rafmacalaba/datause-encoder-data huggingface dataset:
(2021). However, there remains no visible difference between time spent in orange and red categories compared to green and yellow within each year. Results by educational level in Appendix Table A.4 confirm our previous results that younger children were more affected, but once again we find no statistically significant differences of longer time spent in either the orange or red category in dropout. While we find some differential effects in other outcomes, we are cautious with their interpretation given the number of estimated parameters and hypothesis tests carried out. Considering the complete set of results that differentiate by color category, we interpret the findings as suggesting that greater exposure maintains its negative effects on educational outcomes in Guatemala, but there is no difference between the two highest infection categories.

The results presented in this section depend on the assumption that outcome trends in municipalities with greater and lower warning indices were parallel before the pandemic. In order to test the validity of this assumption, we carry out a placebo experiment. The values of the warning index are equal to zero before 2020 and we only have this information for 2020 and 2021. One way to test whether pre-period trends are parallel is to assume that the pandemic began in 2019. For this procedure, we drop data from 2021. Then, we assign the warning index values from 2020 to 2019 and the indices from 2021 to 2020, as if the pandemic had begun one year earlier. We then estimate differential effects of the warning index by year (for 2019 and 2020, respectively). If parallel trends hold, the coefficient on the interaction should be statistically insignificant or if trends were reversed, they should be of the opposite sign than our estimates in previous tables.

Table 6 presents the results of this placebo experiment. The coefficients for the interaction between the “placebo” warning index and 2019 for dropout are all insignificant, suggesting that trends in this outcome were parallel between municipalities with higher and lower warning indices. The coefficient on the warning index with 2020 confirms the increase in dropout we observe in 25
**Box 4 Sample from the Rotter Internal-External (I-E) Locus of Control Scale (1966)** a. Many of the unhappy things in people’s lives are partly due to bad luck. b. People’s misfortunes result from the mistakes they make. a. In the long run, people get the respect they deserve in this world. b. Unfortunately, an individual’s worth often passes unrecognized no matter how hard he tries. a. In my case, getting what I want has little or nothing to do with “luck”. b. Many times we might just as well decide what to do by flipping a coin. _Response option is a or b. In the Valecha (1972) scale, the respondent is further asked to indicate to what extent the statement is (1) much closer or (2) slightly closer to his or her opinion._

This scale has proven popular across disciplines due to its high internal validity. For instance, within economics it was used by Heckman et al. (2006) and Heckman and Kautz (2012) to measure its predictive ability on a range of long-term success outcomes for children who received early childhood programs or formal schooling.

Since it was first proposed, the measurement of locus of control has evolved in response to two sets of critiques. The first, laid out in Reid and Ware (1973), is that LOC is a multidimensional construct consisting of several different factors and should be measured as such. In response, Levenson (1981) developed the Internality, Powerful Others and Chance (IPC) scale to distinguish multiple dimensions within the external side of the LOC continuum (specifically, whether individuals expect outcomes to be a function of chance, luck, or fate, or to be under the control of powerful others). Both the Rotter I-E scale and the Levenson IPC scale have been used and validated in Sub-Saharan Africa for well-educated, professional sub-populations (e.g., Stocks et al. 2012 in South Africa and Abbas 2016 in Nigeria). One of the few applications of these scales to low-literacy populations was conducted by Bernard et al. (2014) in Ethiopia, using the IPC scale. The Cronbach alpha estimates at baseline showed adequate internal consistency: α=0.68 for the Chance subscale, α=0.74 for the Powerful Others subscale, and α=0.75 for the Internality subscale. Bossuroy and Tiongson (forthcoming) find similar Cronbach alphas in Niger using the nationally-representative National Survey on Household Living Conditions and Agriculture. Among studies that assessed gender differences, female respondents tended to have greater external locus of control than male respondents.

The second line of critique began with Dixon, McKee and McRae (1976), who criticized the measurement of generalized LOC as a “sledgehammer” approach and instead advocated situation-specific measurement. As a result, a wide range of domain-specific locus of control scales have been developed, mostly for education- and health-related activities, such as the Multidimensional Health Locus of Control 17
Table 31. Indonesian Exports, ICT use, and Log of Skills Measures Excluding Industries Using U.S. O*Net database. All workers. 2011-2015

