rafmacalaba/datause-encoder-data huggingface dataset:
# **_Electricity_**

The information base on electricity tariffs is much more extensive than that available for the case of water. The analysis draws on the regional database developed by OLADE (2004) that provides average tariffs at the country level for different customer classes. As a result, it is possible to draw a more comprehensive picture as to the current extent of cost recovery for electricity.

The results show that 13 Latin American electric utilities made substantial efforts to raise their residential tariffs in nominal terms at an average annual rate of 22% over the period 1990/02 (Figure 2). Nevertheless, these gains were largely wiped out by inflation, with real residential tariffs rising by no more than 2% per year on average over the same period. In the case of industrial customers, electricity tariffs rose in nominal terms at an average annual rate of 18% over the period 1997/03. However, in real terms, this translated into an average annual decline of 1% over the same period. In contrast to the water sector, industrial electricity tariffs tend to be lower than residential tariffs.

It is interesting to compare tariff trends in Latin America with those observed in other regions of the world (Figure 3). Looking at the period 1995/00, for which comparable data is available, South Asia, Eurasia and East Asia sustained much larger real tariff increases than Latin America of between 4-9% per year in local currency terms. However, real residential electricity tariffs in OECD countries declined in real terms over the same period. Notwithstanding these differences in trend, residential electricity tariffs in Latin America remain the highest of any developing region in absolute terms, although they still fall substantially short of those in OECD countries (Figure 4).

9
|**Table 1: Volume of r**|**egistered sal**|**es and leases**<br>**Land sal**|**for agricult**<br>**es**|**ural: Jan. 202**|**1-Aug. 2024**<br>**La**|**nd leases**|||
|---|---|---|---|---|---|---|---|---|
||**No. of**<br>**Sales**|**Area (ha)**|**Price**<br>**(US$/ha)**|**No. of**<br>**Leases**|**Area (ha)**|**Price**<br>**(US$/ha)**|**Term**<br>**(years)**|**No. of**<br>**Mortgages**|
|**Panel A: Entire sa**|**mple**||||||||
|<br>Total|<br>389,344|910,092|981.2|1,974,319|4,339,537|89.2|12.5|1,866|
|w price bef. 1/23|<br>84,053|<br>213,800|1,074.0|<br>531,722|<br>1,175,480|88.5|13.0||
|<br>w price aft. 1/23|223,030|501,892|945.2|401,380|888,351|90.1|12.4||
|<br>**Panel B: By buyer**|<br>**type & land**|<br>**use category**|||||||
|<br>Individual/FOP|<br>369,267|<br>853,455|968.8|112,940|365,261|51.5|12.8||
|Legal entity|<br>20,077|<br>56,637|1,248.5|<br>1,861,379|<br>3,974,276|91.9|12.4||
|<br>Personal|<br>164,299|<br>198,087|<br>998.5|<br>518,685|<br>809,279|87.4|12.5|904|
|Commercial|<br>225,045|<br>712,004|975.6|<br>1,455,634|<br>3,530,258|89.9|12.5|962|
|**Panel C: By year**|||||||||
|<br>2021|90,074|238,407|1,133.3|754,617|1,692,628|90.0|12.7|493|
|2022|<br>74,825|<br>167,362|<br>988.7|<br>377,931|<br>763,162|85.2|12.7|398|
|2023|<br>119,715|<br>259,708|936.0|<br>552,106|<br>1,209,667|92.3|12.2|534|
|2024<br>_Source:_Own comput|104,730<br>ation based o|<br>244,615<br>n parcel-level s|956.2<br>tatistics from|<br>289,665<br>Ukraine regi|<br>674,080<br>stry of rights o|86.6<br>n property.|12.1|441|

_<mark>Source:</mark>_ <mark>Own computation based on parcel-level statistics from Ukraine registry of rights on property.</mark> _<mark>Note:</mark>_ <mark>Transaction prices are median prices if prices are reported. Data for sales starts from July 2021, i.e., after the opening of sales market for agricultural land. Mortgages are for agricultural land only. In addition to the 1,866 mortgages involving agricultural land, there were 9,289 mortgages involving non-agricultural land (7,439 for residential, 1,710 for industrial, and 140 for recreational land, respectively).</mark> 22
Papua New Guinea where the migrants believe they would be earning relatively high incomes at home and getting charged high tax rates, and $17,000 in New Zealand.

