rafmacalaba/datause-encoder-data huggingface dataset:
7 remove any bias caused by selection on observable variables, but would leave the possibility of bias due to selection on _unobservable_ variables. It is plausible, for example, that people enrolling in NCMS have unobserved characteristics that predispose them to higher out-of-pocket spending—i.e. the scheme suffers from adverse selection. A single difference between the treated and untreated after the program’s introduction—even after matching—would give an upward biased estimate of the program’s impact, and may even show the program increasing out-of-pocket spending. This turned out to be the case with the dataset used in this paper. By combining matching with double differencing we can remove any bias due to selection on _timeinvariant_ unobservables. This is analogous in a regression setting to estimating a fixed-effects model on panel data collected before and after the program’s implementation, while single differencing with matching is analogous to estimating a regression on data collected after the program’s implementation. The identification assumptions in the regression and matching approaches are the same, but the matching method has the attraction of not requiring the specification of a model (let alone a linear model) for the outcome; all that is required as far a model is concerned is the estimation of a probit (or similar) to obtain the propensity scores (cf. e.g. Jalan and Ravallion 2003; Ravallion 2007).

It is conceivable that NCMS may have spillover effects on nonmembers. If this is the case, we need to be clear about what is being measured when comparisons are made between members in the scheme and the two comparison groups (Janssens 2005). Suppose that facilities get upgraded as a result of NCMS or in anticipation of NCMS’s rollout. These changes may improve the quality of care or other attributes of service use, such as convenience. Nonmembers as well as members may benefit, and increase their utilization. Or suppose that following NCMS’s introduction, providers start following guidelines and protocols on the use of drugs and diagnostic tests, and that they abide by them for nonmembers, as well as members. Even nonmembers would benefit in terms of lower out-of-pocket spending and better quality care, and may be encouraged to use facilities more. Then comparing members and suitably matched nonmembers in NCMS counties would understate the _total_ benefits of NCMS; it would provide a measure of the _net_ benefits accruing to NCMS members—the extra benefits accruing to people in NCMS counties who opted to join NCMS. Or it may be the case that non-members are _adversely_ affected by NCMS. For example, providers may respond to NCMS cost-containment measures by inducing more demand for their services among nonmembers. So, while NCMS
<!-- Start of picture text --> Broad Norm: Men and women should have equal opportunities<br>Personal Belief and Perceived Community Norms<br>D : : : : : : : : : : : : : : : : ; : :<br>United States of America . . . - - . . . . . . . . . : =? .<br>Visa : : : : : : : : : : : : : -——_— # : :<br>Netherlands ee<br>United Kinedam : : : : . : . : : : : : : ee<br>Bangladesh : : : : : : : : : : : : OOOO : :<br>Laos —_—_—<br>Belgium. : : : : : : : : : : : : : : 14——————<br>Jamaica : : : : : : : : : : : : : ———_——____—-* :<br>= : : : : : : : : : : : : : 4 : : : 5 :<br>Coun 2: : : : : : : : : : : : . ° : : : : : :<br>Japan |<br><!-- End of picture text -->

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Policy ReseaRch WoRking PaPeR 4624