||||||No|n-routine<br>|skill measure<br>|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Ana|lytical|||Interp|ersonal|||M|anual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000412||0.000407|0.000333|0.000478||0.000475|0.000403|-0.000488||-0.000481|-0.000370|
||(0.000134)***||(0.000134)**|*(0.000127)***|(0.000151)***||(0.000151)***|(0.000148)***|(0.000202)**||(0.000201)*|* (0.000216)*|
|Share of hhlds with computer||-0.0733|-0.0727|-0.0814||-0.0615|-0.0606|-0.0690||0.118|0.117|0.130|
|||(0.0565)|(0.0569)|(0.0573)||(0.0684)|(0.0685)|(0.0691)||(0.0737)|(0.0744)|(0.0758)*|
|IDN exports per worker *||||0.00174||||0.00167||||-0.00262|
|Share of hhlds with computer||||(0.000781)**||||(0.000887)*||||(0.00103)**|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.868|0.868|0.868|0.868|0.637|0.638|0.637|0.637|0.706|0.707|0.706|0.706|

|||Ma|nual|Routine skil|l measures|Cog|nitive||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|-0.000377||-0.000376|-0.000293|0.000520||0.000510|0.000402|
||(0.000169)**||(0.000169)**|(0.000179)|(0.000187)***||(0.000186)**|*(0.000197)**|
|Share of hhlds with computer||0.0425|0.0410|0.0507||-0.142|-0.140|-0.153|
|||(0.0702)|(0.0699)|(0.0712)||(0.0737)*|(0.0747)*|(0.0759)**|
|IDN exports per worker *||||-0.00194||||0.00256|
|Share of hhlds with computer||||(0.000927)**||||(0.000939)***|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.963|0.963|0.963|0.963|0.812|0.813|0.813|0.812|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2004 U.S. O*Net database, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and linear province time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

48
29 Table S. 6: State-level regressions of caloric intake on Total Energy Expenditure (TEE)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES|State cal.|State cal.|∆cal. 1983-2005|∆cal. 2005-2012|
|State TEE||0.689***|||
|||(0.251)|||
|∆TEE|||0.440|0.666*|
||||(0.630)|(0.332)|
|round43|-8.394|-7.189|||
||(24.181)|(23.798)|||
|round50|-45.295|-60.667|||
||(36.081)|(38.507)|||
|round55|-15.561|-29.320|||
||(35.601)|(37.377)|||
|round61|-101.569**|-128.567***|||
||(42.070)|(43.343)|||
|round66|-167.331***|-184.037***|||
||(42.201)|(37.649)|||
|round68|-139.499***|-152.540***|||
||(41.439)|(41.625)|||
|Constant|2,185.355***|714.280|-122.089***|-24.294*|
||(31.172)|(536.167)|(40.467)|(13.028)|
|Observations|222|222|31|32|
|R-squared|0.757|0.768|0.013|0.086|
|State FE|Yes|Yes|No|No|

_Source_ : Authors’ calculations using India Time-Use Survey 1998-1999, National Family Health Survey rounds 2, 3, and 4 and National Sample Survey rounds 38, 43, 50, 55, 61, 66 and 68.

_Note_ : Standard errors clustered by state in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Columns 1 and 2 use the full panel of state caloric intake and TEE, with state fixed effects. Columns 3 and 4 are the regression equivalents of Figure 2 in the paper, using only the within-state long-difference for two periods.
Table 3

|**Leverage Ratio of Private Infrastructure**<br>**Projects**<br>**(by sector)**|
|---|
|Sector<br>Mean<br>Sample Size<br>(standard deviation)|
|Power<br>73.07<br>28<br>(8.15)|
|Roads<br>63.07<br>15<br>(15.43)|
|Transport<br>77.86<br>7<br>(8.09)|
|Telecoms<br>61.25<br>4<br>(2.50)|
|Waste/Water<br>75.00<br>4<br>(12.25)|
|Gas<br>67.00<br>1|

* Percentage ratio of total debt (book value) to a project's investment cost

Source: Authors' estimates based on a sample of infrastructure projects closed between 1994 and 1996 (see below).

# **III. Analytical Framework**

To motivate the discussion of determinants of the risk premia on private foreign currency loans, we begin with a highly simplified model of bank lending with exogenously specified probability of country default, or financial distress. The key factor that distinguishes a foreign loan from a domestic one is the presence of country risk. The importance of country risk in internationally-funded infrastructure projects is highlighted by the fact that even if a project is commercially viable, its ability to service its foreign debt or equity depends on broader governmental policies regarding capital mobility and currency convertibility, which are beyond the control of the project entity. Technically, consider the required return to creditors, or cost of debt capital, from a particular project in a given country. This required rate of return is denoted by i and can be expressed as: _i= r_ + _s,_ where r is the risk-free rate of interest, and _s_ is a variable reflecting the market's combined assessment of country and project risk. In what follows, we develop a simple model of how _s_ is determined as a function of specific project and country risk factors.

