rafmacalaba/datause-encoder-data huggingface dataset:
17 <!-- Start of picture text --> PROPOSED AND OCFIC1AL WASURES OF<br>INTEREST ON PRIVATE FOREIGN ASSETS<br>6000-<br>40:00<br>3000<br>200D<br>1000<br>0.<br>1S80 19B11982 19831984 1985 tS86 1987 19881989 1990<br>yew<br>lProposed Ofd<br><!-- End of picture text --> Figure 6 The drop in domestic and, in particular,<sup>public savingin1985-86 was by</sup> and large a temporary phenomenon: it<sup>was a consequence of the collapse in oil</sup> prices and was more than offset in<sup>later years through a deepening of the non-</sup> oil primary fiscal surplus. While foreign<sup>saving was clearly influenced by</sup> external events, it dropped after the debt<sup>crisis and it rose with the</sup> collapse of oil prices in the mid 1980s,<sup>it was as well strongly influenced by</sup> real exchange rate fluctuations. As Figure<sup>7 illustrates, the real peso</sup> devaluations in 1982 and in 1985-86 exerted<sup>an adverse impact on public</sup> finances while the subsequent real peso appreciation<sup>exerted the opposite</sup> effect in the late 1980s.

Private saving fluctuated substantially<sup>during the period. Figure 1</sup> even suggests a cyclical pattern. An interesting<sup>feature, as Figure 8</sup> illustrates, is that fluctuations in the private<sup>saving rate stem primarily</sup> from fluctuations in disposable income;<sup>private consumption also oscillates,</sup> lagging somewhat in response to changes in<sup>disposable income, but within a</sup> much narrower band. As mentioned, mucb of<sup>the variability in disposable</sup> income resulted from public sector adjustments<sup>to the debt and oil crisis. A</sup> more dissaggregated analysis of private saving,<sup>however, is left to</sup> subsections 3.3. and 3.4 where we examine the<sup>performance of consumption and</sup> disposable income sub-components.
100

-

-

Table 34: ACCESS FUNCTIONS (PROBIT RESULTS)

||Public|Sector|Priva<br> <br>Formal|te<br>Sector|
|---|---|---|---|---|
|Intercept|-5.647|<br>(5.8)|-6.157|(6.8)|
|Years of Primary Schooling|-.059|(0.9)|-.039|(0.4)|
|Years of Secondary Schooling<br>Years of Higher Schooling|-.066<br>.206|(1.4)<br> <br>(2.1)|<br>.088|(1.2)|
|Years of Secondary VTE|-.049|(0.9)|||
|Years of Post-Secondary<br>VTE|.648|<br>(2.1)|<br>.042|(0.4)|
|Years of Teacher Training|1.53|(2.9)|||
|Years of Apprenticeship|-.121|(2.3)|-.085|(2.2)|
|CEPE|.865|<br>(2.3)|<br>.269|(0.9)|
|BEPC|1.44|(2.8)|<br>.384(*|) (0.6)|
|Secondary VTE Diploma|1.56|(3.6)|||
|Post-Secondary<br>VTE Diploma|-.176|(0.2)|<br>.990|(1.9)|
|Teacher Diploma|.210|<br>(0.3)|||
|Other Diploma<br>Age<br>(Age)<sup>2</sup>|1.01<br>.261<br>-.0026|(1.7)<br> <br>(4.8)<br> <br>(3.8)|<br>_<br> <br>.289<br>-.0032|_<br>(5.5)<br>(4.5)|
|Female with Child <5 Years|.461|<br>(2.2)|<br>.253|(0.9)|
|Female without Child <5 Years|.578|<br>(2.0)|-.380|(0.9)|
|Non-Ivorian|-2.37|(4.9)|-.015|(0.1)|
|Years of Residence Since Migration<br>(Years of Residence Since Migration)<br>2|-.006<br>-.0005|(0.2)<br> <br>(0.8)|<br>.010<br> <br>.0003|(0.3)<br>(0.3)|
|Household Enterprises|-.233|(2.0)|-.005|(0.1)|
|Abidjan|-.585|(3.5)|<br>.364|(1.8)|
|Rural Areas|-.510|(1.9)|-.057|(0.2)|
|Log- likelihood<br>Chi- squared<br>Significance<br>Level<br>xCorrect Predictions<br>Sample|-2<br>3<br>.<br>All empl|19.30<br>60.85<br>1E-6<br>82<br>oyees|-157.<br>237.<br>.1E-<br>83<br>All priva<br>Sector<br>Employee|28<br>09<br>6<br>te<br>s|

Note: Absolute asymptotic t-value in parentheses.