This calculation assumes that there is no tax evasion. To check this assumption we compare the difference between reported gross and reported net incomes in each of our five countries to what we would calculate the tax payment to be based on the national tax tables. The actual tax reported by non-migrants and return migrants in their home countries are very similar to what we calculate as the tax they should be paying according to the tax rates. It therefore seems reasonable to assume that migrants would also pay the amount of tax we estimate if they were working in their home countries.

Next, we calculate the sales tax these migrants would have paid at home if they were consuming these counterfactual incomes in their home country. All five countries have sales taxes or goods and services taxes, with tax rates ranging from 5% in Micronesia, 10% in Papua New Guinea, and 12.5% in Ghana and New Zealand to 15% in Tonga. The Tongan government estimates that its goods and services taxes apply to approximately 60 percent of household consumption.<sup>15</sup> We assume the same applies to the other three developing countries and to 90 percent of consumption for New Zealand, and that all income is spent to arrive at the sales tax figures in the next two columns of Table 6.<sup>16</sup> This is highest in New Zealand at $7055 per migrant, reflecting the higher incomes and greater coverage of the sales tax net in this country, and lowest in Micronesia where the low tax rate and lower incomes means the lost sales tax revenue is only $383 per migrant.

Offsetting these fiscal costs of high-skilled emigration are two main fiscal benefits. The first is that if monetary remittances are spent on consumption items and the government has a sales tax, then the government gains a share of these remittances. We assume that 100 percent of monetary remittances are spent, and that again 60 percent of this spending is taxable. Whilst there is debate in the literature as to the extent to which remittances are used for nonconsumption purposes, surveys typically find at least 80 percent of remittances are consumed, and even investments in items like materials for starting a business may be taxable. Nonetheless,

> 15 ‘Utoikamanu (2006) “Consumption tax: The Tongan experience” http://archives.pireport.org/archive/2007/July/07-27-rp.htm.

> 16 Statistics New Zealand estimates that G.S.T. applies to 91 percent of the CPI.

23
**<u>Table 2.</u>** <u>Descriptive statistics of analytical variables (</u> _<u>weighted</u>_ <u>)</u>

||**Pooled S**<br>**_Mean_**|**ample**<br>**_SD_**|**Women**<br>**_Mean_**|<br>**_SD_**|**Men**<br>**_Mean_**|**_SD_**|**_T-tests_**|
|---|---|---|---|---|---|---|---|
|**_Outcome Variables_**||||||||
|Employed|0.68|0.47|0.68|0.47|0.68|0.47|0.030|
|Unemployed|0.05|0.23|0.06|0.23|0.05|0.21|0.010|
|Not in the labor force|0.27|0.45|0.27|0.44|0.28|0.45|-0.040**|
|Self-employment (self/family)|0.44|0.5|0.52|0.5|0.33|0.47|0.191***|
|Wage employment|0.24|0.43|0.15|0.36|0.35|0.48|-0.167***|
|**_Demographic variables_**||||||||
|Male|0.44|0.5|-|-|-|-|-|
|Age|36.6|12.09|36.63|12.35|36.56|11.75|-0.314|
|Married/ cohabiting|0.61|0.49|0.62|0.49|0.6|0.49|0.019|
|At least secondary education|0.65|0.48|0.61|0.49|0.69|0.46|-0.0921***|
|Male household head|0.72|0.45|0.56|0.5|0.94|0.24|-0.343***|
|Muslim|0.53|0.5|0.49|0.5|0.58|0.49|-0.0757***|
|Spouse is present|0.59|0.49|0.57|0.5|0.62|0.49|-0.0593**|
|Household size|4.1|2.56|4.29|2.46|3.87|2.67|0.342**|
|Poor|0.32|0.47|0.3|0.46|0.35|0.48|-0.0464**|
|Urban residence|0.86|0.35|0.88|0.32|0.83|0.38|0.0635***|
|Ashanti region|0.56|0.5|0.6|0.49|0.52|0.5|0.0788***|
|Observations|2,478||1375||1103|2478||

