rafmacalaba/datause-encoder-data huggingface dataset:
Table A.1: Procurement and budget execution sources

|State|Procurement|Budget Execution|
|---|---|---|
|CE|https://api.tce.ce.gov.br/|https://api.tce.ce.gov.br/|
|MG|https://dadosabertos.tce.mg.gov.br|https://dadosabertos.tce.mg.gov.br|
|PB|https://dados.tce.pb.gov.br|https://zeoserver.pb.gov.br/portaltcepb/tcepb/servicos/dados-abertos-do-sagres-tce-pb|
|PE|https://sistemas.tce.pe.gov.br/DadosAbertos|https://sistemas.tce.pe.gov.br/DadosAbertos|
|PR|https://servicos.tce.pr.gov.br/TCEPR/Tribunal/Relacon/Dados/DadosConsulta/Consolidado|https://servicos.tce.pr.gov.br/TCEPR/Tribunal/Relacon/Dados/DadosConsulta/Consolidado|
|RS|http://dados.tce.rs.gov.br|http://dados.tce.rs.gov.br|
|SP||https://transparencia.tce.sp.gov.br/conjunto-de-dados|

**Notes:** This table presents urls with data sources for each state used in this paper.

Table A.2: Procurement methods

|Purchasing method|Competitive|Characteristics|Contract size|
|---|---|---|---|
|Reverse auction<br>(_Pregão_)|Yes|Reverse auction, open to any interested frm.<br>Online or in-person. Of-the-shelf goods.<br>Multiples bids per participant.|Any value|
|Waiver<br>(direct contracting)|No|Small purchases.|Up to 17,600 BRL|
|Invitation to tender<br>(_Convite_)|Yes|Participants are invited. Minimum of 3 bidders.<br>Uninvited frms are allowed to participate.<br>One bid per participant.|Up to 176,000 BRL|
|Competitive bidding<br>(_Concorrência_)|Yes|Open to any interested bidder.<br>One bid per participant.|Any value|
|Submission of prices<br>(_Tomada de preços_)|Yes|Bidder must be previously registered.<br>One bid per participant.|Up to 1,430,000 BRL|
|Direct contracting|No|There is onlyone supplier.|-|
|Contest|Yes|Artistic, scientifc or technical work.|-|

**Notes:** Contract size refers to the purchases of products and services other than construction (see Federal Decree 9,412, of June 18, 2018, for more details ). Thresholds for construction are different (33,000 BRL). The maximum contract size for direct contracting changed in 2018 from 8,000 BRL to 17,600 BRL.

41
**Table 6. Fairlie decomposition of the gender gap in labor supply**

|**Sample used for coefficients**|**Women/men**|**Women/men**|**Women/men**|**Women/men**|
|---|---|---|---|---|
||**pooled**|**teenagers**|**young adults**|**prime-aged**|
||(1)|(2)|(3)|(4)|
|**Number of observations**|4972|1708|1715|1549|
|**Women**|2650|878|913|859|
|**Men**|2322|830|802|690|
|**Probability of labor supply of men**|0.4672|0.2130|0.5071|0.6845|
|<br>**Probability of labor supply of women**|0.2834|0.1554|0.2993|0.4188|
|<br>**Difference between men and women**|0.1838|0.0576|0.2079|0.2657|
|**Explained component of thegap**|0.0458|-0.0135|0.0510|0.0237|
||24.9%|-23.5%|24.5%|8.9%|
|**Variables:**|||||
|**Age**|0.0077|-0.0049|0.0040|0.0012|
|**s.e.**|0.0007|0.0021|0.0009|0.0005|
|**% contribution to the gender gap**|16.9%|-36.2%|7.8%|5.0%|
|**Education**|0.0024|-0.0127|0.0041|0.0044|
|**s.e.**|0.0007|0.0028|0.0024|0.0015|
|**% contribution to the gender gap**|5.3%|-93.9%|8.1%|18.7%|
|**Marital**|0.0328|-0.0026|0.0544|0.0309|
|**s.e.**|0.0132|0.0061|0.0183|0.0415|
|**% contribution to the gender gap**|71.6%|-19.3%|106.7%|130.4%|
|<br>**Children**|-0.0035|0.0027|-0.0152|-0.0197|
|**s.e.**|0.0105|0.0036|0.0129|0.0328|
|**% contribution to the gender gap**|-7.7%|20.0%|-29.8%|-83.2%|
|<br>**Early marriage**|-0.0061||-0.0159|0.0028|
|<br>**s.e.**|0.0042||0.0085|0.0088|
|**% contribution to the gender gap**|-13.2%||-31.2%|11.7%|
|<br>**Parentless**|0.0007|-0.0007|0.0003|0.0010|
|**s.e.**|0.0002|0.0005|0.0003|0.0007|
|**% contribution to the gender gap**|1.5%|-5.3%|0.5%|4.1%|
|<br>**Number of siblings**|-0.0039|-0.0080|-0.0062|-0.0007|
|<br>**s.e.**|0.0013|0.0037|0.0027|0.0018|
|**% contribution to the gender gap**|-8.6%|-59.3%|-12.2%|-3.1%|
|<br>**Household size**|0.0032|0.0021|0.0071|0.0011|
|**s.e.**|0.0012|0.0039|0.0024|0.0011|
|**% contribution to the gender gap**|6.9%|15.2%|13.9%|4.7%|
|**Education of father**|0.0014|0.0012|0.0016|0.0003|
|**s.e.**|0.0005|0.0012|0.0008|0.0010|
|**% contribution to the gender gap**|3.0%|9.0%|3.1%|1.5%|
|**Education of mother**|0.0004|0.0008|-0.0016|0.0000|
|**s.e.**|0.0004|0.0005|0.0011|0.0005|
|**% contribution to the gender gap**|0.8%|5.9%|-3.1%|0.2%|
|<br>**Worked while studying**|0.0013|-0.0011|0.0035|0.0006|
|<br>**s.e.**|0.0004|0.0006|0.0020|0.0005|
|**% contribution to the gender gap**|2.9%|-8.5%|6.9%|2.5%|
|<br>**Rural areas**|0.0095|0.0097|0.0151|0.0017|
|**s.e.**|0.0009|0.0018|0.0020|0.0013|
|**% contribution to thegendergap**|20.6%|71.6%|29.6%|7.1%|

_Note: The educational group includes such variables as secgen specsec univ. The marital status group: wengaged mengaged marrdivwid1w marrdivwid1m. The children group: children: kid1w kid2w kid3w kid1m kid2m kid3m. The early marriage group: earlymarrw earlymarrm. The parentless group: nofather nomother. The education of father: vocsec_f univ_f. The education of mother: vocsec_m univ_m. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 30
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- 31 -

capture the growth in the tax base, a ongoing system of cadastral maintenance is required.

Maintenance of the fiscal cadastre is needed to capture two principle types of changes. First are changes in property characteristics. New parcels come into existence, through subdivision or annexation. Ownership changes, new buildings are constructed or existing ones improve. In order to maintain comprehensive tax coverage, these changes must be incorporated in the fiscal cadastre on an ongoing basis. In principle, most of these changes could be flagged by other agencies of government. The agency responsible for subdivision approval could notify the tax authority when new parcels are created; the registrar of deeds could notify it of changes in ownership; the building permits agency could flag new construction. Many LDC property tax codes call for this form of information pooling. As a maintenance technique, it is not notably successful. Many such changes occur outside the formal system: land is subdivided illegally, construction is undertaken without permits. Even where formal procedures are followed, the agencies concerned attach a low priority to furnishing this information to the tax authorities. Successful maintenance is instead a matter of maintaining a permanent staff whose responsibility is to monitor changes in property characteristics by ongoing visual inspection.

The second category of maintenance involves changes in price. In principle, changes in prices need not concern the valuation office. Where inflation increases the cost of providing local services, the office can merely increase the tax rate to generate additional revenues. As long as relative property values remain unchanged, the burden of the property tax would continue to be distributed equitably. Some countries, notably the U.K., follow this approach. Over time this
- 21 -

to be limited perhaps to about 4–5 bidders. Although one can still expect the conventional competition effect in a static sense, it is important to recall that the entry decision of potential bidders is potentially endogenous. There are a number of prospective bidders, who are interested but may decide not to be involved into the selection process. Hence, the observed distribution of bids will be much different from the distribution of bids that would have been submitted if those potential bidders have participated.

Because of this partial observability, the paper applied the double selection model to procurement data from road development projects in developing countries and examined why some companies did not participate or were not allowed to participate in the bidding process. The estimation results highlight several challenges in infrastructure procurement. Bidders are resource-constrained and self-selective. A few companies can apply for larger road projects. At the same time, auctioneers also tend to limit participation as the contract size increases. They prefer to contract with only experienced contractors. This is the reason the market competition ends up being rather restricted, resulting in high infrastructure procurement costs. The estimated equilibrium bid strategy suggests possible economies of scale; government procurement costs could be saved by increasing the contract size. But it would risk jeopardizing the expected competition effect because, again, larger projects could attract fewer bidders.
26 worker wage arrears) tend to show inverse relations, as evidenced by the negative value of the 13 coefficients. This is the logical complement of the direct relationship of the supply-side factors: the presence of greater need in a region tends to lead to a reduction in national economy expenditures, not the increase one might expect to meet those needs.

_Table 14. National Economy Expenditures, Multivariable Regression Results, 1992-95_

||**1992***<br>**(N=86)**|**1993**<br>(N=78)|1994<br>(N=78)|1995<br>(N=86)|
|---|---|---|---|---|
|Pre-transfer**regional**|.367|.351|.386|.391|
|**revenue**|(28.4)|(10.6)|(16.3)|(22.4)|
|**Federal transfers**|.140|.269|.293|.244|
||(8.1)|(8.5)|(16.2)|(8.9)|
|**Household incomes**||.030|.030|.022|
|||(2.5)|(4.1)|(4.4)|
|**Doctors per 10,000**|3.32||3.66||
||(2.2)||(3.2)||
|**Wage arrears (1993 data)**||-.311|-.449||
|||(-2.0)|(-3.4)||
|**Constant**|-104.6|4.44|-230.47|-81.0|
||(-1.6)|(.16)|(-4.8)|(-3.9)|
|**AdjustedR**<sup>**2**</sup>|.92|.86|.96|.94|

*Excluding Koryaksky AO.

The most obvious observation from Table 14 is that the inclusion of additional variables in the regressions does not unambiguously improve their explanatory force relative to the twovariable regressions in Table 9, as indicated by the adjusted R2 values.<sup>25</sup> Of note in both Tables 9 and 14 is the stability of pre-transfer regional revenues as a determinant of national economy expenditures, as demonstrated by the 3 coefficients: holding all else constant, for every extra 1991 ruble of pre-transfer revenue, regions spent an additional 0.37-0.44 rubles on national economy expenditures in the period 1992-95 in the Table 9 regressions, and analogously, 0.35-0.39 rubles in the Table 14 regressions.

# VI.2. Housing

The explanatory power of the two-variable regressions used to explain overall national

**24** In order to effect the transition from the two-variable regressions in Table 9 to the multivariable regressions in Table 14, all of the independent variables under examination were added one at a time to the twovariable regressions. Significant results for the third variable then led to that variable being used in a threevariable regression, to which fourth variables were added one at a time, and so on. This process was stopped when no addition of an independent variable resulted in significant results for that variable.

**25** For some of the independent variables in the Table 14 regressions data were not available for all regions, leading to the drop in both the number of observation (N) and the adjusted R<sup>2</sup> in 1993 in the Table 14 regressions relative to Table 9.
- Ma, J., Baum, S., Pender, M., & Bell, D. (2015). Trends in College Pricing 2015. _New York, NY: The College Board_ .

- Mayer, A., Patel, R., & Gutierrez, M. (2015). _Four‐Year Effects on Degree Receipt and Employment Outcomes from a Performance‐Based Scholarship Program in Ohio_ (SSRN Scholarly Paper No. ID 2594482). Rochester, NY: Social Science Research Network.

- Myers, D., Olsen, R., Seftor, N. S., Young, J., & Tuttle, C. (2004). The Impacts of Regular Upward Bound: Results from the Third Follow‐Up Data Collection.

- OECD. (2012). Indicator B5 How much do tertiary students pay and what public support do they receive? In _Education at a Glance_ (pp. 272–285). Retrieved from http://www.oecd‐ilibrary.org/content/chapter/eag‐2012‐20‐en

- Olson, L., & Rosenfeld, R. A. (1985). Parents, students, and knowledge of college costs. _Journal of Student Financial Aid_ , _15_ (1), 4.

- Oreopoulos, P., Brown, R. S., & Lavecchia, A. M. (2014). _Pathways to Education: An Integrated Approach to Helping At‐Risk High School Students_ (Working Paper No. 20430). National Bureau of Economic Research.

- Oreopoulos, P., & Dunn, R. (2012). _Information and College Access: Evidence from a Randomized Field Experiment_ (Working Paper No. 18551). National Bureau of Economic Research.

- Page, L., Castleman, B., & Sahadewo, G. A. (2016). _More than Dollars for Scholars: The Impact of the Dell Scholars Program on College Access, Persistence and Degree Attainment_ .

- Page, L., & Scott‐Clayton, J. (2016). Improving college access in the United States: Barriers and policy responses. _Economics of Education Review_ , _51_ , 4–22.

- Pallais, A. (2013). _Small Differences that Matter: Mistakes in Applying to College_ (Working Paper No. 19480). National Bureau of Economic Research.

- Paulsen, M. B., & St. John, E. P. (2002). Social Class and College Costs: Examining the Financial Nexus between College Choice and Persistence. _The Journal of Higher Education_ , _73_ (2), 189–236.

- Peter, F. H., & Zambre, V. (2017). Intended college enrollment and educational inequality: Do students lack information? _Economics of Education Review_ , _60_ , 125–141.

- Pistolesi, N. (2017). Advising students on their field of study: Evidence from a French University reform. _Labour Economics_ , _44_ , 106–121.

- Richburg‐Hayes, L., Patel, R., Brock, T., Campa, D. la, Elijah, Rudd, T., & Valenzuela, I. (2015). _Providing More Cash for College: Interim Findings from the Performance‐Based Scholarship Demonstration in California_ (SSRN Scholarly Paper No. ID 2625711). Rochester, NY: Social Science Research Network.

- Rodríguez‐Planas, N. (2012). Longer‐Term Impacts of Mentoring, Educational Services, and Learning Incentives: Evidence from a Randomized Trial in the United States. _American Economic Journal: Applied Economics_ , _4_ (4), 121–139.

- Rosinger, K. (2016). _Can Simplifying Financial Aid Information Impact College Enrollment and Borrowing? Experimental and Quasi‐Experimental Evidence_ .

40
Table 3: Robustness tests

|Dependent variable|||Number <br>(inverse|of Daes<br> hyperbo|h recruits<br>lic sine)||||
|---|---|---|---|---|---|---|---|---|
||A<br>sp|lternativ<br>ecifcatio|e<br>ns||Sample<br>restrictions||Spa<br>corre|tial<br>lation|
||2014<br>values|Other<br>media|Emi-<br>gration|No<br>Tunis|No<br>mountain|No<br> outliers|20 km<br>radius|100 km<br>radius|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_Panel A: OLS with delega_|_tion FE_||||||||
|Internet access (rate)|0.072<br>|0.074<br>|0.073<br>|0.075<br>|0.075<br>|0.061<br>|0.072<br>|0.072<br>|
||(0.028)|(0.028)|(0.028)|(0.028)|(0.029)|(0.025)|(0.028)|(0.032)|
|Radio ownership (rate)||-0.020<br>(0.022)|||||||
|TV ownership (rate)||0.039<br>(0.041)|||||||
|Emigration (rate)|||0.022<br>(0.031)||||||
|N<br>R2|7878<br>0.228|7878<br>0.228|7878<br>0.228|7248<br>0.222|7489<br>0.230|7788<br>0.208|7878<br>0.228|7878<br>0.228|
|_Panel B: 2SLS second sta_|_ge_||||||||
|Internet access (rate)|0.255|0.369|0.312|0.318|0.306|0.359|0.318|0.318|
||(0.133)|(0.177)|(0.157)|(0.156)|(0.166)|(0.164)|(0.168)|(0.165)|
|Radio ownership (rate)||-0.058<br>(0.029)|||||||
|TV ownership (rate)||-0.041<br>(0.061)|||||||
|Emigration (rate)|||0.053<br>(0.044)||||||
|Kleibergen-Paap Test|85.730|58.512|66.124|70.412|61.072|64.488|138.982|38.306|
|N|7878|7878|7878|7248|7489|7788|7878|7878|

_Notes:_ Standard errors are reported in parentheses and are corrected for spatial correlation following Conley (1999) using 50 km (columns 1-6), 20 km (column 7), or 100 km (column 8) distance cutoffs. All specifications include population (log), unemployment (rate), marital status (marriage rate), area (log), age group (dummies) and education group (dummies) as controls. Column 1 further controls for 2014 nighttime lights (log) and 2014 rainfall (log), while columns 2-8 use 1995-2010 nighttime lights (log) and 1995-2010 rainfall (log) as in Table 2. Panel A reports OLS estimates, which also account for delegation FE. Panel B reports 2SLS estimates that use lightning strikes (log) as an instrument for internet access (rate). Columns 1-3 and 7-8 use the entire sample, while column 4 removes delegations in the Tunis metropolitan area (21 delegations), column 5 removes delegations with the highest coverage of mountains (13 delegations), and column 6 removes delegations with the highest number of Daesh recruits per capita (3 delegations). Variable definitions are available in Appendix Table A1 and summary statistics in Table 1.
|Household (food)||Cell (no|n-food)|||
|---|---|---|---|---|---|
|||Wile contribu|tion|Husband contri|bution|
|Amount<br>(p. cap. inlog)|Amount<br>(p. cap. in log)<br>(|Amount<br>p. cap. in log)|Share|Amount<br>(p. cap. in log)|Share|
|(1)|(2)|(3)|(4)|(5)|(6)|
|PanelA: equation (3), incl. time t and #, union FE!||||||
|Estimatedriskofpolygamy,F,<br>-3.062***<br>|-4.696*<br>|16.150"<br>|0.903**<br>|11.783*<br>|—0.925*<br>|
|<br><br>(0.849)|(2.949)|(6.269)|(0.410)|<br>(6.251)|<br>(0.502)|
|Number of observations<br>1532|1548|1548|1458|1548|1548|
|Numberofunions<br>766|774|774|729|774|774|
|<br><br>Within R2<br>0.019|0.016|0.056|0.011|0.016|0.15|
|Panel B: equation (4), incl. time-varying controls X,,|t and ¢°, union FE|||||
|Estimatedriskofpolygamy,F;<br>-2.140"**<br>|-3.650<br>|16.805**<br>|0.870*<br>|17.631**<br>|1.0728<br>|
|<br><br>(0.716)|(2.978)|(6.810)|<br>(0.482)|(6.851)|(0.563)|
|Numberofobservations<br>1470|1482|1482|1396|1482|1482|
|<br><br>Number of unions<br>735|TAL|741|698|741|741|
|Within R2<br>0.10|0.024|0.066|0.018|0.025|0.16|
|Panel C: equation (5), incl. 1st-step controls x time X|, x t, X,,<br>t and,|union FE*||||
|Estimated risk of polygamy, F,<br>-1.779*|-6.381*|1.420|-0.155|16.452*|1.484"|
|(0.922)|(3.466)|(8.397)|(0.613)|(9.466)|(0.769)|
|Numberofobservations<br>1470|1482|1482|1396|1482|1482|
|<br><br>Number of unions<br>735|741|741|698|741|7Al|
|Within R2<br>0.15|0.058|0.13|0.097|0.060|0.21|
|Unconditionalmean(wave1)<br>11.8|10.3|4.40|0.25|8.04|0.61|

Panel union fixed effect model estimates. Standard errors in parentheses are bootstrapped (300 replications); + p<0.12, * p<0.10, ** p<0.05,*** pO. Dependent variables: col (1): Level of per capita household food consumption (in log); col. (2): Level of per capita cell non-food consumption; col. (3): Level of per capital cell non-food consumption funded by the wife (in log), col (4.) share of cell non-food consumption funded by the wife; col. (5): Level of per capital cell non-food consumption funded by the husband (in log), col (4.) share of cell non-food consumption funded by the husband. Sample: monogamous unions in the two waves; All regressions include union fixed-effects * Controls not shown: time (in months) between the two survey waves and its square Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents. * Control not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents, all step-1 controls (see Table 1) interacted with time Data source: PSF Survey, waves | and 2 (2006-2007 and 2010-2012)
64 that these country's strong egalitarian public-health programs could improve access **and use** of health care even among the least educated mother* (Bohn, 1980). Other economic hypotheses for **the differences** in the relative magnitude of schooling effects on child **mortality are analyzed** in Colombia (Rosenzweig **and** Schultz, 1982b), **and** discussod **generally elsewhere** (Schultz, 1984; Thomas at *1.. 1987).

Is **education** simply correlated with **the use** of **more** health inputs, or does education provide **a** mother with **the capacity** to cope with health risks and better manage her child's environment? In Colombia an analysis of the 1973 **census** indicated that controlling for household incom or husband's education or **the** marital status of the **mother** did not eliminate or **even** greatly **reduce the** indepondent role of mother's education **as** a partial explanation for her children's survival (Schultz, 1980a). Studios elsewhero showed that it **ws** possible to control for many lifetime events and changes in socioeconouic status in relation to child mortality **rates** and still mother's education had its effect (Farah and Preston, 1982). In Brazil a third of the mother's education effect seems to operate through family income (Thomaz sat al, 1987). However, in addition to influencing child mortality, mother'p education undoubtedly influences many of **these** intercorrelated variables, such as migration, labor market behavior, **use** of health **care, and** modern attitudes. Therefore, to control statistically for all **these** types of variables is likely to understate **the** net effcet of schooling **(Kensch** atAl., 1986).

The puzzle rnmains why mother's education explains more of **the** variation in child mortality than other variablos describing **the** individual 's access to health care, or prices, or even totoil family income that could be spent on health care? Three competing hypotheses **are** (1) that **the more educated mother**
6 Table 11_ Infant mgrtalitX ratest DHS. the Wo2rld Bank. and the VA

|1|971-7|5|19|76-80||1981|-86|
|---|---|---|---|---|---|---|---|
|DHS|WB|UN|DHS|UN|DHS|WI|UN|
|Burundi<br>100|132|135|103|130|75|115|124|
|Liberia<br>192|112|155|164|143|144|89|132|
|Mali<br>170|196|203|156|191|108|-33|180|
|Senegal<br>120|157|162|97|154|86|124|142|
|Sri Lanka<br>40|<br>37|56|39|48|25|29|39|
|Thailand<br>55|<br>60|65|41|56|35|51|48|
|Morocco<br>104|115|122|92|110|73|93|97|
|Dominican<br>Rep.<br>80|<br>88|94|75|84|68|51|75|
|Ecuador<br>97|<br>87|95|72|82|58|50|70|
|Guatemala<br>92|<br>87|95|86|82|73|66|70|
|Trinidad<br>&Tobago 47|<br>28|30|37|26|26|29|24|

Note: 1971-75 refers to 1975 for the World Bank. 1981-86 refers to any year between 1981 and 1986 for the World Bank. For DHS, 1971-75 sometimes covers 1972-76 or 1972-77. For the UN, 1976-80 is 1975-80, and 1981-86 is 1980-85.

Note, in the above example, only the DHS data are fully based on actual observations. World Bank and UN data uve both actual data and resort to irn.er- and extrapolations for years with missing data. It is, however, Impossible for the data user to discern the actual from the projected or "filled" data, without inside knowledge of the data processing.

In addition, actual data from DHS, the Bank or the UN differ in their nature. Bank and UN data, are a compilation of survey data such as those from DHS, censuses, and civil regirtration sources, based on the respective organization's best judgment. Thus, the final Bank and UN data may differ as they reflect adjustments and accuracy cross-checks based on other available estimates.