(*) Includes "other diploma"
_30. Have you ever received any vaccination, such as against diseases like polio or measles?_ Yes; No.

_31. Have your kids ever received any vaccine, such as against diseases like polio or measles?_

# Yes; No.

# <u>Online experiment</u>

_32. Do you know if a vaccine for COVID-19 is available in this country?_ Yes; No.

_33. Have you been vaccinated for COVID-19 (at least one dose)?_ Yes; No.

_34. [CONTROL] Do you plan to get the COVID-19 vaccine?_ Yes; No; Unsure.

_34. [EXPERT ADVICE] The COVID vaccines available in PNG are considered safe and highly effective by national and international experts. Do you plan to get the COVID-19 vaccine?_ Yes; No; Unsure.

_34. [SOCIAL NORMS] Papua New Guineans are getting vaccinated against COVID-19! More than 20,000 have done it so far. Help us protect our communities! Do you plan to get the COVID-19 vaccine?_ Yes; No; Unsure.

_34. [RELATIVE SAFETY] COVID-19 vaccines are safe – there have been no severe side effects reported, compared to hundreds of deaths due to COVID-19 in PNG. Do you plan to get the COVID-19 vaccine?_ Yes; No; Unsure.

_35. What is your biggest concern with regards to the COVID-19 vaccine?_ Side effects; I am not worried about COVID-19; It may not work; I am against vaccines in general; I already had COVID-19; The vaccine type I want is not available; I don't trust public health institutions/pharmaceutical companies; I am against the COVID-19 vaccine because of my religion; Something else.

_36. Who of the following do you trust the most to give you health and medical advice?_ Family; Friends; Religious leaders; Doctors/nurses/pharmacists; Community leaders; Scientists and epidemiologists; Celebrities and social media influencers; Political leaders.

50
Cogan, J. F. (1981). Fixed costs and labor supply, _Econometrica_ , 49(4): 945–63.

Di Maio, M. and T., Nandi (2012).The effect of the Israeli-Palestinian conflict on child labour and school attendance in the West Bank, _Journal of Development Economics_ , forthcoming.

Donaldson, D. (2010). Railroads of the Raj: Estimating the Impact of Transportation Infrastructure, NBER Working Paper No. 16487.

Faber, B. (2012). Trade integration, market size and industrialisation: Evidence from China’s national trunk highway system, mimeo.

Gutierrez-i Puigarnau, E. and J.N. van Ommeren (2010). Labour supply and commuting, _Journal of Urban Economics_ , 68(1): 82–89.

IDF Military Advocate General (2012). Legal issues in Judea & Samaria: Movement and access restrictions, Israel Defence Forces, available at: <u>http://www.law.idf.il/602-2215en/Patzar.aspx#paragraph_8</u> International Monetary Fund (2010). Macroeconomic and fiscal framework for the West Bank and Gaza: Sixth review of progress, Staff Report for the meeting of the Ad Hoc Liaison Committee, September.

Ksoll, C., Macchiavello, R. and A. Morjaria (2010). The effect of ethnic violence of an exportoriented industry, mimeo.

Jaeger, D. and D. Paserman (2008). The cycle of violence? An empirical analysis of fatalities in the Palestinian-Israeli conflict. American Economic Review 98 (4): 1591-1604.

Mansour, H. (2010). The effects of labor supply shocks on labor market outcomes: Evidence from the Israeli-Palestinian conflict, _Labour Economics_ , 17(6): 930-939.

Miaari, S.H. and Sauer, R.M. (2011). The labor market costs of conflict: Closures, foreign workers, and Palestinian employment and earnings. _Review of Economics of the Household_ , 9: 129-148.

Miaari, S., A. Zussman and N. Zussman (2012a). Ethnic conflict and job separation, _Journal of Population Economics_ , Vol. 25(2): 419-437.

Miaari, S., A. Zussman, and N. Zussman (2012b). Employment Restrictions and Political Violence in the Israeli-Palestinian Conflict, Discussion Paper 2012.04, Bank of Israel.

