way in practice since with the rise of the stock of unemployment in most transition economies to levels comparable with OECD countries, passive measures have crowded out ALMPs. But even where this was not the case, like in the Czech Republic, ALMPs have only marginally contributed to the lowering of the unemployment rate. Having looked at various determinants contributing to the extremely low unemployment rate in the Czech Republic, in comparison with other transition economies and with OECD countries, the conclusion was reached that ALMPs explained only a small fraction of this unemployment rate differential. To remove misperceptions by the public, one important task of policy makers in CEE has to be to bring across the message that even under the best circumstances ALMPs can only play a marginal role in bringing down the unemployment rate.

Despite their marginal role, ALMPs can be useful also in transition economies. However, the situation in CEE labour markets is different from that in OECD countries which prohibits a mechanical application of OECD ALMPs to these labour markets. Because of severe and persistent shortages in capital and managerial ability labour demand is and will stay in the medium term weak while labour supply, among others for demographic reasons, will be abundant. Consequently, labour shed in those enterprises which restructure or are liquidated cannot be absorbed in its entirety by the expanding private sector which leads to unemployment also affecting core groups of employment and to more competition among the unemployed than this is the case in OECD countries. Certain problem groups which were identified in the Hungarian and Polish case as those who are older and less educated seem to have very poor prospects for future employment. In these two countries, also women have lower probabilities of regaining employmen **t** han men
# **Table 4:  ITT - heterogeneity according to village level characteristics**

|**Program Effect by subgroup**|**Weight/age**<br>**z-scores***|<br>**Exclusiv. BF**<br>**first 6m**|**meal: puree/**<br>**boiled**|**Toilet: hole in**<br>**the ground**|
|---|---|---|---|---|
|||_Zone_|||
|Rural|0.160***<br>_(0.050)_||||
|Urban|0.119<br>_(0.093)_||||
|||_Poverty_|||
|Lowest tercile|0.330***|0.156|0.306**|0.288|
||_(0.082)_|_(0.106)_|_(0.121)_|_(0.221)_|
|Middle tercile|0.001|0.155|_0.223_|0.045|
||_(0.078)_|_(0.111)_|_(0.138)_|_(0.178)_|
|Upper tercile|0.144**|0.402***|0.213*|0.290*|
||_(0.069)_|_(0.100)_|_(0.125)_|_(0.170)_|
||_Proxim_|_ity of rural road_|||
|Yes|0.246***|0.247***|0.123|0.407**|
||_(-0.060)_|(0.092)|_(0.123)_|_(0.168)_|
|No|0.053|0.220**|_0.277***_|_0.047_|
||_(-0.064)_|(0.095)|_(0.104)_|_(0.155)_|
||<br>Sec|ondary school|||
|Yes|0.154***|0.208***|0.238***|0.202|
||_(-0.052)_|_(0.073)_|_(0.079)_|_(0.127)_|
|No|0.154*|0.137|0.218|0.338|
||_(-0.083)_|_(0.122)_|_(0.175)_|_(0.211)_|
|||_Hospital_|||
|Yes|0.234***|0.105|0.358***|0.079|
||_(-0.088)_|_(0.114)_|_(0.135)_|_(0.230)_|
|No|0.129**|0.217***|0.168*|0.266**|
||_(-0.052)_|_(0.077)_|_(0.092)_|_(0.125)_|
|||_Electricity_|||
|Yes|0.210**|0.218**|0.253**|0.222|
||_(-0.104)_|_(0.089)_|_(0.104)_|_(0.164)_|
|No|0.144***|0.193**|0.190*|0.251*|
||_(-0.049)_|_(0.090)_|_(0.107)_|_(0.148)_|
|_T_|_ype of Water _|_source in the comm_|_unity_||
|Piped-in water/public fountains|0.112|0.094|0.370**|-0.255|
||_(-0.083)_|_(0.190)_|_(0.163)_|_(0.246)_|
|Protected wells|0.206*|-0.240*|0.065|-0.192|
||_(-0.118)_|_(0.140)_|_(0.209)_|_(0.303)_|
|Unprotected wells|0.317***|0.081|0.543***|0.532*|
||_(-0.111)_|_(0.168)_|_(0.175)_|_(0.284)_|
|Spring, rain water|0.115|0.220*|-0.174|0.314|
||_(-0.114)_|_(0.120)_|_(0.200)_|_(0.236)_|
|River, lake|-0.002|0.490***|0.437***|0.360*|
||_(-0.084)_|_(0.134)_|_(0.141)_|_(0.192)_|

