rafmacalaba/datause-encoder-data huggingface dataset:
was administered to a sample of 1,000 households with an HIV+ positive member, and 600 randomly sampled neighbor households in the catchment area of the facility. We identified HIV/AIDS patients either by contacting the health facility where they received care or via association of PLWHA. We selected them randomly proportional to the number of HIV/AIDS patients attending each facility. We obtained informed consent from the patients before interviewing their household. In the follow-up survey, 85 percent of the baseline households were re-interviewed. The rate of attrition from the baseline sample was not statistically different between the treatment and comparison groups (15 percent each).

The outcome measures are constructed using data from the household surveys. The outcome for individual HTC is an indicator variable for whether the individual has ever been tested for HIV. For the purpose of the HTC analysis, we exclude identified HIV/AIDS patients who by definition are HIV positive and are aware of their HIV status. The sample is further restricted to individuals aged 15 or older. At baseline, the sample is comprised of 438 individuals in the treatment group and 445 in the comparison group. Individuals present at follow-up but not at baseline were not different based on standard socio-demographic characteristics.

For the analysis of couple testing, we create an indicator using the question of whether or not the most recent sexual partners the respondents had in the 12 months prior to the survey had ever been tested for HIV. We further combine the responses about each respondent’s individual testing and the testing of their sexual partners to create an indicator variable for whether both partners in the couple/sexual partnership have ever been tested. We then restrict the sample to individuals living with their sexual partners and who self-reported having had sex in the 12 months prior to the survey. For this analysis, the unit of observation is the couple and we include only one report by couple to avoid double-counting.

# _3.3. Summary statistics and balance at baseline_

Table 2 reports the baseline means of facility characteristics in 2006. Confirming that the evaluation design achieved balance of observed characteristics at baseline between the facilities in the treatment and comparison groups, there are no significant differences between the treatment and comparison groups in terms of rural location, proportion of district hospitals in the sample of facilities, proportion of facilities that are government-assisted or public, size of catchment 8
<!-- Start of picture text --> Correlation with Earnings<br>0.087***<br>All -—e—H<br>~ 0.075**<br>Intra .<br>~ 0.051**<br>Inter -—_—+<br>7<br>Positive Self Concept~ 0.045"<br>~<br>Emotional. Regulation. 0.017<br>~<br>Perseverance H—e—40.192***<br>-<br>ngs gs<br>Personal Initiative -————+——_H0.066**<br>Problem Solving & DecMaking | }+—e—<br>Self Control 9<br>Empathy rf<br>Expressiveness ——<br>Interpersonal Relatedness -9,90<br>Teamwork og<br>0.040 0.000 0040 0080 O<:120 0.160<br><!-- End of picture text -->

<!-- Start of picture text --> e Female Male<br><!-- End of picture text -->
# The Impact of Private Sector Internship and Training on Urban Youth in Kenya

## **Maddalena Honorati**

JEL Classification: I28, J24, J68, O12, C93 Keywords: vocational training, internship, active labor market programs, randomized experiment, private provision.

Acknowledgments: This paper reports findings from the impact evaluation study conducted jointly by the Kenya Private Sector Alliance (KEPSA), Ipsos Synovate, and the World Bank. The team includes Ehud Gachugu (KYEP Director), Josephine Otieno (KEPSA Project Monitoring and Evaluation Officer), Linda Thompson (previous KEPSA Director), Cornelia Tesliuc (World Bank and current project Task Team Leader), Arvil Van Adams (World Bank), Sara Coll‐Black (World Bank), Yasser El Gammal (original Task Team Leader), Samuel Muthoka (Ipsos), Joy Kiiru (Ipsos), Isaac Kamande (GoK‐Director of Youth Affairs), and Alice Githu (GoK‐KYEP Coordinator). Ipsos Synovate carried out the survey and the data collection on which the paper is based. Research assistance was provided by Andres Ham (University of Illinois) and field supervision by Manuela Bucciarelli (World Bank consultant). We are extremely grateful to Owen Ozier, Julieta Trias, Kathleen Beegle, Andrew Brudevold, Omar Arias and Emanuela Galasso for their constructive comments as well as to David Newhouse, Marcus Goldstein, and Alaka Holla for technical guidance during the design stage. The findings, interpretations and conclusions expressed in this paper are entirely those of the author. 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, the Government of Kenya, or KEPSA.
Policy Research Working Paper 10957

# **Abstract**

While populations in low- and middle-income countries are exposed to some of the highest levels of air pollution and its consequences, the majority of economics research on the topic is focused on high-income settings where there is greater data availability. This paper compares and evaluates the three principal sources of air pollution data (regulatory-grade monitors, satellites, and low-cost monitors) in a Sub-Saharan African context in terms of the accuracy of measurements of inhalable fine particulate matter across spatial and temporal frequencies and their performance when studying policy impacts. Satellite data is closely aligned with data from the regulatory-grade monitors at lower temporal frequencies. The low-cost monitors underestimate the amount of fine particulate matter relative to the other data sources. Calibration, especially context-specific calibration, of the low-cost monitors’ data improves its alignment with other data sources. The paper uses each data source to evaluate the air pollution externality of mobility reduction policies using a difference-in-differences design and finds similar results, especially in terms of percent reduction. The paper considers policy makers’ constraints to air pollution monitoring in low-income settings and demonstrates that co-locating one regulatory-grade monitor in a network of low-cost monitors can capture the spatial variation of pollution across an urban area and achieve better accuracy than either of these data sources alone. This provides a framework for policy makers to generate the data needed to evaluate environmental policies and externalities.

This paper is a product of the Development Impact Group, Development Economics. 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 smilusheva@worldbank.org and bridgeth@IADB.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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 4:  Electoral rules, middle class income shares, and leader years in office (dependent variable:** sharegain **going from** MarketP **to** gross **income)**

||(1)<br>Electoral|(2)<br>rules|(3)<br>Income sh<br>middle to<br>top to mid|(4)<br>are ratios,<br>bottom and<br>dle quintiles|(5)<br> <br>Leader y<br>office|(6)<br>ears in|
|---|---|---|---|---|---|---|
||Bottom<br>quintile|Top<br>quintile|Bottom<br>quintile|Top<br>quintile|Bottom<br>quintile|Top<br>quintile|
|Market share of respective<br>quintile, includes public<br>pensions|-0.82<br>(0.000)|-0.12<br>(0.003)|-0.86<br>(0.000)|-0.22<br>(0.010)|-0.73<br>(0.000)|-0.10<br>(0.005)|
|Continuous years of|-0.029|-0.042|-0.002|-0.051|-0.0063|-0.065|
|competitive elections|(0.42)|(0.19)|(0.94)|(0.087)|(0.84)|(0.024)|
|**Largest govt. party is left**<br>**wing**|**0.35**<br>**(0.049)**|**-0.46**<br>**(0.011)**|**0.31**<br>**(0.077)**|**-0.40**<br>**(0.028)**|**0.33**<br>**(0.069)**|**-0.40**<br>**(0.028)**|
|**Age of largest govt. party**<br>**if left-wing**|**-0.005**<br>**(0.044)**|**0.009**<br>**(0.044)**|**-0.006**<br>**(0.043)**|**0.009**<br>**(0.006)**|**-0.006**<br>**(0.022)**|**0.008**<br>**(0.01)**|
|Political system (1 =|0.003|-0.13|||||
|presidential, 2 = semi-pres;<br>3 = parliamentary)|(0.99)|(0.66)|||||
|Mean District Magnitude|-0.0046|0.0062|||||
|<br>House|(0.44)|(0.46)|||||
|Proportional (0) or plurality|1.73|-1.50|||||
|<br>(1) lower house elections|(0.036)|(0.035)|||||
|Ratio of Middle to Bottom|||0.002|-0.006|||
|quintile income shares|||(0.44)|(0.12)|||
|Ratio of Top to Middle|||-0.33|0.48|||
|quintile shares|||(0.056)|(0.066)|||
|Chief Executive Years in|||||-0.021|0.046|
|Office|||||(0.47)|(0.16)|
|Log of ppp-adjusted per|-0.40|0.37|-1.18|1.17|-1.23|1.25|
|<br>capita income, 2005 US|(0.71)|(0.74)|(0.11)|(0.17)|(0.071)|(0.13)|
|<br>dollars|||||||
|Log of total population|-0.95|-0.87|1.24|-2.58|1.07|-2.66|
||(0.75)|(0.76)|(0.58)|(0.27)|(0.62)|(0.22)|
|Percent of population that is|<br>-0.15|0.052|-0.074|0.015|-0.084|-0.0048|
|<br>rural|<br>(0.12)|(0.56)|(0.31)|(0.84)|(0.19)|(0.94)|

18
# **Appendix Table 7. Effect of preschool availability on female’s occupation conditional on employment**

||(1)<br>Professional|(2)<br>Manager|(3)<br>Clerk|(4)<br>Sales|(5)<br>Service|(6)<br>Agricultural|(7)<br>Production|
|---|---|---|---|---|---|---|---|
|**Panel A: Public**||||||||
|Public * Eligible|-0.003|-0.002|0.004|-0.023**|0.009|-0.007|0.006|
||(0.007)|(0.002)|(0.006)|(0.010)|(0.010)|(0.010)|(0.007)|
|Public Preschools|0.001|0.001|-0.005|0.002|-0.004|0.001|0.001|
||(0.005)|(0.001)|(0.004)|(0.011)|(0.009)|(0.010)|(0.009)|
|Eligible Child|-0.001|0.001|-0.009***|0.006|-0.004|0.021***|-0.004|
||(0.002)|(0.001)|(0.002)|(0.004)|(0.003)|(0.004)|(0.003)|
|FDR q-value|0.600|0.600|0.600|0.141|0.600|0.600|0.600|
|**Panel B: Private**||||||||
|Private * Eligible|0.000|0.000|-0.000|-0.001|-0.001|-0.000|0.002**|
||(0.001)|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Private Preschools|0.002***|0.000|0.001|0.004***|-0.003**|-0.003***|-0.000|
||(0.001)|(0.000)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|
|Eligible Child|-0.003|-0.001|-0.007***|0.009|-0.000|0.020***|-0.011**|
||(0.003)|(0.001)|(0.003)|(0.006)|(0.005)|(0.006)|(0.005)|
|FDR q-value|0.841|0.841|0.841|0.841|0.841|0.841|0.298|
|Observations|125,047|125,047|125,047|125,047|125,047|125,047|125,047|
|Mean|0.075|0.003|0.044|0.223|0.150|0.319|0.113|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dummy for being employed in a certain occupation category indicated in column heading, conditional on being employed, on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. Comparison group includes all mothers of children of the wrong ages and non-mothers. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q-values for the interaction coefficient of preschool density and eligible child are computed over all 7 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows. See Table 8 for a list of top-3 occupations per category.

75
Policy Research Working Paper 8864

# **Abstract**

What sorts of conditions make some countries more prone to corruption than the others? This is an important question for understanding how corruption arises and how to combat it. The present paper attempts to answer this question by exploring the link between the size of the country and corruption. Economic theory suggests advantages and disadvantages of being a large country. Fixed costs in monitoring and punishing corrupt politicians and bureaucrats implies lower corruption in larger countries. However, congestion or administrative costs may escalate with country size. Further, greater diversity in the larger countries implies that such countries may find it harder to reach a consensus on growth-enhancing anti-corruption reforms. Thus, the corruption and country size relationship is an empirical issue. Using firm-level survey data for 135 countries, this paper finds that the level corruption experienced by the firms is positively correlated with country size. This holds for a measure of overall corruption and petty corruption that arises in availing specific government services. According to a conservative estimate, moving from a country the size of Namibia (25th percentile level in size) to a country the size of Morocco (75th percentile level) is associated with an increase in the level of overall corruption by 0.28 percentage point or about 23 percent of its mean value. The results are robust to several controls, alternative corruption measures, sample alternations, and different country size measures.

This paper is a product of the Global Indicators Group, Development Economics. 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 mamin@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
such jobs more comparable to the private sector in terms of accountability, work hours, and other conditions may help, as well as efforts to promote and celebrate the private sector. Universities may be able to play a better role here in promoting different career options for graduatestypically Jordanian universities provide limited support to graduates in the form of job fairs, career development offices, and internship opportunities to help broaden their horizons as to the range of potential jobs. More work is also needed to better examine the long-term impacts of taking lower prestige jobs in the short-term on future job prospects, in order to help dispel beliefs that this is economically costly. It is less clear whether there are policy actions that can help reduce the social costs of taking a less prestigious job, although this process may occur slowly as a response to some of the other changes proposed.

# **References**

Agrawal, Ajay, Nicola Lacetera, and Elizabeth Lyons (2013) “Does information help or hinder job applicants from less developed countries in online markets”, _NBER Working Paper_ no. 18720.

Almeida, Rita, Behrman, Jere, and Robalino, David (2012) “The Right Skills for the Job? Rethinking Training Policies for Workers”. Mimeo. The World Bank.

Al-Zoubi, Marwan (forthcoming). “ Developing the differential norms for Jackson personality test using a large Jordanian employee sample.” (in Arabic) _Jordanian Journal of Social Sciences_ .

Angel-Urdinola, Diego, Amina Semlali, and Stefanie Brodmann (2010) “Non-Public Provision of Active Labor Market Programs in Arab-Mediterranean Countries: An Inventory of Youth Programs”, Mimeo. The World Bank Barcucci, Valentina and Nader Mryyan (2014) “Labour market transitions of young women and men in Jordan”, ILO Work4Youth Publication Series No. 14.

Barnett, Andy, Bruce Yandle and George Naufal (2013) “Regulation, Trust, and Cronyism in Middle Eastern Societies: The Simple Economics of ‘Wasta’” _The Journal of Socio-Economics_ , 44: 41-46.

Beam, Emily (2014) "Incomplete Information and Migration Decisions: Do Job Fairs and Information Work?" Mimeo. National University of Singapore.

Beam, Emily, David McKenzie and Dean Yang (2014) “"Unilateral Facilitation Does Not Raise International Labor Migration from the Philippines", Mimeo. World Bank.

26
Mani, Sunil (1990) "Effective Incentives in Indian Agriculture: Case of Natural Rubber." Mimeo. November.

(1991) "Effective Incentives in Indian Agriculture: Case of Flue-Cured Virginia Tobacco." Mimeo. July.

(1992) "Effective Incentives in India's Agriculture: Case of India's Coconut! Copra Sector". Mimeo . March.

(1992) "India's Flue Cured Virginia Tobacco." _Journal of Indian School of Political Economy:_ April - June.

(1992) "Government Intervention in Commercial Crop Development: India's Natural Rubber Industry." _Economic and Political Weekly,_ June 27.

Pursell, Garry (1986) "Effective Incentives for Wheat and Rice: Some Implications of Gulati's Study." Nov. Mimeo.

(1993) "NotesonTradeLiberalisationandDeregulation." 1993.Presented at seminar on agricultural reforms, Indira Gandhi Institute of Development Research, Bombay Jan. IOto 12, 1993.