Michaels, G. (2008). The effect of trade on the demand for skill — Evidence from the interstate highway system, _Review of Economics and Statistics,_ Vol. 90(4): 683-701 _._ Miguel, E. and G. Roland (2011). The Long Run Impact of Bombing Vietnam, _Journal of Development Economics_ , Vol. 96(1): 1-15.

32
husbands’ well-being. We measure these effects both during implementation and nine months after the program ceased.

The Djiboutian public works program stands out due to its explicit gender focus: women, but particularly constrained pregnant women or mothers of young children, were the principal recipients. The program offered eligible women 50 consecutive days of work within the boundaries of their neighborhoods, thereby overcoming any geographic barriers that might limit their participation (Tucker, 2008). The works themselves entailed anything from cleaning services (e.g. garbage collection, particularly plastic bags), light labor-intensive community activities, to small artisanal projects. Participants were paid wages equivalent to 80 percent of Djibouti’s official minimum wage. Women in eligible households who could not or did not wish to participate in the program could delegate their offer to any other adult member of their household, male or female. In specific cases, delegation to adults outside a woman’s household was permitted.

Our analysis yields six main findings. First, the program has almost universal take-up: we estimate that 92% of the households that were offered the opportunity to participate in program accept it. Surprisingly, more than three-quarters of the women in participating households choose to work themselves, rather than delegating the offer to a household member or outside family member or friend. This result is notable, given that the target population has high care burdens (i.e., female participants with young or in utero children). In addition, almost all the women who do not accept the public works offer delegate the opportunity and the corresponding direct income payment to another adult. Interestingly, in a setting where marriage is almost universal, the women who delegate their offer do so to someone outside of their household, and not to their husbands or even to another household member.

Second, the near universal program take-up results in a 55-percentage-point increase in women’s employment, after accounting for incomplete take-up, delegation, and limited crowding out of self-employment. There is no labor response from husbands (mainly limited to instances of delegation) or other household members. After the short-term employment opportunity ended, women either revert to remaining unemployed or to searching for employment.

Third, based on detailed time-use data, we document that beneficiary women accommodate the additional outside earning opportunity by assigning some of their household chores to other household members and entrusting the care of their children to other women, including female neighbors.

Fourth, we estimate the average participant’s net income gains during the program and find that it was very close to the gross wage weekly transfer (with an estimated 16 percent of foregone income). These net income gains correspond to a 30 percent increase in household labor income. In line with the temporary nature of the program, women save most of their 3
Policy Research Working Paper 9781

# **Abstract**

Recent work draws attention to the fragility of domestic tax revenues—a vital resource for the developing world—to illicit financial flows. To cope with two major challenges in the illicit financial flows–tax revenues relationship— related to the mere illicit financial flows measurement and reverse causality—this paper exploits the Financial Action Task Force data using an impact assessment analysis. Estimations reveal a significant tax revenue loss in countries associated with important illicit financial flows with respect to comparable countries without important illicit financial flows. Moreover, this causal effect—estimated as being economically meaningful—is supported by a large robustness section, and in particular remains unchanged when using several “doubly robust” estimators. Lastly, it unveils heterogeneities in the impact of illicit financial flows on tax revenues, related to the type of tax—a significant loss for indirect but not for direct taxes—and the considered environment. Therefore, policies combating illicit financial flows—for example, by developing institutions or a sound financial system, as shown by the estimations—may provide additional tax revenues for the developing world.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at psawadogo2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 6. WTP Determinants with the Two-Stage Approach**