Assessment of a country risk premium can rely on ratings assigned by credit rating agencies to the country's foreign currency debt obligations, andlor information on secondary market trading of sovereign bond issues, if available. In assigning such ratings, agencies are known to take into account numerous economic, social, and political factors. In a study of the behavior of two US credit agencies, the Moody's and Standard and Poor's, Cantor and Packer (1996) found that six factors appear to play an important role 11
# **Section 1: Introduction**

One of Africa’s most challenging problems is the HIV/AIDS epidemic ravaging the continent. There has been extensive research done on different aspects of the epidemic to try to understand the reasons it has spread so extensively and how to best target prevention efforts. A great deal of evidence from a variety of contexts shows that divorced, separated, and widowed individuals in Sub-Saharan Africa have an increased risk of being infected with HIV. Less has been written about what happens to these widows and divorced individuals (Caldwell 2007). If they have a high likelihood of being HIV positive and they are reinitiating sexual activity, it is important to consider their next steps after divorce or widowhood when trying to understand the way HIV may be spread.

Using nationally representative surveys across 13 African countries, this paper will investigate the proportion of those divorced, separated, and widowed that remarry and their risk of HIV infection. In order to better understand the magnitude of the problem, we compare the number of individuals and the degree of HIV prevalence of those remarried to formerly married individuals. Using multivariate regression analysis, we also analyze possible confounding factors to establish that the increased HIV prevalence of remarried individuals is truly associated to remarrying and not some other variable. We conclude that, in almost all of the countries examined, there are high rates of remarriage and these remarried individuals have significantly higher rates of HIV prevalence than the adult population in general and than other married individuals. We do not claim that the association between remarriage and HIV infection is causal as clearly the causality is more likely to run from HIV infection to divorce or widowhood and then remarriage. But we stress that remarried individuals constitute a large segment of the population that is highly vulnerable to HIV/AIDS but has not been clearly identified as such by existing prevention efforts.

2
- Xu, D. and Q. Feng, 1994. Dangerous Glacier Lakes and Their Outburst Features in the Tibetan Himalayas. Bulletin of Glacier Research 12: 1-8.

- Xu, Z.X., T.L. Gong, and J.Y. Li, 2008. Decadal trend of climate in the Tibetan Plateau— regional temperature and precipitation. Hydrological Processes 22(16): 3056-3065.

- Xu, J., R.E. Grumbine, A. Shrestha, M. Eriksson, X. Yang, Y. Wang, and A. Wilkes, 2009. The Melting Himalayas: Cascading Effects of Climate Change on Water, Biodiversity, and Livelihoods. Conservation Biology 23: 520-530.

- Yamada, T., and C.K. Sharma, 1993. Glacier Lakes and Outburst Floods in the Nepal Himalaya. IAHS Publications-Publications of the International Association of Hydrological Sciences 218: 319-330.

- Yang, B., X. Kang, J. Liu, A. Bräuning, and C. Qin, 2010. Annual Temperature History in Southwest Tibet During the Last 400 Years Recorded by Tree Rings. International Journal of Climatology 30: 962-971.

- Yang, X., T. Zhang, D. Qin, S. Kang, and X. Qin, 2011. Characteristics and Changes in Air Temperature and Glacier’s Response on the North Slope of Mt. Qomolangma (Mt. Everest). Arctic, Antarctic, and Alpine Research 43: 147-160.

- Yang, X., Y. Zhang, W. Zhang, Y. Yan, Z. Wang, M. Ding, and D. Chu, 2006. Climate Change in Mt. Qomolangma Region Since 1971. Journal of Geographical Sciences 16: 326-336.

- Yao, T., J. Pu, A. Lu, Y. Wang, and W. Yu, 2007. Recent Glacial Retreat and Its Impact on Hydrological Processes on the Tibetan Plateau, China, and Surrounding Regions. Arctic, Antarctic, and Alpine Research 39: 642-650.

- Yasunari, T.J., P. Bonasoni, P. Laj, K. Fujita, E. Vuillermoz, A. Marinoni, P. Cristofanelli, R. Duchi, G. Tartari, and K.M. Lau, 2010. Estimated impact of black carbon deposition during pre-monsoon season from Nepal Climate Observatory–Pyramid data and snow albedo changes over Himalayan glaciers. Atmospheric Chemistry and Physics 10: 66036615.

- Ye, Q., T. Yao, S. Kang, F. Chen, and J. Wang, 2006. Glacier Variations in the Naimona’nyi Region, Western Himalaya, in the last three decades. Annals of Glaciology 43: 385-389.

- Yong, N., Y. Zhang, L. Liu, and J. Zhang, 2010. Glacial change in the vicinity of Mt. Qomolangma (Everest), central high Himalayas since 1976. Journal of Geographical Sciences 20: 667-686.

46
|**Tab**|**le 2. Test Sco**<br>**Mathem**|**res by Stra**<br>**atics**|**tum**<br>**Span**|**ish **|
|---|---|---|---|---|
|**Stratum **|**Mean**|**SD **|**Mean**|**SD **|
|Community School|47.3|5.7|52.0|5.2|
|Indigenous School|45.8|5.4|51.5|5.1|
|Public rural school|48.2|6.0|54.0|6.2|
|Public urban school|49.4|5.9|55.6|6.3|
|Private urban school|53.0|6.5|62.9|8.4|
|National|48.7|6.1|54.9|6.8|

Source: Primary Education Assessment Survey, second round 1997 Table 3 shows classroom size, which can be taken as a measure of relative school productivity among stratum. Surprisingly, indigenous schools perform better in this indicator than community schools given that the scoring difference between them is not significant. Classroom size does not differ significantly between private urban schools and public urban schools although variance is greater in the latter.

|**Table 3. Classroom size by Str**<br><br>|**atum**<br>|
|---|---|
|**Stratum **<br>**Mean**|**SD **|
|Community School<br>23.0|1.2|
|Indigenous School<br>22.5|8.0|
|Public rural school<br>21.5|7.1|
|Public urban school<br>24.6|3.5|
|Private urban school<br>24.3|4.5|
|National<br>22.6|6.6|

Source: Primary Education Assessment Survey, second round 1997.

Tables 4-5 below show the distribution of students by learning achievement quintiles. About 45% of students in private urban schools are enrolled in the top quintile of schools, compared to only 6.4 percent of the students from indigenous schools, which has the highest percentage of students enrolled in the bottom quintile of Mexican schools. These results are more pronounced in Spanish, since 61.4 percent of the students in private urban schools are enrolled in the best 20% schools, compared to only 4.0 percent of the students from indigenous schools, which also have the largest percentage of students enrolled in the lowest 20%. The distribution of students enrolled in public urban schools is evenly distributed across quintiles. The distribution of students in public rural schools is biased toward the lowest quintile.

10
Policy Research Working Paper 5243

# **Abstract**

To examine the relationship between patient satisfaction and doctor performance, the authors observed 2,271 interactions between 29 “Red flags” are indicators of potential issues regarding governance failure, collusion or corruption in projects. While some specific red flags can be powerful indicators of issues to be addressed, the hypothesis of this paper is that many proposed red flags are potentially too ubiquitous and randomly distributed to be useful as indicators of significant governance failure. The paper examines project documentation from a small sample of World Bank water and sanitation projects in an attempt to collect data on the presence or absence of 13 commonly accepted red flags. This paper finds that: (i) almost every contract reviewed raised at least one of 13 red flags analyzed; (ii) potentially tainted contracts did not exhibit notably more red flags than control contracts; and (iii) the occurrence of multiple red flags in the same contract was rare enough to suggest that joint occurrence was largely by chance, not as a result of a strongly causal inter-relationship between flags. The ubiquity and apparent randomness of these red flags suggests that their roll-out as a monitoring tool requires additional thought as to interpretation, context and use. The paper examines an alternate tool for uncovering potential problem projects—supplier concentration. Across a very small sample, there does appear to be a relationship between such concentration and potential problem projects. 2 doctors and their patients in This paper—a product of the Finance, Economics and Urban Division in the Sustainable Development Department—is part of a larger effort in the department to learn about approaches to improve governance and reduce corruption in construction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ckenny@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
ICT Adoption and Wage Inequality: Evidence from Mexican Firms<sup>_∗_</sup> Leonardo Iacovone<sup>_†_</sup> Mariana Pereira-L ́opez<sup>_‡_</sup> JEL Classifications: E23, J23, J24, J31, L25, O33 Keywords: ICT, jobs, labor demand, skills, technical change, Mexico

> _∗_ ACKNOWLEDGEMENTS: We thank Jos ́e Mercado, Adriana Arenas, Natalia Volkow, Liliana Martinez, Carmen M ́arquez, and Andrea L ́opez from INEGI for their support with the data use in compliance with the confidentiality requirements set by the Mexican Laws. We also want to thank Pablo Gordillo Couti ̃no for his support with the data. Finally, thanks to Stephen O’Connell and Carlos Corseuil for their valuable comments and suggestions and to all the participants of the Author’s Workshop on Digital technology adoption, skills, productivity and jobs in Latin America as well as to the participants of the Special Session on Digital Technology, Skills and Labor Policy in Latin America of the ASSA conference 2017 for their comments. All remaining errors are naturally our own.

> *This work has benefited from funding by the World Bank’s Latin America and Caribbean’s Chief Economist Office, under the regional study “Digital technology adoption, skills, productivity and jobs in Latin America”.

> _†_ World Bank, email: liacovone@worldbank.org (Corresponding Author)

> _‡_ World Bank and Universidad Iberoamericana Ciudad de M ́exico, email: mpereiralopez@worldbank.org