rafmacalaba/datause-encoder-data huggingface dataset:
pattern is even more widespread when regional rural to urban areas are compared. In the Northeast, 58 percent of the rural population is below 25 years of age and 35 percent is of working age, roughly speaking.<sup>6</sup> This compares to 44 and 44 percent, respectively, in the Pampeana region. Moreover, findings indicate that 60 percent of Argentina’s rural population consists of children, youth and old people in rural Argentina.

|Table2.2:Age Cohorts a|s a Share of Total, Urba|n, andRural<br>Age C|Population,20<br>ohorts|01|
|---|---|---|---|---|
||0-14|15-24|25-64|65+|
||Total Argentina||||
|Total Argentina|28.3|17.6|44.2|9.9|
|Total Pampeana|25.6|17.1|45.9|11.4|
|Total Cuyo|29.8|17.9|43.4|8.8|
|Total Northwest|34.1|19.3|39.9|6.7|
|Total Northeast|36.4|18.8|38.8|6.1|
|Total Patagonia|31.5|17.9|44.5|6.0|
||Urban Argentina||||
|Total Urban Argentina|27.6|17.6|44.7|10.1|
|Urban Pampeana|25.4|17.2|45.9|11.5|
|Urban Cuyo|28.9|17.9|44.1|9.1|
|Urban Northwest|33.0|19.5|40.9|6.6|
|Urban Northeast|35.3|19.1|39.7|6.0|
|Urban Patagonia|31.6|18.1|44.5|5.8|
||Total Rural Argentina||||
|Total Rural Argentina|34.2|17.3|40.1|8.3|
|Total Pampeana|28.9|16.0|44.7|10.5|
|Total Cuyo|34.1|18.2|40.4|7.3|
|Total Northwest|38.2|18.4|36.1|7.3|
|Total Northeast|40.0|18.1|35.6|6.2|
|Total Patagonia|31.4|17.0|43.9|7.7|
||<br>GroupedRural Argenti|na|||
|Total Grouped Rural Argentina|33.0|17.1|40.5|9.4|
|Total Pampeana|28.2|15.8|43.6|12.4|
|Total Cuyo|33.6|18.3|40.7|7.4|
|Total Northwest|38.5|18.6|36.3|6.5|
|Total Northeast|40.9|18.1|35.2|5.8|
|Total Patagonia|36.5|17.5|40.3|5.7|
||DispersedRural Argenti|na|||
|Total Dispersed Rural Argentina|34.8|17.4|40.0|7.8|
|Total Pampeana|29.4|16.1|45.5|9.1|
|<br>Total Cuyo|34.3|18.2|40.3|7.2|
|<br>Total Northwest|38.0|18.3|36.0|7.7|
|Total Northeast|39.9|18.1|35.7|6.3|
|Total Patagonia<br>_Source:_Own calculations based on IND|27.7<br>EC National Population Cens|16.6<br>us,2001.|46.6|9.1|

6 In Jujuy, Misiones, Salta, and Santiago del Estero less than 35 percent of the population is in the prime working age (see Appendix A).

9
Appendix Table 4a. Estimates of the effect of the crisis on changes in child anthropometrics for children under three years old in migrant households by migration definition: Panel cohort analysis

