rafmacalaba/datause-encoder-data huggingface dataset:
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 53 Marhuenda et al. (2017) extended the EB procedure based on the nested error model to the two-fold nested error model of Stukel and Rao (1999) given in (5.10), which includes subarea (e.g. cluster) effects nested within the area effects. This procedure has now been incorporated into the `sae` Stata package. Marhuenda et al. (2017) obtained clear losses in efficiency of EB estimators of poverty indicators when the random effects are specified only for the subareas (e.g. clusters), but estimation is desired for areas. Recently, Guadarrama, Morales and Molina (2020) have further extended the EB procedure by considering a two-fold model with time effects nested within the area effects, following an AR(1) process.

EB procedure under the nested error model (5.1) requires normality of area effects and unit-level errors, and this may be achieved by using a transformation of the welfare as the model response. In practice, the most common transformation for monetary variables, typically right-skewed and displaying heteroscedasticity, is the natural logarithm. When estimating area means of the original variables in a model with log transformation, Molina and Mart ́ın (2018) studied the analytical EB predictors and obtained second-order correct MSE estimators with a closed form. Often, a positive constant is added to the monetary variable before taking log transformation. Adding this constant is not only needed to have positive values; it is often necessary to shift the values far from zero, where the log function is less steep. Note that values _zdi_ close to zero have log( _zdi_ ) tending to minus infinity, which causes outliers on the left tail of the distribution, see Figure 4.7 of Corral et al. (2022). Hence, the constant is required to achieve approximate normality. Other transformations different from log, such as those from the Box-Cox or power families, can be taken, but making the shift previously is likely needed as well. The constant might be chosen by fitting the model to a grid of values in the range of the monetary variable, and selecting the value for which a skewness measure of model residuals is closest to zero. Rojas-Perilla et al. (2020) study data-driven transformations of the target variable in the EB method.
- **Cullen, Julie Berry, Nicholas Turner, and Ebonya Washington** , “Political Alignment, Attitudes toward Government, and Tax Evasion,” _American Economic Journal: Economic Policy_ , August 2021, _13_ (3), 135–66.

- **EU Tax Observatory** , “Global Tax Evasion Report,” 2024.

- **Garbinti, B., J. Goupille-Lebret, M. Muñoz, S. Stantcheva, and G. Zucman** , “Tax Design, Information, and Elasticities: Evidence From the French Wealth Tax,” Working Paper 31333, National Bureau of Economic Research June 2023.

- **Guyton, John, Patrick Langetieg, Daniel Reck, Max Risch, and Gabriel Zucman** , “Tax Evasion at the Top of the Income Distribution: Theory and Evidence,” Working Paper 28542, National Bureau of Economic Research March 2021.

- **Johannesen, Niels, Daniel Reck, Max Risch, Joel Slemrod, John Guyton, and Patrick Langetieg** , “The Offshore World According to FATCA: New Evidence on the Foreign Wealth of U.S. Households,” Working Paper 31055, National Bureau of Economic Research March 2023.

- **, Patrick Langetieg, Daniel Reck, Max Risch, and Joel Slemrod** , “Taxing Hidden Wealth: The Consequences of U.S. Enforcement Initiatives on Evasive Foreign Accounts,” _American Economic Journal: Economic Policy_ , 2020, _12_ (3), 312–346.

- **Langenmayr, Dominika** , “Voluntary Disclosure of Evaded Taxes—Increasing Revenue, or Increasing Incentives to Evade?,” _Journal of Public Economics_ , 2017, _171_ (7), 110–125.

- **Leenders, Wouter, Arjan Lejour, Simon Rabaté, and Maarten van ’t Riet** , “Offshore tax evasion and wealth inequality: Evidence from a tax amnesty in the Netherlands,” _Journal of Public Economics_ , 2023, _217_ , 104785.

- **Londoño-Vélez, J. and J. Ávila-Mahecha** , “Behavioral Responses to Wealth Taxation: Evidence from Colombia,” September 2023.

- **Londoño-Vélez, Juliana and Javier Ávila-Mahecha** , “Enforcing Wealth Taxes in the Developing World: Quasi-Experimental Evidence from Colombia,” _American Economic Review: Insights_ , June 2021, _3_ (2), 131–48.