* p<0.10, ** p<0.05, *** p<0.01

25
# Table 1: AN EXPORT-EXTERNALITY MODEL

|(1)|Q = F(M, DD; u)|CES aggregation function|
|---|---|---|
|(2)|X = AG(E, DS; D)|CET transformation function|
|(3)|Pr= R.Im.(1 + tm)|Import price|
|(4)p|c<br>=R.,rC(1<br>+ te)|Export price|
|_(5)_|**pt**<br>Pd.(1 + td)|Tax-ridden domestic price|
|(6)|Pq =f1(pc, pt;_a)_|Consumer price|
|(7)|px=gl(pC, pd;_n)_|Producer price|
|(8)|M/DD=f2(Pm, P';_a)_|Import demand equation|
|(9)|E/DS = g2(Pr, Fd; 0)|Export supply equation|
|(10)|A=A.(E/EO)-"|Export externality (E > Eo,q>_0,_A=A if E<EO)|
|(11)|fm.M = xr'E|Balance of trade constraint|
|(12)|DD- Ds =0|Domestic demand = supply|
|'Variables|||
|M, E|= Imports, exports||
|DD, Ds|= Demand and supply of|the domestic good|
|Q|= Composite consumer g|ood|
|X|= Fixed aggregate comp|osite production|
|**tim,**me|= Fixed world prices of i|mports and exports|
|R<br>|= Conversion factor or "<br>|nominal' exchange rate<br>|
|Pm_c_<br>|= Domestic prices of im<br>|ports and exports<br>|
|pdpt|=Domestic prices of do|mestic sales, D, exclusive and inclusive of sales tax|
|pq, px|= Domestic prices of co|mposite consumer good, 0, and composite output, X|
|Parameters|||
|_a,a _|= Elasticity of substituti|on (CES), elasticity of transformation (CET)|
|**ip,**Eo|=Externality parameters||

-11-
data set has around 3,000 observations equally distributed between the manufacturing sector and other sectors excluding agriculture (see Table 2); manly services and within services wholesale and retail. The survey targeted formal firms only and excluded micro firms, although a few firms on the survey are de facto micro when surveyed and have less than 5 employees.

_<u>Table 2 Number of Firms according to their size, sector and country</u>_

|||**Manufacturin**|**g**|**S**|**ervices and ot**|**hers**||
|---|---|---|---|---|---|---|---|
||**small(<20)**|**Medium**<br>**(20-99)**|**Large (100**<br>**and over)**|**small(<20)**|**Medium**<br>**(20-99)**|**Large (100**<br>**and over)**|**_Total_**|
|Ghana|193|66|18|164|84|14|**_539_**|
|DRC|108|48|14|140|39|6|**_355_**|
|Tanzania|163|68|35|170|80|12|**_528_**|
|Uganda|129|50|25|168|55|11|**_438_**|
|Zambia|131|109|21|200|51|21|**_533_**|
|Kenya|104|100|76|153|81|31|**_545_**|
|**_Total_**|**_828_**|**_441_**|**_189_**|**_995_**|**_390_**|**_95_**|**_2938_**|

Source: Author’s own elaboration from enterprise surveys The ICT section provides information on computer and software use and different types of internet use.<sup>12</sup> In the next sub-sections, we provide a detailed overview of computer and internet use for the six African countries of our sample.

# **5.2 Computer use**

_On average, around 2/3 of African firms have at least one employee using a computer regularly_ . Table 3 shows the percentage of firms in these six countries which has at least one person using a computer regularly. On average, 62.9% of African firms in these countries has a regular computer user in 2013, percentage comparable to Australian firms in 1997/98, according to Loundes (2002). However, differences are substantial across countries, since it is only 40.9% in Tanzania yet it reaches to nearly 90% in Kenya. Recent data from the European Union (EU) shows that 97% of firms located in this region has a computer. Therefore, we may infer that Kenya is more similar to European countries than to other parts of Africa.

_This percentage is greater in services in most of the countries, yet differences are small on average. A_ frican countries show similar pattern as EU countries: Services having a higher percentage of firms with computer than manufacturing. Zambia is the only country showing a percentage in manufacturing higher than in services. Across sectors, the manufacturing sector shows more variance than services. While the minimum in services is over 60% (Hotel and Restaurants, 60.1%), in manufacturing the minimum use reaches 36.9% in Garments.

> 12 The survey also provides information on mobile phone use.

13
frequent. Figure S1.6 in appendix S1 confirms that the differences are more important at higher levels of consumption.<sup>12</sup> As may be expected, we find more inequality in PSF than in ESPS data. Using per capita household expenditures, the Gini index is 47.1%, quite a bit higher than what we find in ESPS (38.9%) (using the series restricted so as to be comparable to PSF) and in the WDI (40.3% for 2011).<sup>13</sup> With such level of inequality, Senegal would be placed about 37 ranks higher in the ranking of countries by inequality level (computed from standard consumption data), from the 62nd position to the 25th one, between Venezuela and Chile.<sup>1415</sup> As we shall see in the next section, factoring in intra-household inequality will push Senegal even upward on the ladder of inequalities.

How does PSF modify the assessment of poverty? In order to answer this question we first need to define poverty. Two poverty lines are selected, following the basic needs approach. The lowest, nutrition, line corresponds to the cost of the food basket that provides at least 2400 kcal per day. The second line is a basic needs poverty line, used as the national poverty threshold in Senegal. It is obtained by augmenting the food poverty threshold with the amount of resources that is necessary to cover individual basic needs other than nutrition. This amount is established through the observation of the average non-food consumption of households for which food consumption per adult equivalent belongs to an interval of plus or minus 5% around the food poverty threshold (see Appendix S1).

Values of the two poverty lines for Dakar, other towns and rural areas separately are reported in table 2. The nutrition poverty line is very close to the $1.25 (PPP 2005) international line (that would be equal to 366 CFA francs at the time of our survey). A $2 line would correspond to 586 CFA francs.

14
effects across all domains for children at age 8; around 21 percent for the language and cognitive domain and 3 to 6 percent for the social competence and emotional maturity domains. The results for primary school enrollment are rather similar across methods.

As before, we also unpack our EDI analysis into children from villages of high, medium, and low quality preschools. These results are presented in Tables 8 and 9. Once again, we focus on the EDI domains where we observe significant gender gaps, which are social competence, emotional maturity, and language and cognitive development. We find that gender differences in preschool enrollment explain gender differences in social competence and language and cognitive development in areas with high quality preschool if the standard decomposition method is used, but the effects become mostly insignificant if the extended decomposition method is used. This suggests that children’s background characteristics (control variables) correlate with the quality of preschool services. In the extended decomposition model, the effects of gender differences in primary school enrollment are observed at age 8, and are mostly visible for the social competence and language and cognitive domains. An interesting pattern occurs for the parenting variables. Using the standard method, gender differences in parenting are important for the gender differences in the social competence and emotional maturity domains of the EDI for children living in villages with high quality preschools, and for children living in villages with low quality preschools. The significant coefficient on parenting is also observed in the extended model (Table 9), although the effect is weaker. It is not clear what drives this pattern.

# **7. Discussion and Conclusion**

We find consistent gender gaps in total test scores at all ages, with substantial female advantage. The gender gaps in total test scores are driven by differences in children’s performance in the language and mathematics portion of the test, but not by differences in performance in the abstract reasoning section of the assessment. Our findings here are consistent with previous studies. Girls outperform boys in language as early as age 6 (by 0.252 SD), which is similar to the 0.162 SD female advantage observed in the U.S. among kindergarteners (Cornwell et al. 2012, DiPrete and Jennings 2012). However, our results diverge from earlier studies that have observed girls losing ground to boys in mathematics during primary school, both in high-income countries (Fryer and Levitt 2010) and in lower- and middle-income countries (Bhardwaj et al. 2015, Dickerson et al. 2013). Instead, we find a female advantage in mathematics during the first few years of 18
|Percent rainfall loss interacted with|||
|---|---|---|
|No. of Months after October (t)|0.0014***|0.00|
|t<sup>2</sup>|-0.0002***|0.00|
|t<sup>3</sup>|0.0000*|0.06|
|After 2005 * t|0.0005|0.38|
|After 2005 * t<sup>2</sup>|-0.0001|0.23|
|After 2005 * t<sup>3</sup>|0.0000|0.21|
|High roads * t|0.0002|0.52|
|High roads * t<sup>2</sup>|0.0001|0.31|
|High roads * t<sup>3</sup>|-0.0000**|0.02|
|High roads after 2005 * t|-0.0011***|0.01|
|High roads after 2005 * t<sup>2</sup>|0.0002*|0.07|
|High roads after 2005 * t<sup>3</sup>|-0.0000|0.25|
|Cash PSNP district * t|-0.0006|0.15|
|Cash PSNP district * t<sup>2</sup>|0.0001|0.15|
|Cash PSNP district * t<sup>3</sup>|-0.0000|0.21|
|Cash PSNP district after 2005 * t|-0.0004|0.50|
|Cash PSNP district after 2005 * t<sup>2</sup>|0.0001|0.62|
|Cash PSNP district after 2005 * t<sup>3</sup>|-0.0000|0.66|
|Food PSNP district * t|0.0008**|0.03|
|Food PSNP district * t<sup>2</sup>|-0.0002**|0.04|
|Food PSNP district * t<sup>3</sup>|0.0000**|0.05|
|Food PSNP district after 2005 * t|-0.0016***|0.00|
|Food PSNP district after 2005 * t<sup>2</sup>|0.0003***|0.01|
|Food PSNP district after 2005 * t<sup>3</sup>|-0.0000**|0.02|
|N|126,306||
|Number of markets|82||