|Migratio|n to U.S. or M|exico three of|fouryears|Mi|gration to U.S|. at least on|eyear|
|---|---|---|---|---|---|---|---|
|HAZ|Stunted|WAZ|Underweight|HAZ|Stunted|WAZ|Underweight|
|_Panel A: Difference-in-difference (DD) m_|_odels_|||||||
|_DD without controls_||||||||
|Migrant household = 1<br>0.493***|-0.141**|0.299**|-0.051|0.600***|-0.182***|0.395***|<br>-0.090**|
|(0.166)|(0.054)|(0.127)|(0.046)|(0.143)|(0.049)|(0.127)|(0.041)|
|Year 2012 = 1<br>0.085|-0.054|-0.236***|0.017|0.086|-0.057|-0.200***|<br>0.019|
|(0.095)|(0.038)|(0.064)|(0.023)|(0.105)|(0.040)|(0.071)|(0.023)|
|DD<br>-0.366***|0.122**|-0.146|0.010|-0.282**|0.103**|-0.209|0.001|
|(0.134)|(0.057)|(0.151)|(0.052)|(0.129)|(0.050)|(0.144)|(0.052)|
|Constant<br>-2.313***|0.615***|-1.140***|0.218***|-2.395***|0.643***|-1.201***|<br>0.237***|
|(0.107)|(0.034)|(0.069)|(0.024)|(0.108)|(0.035)|(0.070)|(0.023)|
|_Individual fixed effects DD with age co_|_ntrols_|||||||
|DD<br>-0.247*|0.078|-0.081|-0.005|-0.180|0.046|-0.127|0.001|
|(0.139)|(0.056)|(0.170)|(0.062)|(0.148)|(0.052)|(0.167)|(0.059)|
|<br>_Individual fixed effects DD with age co_<br> <br>|<br>_ntrols and clu_<br>|<br>_ster-level tim_<br>|<br>_e trends_<br>|||||
|DD<br>-0.370**|0.133**|-0.224|0.023|-0.209|0.076|-0.214|0.030|
|(0.164)|(0.062)|(0.190)|(0.076)|(0.147)|(0.062)|(0.167)|(0.063)|
|N<br>830|830|824|824|830|830|824|824|
|_Panel B: Triple difference (DDD) models_<br>|_comparing wi_|_th children 36_|_-72 months old_|_using same mi_|_gration defin_|_ition_||
|_DDD without controls_||||||||
|DDD<br>-0.332*|0.123|-0.216|0.009|-0.378**|0.186***|-0.262*|-0.002|
|(0.181)|(0.074)|(0.200)|(0.064)|(0.165)|(0.066)|(0.145)|(0.061)|
|_Individual fixed effects DDD with age_<br>|_controls_|||||||
|DDD<br>-0.288*|0.104|-0.183|0.008|-0.308|0.148**|-0.240|-0.013|
|(0.166)|(0.074)|(0.212)|(0.079)|(0.192)|(0.073)|(0.165)|(0.072)|
|<br>_Individual fixed effects DDD with age c_|<br>_ontrols and cl_|<br>_uster-level ti_|<br>_me trends_|||||
|DDD<br>-0.336*|0.117|-0.246|0.040|-0.379**|0.158*|-0.335*|0.029|
|(0.188)|(0.090)|(0.214)|(0.082)|(0.188)|(0.084)|(0.174)|(0.074)|
|N<br>1782|1782|1756|1756|1782|1782|1756|1756|

Notes: Samples include balanced panels of all children with valid anthropometric measurements in each age group. Results based on children under three years old in 2008 in Panel A and under six years old in 2008 in Panel B. Age controls are binary indicators for age-in-months at measurement; cluster level time trends are binary indicators for each cluster in 2012. Migrant household is defined as a household with at least one migrant in the U.S. in three of the four years 2005-08 for children under three years old in 2008 and three of the four years for children three to six years old in 2008. Standard errors estimated allowing for clustering at the community level in parentheses. *** indicates significance at p<0.01, ** at p<0.05, and * at p<0.10.
# **1. Introduction**

1. **Colombia has one of the most unequal income distributions in the world.** In 2010, the country had the 2<sup>nd</sup> highest Gini coefficient in Latin America (0.554)<sup>2</sup> and the 7<sup>th</sup> highest worldwide with inequality levels comparable to countries such as Haiti and Angola (Figure 1). Colombia is also the most unequal among the CIVETS group of emerging market economies with a Gini coefficient higher than South Africa.<sup>3</sup> Among the upper-middle income group, only Namibia (the most unequal in the World) has a Gini coefficient higher than Colombia.  The Gini coefficient of the group of OECD countries, which Colombia aspires to join, is substantially lower (0.307).<sup>4</sup>

2. **While Latin American and upper-middle income countries have become less unequal over the past two decades, inequality in Colombia has tended to remain high.** The Gini coefficients in upper-middle income countries and in Latin America have declined since the 1980s. Brazil is a good example of this trend, but it is not the only one. As Colombia‟s Gini coefficient rose somewhat over this period, it moved from a position above average towards the most unequal part of the upper-middle income and regional groups. Colombia has had a higher level of inequality than Brazil since 2008.