Note: Each line reports the coefficients from a separate regression. The subsamples of poverty refer to terciles of incidence (headcount index) of the commune where village is residing. The headcount cutoff are (0, 0.67], (0.67, 0.79], (0.79,1]. The subsamples of distance terciles correspond to cutoffs [0,8], [9,28], [29,180]. Below in parentheses are standard deviations based on errors clustered at the village level. All reported results are PS weighted estimates. Significance level: .01 - ***; .05 - **; .1 - ** 25
# **Endnotes**

Some specific features of this two-sector model stem from the works of Bruno (1976). Frenkel and Mussa (1985), and De Grauwe (1985 and 1989).

2. The definition of the real exchange rate q S P, * - Pv _P7_ - _PA,_ is consistent with the assumptions and purpose of this model. Edwards (in Calvo at al., 1989) shows four other real exchange rate definitions. For simplification the model assumes that all government expenditure is on nontradables 3 For a fixed exchange rate, d.s _0,_ and constant world market price of tradables, dp,* _0,_ the domestic inflation rate depends upon the change in the price of nontradables only, _dlp di (I-a)djP XC/ I._ An x percent increase in the price of nontradables raises the domestic price level by _(I-a)x_ percent.

4. As a price taker, a small open economy faces infinitely elastic demand for its tradables if there is perfect substitutability between foreign and domestic traded goods. De Grauwe (1985) explains the consequences of such a case.

'5. This analysis holds as long as there is no change in the relative productivity levels in the home country and abroad. This model assumes no productivity growth and thus no change in the price of nontradables as a result of the productivity growth differential between the tradables and nontradables sectors For models explaining the price effects of the productivity growth differential see Froot and Rogoff (1994) or De Grauwe (1985) 6 Output of nontradables is a function of the relative price of nontradables, output of tradables is a function of the real exchange rate With a given price of tradables and identical consumption baskets in the home country and abroad, productivity in the domestic tradables 20
_Table 22_

|Dependent variable: F|iscal adju|stment wh|en neede|d based o|n Escolano|et al.(20|14)||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure, CE|-0.020|-0.026|-0.028|-0.066|-0.048|-0.041|-0.060|-0.062|
|(% of GDP)|(0.063)|(0.055)|(0.062)|(0.060)|(0.064)|(0.061)|(0.060)|(0.055)|
|Rigid expenditure, SS|-0.025**|-0.038***|-0.026**|-0.034***|-0.022***|0.106*|-0.121***|-0.079**|
|(% of GDP)|(0.012)|(0.011)|(0.011)|(0.011)|(0.007)|(0.057)|(0.033)|(0.032)|
|Economic factors|||||||||
|GDP growth (t-1)||-0.296||0.311|0.465|0.383|0.245|0.341|
|||(1.387)||(1.441)|(1.444)|(1.450)|(1.432)|(1.436)|
|Inflation (t-1)||2.625***||2.731***|3.609***|3.370***|3.701***|2.587**|
|||(0.774)||(0.996)|(1.048)|(0.999)|(0.997)|(1.074)|
|U.S. Interest rate||-0.129**||-0.167**|-0.175***|-0.197***|-0.201***|-0.180***|
|||(0.051)||(0.063)|(0.065)|(0.055)|(0.069)|(0.050)|
|Political factors|||||||||
|Gini|||0.008|0.007|0.009|0.054**|0.009|0.001|
||||(0.009)|(0.011)|(0.011)|(0.022)|(0.012)|(0.010)|
|Rule of law|||-0.224***|0.092|0.129|0.122|-0.061|0.096|
||||(0.062)|(0.124)|(0.125)|(0.121)|(0.112)|(0.126)|
|Margin of Majority|||0.437|0.154|0.118|0.272|0.150|-0.944*|
||||(0.383)|(0.357)|(0.361)|(0.339)|(0.351)|(0.550)|
|Election's year|||-0.111|-0.087|-0.073|-0.068|-0.070|-0.074|
||||(0.079)|(0.075)|(0.070)|(0.071)|(0.075)|(0.080)|
|Interactions|||||||||
|EMEs * Rigidity|||||-0.058*<br>(0.033)||||
|Gini * Rigidity||||||-0.004**<br>(0.002)|||
|Rule of law * Rigidity|||||||0.018***<br>(0.006)||
|Majority * Rigidity||||||||0.088<br>(0.059)|
|Constant|0.807|1.384**|1.247|1.180|0.972|-0.996|1.882**|1.948**|
||(0.600)|(0.555)|(0.815)|(0.981)|(0.973)|(1.428)|(0.877)|(0.905)|
|Observations|185|183|165|163|163|163|163|163|
|R-squared|0.021|0.139|0.090|0.185|0.200|0.224|0.214|0.206|
|Number of ifscode|60|60|53|53|53|53|53|53|
|CountryFE<br>Robust standard error<br>*** p<0.01, ** p<0.05|Yes<br>s clustere<br>, * p<0.1|Yes<br>d by coun|Yes<br>try in pare|Yes<br>ntheses|Yes|Yes|Yes|Yes|