- **Ministerio de Economía** , “Impuesto sobre los Bienes Personales: Evolución de Alícuotas, Mínimos no Imponibles y Mínimo Exento,” Technical Report January 2022.

- **OECD** , _Update on Voluntary Disclosure Programmes: A Pathway to Tax Compliance_ , OECD Publishing, August 2015.

22
# **Appendix S3: inequality measures, using per adult equivalent consumption.**

**Table S3.1: Inequality measures, on per adult equivalent consumption.**

||Gini|90/10|75/25|Mean log<br>dev|Theil-T|
|---|---|---|---|---|---|
|_Per adult equivalent_<br>_household consumption_||||||
|Total consumption|45.28|7.14|2.84|0.35|0.40|
||(1.10)|(0.35)|(0.09)|(0.02)|(0.03)|
|Non-food consumption|61.55|17.91|4.99|0.73|0.79|
||(1.29)|(0.90)|(0.24)|(0.03)|(0.05)|
|Food consumption|37.36|5.52|2.31|0.23|0.24|
||(0.70)|(0.25)|(0.06)|(0.01)|(0.01)|
|_Per adult equivalent cell_<br>_consumption_||||||
|Total consumption|47.25***|7.90***|2.86|0.38***|0.45***|
||(1.05)|(0.39)|(0.08)|(0.02)|(0.03)|
|Non-food consumption|65.32***|22.97***|5.47**|0.85***|0.94***|
||(1.15)|(1.59)|(0.24)|(0.03)|(0.05)|
|Food consumption|38.41***|5.88***|2.39**|0.25***|0.26***|
||(0.68)|(0.26)|(0.07)|(0.01)|(0.01)|

Source: « Pauvreté et Structure Familiale » (PSF) survey, authors’ calculations.

Note: The first panel presents inequality measures computed using per adult equivalent household consumption as a measure of individual consumption. The second panel presents inequality measures computed using per adult equivalent cell consumption. _Equivalence scale used:_ adult: 1; child 0 to 14: 0.5; N=1762, Bootstrap standard errors (250 replications) between parentheses. *,**,***: difference with per adult eq. household consumption inequality measure is significant at the 10%, 5% or 1% level (see Note 9 and Appendix S2 for details).

17
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Senegal > Electricity investments > Macro results**

||||Reduction in<br>th bli|Ele|ctricity<br>It|Fi|I|
|---|---|---|---|---|---|---|---|
|Variable|Definition|Reference|oer puc<br>expenditure|VAT|mpor<br>duties|oregn<br>aid|ncome<br>tax|
|Ym|Aggregate household income|1,500,000.00|0.39|0.91|0.90|0.93|1.02|
|EV|Equivalent variation||0.38|0.84|0.84|0.92|–1.11|
|s|Wage|1.00|–0.34|2.02|2.00|2.11|2.54|
|yg|Government income|539,000.00|0.69|5.61|5.61|5.61|5.61|
|g|Total government expenditure|295,000.00|–4.13|4.84|4.84|4.84|4.84|
|ye|Firm income|935,000.00|1.25|0.41|0.41|0.38|0.23|
|It|Total private investment|860,000.00|1.20|0.41|0.23|–0.29|0.16|
|GDP|GDP|2,100,000.00|0.82|0.82|0.82|0.81|0.81|
|e|Nominal exchange rate|1.00|–0.31|0.11|–1.09|–4.39|–0.61|

# **Senegal > Electricity investments > Sectoral results**

||||Reduction in<br>|E|lectricity<br>|||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign aid|Income<br>tax|
||Crop agriculture|252,000.00|0.07|0.00|0.01|0.03|–0.06|
||Export agriculture|221,000.00|1.25|–0.15|–0.14|–0.24|0.05|
|Va<br>|Industries|361,000.00|0.96|–0.31|–0.27|–0.41|0.03|
|Value added or<br>output|Construction|91200.00|4.32|3.56|3.57|3.89|3.73|
||Private services|995,000.00|1.33|0.91|0.88|0.79|0.69|
||Public services|180,000.00|–3.53|3.51|3.55|4.21|3.71|
||Crop agriculture|1.02|0.44|0.76|0.59|–0.22|–0.91|
||Export agriculture|1.02|–0.27|0.54|0.45|–0.40|0.01|
|pq|Industries|1.14|–0.19|1.54|1.55|–1.73|–0.31|
|Market prices|Construction|1.01|4.56|4.89|4.82|4.14|4.57|
||Private services|1.02|–1.59|–0.72|–0.87|–1.75|–1.70|
||Public services|1.00|–0.62|1.29|1.25|0.61|1.10|
||Crop agriculture|1.00|1.80|1.20|1.68|2.74|–0.84|
|r|Export agriculture|1.00|2.17|0.92|0.92|0.77|1.95|
|Rental rate of|Industries|1.00|2.11|–0.76|–0.64|–1.11|1.15|
|capital|Construction|1.00|14.68|12.48|12.53|14.67|14.18|
||Private services|1.00|–0.59|–0.64|–0.82|–1.21|–1.33|