3. **Inequality in labor incomes has fallen throughout the region, as a result of reduced educational inequality, though Colombia has not experienced this trend.** Given their high share of total income (averaging 73 percent in the LCR region), labor incomes are the main drivers of income inequality (World Bank, 2011). Reduced regional labor income inequality can be explained by a reduction in skill premiums. In Colombia, however, skill premiums have not fallen owing to an inadequate expansion of education across the population (Azevedo et al, 2012).

4. **There is also evidence to suggest that fiscal redistributive policies have played a positive role in reducing inequality in Latin America over the past decade.** Cornia (2010) finds statistical evidence of the importance of progressive taxation and social spending, in addition to improvements in educational inequality and favorable terms of trade. Similarly, Lopez-Calva and Lustig (2010) emphasize the importance of larger and better targeted conditional cash transfer programs, in addition to a shrinking wage gap between skilled and lowskilled workers owing to expanded access to education. Finally, Soares _et al_ (2009) find that conditional cash transfer programs explain about 21 percent of the fall in inequality in Brazil and Mexico, and 15 percent in the case of Chile.

> 2 Source: World Bank (2011).  The Gini coefficient of inequality takes values between 0 and 1 (or 100 percent), where a high value indicates a high level of inequality.  Throughout the paper income inequality is measured using the Gini coefficient.

> 3 The CIVETS group of countries consist of Colombia, Indonesia, Vietnam, Egypt, Turkey, and South Africa. 4 Source: OECD (2012).

2
The level of vulnerability to nontariff barriers, as measured by the share of imports subject to NTBs, reflected the differences in export baskets among CEE-5 countries. The average NTB ratio was larger for countries with higher shares of agricultural products, iron and steel, and textile and clofting in their exports. For instance, Bulgaria and Hungary had the highest NTB coverage. ratios, .mainly because of the high shares of agricultural products. At the other end, imports from Poland and Romania were least-affected by NTBs because of the less restrictive access for labor-intensive engineering and consumer goods as weil as fuels and ores and metals.

Table 2: Pre-Agreemeut Market Access to the EQ Industrial Products versus Other Goods

||Index, 19<br>1988=1|91<br>00|NTB Co<br>Indust|verage Ratio<br>Otler|Simple A<br>Indust.|verage Tariff Rate-<br>Other||
|---|---|---|---|---|---|---|---|
||Indust.|Other|(in pe|rcent)|(in p|ercent)|-|
|Bulgaria|162|165|22.5|48.3|6.9|-<br>11.6||
|<br>FCSK|196|163|24.0|52.5|7.0|11.7||
|Hungary|188|156|24.2|57.7|0.1|9.4||
|<br>Poland|202|165|23.6|48.6|0.1|10.5||
|Romania|68|85|28.4|59.8|0.0--|<br>8.6||

# Sources: Derived from the UN COMTRAfDE and UNCTAD-World Bank SMART data bases.

# V. THE SCOPE OF PREFERENTIAL TREATMENT OF INDUSTRIAL PRODUCTS IN ITAs-

The lTAs distinguish six various groups of idustrial products with different schedules of transition to free trade and different mixes of trade liberalizing measures, i.e, tariff and nonmariff measures. As for the latter, quantitative restictions on imports to tl. EC are abolished on the day of entry into force of the Agreements. The groups of indLstrial products subject to different time schedules are as follows: (i) the "one-year-delayed' free trade group (duty-free access envisaged in the second year of the rrAs); (ii) the "four-year-delayed" free trade group, i.e., at the beginning of the fifh year tarfs are eliminted (iii) the "quotalfive-year-delayed" free trade group (free trade at the beginning of the sixth year of the ITAs); (iv) the ECSC group (tariffs on steel fully are eliminated by the end of the fifth year and tariffs on coal imports .. . . . .. . .~~~~~
- Schwartz, J. (1994a). Air Pollution and Daily Mortality: A Review and Meta-analysis. _Environmental Research_ 64:36-52.

- Schwartz, J. (1994b). Air Pollution and Hospital Admissions for the Elderly in Detroit, Michigan. _Am. J. Respir. Crit. Care Med._ 150:648-55.

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- Schwartz, J. (1994d). Ozone, PM1o, and hospital admissions for the elderly in Minneapolis-St Paul, Minnesota. _Arch. Env. Health_ 49(5):366-74.