- 51 -
**Table 13: Stallings: Student test scores regressed on classroom observation variables (level 1) and teacher test scores**

|||All te|achers||Te|achers with|test score <|=0|Te|achers wit|h test score|>0|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Share of time teaching|-0.1878|-0.425|-0.1921|-0.422|-0.0836|-0.353|-0.0661|-0.393|-0.2405|-0.109|-0.2995|-0.150|
||(0.3188)|(0.267)|(0.3202)|(0.267)|(0.6409)|(0.643)|(0.6465)|(0.633)|(0.5556)|(0.334)|(0.5335)|(0.334)|
|Share of time lost|-0.7645|-0.640|-0.8052|-0.633|-1.0848|-1.778***|-1.1037|-1.788***|-0.3623|-0.653|-0.4297|-0.642|
||(0.5050)|(0.460)|(0.5102)|(0.458)|(0.9959)|(0.634)|(1.0084)|(0.654)|(0.6527)|(0.584)|(0.6837)|(0.599)|
|Availability of materials|0.4661|-0.178|||-0.0566|-1.143|||1.5375|0.978|||
||(0.8967)|(0.705)|||(1.0873)|(0.761)|||(1.1090)|(0.841)|||
|Classroom environment|-0.1510|0.0446|||-0.1722|0.0507|||-0.1290|-0.0661|||
||(0.2204)|(0.208)|||(0.3036)|(0.238)|||(0.2762)|(0.185)|||
|Stallings PC1|||-0.0074|-0.000460|||-0.0397|-0.0507|||0.0566|0.0393|
||||(0.0709)|(0.0544)|||(0.0857)|(0.0597)|||(0.0812)|(0.0562)|
|Teacher Test Score|-0.0368|0.0171|-0.0487|0.0201|0.1397|0.00641|0.1288|0.0232|-0.3774|-0.343|-0.4326|-0.369|
||(0.1106)|(0.0767)|(0.1101)|(0.0775)|(0.0926)|(0.0751)|(0.0868)|(0.0789)|(0.3089)|(0.276)|(0.3204)|(0.292)|
|Observations|4,619|4,586|4,619|4,586|2,333|2,300|2,333|2,300|2,286|2,286|2,286|2,286|
|R-squared|0.027|0.230|0.024|0.229|0.050|0.295|0.049|0.293|0.040|0.259|0.030|0.257|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Teacher Test|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|F-Test: Observation vars.|0.434|0.381|0.421|0.254|0.691|0.049|0.625|0.044|0.584|0.454|0.737|0.418|
|F-Test: Student vars.||0.011||0.011||0.000||0.000||0.004||0.004|
|F-Test: Household vars.||0.000||0.000||0.000||0.000||0.000||0.000|
|F-Test: Teacher vars.||0.116||0.113||0.016||0.018||0.010||0.005|
|F-Test: School vars.||0.000||0.001||0.062||0.114||0.000||0.000|

Note: Standard errors clustered at the teacher level in parentheses. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

53
Table 3 **Econometric Results**

||**Lan**|**d Rental Reg**<br>|**imes**|
|---|---|---|---|
||Rent-In|Autarchy|Rent-Out|
|**Investment Intensity,**k||||
|_Tenure Security Effect,aklaS_||-0.01<br>(0.02)||
|_Transfer Rights Threshold Effects,_||-0.06||
|_aT1' am,>,o;_||(0.12)||
|_a/lo /Ma_<br>_<o;_||_-0.59**_<br>(0.16)||
|_Non-Separability,akl/ar_**_c_**|**-0.06****|-0.03**|-0.02|
||(0.01)|(0.01)|(0.04)|
|_Transfer Rights Dependence: dk/aMl_|0.42**|**0.06***|0.14|
||(0.18)|(0.04)|(0.72)|
|_Pure Regret: Dk/la _|-0.6|-0.90**|1.89|
||(0.46)|(0.41)|(1.84)|
|_Regret Mitigation_|-0.01|-0.01|0.01|
|_ak-larc'DMI_|(0.01)|(0.01)|(0.03)|
|_akla4DMI_|-0.27|0.001|-0.07|
||(0.24)|(0.08)|(0.92)|
|**Labor Intensity,**X||||
|_Transfer Rights Threshold Effects,_||-0.03||
|aTll/am,||(0.12)||
|_aI,°/aM,_||_-0.55**_||
|||(0.15)||
|_Non-Separability,at/ar'_|**-0.06****|-0.03**|-0.4|
||(0.01)|(0.01)|(0.04)|
|_Transfer Rights Dependence:aJ/aMl_|0.03|**0.04**|0.32|
||(0.15)|(0.03)|(0.51)|
|_Pure Regret:akla/4_|-1.26**|-0.67*|1.89|
||(0.41)|(0.36)|(1.14)|
|_Regret Mitigation_|-0.002|-0.006|-0.02|
|_akI/caMc_|(0.01)|(0.006)|(0.03)|
|_akla4aM,_|0.09|-0.02|-0.07|
||(0.21)|(0.07)|(0.65)|

Standard deviations computed from the inverse of the Hessian are reported in the parentheses.

*Significant at the 10% significance level.

** Significant at the 5% significance level.

38
# **Markets for Cotton By‐Products: Global Trends and Implications for African Cotton Producers**

The recent commodity boom, and especially the price increases for edible oils combined with the relatively stagnant prices for cotton, has reawakened interest in the potential of cotton by‐products as a complementary source of revenue for cotton growers. Cotton oil, like other edible oils, has received particular attention in this regard because of its potential use as a feedstock for biofuels. The expan‐ sion of world demand for biofuels, including edible‐oil‐based biodiesel, has raised hopes that the development of biofuel industry might transform the Afri‐ can edible oil industry and the prospects for African cotton producers.

A key conclusion of a recent multi‐country cotton study by the World Bank (Tschirley, Poulton, and Labaste 2009) is that cotton by‐products in general have a growing market and are potentially an important complementary source of revenue for cotton growers. The same study also concludes that the markets for cotton by‐products in Sub‐Saharan Africa are not well developed and have considerable scope for improvement, especially in the context of the prices re‐ ceived by farmers (Gergely and Poulton 2009). On the other hand, cotton by‐ products are an area that has not received much attention.<sup>**1**</sup> To assess more thoroughly the prospects of cotton by‐products industries in Sub‐Saharan African countries, this paper analyzes the global market for cot‐ ton oil in the context of the global vegetable oil market and demand for biofuel feedstock, and then compares the structure and recent behavior of cotton by‐ products industries in four African countries—Uganda, Tanzania, Benin, and Burkina Faso. The paper also looks briefly at the United States, not only because its much longer‐established industry provides a useful benchmark, but also be‐ cause of the nature of cotton ownership. Because cotton in most countries (in‐ cluding SSA) is primarily a smallholder crop, seed cotton is typically bought by traders or ginning companies at the farm level, and thus the ownership of cotton is transferred. In the United States (as well as some other cotton producing coun‐ tries such as Australia and to a lesser extend Brazil), cotton growers retain own‐ ership of the crop after ginning. Further, because many cotton producing coun‐ tries (notably several in West and Central Africa and Central Asia) have few gin‐ ning companies, the markets for by‐products are characterized by oligopolistic

> **1** In a study undertaken at about the same time as the present study, Kelly and others (2010) ex‐ amine at the cotton by‐products sectors in Benin, Burkina Faso, Chad, and Mali. That study’s key objectives are to describe the evolution, organization, and performance of the by‐products indus‐ tries as well as their institutional, political, and technological constraints.