# **Abstract**

There are several possible explanations for the observed changes in inequality, the returns to education, and the gap between the wages of informal and formal salaried workers in Argentina over the period 1980-2002. Largely due to the lack of evidence for competing explanations, skill-biased technical change is the most likely explanation for the increases in the returns to education that occurred in the 1990s. Using a semi-parametric re-weighting variance decomposition technique and data from the Permanent Household Survey, the authors show that during the same period there was an increase in the returns to unobserved skill. This finding lends support to the hypothesis that skill-biased technical change has been a main driver of increases in inequality in Argentina. The pattern of changes suggests that the growth in returns to unobserved skill may have been partly responsible for the relative deterioration of informal salaried wages during the 1990s This paper—a product of the Poverty Sector Unit, Latin America and the Caribbean Region—is part of a larger effort in the department to understand poverty and inequality in Latin America and the Caribbean. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gdemombynes@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
|Self Employed|P10<br>P50<br>P90|(7)<br>(8)<br>(9)|-0.009<br>0.007<br>0.08<br>[0.012]<br>[0.014]<br>[0.028]***|5,239<br>5,239<br>5,239<br>0.016<br>0.495<br>1.217<br>[0.194]<br>[0.297]*<br>[0.518]**|5,239<br>5,239<br>5,239|ression of the city variables reported at the top of|teristics. Panel B reports the instrumental variable|or enforcement. City level characteristics include|, its square and interactions with the number of|with the number of inspectors per firm in the state|ntrols for income per capita, population, average||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|arners|P90|(6)|0.016<br>[0.024]|5,240<br>0.497<br>[0.473]|5,240|cients of the reg|city level charac|an instrument f|state capital city|and interactions|he city level co||
|rmal Wage E|P50|(5)|-0.011<br>[0.012]|5,240<br>0.033<br>[0.227]|5,240|estimated coeffi|bles and several|m in the state as|distance to the|osts, its square|also includes t||
|Info<br>Formal Wage Earners|P10<br>P50<br>P90<br>P10|(1)<br>(2)<br>(3)<br>(4)|Log Inspected Firms per firm in city<br>-0.085<br>-0.148<br>-0.068<br>-0.016<br>[0.016]***<br>[0.028]***<br>[0.027]**<br>[0.010]<br>Panel A: OLS|Observations<br>5,237<br>5,237<br>5,237<br>5,240<br>Log Inspected Firms per firm in city<br>-0.540<br>-1.723<br>-1.642<br>-0.208<br>[0.325]*<br>[0.664]***<br>[0.626]***<br>[0.166]<br>Panel B: IV|Observations<br>5,237<br>5,237<br>5,237<br>5,240|Standard errors in brackets, * significant at 10%; ** significant at 5%; *** significant at 1%. Panel A reports the|each column on the log of the number of inspected firms per firm in the city, controlling for state level dummy varia|estimates using city distance to the nearest labor office interacted with the number of state level inspectors per fir|distance to the nearest labor office, its square and interactions with state level measures of institutional quality,|inspectors per firm in the state and interactions with state level measures of institutional quality, city transportation c|and interactions with state level measures of institutional quality, city altitude, city latitude and city longitude. It|schooling and share of urban population in 1991, 1980 and 1970.|
**Table 10: Including Ethno-linguistic Fragmentation, with DIVIDED**

||(1)|(2)|(3)|
|---|---|---|---|
||Total<br>Expenditure|Total<br>Tax<br>Revenues|Deficit|
|Divided|-0.025<br>(0.017)|-0.02<br>(0.012)*|0.013<br>(0.008)*|
|Ethno-linguistic|-0.90|-0.056|0.041|
|Fragmentation|(0.065)|(0.040)|(0.022)*|
|Turnout|0.047<br>(0.045)|0.037<br>(0.033)|0.02<br>(0.017)|
|Majoritarian System|0.001|-0.012|0.0004|
|(=1, 0 if Proportional)|(0.017)|(0.012)|(0.007)|
|Presidential System|-0.032|-0.030|0.0008|
|(=1, 0 if Parliamentary)|(0.019)*|(0.014)**|(0.007)|
|Regular|-0.017|-0.004|-0.01|
|Elections<br>(=1, 0 otherwise)|(0.014)|(0.01)|(0.007)|
|Observations|174|174|191|
|R-squared|0.49|0.64|0.25|

Robust standard errors in parentheses, clustered by country.

* significant at 10%; ** significant at 5%; *** significant at 1%. Controls as in Table 1. Countries included in the sample: Argentina, Australia, Austria, Bangladesh, Belgium, Belize, Bolivia, Botswana, Brazil, Canada, Chile, Colombia, Costa Rica, Cyprus, Denmark, Ecuador, El Salvador, Finland, France, Germany, Ghana, Greece, Guatemala, Honduras, Hungary, Iceland, India, Ireland, Italy, Japan, Malawi, Malaysia, Mexico, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Norway, Pakistan, Paraguay, Peru, Philippines, Portugal, Senegal, South Africa, Spain, Sri Lanka, Sweden, Switzerland, Thailand, Turkey, Uganda, United Kingdom, United States, Uruguay, Venezuela, Zambia, Zimbabwe

36
**Table A.9. Restricted irrigation: median norovirus infection risks from the ingestion of 1−10 mg of wastewater-saturated soil per day for 300 days per year estimated by 10,000 Karavarsamis-Hamilton Monte Carlo simulations**<sup>_a_</sup>

|**Noroviruses**|<br>**Median norovirus**|
|---|---|
|**per 100 g soil**|**infection risk pppy**|
|100−1000|0.98|
|10−100|0·32|
|1−10|3·7 × 10<sup>−2</sup>|

> _a_ Assumptions: soil quality per 100 g taken, as a worst-case scenario, as the wastewater quality per 100 mL; 105 noroviruses per 10<sup>5</sup> _E. coli_ ; no pathogen die-off; dose-response data (Teunis _et al_ ., 2008) ± 25%.

_Source:_ Mara and Sleigh, 2010c.

**Table A.10. Restricted irrigation: median** **_Ascaris_ infection risks from the ingestion of 1−10 mg of wastewatersaturated soil per day for 300 days per year estimated by 10,000 Karavarsamis-Hamilton Monte Carlo simulations**<sup>_a_</sup>

|**_Ascaris_eggs**|<br>**Median****_Ascaris_ infection**|
|---|---|
|**per kg soil**|**risk pppy**|
|100−1000|0·14|
|10−100|1.5 × 10<sup>−2</sup>|
|1−10|1·5 × 10<sup>−3</sup>|

> _a_ Assumptions: soil quality (eggs per kg) taken, as a worst-case scenario, as the wastewater quality (eggs per liter); _N_ 50 = 859 ± 25% and α = 0·104 ± 25%; no pathogen die-off.

_Source:_ Mara and Sleigh, 2009c.

88
and easy job match. The employers who used social networks as a common mode of job advertisements filled vacancies in a shorter period and felt less difficulty in filling vacancies compared with the employers who did not so commonly use social networks. This finding suggests that social networks allow employers to quickly and easily fill vacancies. Social networks do not necessarily bring the best workers, but employers set lower wages for worse workers.

In developing countries, many job seekers may have insufficient financial sources for job search and be very responsive to search costs. In particular, there may be a substantial number of poor job seekers who cannot help but using social networks due to lack of credits even if their returns to finding good-match jobs through open channels are sufficiently high.

This paper relates to the broad literature on social networks in labor markets (Beaman, 2016; Topa, 2011; Ioannides and Datcher Loury, 2004 for excellent reviews). We contribute to it by providing solid empirical evidence showing that social networks serve as fast and easy but narrow-range job matching. This aspect of social networks in labor markets, as well as the negative consequence that network-based job search causes mismatches, has been largely missed in the literature. The aspect is studied only by Bentolila et al. (2010) and Pellizzari (2010), to the best of our knowledge. Most of the previous studies highlight other aspects, such as implied by the screening and the peer effects hypotheses, which lead to the positive consequence that social networks generally improve labor market efficiency and do not bring about job mismatches.