38
|SYS<br>GMM<br>(8)<br>0.721***<br>(0.0628)<br>0.432**<br>(0.204)<br>0.434**<br>(0.216)<br>0.00443<br>(0.0135)<br>1.783***<br>(0.374)<br>-2.144***<br>(0.482)<br>yes<br>0.000<br>0.374<br>0.467<br>476<br>91<br>86<br>balanced sample<br>lues reported for<br>mented for using<br>mn (4) shows the<br>ents, the system|
|---|
|SYS<br>GMM<br>(7)<br>0.678***<br>(0.0540)<br>0.497**<br>(0.197)<br>0.473**<br>(0.215)<br>-0.00002<br>(0.0125)<br>0.858*<br>(0.489)<br>-1.702*<br>(0.999)<br>yes<br>0.000<br>0.544<br>0.588<br>476<br>91<br>90<br>mple is an un<br>tions. The va<br>ey are instru<br>rticular, colu<br> these instrum|
|SYS<br>GMM<br>(6)<br>0.648***<br>(0.0571)<br>0.582**<br>(0.238)<br>0.508**<br>(0.215)<br>0.0197<br>(0.0140)<br>-0.126<br>(0.0911)<br>yes<br>0.000<br>0.521<br>0.291<br>476<br>91<br>91<br>mator. The sa<br> serial correla<br>termined. Th<br>lumns. In pa<br>n addition to|
|SYS<br>GMM<br>(5)<br>0.593***<br>(0.0698)<br>0.0112<br>(0.310)<br>0.498*<br>(0.274)<br>0.00452<br>(0.0134)<br>0.0467**<br>(0.0227)<br>yes<br>0.000<br>0.836<br>0.247<br>423<br>85<br>76<br>m GMM esti<br>Bond test for <br>ted as pre-de<br>ll the other co<br>ness check. I<br> one period.|
|SYS<br>GMM<br>(4)<br>0.577***<br>(0.0695)<br>0.676**<br>(0.276)<br>0.754***<br>(0.280)<br>0.0291<br>(0.0185)<br>yes<br>0.000<br>0.604<br>0.0662<br>476<br>91<br>62<br>ne step syste<br>s of Arellano-<br>bles are trea<br>ond lags in a<br>ags for robust<br>erences lagged|
|SYS<br>GMM<br>(3)<br>0.621***<br>(0.0637)<br>0.596**<br>(0.263)<br>0.682***<br>(0.260)<br>0.0271<br>(0.0168)<br>yes<br>0.000<br>0.571<br>0.241<br>476<br>91<br>74<br>arentheses. O<br>re the p-value<br> All the varia<br>wn ...rst to sec<br>l the second l<br> the ...rst di¤|
|F.E.<br>OLS<br>(2)<br>0.352***<br>(0.0550)<br>-0.326<br>(0.492)<br>-0.268<br>(0.454)<br>-0.224*<br>(0.119)<br>yes<br>0.327<br>476<br>91<br>country in p<br>nd AR(2) a<br>ent validity. <br>d for their o<br>truments til<br> variables in|
|Pooled<br>OLS<br>(1)<br>CLt�5<br>0.770***<br>(0.0375)<br>Human capitalt�5<br>0.536***<br>(0.183)<br>Total emigration ratet�5<br>0.144<br>(0.0938)<br>Log populationt�5<br>-0.00656<br>(0.00506)<br>Log GDP per capitat�5<br>Share tertiary ed. migrantst�5<br>Human capitalt�5*Soc. dummy<br>Total emigration ratet�5*Soc. dummy<br>Human capitalt�5*Soc. dummy*d90<br>Total emigration ratet�5*Soc. dummy*d90<br>Time dummies<br>yes<br>R-squared<br>0.692<br>AR(1) test<br>AR(2) test<br>Hansen J test<br>Observations<br>476<br>N. countries<br>N. instr.<br>*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors clustered by <br>comprising data at ...ve year interval between 1980 and 2005. AR(1) a<br>the Hansen J test are the p-values for the null hypothesis of instrum<br>their own ...rst to third lags in columns 3 and 6. They are instrumente<br>same speci...cation as column (3), but now reducing the number of ins<br>GMM also uses as instruments for the level equations the explanatory|

18
**Table 2: Incidence of sickness and use health services by the sick, Uganda 1999 and 2002**

||**All**|**1**<sup>**st**</sup>**quintile**|**2**<sup>**nd**</sup>|**2002**<br>**3**<sup>**rd**</sup>|**4**<sup>**th**</sup>|**5**<sup>**th**</sup>**quintile**|
|---|---|---|---|---|---|---|
|Sick in previous 30 days|28.22|26.28|27.45|28.56|30.99|28.73|
|Children|54.66|56.60|57.39|57.20|53.60|46.11|
|Adults|45.34|43.40|42.61|42.80|46.40|53.89|
|Workdays lost|7.27|7.45|7.27|7.19|7.17|7.26|
|Visited hospital when sick|78.72|70.07|78.46|80.51|81.73|85.26|
|Government hospital|30.15|44.72|32.72|27.63|24.74|19.41|
|Private hospital|69.85|55.28|67.28|72.37|75.26|80.59|
|Did not go to hospital when sick|21.28|29.93|21.54|19.49|18.27|14.74|
|Sickness mild|38.93|31.35|31.52|42.64|46.86|59.93|
|Long distance|26.14|29.41|29.81|25.56|22.01|15.56|
|High cost|34.92|39.24|38.67|31.80|31.13|24.51|
|Observations|50,256|10,067|10,043|10,048|10,052|10,046|
|||||**1999**|||
||**All**|**1**<sup>**st**</sup>**quintile**|**2**<sup>**nd**</sup>|**3**<sup>**rd**</sup>|**4**<sup>**th**</sup>|**5**<sup>**th**</sup>**quintile**|
|Sick in previous 30 days|27.49|25.38|27.67|27.65|29.29|27.94|
|Children|52.47|58.36|54.89|53.99|48.61|45.73|
|Adults|47.53|41.64|45.11|46.01|51.39|54.27|
|Workdays lost|8.30|8.24|8.18|8.17|8.66|8.25|
|Visited hospital when sick|68.60|57.63|64.80|68.35|73.99|79.59|
|Government hospital|33.86|42.80|35.89|36.09|31.24|25.33|
|Private hospital|66.14|57.20|64.11|63.91|68.76|74.67|
|Did not go to hospital when sick|31.40|42.37|35.20|31.65|26.01|20.41|
|Sickness mild|34.52|24.34|30.70|35.80|39.40|57.18|
|Long distance|15.92|17.95|17.64|14.70|16.52|9.00|
|High cost|49.57|57.71|51.66|49.50|44.08|33.82|
|Observations|57,058|11,413|11,412|11,415|11,412|11,406|
|Source: Own calculation from UNHS|1999 and prelim|inary data for UNH|S 2002||||

21
# Appendix G:

A note on the relationship between sector specific capital and the elasticity of supply in applied general equilibrium models of imperfect competition<sup>_∗_</sup> Edward J. Balistreri David G. Tarr Colorado School of Mines The World Bank The models developed by Balistreri et al. (2009) and Jensen et al. (2008) to analyze services liberalization in Kenya and Tanzania utilize a specific-factor formulation. The specificfactor formulation facilitates a calibration of the FDI and domestic service responses. This is important because the empirical evidence [Hummels and Klenow (2005)] indicates that varieties expand less than proportionately to market size. The expansion of services bids up the price of the specific factor resulting in increasing costs (upward sloping supply). These increasing costs ensure that the varieties expand less than proportionately to market size. The predetermined elasticity of supply controls the magnitude of these effects. This note outlines the calibration procedure.

One can calibrate a linearly-homogeneous (constant-returns) Constant Elasticity of Substitution (CES) technology to an arbitrary price elasticity of supply if some of the input value is allocated to a specific factor. In the context of the Kenyan and Tanzania models the supply elasticity applies to the composite input that is used in both fixed and variable costs associated with the services sectors.

To simplify the presentation, consider the composite input for a single type of firm (say domestic firms) and for a single industry (say Communications). Let the quantity of this

> _∗_ This note is largely based on lecture notes from Thomas F. Rutherford’s graduate course on Computational Economics at the University of Colorado (late 1990’s)

1
first hub were set up in each district.<sup>28</sup> The installation of the first hub always postdates the installation of the first kiosk in a district. Thus at most two effects can be identified - the of kiosks without hubs and the of kiosks with hubs.

- [Insert Table 2 about here]

The state marketing board collects monthly data on prices and quantities crops sold in the _mandis_ of the state. In a particular _mandi_ , different crops of varying quality are sold each day at varying rates. _Mandi_ officials record the price and quantity sold of every transaction. At the end of the day, a daily minimum price, maximum price and mode price as well as total quantity sold for every crop are recorded by _mandi_ officials.<sup>29</sup> From all the daily entries for a given month, a monthly minimum, maximum and mode price as well as total monthly sales are calculated.<sup>30</sup> The monthly price and sales data is provided by each _mandi_ to the central state marketing board, from where I obtained the data for a period of 66 months (from April 2000 to September 2005). With no measures of average price in a month, the mode price can be considered the best available proxy for mean price. Additional price data in a sub-sample of _mandis_ is available from April 1998. Data on individual transactions and on daily prices and sales is not available.<sup>31</sup> It is important to point out that I observe the price of crops sold in government regulated wholesale _mandis_ . This data does not include the price posted or offered by ITC. Moreover, these are raw monthly prices without any quality grading, which is a major drawback since quality may have changed in response to the intervention. An overall increase in the quality of soybean produced will tend to bias my results towards finding a positive price effect of the intervention. This concern will be discussed at length in the subsequent sections.

> 28Kiosks were built really rapidly in each district. Thus the date of first kiosk is to be interpreted as essentially the date that all kiosks were built in a district.

> 29The daily minimum price is the lowest price that prevailed that day. The daily maximum price is the highest price that prevailed that day. The daily mode price is the price at which the highest quantity of a crop is sold in a day.

> 30The monthly minimum price is the lowest prevailing price in a given month (minimum of daily minimums). The monthly maximum price is the highest prevailing price in a given month (maximum of daily maximums). The monthly mode price is the price that is associated with the highest quantity of a crop sold in a month (mode of daily modes associated with highest quantity sold).

> 31Daily price and sales recorded in individual _mandis_ are not compiled by the state marketing board. Moreover, individual _mandis_ do not typically keep records of historical daily price and sales information.

15
OECD countries, have housing built of concrete rather than mud and have greater access to communications, which can help in coping during natural disasters. Finally, Ethiopian households that receive international remittances tend to rely more on their own cash reserves during shocks to food security, and less on selling productive assets such as household assets or livestock.

The rest of the paper is organized as follows. The next section reviews the literature on natural disasters, migration and remittances. Section 3 presents cross-country analysis on the _ex-post_ response of remittances to natural disasters. In section 4, we explore using household survey data to analyze ex-post responses and ex-ante preparedness. Section 4.1 considers how remittances to Bangladesh helped households in maintaining consumption after a severe flood (a rapid-onset but predictable disaster) in 1998. Section 4.2 considers for Burkina Faso and Ghana whether remittance-receiving households are ex-ante better prepared for disasters such as earthquakes and landslides. This section provides an analysis of how recipient households often use remittances for investment in stronger housing and improving access to communication, which can help in reducing vulnerability to natural disasters.<sup>2</sup> Section 4.3 explores the coping strategies used by remittance-recipient and non-recipient households in Burkina Faso with predictable and recurrent droughts. Section 5 concludes.