**_Page_ 1**
|National Aids Control Project|Salim J. Habayeb|
|---|---|
|National Aids Control Project|Salim J. Habayeb|
|National Aids Control Project|Salim J. Habayeb|
|National Aids Control Project|Salim J. Habayeb|
|National Aids Control Project|Salim J. Habayeb|
|Punjab Middle SchoolingProject|Fredrick Golladay|
|Punjab Middle SchoolingProject|Fredrick Golladay|
|Punjab Middle SchoolingProject|Fredrick Golladay|
|Punjab Middle SchoolingProject|Fredrick Golladay|
|Punjab Middle SchoolingProject|Fredrick Golladay|
|Punjab Middle SchoolingProject|Fredrick Golladay|
|Punjab Middle SchoolingProject|Fredrick Golladay|
|Punjab Middle SchoolingProject|Fredrick Golladay|
|Punjab Middle SchoolingProject|Fredrick Golladay|
|Basic and PrimaryEducation Project|Grant G. Sinclair|
|Basic and PrimaryEducation Project|Grant G. Sinclair|
|Basic and PrimaryEducation Project|Grant G. Sinclair|
|Basic and PrimaryEducation Project|Grant G. Sinclair|
|Basic and PrimaryEducation Project|Grant G. Sinclair|
|Basic and PrimaryEducation Project|Grant G. Sinclair|
|Basic and PrimaryEducation Project|Grant G. Sinclair|
|Basic and PrimaryEducation Project|Grant G. Sinclair|
|Basic and PrimaryEducation Project|Grant G. Sinclair|
|Telecommunications Project(05)|Ritin Singh|
|Telecommunications Project(05)|Ritin Singh|
|Telecommunications Project(05)|Ritin Singh|
|Telecommunications Project(05)|Ritin Singh|
|Telecommunications Project(05)|Ritin Singh|
|Telecommunications Project(05)|Ritin Singh|
|Telecommunications Project(05)|Ritin Singh|
|Telecommunications Project(05)|Ritin Singh|
|Agricultural Extension Project(02)|AnthonyNihal Fernando Warnakulas|
|Agricultural Extension Project(02)|AnthonyNihal Fernando Warnakulas|
|Agricultural Extension Project(02)|AnthonyNihal Fernando Warnakulas|
|Agricultural Extension Project(02)|AnthonyNihal Fernando Warnakulas|
|Agricultural Extension Project(02)|AnthonyNihal Fernando Warnakulas|
|Agricultural Extension Project(02)|AnthonyNihal Fernando Warnakulas|
|Agricultural Extension Project(02)|AnthonyNihal Fernando Warnakulas|
|Environmental Protection and Resource Conservation Project|Aziz Bouzaher|
|Environmental Protection and Resource Conservation Project|Aziz Bouzaher|
|Environmental Protection and Resource Conservation Project|Aziz Bouzaher|
|Environmental Protection and Resource Conservation Project|Aziz Bouzaher|
|Environmental Protection and Resource Conservation Project|Aziz Bouzaher|
|Environmental Protection and Resource Conservation Project|Aziz Bouzaher|
|Environmental Protection and Resource Conservation Project|Aziz Bouzaher|
|Environmental Protection and Resource Conservation Project|Aziz Bouzaher|
|Environmental Protection and Resource Conservation Project|Aziz Bouzaher|
- 18 -

of agglomeration economies to strengthen firm competitiveness. But further efforts are perhaps needed to reinforce the agglomeration effect on the economy.

Second, the estimation results also suggest that transport infrastructure is important to foster firm concentration. Firms are more likely to be located where local connectivity (measured by road density) is high. Similarly, the domestic market connectivity is also found important. The coefficient is significant at -0.37. Firms prefer to be based where the transport costs are lower to buy materials from and send products to the large city.

The global market connectivity is found far more important in terms of magnitude. The coefficient of _TPORT_ is significantly negative at -2.44. Therefore, a large number of new firms are expected to be created at a particular locality, if the transport costs from that place to the port are reduced. This indicates the importance of transport corridor investments to connect better local cities to the port as a package.