Note: *** indicates significant at 1%, ** significant at 5% and * significant at 10%. The regression includes time since harvest (t, t<sup>2</sup> and t<sup>3</sup> ) and year, grain type (by grade) and market fixed effects. Robust standard errors were used to estimate the p-values presented. We allowed for autocorrelation within each panel while allowing the coefficient of AR(1) process to vary across panels.

# **6. Conclusion and policy implications**

Studies on the impacts of drought in Ethiopia have focused predominantly on analyzing the household-level effects by exploring its impacts on food security, poverty, asset holdings, migration decision and so on. In addition to household-level effects, understanding the meso-level impacts of weather shocks is crucial as this is a key mechanism through which droughts influence welfare. The current study is dedicated to analyzing key meso-level effects of localized weather shocks on grain prices and wages, which in turn affect welfare. Further, the study examines when these meso-level effects are observed, providing an understanding of the timing of the impact of drought on welfare.

24
**Table S.1 – continued from previous page**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||(0.176)|(0.313)|(0.340)|(0.273)|(0.027)|
|Secondary School|0.132|0.506<sup>∗</sup>|-0.233|0.316|0.021|
||(0.147)|(0.286)|(0.330)|(0.377)|(0.023)|
|Secondary School×Traced|0.066|-0.399|0.616|-0.017|0.001|
||(0.208)|(0.260)|(0.409)|(0.358)|(0.039)|
|Small business|0.029|0.051|0.055|0.717<sup>∗</sup>|0.047|
||(0.206)|(0.248)|(0.389)|(0.393)|(0.067)|
|Small business×Traced|0.048|0.010|0.004|-0.379|-0.039|
||(0.217)|(0.257)|(0.439)|(0.467)|(0.071)|
|Construction|0.012|0.131|0.354|-0.088|-0.017|
||(0.275)|(0.371)|(0.330)|(0.576)|(0.029)|
|Construction×Traced|0.181|0.062|-0.468|0.032|0.027|
||(0.295)|(0.396)|(0.362)|(0.609)|(0.032)|
|Public sector|0.159|0.016|0.564|0.680<sup>∗∗</sup>|0.002|
||(0.460)|(0.515)|(0.486)|(0.338)|(0.069)|
|Public sector×Traced|0.073|0.349|-0.822|-0.232|0.058|
||(0.442)|(0.513)|(0.527)|(0.466)|(0.074)|
|Constant|8.875<sup>∗∗∗</sup>|8.603<sup>∗∗∗</sup>|7.467<sup>∗∗∗</sup>|6.956<sup>∗∗∗</sup>|0.008|
||(0.478)|(0.687)|(1.078)|(1.281)|(0.063)|
|Observations<br>|1178|1191|1105|1076|1195|
|_R_<sup>2</sup>|0.050|0.085|0.049|0.088|0.040|
|F-test Traced HH|0.95|0.04|0.01|2.65|0.79|
|_p_-value F-test|0.33|0.85|0.92|0.11|0.38|
|F-test interactions variables|0.98|0.92|1.20|2.05|0.87|
|_p_-value F-test|0.48|0.54|0.29|0.03<sup>∗∗</sup>|0.58|
|F-test Traced HH+interactions variables|0.91|1.15|1.60|2.03|1.75|
|_p_-value F-test|0.55|0.33|0.1<sup>∗</sup>|0.03<sup>∗∗</sup>|0.06 <sup>∗</sup>|

_Notes:_ Selective attrition has been tested for the log of dependent variables in 2006. Consumption and spendings are per adult equivalent. We did not trace only one household which had a member working in the extractive sector so it is omitted. Sampling weights are accounted for. Clustered-robust standard errors are in parentheses.<sup>∗</sup> _p_ < 0.10,<sup>∗∗</sup> _p_ < 0.05,<sup>∗∗∗</sup> _p_ < 0.01 34
**Bank Competition in the Middle East and Northern Africa Region**<sup></sup>

Diego Anzoategui, María Soledad Martínez Pería, and Roberto Rocha

**Keywords:** bank competition, market structure **JEL:** G21, L11

>  Diego Anzoategui is a Consultant with the Finance and Private Sector Development Research Group of The World Bank. Maria Soledad Martínez Pería is a Senior Economist in the aforementioned group. Roberto Rocha is Senior Adviser in the Middle East and Northern Africa Region of The World Bank. This paper was written as part of the Flagship Report on Finance for the Middle East and Northern Africa Region. We are grateful to Erik Feyen, Ilias Skamnelos, Zsofia Arvai and to participants in the Middle East and Northern Africa Region Seminar on Bank Competition for many useful comments and suggestions. We thank Subika Farazi and Diego Sourrouille for sharing data. The views and opinions in this paper are those of the authors and do not reflect those of The World Bank and/or its Executive Directors. Corresponding author: María Soledad Martínez Pería, The World Bank, 1818 H St., N.W., MSN 3-300, Washington, D.C. 20433. mmartinezperia@worldbank.org.
Table 6 reports the results by dividing the sample into two periods: 1991-1999 and 20002006.<sup>10</sup> In 2000 the government of Brazil began to implement fiscal adjustments as part of a broad effort to stabilize macroeconomic policy that included the Fiscal Responsibility Law (FRL), which imposed important constraints on sub-national governments.<sup>11</sup> The states show less pro-cyclical behavior for the period from 2000 to 2006 in both primary and personnel expenditures. And even though the interaction term in the tax revenue and ICMS has a negative sign, there are no statistically significant differences between the coefficients of both periods with respect to revenue variables. These results do not necessarily imply that the effects on the coefficients for the period 2000-2006 are due exclusively to the FRL, though they suggest that further exploration of that hypothesis is warranted.

Table 6: Cyclical Sensitivity of Fiscal Policy: Subnational Level by Periods

|Variables|Coefficient for<br>1991-2006|Coefficient for<br>1991-1999||Interaction term<br>(with dummy)|Coefficient<br>2000-2006||
|---|---|---|---|---|---|---|
|**_Revenues:_**|||||||
|Total Revenue|0.273|***<br>0.198|***|0.066|||
||[0.036]|[0.079]||[0.101]|||
|Intergovernment Transfers|0.091|0.009||0.160|||
||[0.066]|[0.102]||[0.131]|||
|Tax Revenue|0.309|***<br>0.338|***|-0.119|||
||[0.030]|[0.062]||[0.088]|||
|V.A.T. (ICMS)|0.312|***<br>0.312|***|-0.138|||
||[0.040]|[0.068]||[0.097]|||
|**_Expenditures:_**|||||||
|Total Expenditure|0.305|***<br>0.583|***|-0.425|***<br>0.158||
||[0.031]|[0.102]||[0.146]|[0.104]||
|Primary Expenditure|0.703|***<br>0.458|***|-0.266|*<br>0.192|*|
||[0.057]|[0.105]||[0.148]|[0.105]||
|Maintenance and Capital|0.340|***<br>0.707|***|-0.598|***<br>0.109||
||[0.088]|[0.161]||[0.225]|[0.156]||
|Personnel Expenditure|0.703|***<br>0.879|***|-0.655|***<br>0.224|*|
||[0.056]|[0.148]||[0.204]|[0.141]||
|Interest Payments|0.516|***<br>0.957|***|-0.801|*<br>0.156||
||[0.126]|[0.329]||[0.462]|[0.324]||
|***, **, and * denote signific|ance at the 1%, 5|%, and 10% levels, r|espe|ctively.|||
|The dummy variable takes t|he value of 1 for t|he period 2000-200|6. E|stimation done wi|th FGLS||

10 A dummy variable is used to capture the potential shift period. The dummy takes the value of 1 for the period 2000-2006.

> 11 See Sturzenegger and Werneck (2006) for more details on the FRL.

14