- Schwartz, J. (1993). Air pollution and daily mortality in Birmingham, Alabama. _American Journal of Epidemiology_ 137(10):1136-1147.

- Schwartz, J., and D.W. Dockery (1992a). Increased mortality in Philadelphia associated with daily air pollution concentrations. _Am. Rev. Respir. Dis._ 145:600-604.

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- Schwartz, J., D. Slater, T. Larson, and W. Pierson. (1993). Particulate Air Pollution and Hospital Emergency Room Visits for Asthma in Seattle. _Am. Rev. Respir. Dis._ 147:826-31.

- Simpson, R. W., G. Williams, A. Petroeschevsky, G. Morgan, and S. Rutherford. (1997). Associations between Outdoor Air Pollution and Daily Mortality in Brisbane, Australia. _Archives of Environmental Health_ 52(6, November/December):442-54.

- SMA. (1999). _Towards an Air Quality Programme 2000-2010, Clean Air Initiative._ Secretaria del Medio Ambiente del Gobiemo del Distrito Federal, Comision Ambiental Metropolitana, Mexico DF.

- Spix, C., J. Heinrich, D. Dockery, J. Schwartz, G. Volksch, K. Schwinkowski, C. Collen. and H. E. Wilchmann. (1993). Air pollution and daily mortality in Erfurt, East Germany, 1980-1989. _Environmental Health Perspectives_ 101(6, November):518-26.

- Sunyer, J., J. Castellsague, M. Saez, A. Tobias, and J. M. Ant6. (1996). Air pollution and mortality in Barcelona. _Journal of Epidemiology and Community Health_ 50(1):S76-S80.

- Thurston, G. D., and Kasuhiko Ito. (1999). Epidemiological Studies of Ozone Exposure Effects. In S. Holgate, J. M. Samet, H. S. Koren, R. L. Maynard, eds., _Air Pollution and Health._ Cambridge UK: Academic Press.

- Toulourni, G., E. Samoli, and K. Katsouyanni. (1996). Daily mortality and "winter type" air pollution in Athens, Greece. A time series analysis within the APHEA project. _J. Epidem and Commun. Health_ 50(Suppl. 1):s47-s51.

- Touloumi, G., S. J. Pocock, K. Katsouyanni, and D. Trchopolous. (1994). Short-term effects of air pollution on daily mortality in Athens: A time series analysis. _Int. J Epidemiol._ 23:957-67.

- U.S. EPA. (1999). _The Benefits and Costs of the Clean Air Act, 1990 to 2010._ Paper prepared for Congress, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA.

_50_
Table 2: Multidimensional poverty measures, censored headcount ratios by dimension and average deprivation shares by category

|**Indicator**|**Weights **|**2008**|**2010**|**2012**|
|---|---|---|---|---|
|_H_: Multidimensional headcount ratio (%)||34.47|30.38|26.97|
|_A_: Average deprivation share among the poor||0.448|0.432|0.425|
|_M_0: Adjusted headcount ratio||0.154|0.131|0.115|
|**Education**|.2||||
|Educational Achievement (%)|0.1|96.30|94.26|94.24|
|Literacy (%)|0.1|43.37|44.76|44.56|
|At least 1 component (%)||96.36|94.78|94.96|
|Average deprivation share in category||0.698|0.695|0.694|
|**Childhood and youth**|.2||||
|School Attendance (%)|0.05|20.06|20.23|18.48|
|Children behind grade (%)|0.05|71.27|72.51|71.69|
|Access to child care services (%)|0.05|29.38|27.91|21.38|
|Child labour (%)|0.05|17.67|16.42|14.58|
|At least 1 component (%)||81.90|82.12|79.00|
|Average deprivation share in category||0.346|0.343|0.315|
|**Labour**|.2||||
|Long term unemployment (%)|0.1|10.12|10.61|11.14|
|Formal employment (%)|0.1|99.03|99.25|99.18|
|At least 1 component (%)||99.12|99.31|99.19|
|Average deprivation share in category||0.546|0.549|0.552|
|**Health**|.2||||
|Health insurance (%)|0.1|53.25|47.78|44.96|
|Health services (%)|0.1|22.99|16.97|19.81|
|At least 1 component (%)||63.51|57.39|55.93|
|Average deprivation share in category||0.381|0.324|0.324|
|**Standard of living**|.2||||
|<br>Water system (%)|0.04|30.27|27.56|30.26|
|Sewage (%)|0.04|32.01|29.21|29.64|
|Floors (%)|0.04|23.44|20.75|19.46|
|Walls (%)|0.04|7.75|7.62|5.76|
|Overcrowding (%)|0.04|40.04|38.38|35.29|
|At least 1 component (%)||68.65|66.00|66.70|
|Average deprivation share in category||0.267|0.247|0.241|