Our empirical findings that social networks use is associated positively with turnover and negatively with salary are exceptional. The literature mostly finds opposite results. For example, using novel datasets of nine large firms, most of which are based in the U.S. and all of which have employee referrals programs, Burks et al. (2015) show that referred workers have lower turnover and similar or higher earnings. Brown et al. (2016) use data of a single firm and observe that referred workers have lower turnover and higher wage. Simon and Warner (1992) and Kugler (2003) also identify positive associations between social networks use and wages in the U.S. Outside the U.S., Hensvik and Skans (2016) and Dustmann et al. (2016) identify the positive associations with wages in Sweden and Germany, respectively. Conducting experiments in an online job marketplace, Pallais and Sands (2016) observe that referred workers have lower turnover. By contrast, only a handful of papers find similar results to ours. Using datasets from the U.S. (the Multi-City Study of Urban Inequality 1992–1994) and Europe (the European Community Household Panel, ECHP), Bentolila et al. (2010) find that workers who found jobs through social networks earned less. Pellizzari (2010) uses the same European data 3
|Woman and Child Development Project|Peter A. Berman|
|---|---|
|Woman and Child Development Project|Peter A. Berman|
|Woman and Child Development Project|Peter A. Berman|
|Woman and Child Development Project|Peter A. Berman|
|Conservation and Sustainable Use of Medicinal Plants Project|Malcolm Alexius Jansen|
|Conservation and Sustainable Use of Medicinal Plants Project|Malcolm Alexius Jansen|
|Conservation and Sustainable Use of Medicinal Plants Project|Malcolm Alexius Jansen|
|Conservation and Sustainable Use of Medicinal Plants Project|Malcolm Alexius Jansen|
|Conservation and Sustainable Use of Medicinal Plants Project|Malcolm Alexius Jansen|
|Conservation and Sustainable Use of Medicinal Plants Project|Malcolm Alexius Jansen|
|Cyclone EmergencyRehabilitation Project|Christian A. Rey|
|Cyclone EmergencyRehabilitation Project|Christian A. Rey|
|Cyclone EmergencyRehabilitation Project|Christian A. Rey|
|Cyclone EmergencyRehabilitation Project|Christian A. Rey|
|Cyclone EmergencyRehabilitation Project|Christian A. Rey|
|Transport and Urban Infrastructure Project|Matar Fall|
|Transport and Urban Infrastructure Project|Matar Fall|
|Transport and Urban Infrastructure Project|Matar Fall|
|Transport and Urban Infrastructure Project|Matar Fall|
|Transport and Urban Infrastructure Project|Matar Fall|
|Transport and Urban Infrastructure Project|Matar Fall|
|Transport and Urban Infrastructure Project|Matar Fall|
|Mulanje Mt. BiodiversityConservation Project|CaryAnne Cadman|
|Mulanje Mt. BiodiversityConservation Project|CaryAnne Cadman|
|Mulanje Mt. BiodiversityConservation Project|CaryAnne Cadman|
|Mulanje Mt. BiodiversityConservation Project|CaryAnne Cadman|
|Mulanje Mt. BiodiversityConservation Project|CaryAnne Cadman|
|Mulanje Mt. BiodiversityConservation Project|CaryAnne Cadman|
|Mulanje Mt. BiodiversityConservation Project|CaryAnne Cadman|
|Mulanje Mt. BiodiversityConservation Project|CaryAnne Cadman|
|Mulanje Mt. BiodiversityConservation Project|CaryAnne Cadman|
|Mulanje Mt. BiodiversityConservation Project|CaryAnne Cadman|
|Coastal and Marine BiodiversityManagement Project|Jean-Christophe Carret|
|Coastal and Marine BiodiversityManagement Project|Jean-Christophe Carret|
|Coastal and Marine BiodiversityManagement Project|Jean-Christophe Carret|
|Coastal and Marine BiodiversityManagement Project|Jean-Christophe Carret|
|Coastal and Marine BiodiversityManagement Project|Jean-Christophe Carret|
|Coastal and Marine BiodiversityManagement Project|Jean-Christophe Carret|
|Coastal and Marine BiodiversityManagement Project|Jean-Christophe Carret|
|Coastal and Marine BiodiversityManagement Project|Jean-Christophe Carret|
|Coastal and Marine BiodiversityManagement Project|Jean-Christophe Carret|
|Coastal and Marine BiodiversityManagement Project|Jean-Christophe Carret|
|Economic RecoveryCredit(03)Project|Manuela V. Ferro|
|Economic RecoveryCredit(03)Project|Manuela V. Ferro|
|Cape Peninsula BiodiversityConservation Project|Christopher James Warner|
|Cape Peninsula BiodiversityConservation Project|Christopher James Warner|
|Cape Peninsula BiodiversityConservation Project|Christopher James Warner|
**Figure 5: Estimated Sector-Level Gravity Model Coefficients, 1970-99.**