Pursell, Garry and Ashok Gulati (1993) Liberalising Indian Agriculture : An Agenda for Reform. World Bank Policy Research Working Paper WPS 1172. September 1993.Subsequently published in Robert Cassen and Vijay Joshi (eds) 1995. India: The Future of Economic Reform. Oxford University Press, New Delhi. Pp 261-3111.

Sharma, Pradeep K. (1991) "Effective Incentives in Indian Agriculture: Role of Transport and Marketing Costs." Mimeo. March.

(1991) "Effective Incentives in India's Agriculture: Case of Coarse Cereals". September. Mimeo.

xxxvii
10 Figure 6: The U.S. economy and commodity markets <!-- Start of picture text --> (A) U.S. share of global consumption, (B) U.S. share of global production,<br>2015 2015<br><!-- End of picture text --> <!-- Start of picture text --> Percent Percent<br>Cotton Cotton<br>Edible oils Edible oils<br>Grains Grains<br>Natural gas Natural gas<br>Oil Oil<br>Coal Coal<br>Iron ore Iron ore<br>Tin Tin<br>Lead Lead<br>Zinc Zinc<br>Nickel Nickel<br>Copper Copper<br>Aluminum Aluminum<br>0 10 20 30 0 10 20 30<br>(C) U.S. share of global crude oil (D) Oil and gas production, 2015<br>consumption and production<br>Percent of world total Quadrillion Btu per day<br>30 Production Consumption 60 Oil Natural gas<br>25<br>20 40<br>15<br>10 20<br>5<br>0 0<br>U.S. RussiaSaudi U.S. RussiaSaudi U.S. RussiaSaudi<br>Arabia Arabia Arabia<br>1991Q1 1992Q3 1994Q1 1995Q3 1997Q1 1998Q3 2000Q1 2001Q3 2003Q1 2004Q3 2006Q1 2007Q3 2009Q1 2010Q3 2012Q1 2013Q3 2015Q1 2016Q3 1990 2008 2015<br><!-- End of picture text --> Sources: Haver Analytics, World Bank, BP Statistical Review of World Energy Efficiency, U.S. Energy Information Administration.

A.B. Data for metals represent refined consumption and production. Iron ore consumption is estimated with crude steel production. Grains include wheat, maize and rice; edible oils include coconut oil, cottonseed oil, palm oil, palm kernel oil, peanut oil, rapeseed oil and soybean oil. Oil includes inland demand plus international aviation and marine bunkers and refinery fuel and loss. Coal includes commercial solid fuels only, i.e., bituminous coal, anthracite, lignite and brown coal, and other commercial solid fuels. Natural gas excludes natural gas converted to liquid fuels but includes derivatives of coal as well as natural gas consumed in gas-to-liquids transformation.

D. Oil and natural gas production in British thermal units (Btu), assuming that 1 barrel of crude oil is equivalent to 5,729,000 Btu and 1 cubic foot of natural gas is equivalent to 1,032 Btu.
Policy Research Working Paper 6030

# **Abstract**

This paper analyzes the impact of two distinct shocks stemming from the cross-border transmission of the 2007-2009 crisis on credit availability for small firms. The paper uses data from AccessBank Azerbaijan which was affected in its liquidity position during the second and third quarters of 2008 by delays in its refinancing. The Azeri real economy was hit by the global crisis from the fourth quarter of 2008 onwards with a combined decline in oil prices, exports, remittances, and domestic demand. Therefore, a pure supply side shock con be contrasted with a real economy shock that hit exactly when the bank’s funding position strengthened again. The paper finds that during the _funding shock_ (potential) borrowers are discouraged from applying for loans. However, for those applications made, the likelihood of loan approval is not affected. The _real economy shock,_ in contrast, reduces the approval likelihood for SME loans in particular, while agro and micro loans are considerably less affected. Finally, bank relationships increase credit availability in good as well as in bad times.

This paper is a product of the Finance and Private Sector Development Unit, 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://econ.worldbank.org. The author may be contacted at gberg@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