# 2. Natural disasters, migration and remittances: Review of the

# literature

This section provides a review of the response of remittances to natural disasters drawing on the macro economic literature and household level studies. Anecdotal and case study evidence seem to suggest that contrary to private international capital flows (which are usually procyclical), remittance flows increase or remain stable after the onset of large shocks such as natural disasters, macroeconomic or financial crises and armed conflicts (Clarke and Wallsten, 2004, World Bank, 2005 and Weiss Fagen and Bump, 2005). Yang (2007) provides cross-country evidence on the response of international flows to hurricanes, and concludes that for poorer countries, increased hurricane exposure is associated with greater remittance flows. In addition, it is estimated that in the Caribbean, a 1 percent decrease in real gross domestic product (GDP) is associated with a 3 percent increase in migrant remittances with a two-year lag (Mishra 2005). Figure 1 and Figure 2 provide certain instances of the response of remittances to large natural disaster in selected countries. These indicate substantial variation in the increase in remittances

> 2 Such income shocks may be factored in the inter-temporal consumption and remitting decisions.

3
|**Table 5. Impact of schoolin**<br>**(sample consists of individ**<br>|**g on**<br>**uals**<br>|**partner's fa**<br>**whose curr**<br>|**mily backgr**<br>**ent union is**<br>|**ound characteris**<br>**their first union**<br>|**tics**<br>**-- 88%)**<br>||
|---|---|---|---|---|---|---|
|**Inlaw lan**|**ho**|**ldings**|**Inlaw sch**|**ooling attainme**|**t**||
||||<br>**Mother-in**|<br>**-law**|<br>**Father-in**|**-law**|
|**Outcomes**<br>**Males**|**Fe**|**males**|**Males**|**Females**|**Males**|**Females**|
|_(1) Schooling only (OLS)_|||||||
|Schooling<br>-0.163||0.531|0.131|0.218|0.178|0.335|
|t<br>-0.81||1.33|**3.66**|**4.23**|**4.43**|**5.37**|
|F<br>0.66||1.76|13.40|17.89|19.59|28.86|
|p-value<br>0.42||0.19|0.00|0.00|0.00|0.00|
|n<br>399||324|379|427|347|412|
|_(2) Schooling (IV)_|||||||
|Schooling<br>-1.851||-0.405|0.217|0.338|0.381|0.845|
|z<br>-1.44||-1.10|**2.34**|**2.79**|**3.32**|**4.96**|
|Over-id<br>6.60||15.61|22.09|17.33|17.40|16.34|
|p-value<br>0.99||0.62|0.23|0.50|0.50|0.57|
|F<br>2.05||1.20|5.43|7.73|10.93|24.44|
|p-value<br>0.15||0.27|0.02|0.01|0.00|0.00|
|n<br>399||324|379|427|347|412|
|_(3) Schooling and height (IV)_|||||||
|Schooling<br>-0.256||0.247|0.260|0.286|0.418|0.729|
|z<br>-1.45||1.11|**2.35**|**2.31**|**3.43**|**4.27**|
|Height<br>0.009||0.255|-0.094|-0.023|-0.070|0.114|
|z<br>0.06||0.73|-1.20|-0.23|-0.64|0.74|
|Over-id<br>22.48||18.32|19.38|18.24|16.12|17.09|
|p-value<br>0.17||0.37|0.31|0.37|0.52|0.45|
|F<br>1.73||0.69|3.04|3.13|5.92|9.06|
|p-value<br>0.18||0.51|0.05|0.05|0.00|0.00|
|n<br>301||248|291|333|266|319|
|_(4) Schooling and health (IV)_|_and_|_family backg_|_round_||||
|Schooling<br>-0.301||-0.094|0.287|0.343|0.352|0.736|
|z<br>**-1.99**||-0.26|**2.44**|**2.75**|**2.71**|**4.36**|
|Height<br>-0.062||0.159|-0.082|-0.019|-0.069|0.132|
|z<br>-0.410||0.41|-1.04|-0.19|-0.59|0.83|
|Over-id<br>16.22||10.75|19.18|17.07|17.03|17.14|
|p-value<br>0.51||0.87|0.32|0.45|0.45|0.44|
|F<br>0.81||5.17|1.64|1.93|3.22|3.43|
|p-value<br>0.57||0.00|0.14|0.08|0.00|0.00|
|n<br>301||248|291|333|266|319|

t- and z-statistics in bold are significant at 0.05 level.

20