<!-- Start of picture text --> log(distance) log(pop_exporter) log(area_exporter) log(pop_importer)<br>1970 1980 1990 2000 1970 1980 1990 2000 1970 1980 1990 2000 1970 1980 1990 2000<br>log(area_importer) landlocked border border*log(distance)<br>1970 1980 1990 2000 1970 1980 1990 2000 1970 1980 1990 2000 1970 1980 1990 2000<br>border*log(pop_exporter) border*log(area_exporter) border*log(pop_importer) border*log(area_importer)<br>1970 1980 1990 2000 1970 1980 1990 2000 1970 1980 1990 2000 1970 1980 1990 2000<br>border*landlocked<br>1970 1980 1990 2000<br>.5 0 .2 1<br>0 .8<br>-.5 0 .6<br>-1-.5 -1 -.2 .2.4<br>-1.5 -1.5 -.4 0<br>.1 1 10 1<br>0 0 5 .5<br>0<br>-.1 -1 0<br>-5<br>-.2 -2 -10 -.5<br>-.3 -3 -15 -1<br>1 .4 1 .4<br>.2 .2<br>.5 0 .5 0<br>0 -.2-.4 0 -.2-.4<br>-.5 -.6 -.5 -.6<br>3<br>2<br>1<br>0<br>-1<br>-2<br><!-- End of picture text -->

**Figure 6: Estimates and Two-Standard Error Bands for the Distance Coefficient, by Sector and over Time.**

<!-- Start of picture text --> 311: Food products 313: Beverages 314: Tobacco 321: Textiles 322: Wearing apparel 323: Leather products<br>324: Footwear 331: Wood products, ex. furnit . 332: Furniture, except metal 341: Paper and products 342: Printing and publishing 351: Industrial chemicals<br>352: Other chemicals 353: Petroleum refineries 354: Misc. petr. and coal prod . 355: Rubber products 356: Plastic products 3 61: Pottery, china, earthenwar e<br>362: Glass and products 36 9: Oth. non-metal. mineral pro d. 371: Iron and steel 372: Non-ferrous metals 381: Fabricated metal prod. 382: Machinery, ex. electric<br>1970 1980 1990 2000 1970 1980 1990 2000<br>383: Machinery, electric 384: Transport equipment 385: Prof. & scient. equip. 390: Other manufacturing<br>1970 1980 1990 2000 1970 1980 1990 2000 1970 1980 1990 2000 1970 1980 1990 2000<br>.5<br>0<br>-.5<br>-1<br>-1.5<br>.5<br>0<br>-.5<br>-1<br>-1.5<br>.5<br>0<br>-.5<br>-1<br>-1.5<br>.5<br>0<br>-.5<br>-1<br>-1.5<br>.5<br>0<br>-.5<br>-1<br>-1.5<br><!-- End of picture text --> 26
Policy Research Working Paper 9920

# **Abstract**

More than 20 developing countries do not publish any data on their sovereign debt. In those that do disclose data, public debt statistics usually do not comply with international standards in terms of coverage and definitions. Some information can be deduced through indirect disclosure of debt statistics to external agents, such as the World Bank and the International Monetary Fund, and this can help minimize data gaps. This paper has two main objectives. First, it measures the extent of transparency in direct reporting and identifies the factors that promote it. The results show that debt transparency is fostered by standardized recording and reporting systems, high levels of external scrutiny (for example, Eurobond issuance and ratings), and the presence of highly skilled staff at the local debt office. Second, the paper describes the reporting ecosystem in which two type of channels (direct and indirect) coexist and provides novel estimates of the data gaps across the two. Cross-comparison of direct reporting and the World Bank– International Monetary Fund Debt Sustainability Analysis shows that deviations in public debt stocks can represent up to 30 percent of national gross domestic product. Based on these results, the paper puts forward a call for action to (i) improve debt transparency by focusing on those factors that best promote transparency; (ii) shifting the focus of multilateral development banks’ operations and technical assistance from indirect to direct reporting; (iii) introducing minimum but enforceable international standards for direct reporting; and (iv) promoting the use of modern and integrated debt recording and reporting systems.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 author may be contacted at drivetti@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