Source: Author’s calculations. Headcounts are censored, i.e. calculated over the poor. See table A.1 for the uncensored headcount levels, calculated over the whole sample. “At least 1 component” denotes censored headcount ratios of individuals deprived in at least one dimension within the category. “Average deprivation share in category” is the average over poor individuals of the number of deprivations divided by the number of dimensions in the category. See table 3 for the changes of censored headcounts over time.

29
We analyze multi-topic household survey data from a total of 22 survey rounds from six countries: Bangladesh (3), Ethiopia (1), Malawi (5), Nigeria (3), Tanzania (6), and Viet Nam (4), with the number of survey rounds for each country noted in parentheses. In the four Sub-Saharan African countries (Ethiopia, Malawi, Nigeria, and Tanzania), the data originate from the nationally-representative, multi-topic household surveys that have been implemented by the respective national statistical offices with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative. Being similar to the LSMS-type surveys supported by the World Bank, the surveys from Viet Nam are implemented biennially by the country’s General Statistical Office (GSO) with technical support from the World Bank. These surveys are generally regarded as being of high quality and are regularly employed by the national governments, international organizations, and academic researchers to provide estimates on household welfare.<sup>9</sup> The data sets include

- i. the Bangladesh Integrated Household Survey (BIHS) 2011/12, 2015, and 2018/19

- ii. the Ethiopia Socioeconomic Survey (ESS), 2018/19 round

- iii. the Malawi Integrated Household Survey (IHS), 2010/11, 2016/17, 2019/20 rounds

- iv. the Malawi Integrated Household Panel Survey (IHPS), 2010 and 2013 rounds

- v. the Nigeria General Household Survey (GHS)–Panel, 2010/11, 2012/13 and 2018/19

- rounds<sup>10</sup>

> 9 For example, Baulch (2011) considers the VHLSSs as having high quality data and heavily uses these surveys for poverty analysis. Other researchers analyze the LSMS-ISA surveys for various topics such as agricultural input uses (Sheahan and Barrett, 2017) or temperature shocks and household consumption (Letta, Montalbano, and Tol, 2018).

> 10 We did not include the 2015/16 round for Nigeria for comparability issues (e.g., the total consumption aggregate for this round does not include health care expenditures and it is not adjusted using temporal and spatial price deflators).

13
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- [37] National AIDS Control Council [Kenya] (2005). Kenya HIV/AIDS Data Booklet 2005. Nairobi, Kenya: National AIDS Control Council.

- [38] Oster, Emily (2005). "Sexually Transmitted Infections, Sexual Behavior, and the HIV/AIDS Epidemic." Quarterly Journal of Economics 120 (2): 467-515.

- [39] Oster, Emily (2007). "HIV and Sexual Behavior Change: Why not Africa?" NBER Working Paper #13049.

- [40] Paxson, Christina H (1992). “Using Weather Variability to Estimate the Response of Savings to Transitory Income in Thailand.” American Economic Review 82 (1): 15-33.

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- [43] Rosenzweig, Mark and Kenneth Wolpin (1993). "Credit Market Constraints, Consumption Smoothing, and the Accumulation of Durable Production Assets in Low-Income Countries: Investments in Bullocks in India." Journal of Political Economy 101 (2): 223-244.