Regarding other explanatory variables, the local market size is found to be important for firms to decide their location. This is consistent with prior expectation. Education is also important, because this would affect the quality of expected labor supply. Interestingly, the coefficient is significant for primary education, but not for secondary education. The results may be interpreted to mean either that higher education attainment is not demanded by businesses given the level of industrialization or that the quality of secondary education is not sufficient from the business point of view.

The impact of electricity access turned out unclear. The coefficients are positive, not negative, and statistically insignificant. One possible reason is that our measurement of power infrastructure may be poor, because our economic cost of power is calculated based on the residential power access data. Firms normally have better access than households. Second, the power cost may not have much variation in the spatial dimension, regardless of the differences observed in our variable. Therefore, this may not strongly influence the firm location choice.
Table A7: Impact of the decree on quality (common linear trends)

||(1)<br>Pre-trial<br>insuf-<br>cient|(2)<br>Decision<br>postponed|(3)<br>Number<br>of articles|(4)<br>Decision<br>length|(5)<br>Appeal|
|---|---|---|---|---|---|
|Entered after decree|0.009|-0.010|-0.167|-0.140|0.027|
|application (_β_)|(0.040)|(0.028)|(0.157)|(0.216)|(0.054)|
|Trend|0.001|0.002***|0.004|-0.001|0.001|
||(0.001)|(0.001)|(0.004)|(0.006)|(0.001)|
|Interaction|0.003|-0.001|0.008|0.023|0.001|
||(0.003)|(0.002)|(0.011)|(0.015)|(0.004)|
|P-value 6pWCBoot|0.868|0.808|0.401|0.716|0.537|
|Chamber FEs|Yes|Yes|Yes|Yes|Yes|
|Calendar month FEs|Yes|Yes|Yes|Yes|Yes|
|Without adj. period|Yes|Yes|Yes|Yes|Yes|
|Pre-mean|0.118|0.055|2.838|5.538|0.536|
|Pre-sd|0.323|0.228|1.591|2.555|0.499|
|R-Squared|0.021|0.042|0.006|0.037|0.040|
|Observations|3832|3832|2742|2741|2742|

<u>Notes:</u> Estimates of case-level impacts of the reform on the quality of pretrial and deliberation proceedings. See Notes on Table 3. 3844 observations for decisoin stage outcomes, except cols 1-2 (censoring as only for cases with at least one decision stage hearing), and cols 3-5 (missing outcomes).

61
specialization in trading differentiated goods under conditions of monopolistic competition – quantities matter for domestic performance too. However, here it is important to isolate the exogenous determinant (or the instrument) of quantities as the relevant predetermined quantity variable. In this vein, the world or regional supply of capital could be an adequate instrument for the supply of capital to the domestic economy, while average growth of all trading partners could be an adequate instrument for the foreign demand of exports by the domestic economy.

Foreign shocks are measured in two ways: the rate of growth or deviation of a foreign variable from its preceding level and the standard deviation of the variable in a given time period. Most empirical growth studies include one or two foreign shock variables based on the first measure, typically as control variables. Here we refer only selectively to recent cross-country empirical studies that quantify the effects of terms of trade, the foreign interest rate, capital inflows, and trade partner growth on domestic growth and growth volatility (Table 1).

The rate of growth of the terms of trade is the most widely used measure of foreign shocks (among representative studies are Easterly, Loayza, and Montiel 1997, FernándezArias and Montiel 2002, Loayza, Fajnzylber, and Calderón 2004, Barro and Sala-i-Martin 2004). In most studies terms-of-trade shocks turn out to be significantly positive determinants of growth.

In a highly integrated world, open economies are also influenced by growth in the rest of the world. Arora and Vamvakidis (2005) find that a 1% increase in economic growth of the countries’ trading partners leads to higher domestic growth by 0.8 percentage points.<sup>5</sup> Other studies have included the ratio of private capital inflows to GDP as a growth determinant and have also evaluated the impact of different types of capital flows on growth (Calderón and Schmidt-Hebbel 2003). Most of these studies have found a positive impact of private capital inflows on growth, with a stronger effect in the case of foreign direct investment.<sup>6</sup>

> 5 They argue that this result is consistent with the literature on the impact of cross-country spillovers (Arora and Vamvakidis 2004, Ahmed and Loungani 1999).

> 6 Most of these studies instrumentalize capital inflows using lagged values, legal origin variables or investor protection measures in order to avoid endogeneity bias from the response of capital flows to growth.

6