|**Variables**|**Log WTP Me**|**an (****)**|**Log WTP Va**|**riance (****)**|
|---|---|---|---|---|
||**Coefficient**|**t-value**|**Coefficient**|**t-value**|
|**_Social, Economic and_**<br>|**_Demographical_**<br>|**_Characteristics_**<br>|||
|Male|-.032|-0.29|-.16|-1.00|
|Age|-.011|-2.06**|-.019|-2.84***|
|Lg(Income)|.21|3.91***|.21|2.66***|
|Secondary Education|.26|2.21**|.37|2.19**|
|Married|-.031|-0.17|-.12|-0.46|
|Donation|.20|1.93*|.21|1.41|
|**_Perception about Envi_**<br>|**_ronment_**<br>||||
|Water important|.064|0.61|.091|0.60|
|Press discussion|.20|1.87*|.18|1.10|
|Government job|-.15|-1.36|-.11|-0.73|
|**_Current use of Puzheh_**|**_ei lake_**||||
|Visit lake|.0021|0.02|-.10|-0.52|
|Notice change|.20|1.79*|.23|1.47|
|Generate income|.28|1.10|.12|0.32|
|Irrigation|-.37|-2.18**|-.23|-0.96|
|**_Variables related to pr_**|**_oject_**||||
|Heard project|.45|1.86*|.57|1.41|
|Living in project|||||
|domain|.14|1.19|-.095|-0.54|
|Project increase|||||
|income|.13|0.93|-.0034|-0.02|
|Project improve view|.069|0.61|.15|0.90|
|Project improve|||||
|swimming condition|.10|0.74|-.045|-0.22|
|Project protect|||||
|ecological system|.23|2.07**|.14|0.86|
|Project improve|||||
|health|-.12|-1.16|-.30|-1.82*|
|Project improve|-.065|-0.57|.044|0.26|

25
_Note:_ As all regressions are run in logs; coefficients around 0 can directly be interpreted as percentage changes. For coefficients deviating sufficiently from 0, effects should be calculated as exp( )—for example, a coefficient of 0.7 means that with every unit increase in the respective variable, investment volumes increase by about 200 percent (that is, _exp(0.7)_ ≈ _2 = 200%)._ ββ Standard errors are clustered on the ward level in parentheses: *** p<0.01 ** p<0.05 * p<0.1

|**Table D.9: Regression results forprobabilit**<br>|**y of obtaining go **<br>(1)<br>|**vernmental suppor**<br>(2)<br>P (any governm<br>|**t**<br>(3)<br>ental support)<br>|(4)<br>|
|---|---|---|---|---|
|VARIABLES|Regression<br>coefficients|Mean marginal<br>effects|Regression<br>coefficients|Mean marginal<br>effects|
|**Region**(Dar es Salaam)|||||
|Dodoma|0.660*|0.0551|0.664|0.0557|
||(0.396)|(0.0387)|(0.409)|(0.0401)|
|Tanga|1.458***|0.167***|1.455***|0.167***|
||(0.361)|(0.0529)|(0.369)|(0.0537)|
|Firm age (years)|-0.00903|-0.000825|-0.00898|-0.000823|
||(0.00762)|(0.000712)|(0.00731)|(0.000687)|
|Number of employees|0.000106|9.69e-06|0.000102|9.38e-06|
||(0.000174)|(1.59e-05)|(0.000174)|(1.60e-05)|
|Log (total operational costs, $/month)|-0.0571|-0.00521|-0.0631|-0.00578|
||(0.0850)|(0.00773)|(0.0868)|(0.00792)|
|Total damages ($)|||7.04e-06|6.45e-07|
||||(8.39e-06)|(7.62e-07)|
|**Experienced flooding on business**<br>**premises**= Yes|||0.00194|0.000178|
||||(0.472)|(0.0434)|
|Time since last serious disaster (months)|0.00769*|0.000702**|0.00711*|0.000652*|
||(0.00390)|(0.000355)|(0.00416)|(0.000382)|
|**Sector**(Accommodation and food service<br>activities, Construction):|||||
|Communication and other services|0.188|0.0197|0.181|0.0191|
||(0.430)|(0.0445)|(0.431)|(0.0447)|
|Manufacturing, Mining, Water, Energy,<br>Agriculture|-0.583|-0.0465|-0.585|-0.0470|
||(0.492)|(0.0407)|(0.495)|(0.0412)|
|Transportation and storage|-0.653|-0.0509|-0.633|-0.0500|
||(0.712)|(0.0492)|(0.722)|(0.0507)|
|Wholesale and retail trade, Repair of<br>motor vehicles and motorcycles|-0.00813|-0.000796|-0.0179|-0.00176|
||(0.431)|(0.0423)|(0.437)|(0.0431)|
|Constant|-2.203***||-2.149***||
||(0.673)||(0.705)||
|Observations|690|690|686|686|

_Note:_ Standard errors in parentheses: *** p<0.01 ** p<0.05 * p<0.1 48
# **Table A8.2 The Effect of PMT on Height-for-Age of Children 12 to 24 Months: Using Exposed Share of Eligible Life to Measure Program Exposure**

||Poole|d OLS|Com<br>Fixed|munity<br>-Effects|
|---|---|---|---|---|
|Year (2000=1)|-0.121|-0.116|-0.208|-0.221|
||[0.104]<br>|[0.105]<br>|[0.144]<br>|[0.151]<br>|
|_Exposed Share_*Year|0.042|0.042|0.464**|0.528**|
||[0.125]|[0.127]|[0.227]|[0.235]|
|Gender (boy=1)|-0.122|-0.124*|-0.219**|-0.214**|
||[0.074]|[0.074]|[0.088]|[0.089]|
|Age|-0.042***|-0.043***|-0.030***|-0.030**|
||[0.011]|[0.011]|[0.012]|[0.012]|
|Mother education (years)|0.008|0.008|-0.007|-0.007|
||[0.008]|[0.009]|[0.009]|[0.009]|
|Father education (years)|0.004|0.002|0.001|0.001|
||[0.009]|[0.009]|[0.010]|[0.010]|
|Mother height (cm)|0.024***|0.024***|0.029***|0.029***|
||[0.007]|[0.007]|[0.008]|[0.008]|
|Father height (cm)|0.031***|0.031***|0.036***|0.036***|
||[0.008]|[0.008]|[0.009]|[0.009]|
|Head of HH (male=1)|0.012|0.023|-0.144|-0.153|
||[0.123]|[0.123]|[0.135]|[0.138]|
|Number of children 5-14 yo in HH|-0.002|0.002|0.026|0.025|
||[0.037]|[0.038]|[0.044]|[0.045]|
|HH owns private toilet (Yes=1)|0.137*|0.134*|0.121|0.119|
||[0.080]|[0.081]|[0.102]|[0.103]|
|HH has sanitation (Yes=1)|0.224**|0.193*|0.133|0.130|
||[0.097]|[0.113]|[0.159]|[0.168]|
|Type of community (urban=1), C|<br>-|<br>0.145||<br>0.113|
|||[0.104]||[0.213]|
|Distance to district capital (km), C|-|-0.001||0.001|
|||[0.002]||[0.003]|
|Distance to bus station (km), C|-|0.010||0.017|
|||[0.006]||[0.012]|
|Land w/ technical irrigation (%), C|-|0.369*||-0.400|
|||[0.211]||[0.422]|
|Comm. has asphalt road (Yes=1), C|-|-0.108||0.061|
|||[0.103]||[0.186]|
|C h t Y=1 C||0095||0088|
|omm. as sewerage sysem (es),|-|-.||-.|
|||[0.086]||[0.165]|
|Comm. has piped water (Yes=1), C|-|0.072||-0.028|
|||[0.093]||[0.161]|
|Constant|-10.234***|-10.107***|-11.447***|-11.551***|
||[1.609]|[1.614]|[1.840]|[1.874]|
|R-squared|0.08|0.09|0.40|0.41|

Notes: _Exposed Share_ is the share of a child’s eligible life exposed to the program and is calculated as described on page 11. n is 1042. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

44
Figure 11.7 Philippines-Policy responses and incentives to borrow <!-- Start of picture text --> Monetary policy: components of reserve money Fiscal policy: fiscal impulse and demand<br>growth pressures<br>I hilippines<br>percent Philippines percent<br>tt0 -4-<br>60 | i<br>0~~~~~~~~~~~~~~~~~~~~~~<br>-2 | 2 /<br>-60 -4<br>1900 1990 ¶991 1992 1993 1994 1995 1996 1997 1919 1990 1991 1992 1993 1994 1995 1090 19i7<br>contribution contribution of net foreign assets to reserve money growth of domestic credit to reserve money growth - Excess demandFiscal impulse<br>resarve money growth (percent of GDP)<br><!-- End of picture text -->

Differentials between domestic deposit rates and Nominal exchange rate and variability of **US** LIBOR unpredictable component of nominal exchange rate movements.

<!-- Start of picture text --> Philippines<br>percent percent 10- Indx- 112<br>30 - Philippines 0 - - 110<br>8- 'n.b,dIrtytl of - 108<br>7 - unoredictable ponat -80<br>20 - -76<br>10 3--<br>0- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~2<br>10Bh t990 1992 1994 1996 ts90 1998~~~~~~~~~~4-I0<br>-10 -2<br>.5 - nominal ... chenge iSa -00<br>- (root aoat78<br>.20 . 78<br>1988s 19090 1092 1 904 1998 g6 1990 19<br>domestic deposit rates i<br>differential between domestic deposit rates and US LtBOR<br>(adjusted for en post exchange rate movements)<br><!-- End of picture text --> The variability of the unpredictable component is given by the estimation of **ani** ARCH model of the nominal exchange rate on a constant and a time trend. Nominal exchange rate index 1990=100.

31
also contributes to our conclusions below that changes in the institutional structure and incentives of schools are fundamental to improving school outcomes.<sup>73</sup>

# **_6.3 Within-country Evidence – Developing Countries_**

The evidence on developed countries, while instructive to them, may or may not be similar in developing countries. Indeed, one concern about the within-country analyses is that they might not provide reliable guidance to other countries. For example, Heyneman and Loxley (1983) argued early in the analysis of school effects that schools may be considerably more important for achievement in developing countries, where family inputs to education may be less.<sup>74</sup> Further, the levels of spending differ by a factor of 100 in comparing some developed and developing countries so that simple translations of results may be inappropriate.

The research on schools in developing countries has been less extensive than that in developed countries. Moreover, the evidence is frequently weaker because of data or analytical problems with the underlying studies. Nonetheless, as Pritchett (2004) convincingly argues on the basis of ample evidence, just increasing spending within current education systems in developing countries is unlikely to improve students’ performance substantially.<sup>75</sup> Overwhelming evidence shows that expansions on the input side, such as simple physical expansion of the educational facilities and increased spending per student, generally do not seem to lead to substantial increases in children’s competencies and learning achievement. The lack of substantial resource effects in general, and class-size effects in particular, has been found across the developing world, including Africa (see, e.g., Kremer (2003); Michaelowa (2001), Latin America (see, e.g., Mizala and Romaguera (2002); Wößmann and Fuchs (2005) and East Asia (see, e.g.,Gundlach and Wößmann (2001); Wößmann (2005a)).

Again, it is necessary to understand the character of the results. In particular, the evidence refers most specifically to overall infusions of resources. They do not deny that some investments are productive. A number of studies provide convincing evidence that some minimal levels of key resources are frequently valuable in promoting student learning. For example, the simple availability of

> 73 These conclusions are also underscored by the evidence on extensive teacher absenteeism in developing countries (see Chaudhury et al. (2006); Banerjee and Duflo (2006)).

> 74 Based on recent international data from TIMSS, however, Hanushek and Luque (2003) question whether this overall conclusion holds.

> 75 See Hanushek (1995), Glewwe (2002), Pritchett (2004), and Glewwe and Kremer (2006) for reviews of research on the determinants of educational quality in developing countries.

66
# **Table 1: Summary Statistics**

|||Mean<br>||
|---|---|---|---|
||1995|2003|2012|
|Years of Education|6.31|7.41|8.92|
|Potential Experience|23.07|22.51|22.32|
|Below Minimum Wage|0.12|0.18|0.16|
|Self-Employment|0.27|0.25|0.21|
|Informal|0.25|0.27|0.21|
|Formal|0.48|0.48|0.57|
|White|0.56|0.54|0.48|
|Black|0.43|0.45|0.51|
|Other|0.01|0.01|0.01|
|Female|0.38|0.40|0.42|
|Rural|0.16|0.12|0.10|
|Northeast|0.25|0.24|0.24|
|North|0.04|0.06|0.06|
|Southeast|0.47|0.46|0.46|
|South|0.16|0.16|0.16|
|Center-West|0.07|0.08|0.08|
|Agriculture, Fishing and Mining|0.15|0.12|0.09|
|Industry|0.15|0.16|0.14|
|Construction|0.07|0.08|0.09|
|Services|0.62|0.64|0.67|
|Earnings in 2005 Reals|780.12|655.01|870.97|
|log(Earnings). 2005 Reals|6.22|6.10|6.48|

Note: All the statistics are calculated over the estimating sample (formal, informal and self-employed of ages 18-65).

31