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30
**<u>Table 9. OLS estimation of cost overruns</u>**

||Coef.<br>|Std. Err.|Coef.<br>S|td. Err.|Coef.<br>|Std. Err.|Coef.<br>|Std. Err.|Coef.<br>|Std. Err.|Coef.<br>|Std. Err.|Coef.<br>|Std. Err.|Coef.<br>|Std. Err.|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|ln_securityduring_|1.245|(0.645) **|||||||||||||2.022|(0.698) ***|
|ln_unexpectedsecurity_|||-0.157|0.5009|||||||||||||
|ln_rainduring_|||||-0.316|(0.265)|||||||||-1.094|(0.336) ***|
|ln_unexpectedrain_|||||||-0.193|(0.204)|||||||||
|ln_num_|||||||||-2.046|(0.710) ***|||||-3.701|(0.975) ***|
|ln_lowbid_|||||||||||0.050|(0.097)|||0.024|(0.092)|
|ln_dayscontracting_|||||||||||||1.460|(0.600) **|0.617|(0.755)|
|ln_cost_|-0.032|(0.821)|0.1131|0.866|0.945|(0.755)|0.842|(0.735)|0.990|(0.658)|0.834|(0.758)|0.029|(0.755)|0.846|(0.869)|
|ln_length_|-1.821|(0.727) **|-1.563|0.7743 **|-1.899|(0.687) ***|<br>-1.749|(0.691) **|-1.671|(0.649) ***|<br>-1.629|(0.863) *|-1.395|(0.702) **|-1.727|(0.750) **|
|ln_lane_|1.072|(3.606)|0.0911|4.6242|2.028|(3.540)|2.409|(3.575)|1.485|(3.448)|1.705|(3.632)|2.410|(3.477)|0.262|(3.918)|
|ln_thickness_|-0.241|(0.493)|0.3371|0.6176|-0.372|(0.508)|-0.227|(0.509)|-0.229|(0.482)|-0.738|(0.522)|-0.170|(0.513)|-0.128|(0.496)|
|ln_gravel_|-0.163|(0.220)|0.1492|0.2943|-0.038|(0.203)|-0.085|(0.221)|-0.053|(0.217)|-0.151|(0.219)|-0.089|(0.218)|-0.231|(0.210)|
|ln_bitumen_|0.941|(0.242) ***|0.9046|0.2979 ***|<br>0.966|(0.271) ***|<br>0.924|(0.263) ***|<br>0.903|(0.249) ***|<br>1.008|(0.265) ***|<br>0.928|(0.269) ***|<br>0.909|(0.246) ***|
|ln_earth_|0.676|(0.299) **|0.4198|0.3362|0.505|(0.250) **|0.515|(0.253) **|0.691|(0.260) ***|<br>0.621|(0.260) **|0.527|(0.248) **|0.855|(0.325) ***|
|ln_brick_|-0.242|(0.396)|0.0338|0.5058|0.103|(0.399)|0.119|(0.401)|0.008|(0.377)|-0.013|(0.431)|-0.002|(0.391)|-0.343|(0.416)|
|ln_gabion_|-0.071|(0.232)|0.2795|0.3225|-0.097|(0.231)|-0.127|(0.226)|0.004|(0.229)|-0.163|(0.238)|-0.108|(0.222)|0.014|(0.234)|
|ln_exavate_|-0.199|(0.131)|-0.041|0.1624|-0.168|(0.135)|-0.186|(0.138)|-0.127|(0.128)|-0.141|(0.151)|-0.227|(0.141) *|-0.191|(0.137)|
|ln_cement_|0.224|(0.234)|0.0198|0.3035|0.049|(0.240)|0.038|(0.239)|0.013|(0.224)|0.052|(0.244)|0.226|(0.252)|0.059|(0.250)|
|D(_postqualify_)|3.954|(2.329) *|0.2357|1.9315|4.056|(2.070) **|3.693|(2.137) *|3.084|(2.075)|3.911|(2.209) *|3.037|(2.033)|4.545|(2.577) *|
|Constant|-6.759|(9.272)|-1.570|(9.934)|-13.976|(8.553)*|-14.052|(8.866)|-14.925|(7.980)*|-15.158|(9.170)*|-7.742|(8.766)|-12.152|(9.071)|
|Obs.|152||95||155||154||155||142||154||138||
|R-squared|0.558||0.638||0.540||0.542||0.559||0.544||0.556||0.624||
|_F_-statistics|9.68||38.17||9.29||8.77||9.61||8.54||8.14||10.58||
|No. of dummy<br>variables:|||||||||||||||||
|Project districts|18||18||18||18||18||18||18||18||

Note: The dependent variable is the cost overrun rate, _overrun_ . Robust standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively.