rafmacalaba/data-use-mentions-extended huggingface dataset:
of total enrollment in private schools, by level <!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>2012 2013 2014 2015 2016<br>Pre-primary Primary Lower secondary Upper secondary<br><!-- End of picture text --> Source: 2012-2014, 2016, Best Education Statistics in Tanzania (BEST); 2015, UNESCO Institute for Statistics.

So what kind of an education market have these policies created in Tanzania? A deep dive into the education market of one region, Morogoro, is presented in the next section.

# 6. What do private schools look like?

In order to understand private schools’ ability to fill the gap in secondary education, a survey of 53 public and 89 private schools, at different levels (pre-primary, primary, and secondary) was undertaken in Morogoro, Tanzania in March 2015. This survey covered all the schools in urban 10
Policy Research Working Paper 11074

# **Abstract**

This study employs a cluster randomized controlled trial and administrative health center data to investigate the effects of authorizing community health workers to deliver a new generation of contraceptive injections directly to women during routine home visits following comprehensive training. The paper observes a significant increase of approximately 70 percent in the administered quantity of these injections, which provide average protection for three months. However, the results suggest that the intervention does not produce a statistically significant change in contraceptive coverage because of significant substitution effects away from long-acting contraceptive implants and intrauterine devices that women might otherwise have adopted.

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 mandreottola@worldbank.org, obasenya@worldbank.org, vorozco@worldbank.org, arndt.reichert@ihe.uni-hannover.de, or paulade@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
**Table B5: Comparison with representative household survey**

|||G-HFLS<br>(1)|GLSS 6<br>(2)|Difference<br>(3)|_p_-value<br>(4)|
|---|---|---|---|---|---|
|Individual Characteristics||||||
|Male||0.412|0.454|-0.042|0.904|
|Age||33.857|35.465|-1.731|0.831|
|Education|Female|9.415|10.022|-0.607|0.931|
||Male|11.233|12.886|-1.653|0.839|
|Employed|Female|0.646|0.768|-0.122|0.749|
||Male|0.731|0.834|-0.102|0.800|
|of which self-employed|Female|0.692|0.732|-0.040|0.935|
||Male|0.503|0.414|0.090|0.887|
|of which in<br>manufacturing|Female|0.075|0.132|-0.057|0.882|
||Male|0.105|0.113|-0.008|0.984|
|of which in trade|Female|0.589|0.437|0.152|0.785|
||Male|0.255|0.177|0.078|0.873|
|of which in services|Female|0.294|0.299|-0.005|0.993|
||Male|0.584|0.399|
|2016|405|405|
|---|---|---|
|2017|406|407|
|2018|409|409|
|2019|411|412|
|2020|414|414|

# **4.2 Hemispheric Seasonal Cycles**

As previously noted, global CO2 concentrations fluctuate seasonally with CO2 absorption and respiration by vegetation over the annual cycle. Seasonal fluctuations are more pronounced in the Northern Hemispheric because it has more plant life. To measure the amplitude of CO2 cycles in the OCO-2 data, we regress measured CO2 on a time trend and compute monthly mean residuals for the Northern and Southern Hemispheres. Figure 1 displays mean residuals by month. The cycle is pronounced in the Northern Hemisphere, with the peak in April, the trough in August, and an annual amplitude of 7.5 ppm. The Southern Hemisphere cycle is much flatter, with the peak in July, the trough in March, and an annual amplitude of 1.5 ppm.

# **4.3 City Concentration Anomalies**

In our model, differences in cities’ economic, demographic and geographic conditions affect residual variations in measured CO2 once global trend growth and seasonal fluctuations are accounted for. We assess the scale of these concentration anomalies by computing residuals from a regression of CO2 on a time trend and hemispheric dummy variables for months. Figure 2 displays the distribution of mean anomalies for the cities in our sample, with a standard deviation of 1.0 ppm, 94.8% of cities in the range [-2,2] ppm and 98.5% in the range [-3,3] ppm. For comparison, human activity currently generates about 40 gigatons of CO2 emissions each year, increasing the atmospheric CO2 concentration by about 2 ppm. The city concentration anomalies in Figure 2 have the same order of magnitude, thus highlighting the global significance of inter-city variation.

Methods for direct conversion of city residuals to CO2 emissions are still in the
Departments issued annual reports with information on disease patterns and associated factors such as seasonal conditions and population movements, and analyzed this information to extrapolate the potential for outbreaks for which advance planning might be necessary. Periodic Sanitary Conferences were convened to discuss and refine overall policy thrusts, and coordinate policies and implementation between provinces.

The Sanitary Departments were entasked with ascertaining local sanitary conditions and improving them; vital registration<sup>8</sup> ; monitoring disease trends; providing technical advice on disease control; and carrying out vaccination programs. They were expected to detect outbreaks early, trace them to their source and extinguish them quickly. Their medical staff was on average better-qualified, better-paid, and had faster promotion avenues than those in the Indian Medical Service. Municipal governments hired their own public health staff, consisting of medical doctors, and “a small army” of supervisors and sanitary inspectors to enforce sanitary regulations. Municipal planning was designed to avert

> 7 This section draws on Harrison (1994), Jeffrey (1988), the essays by Arnold and others in Dyson (1989), Guha (1993), Ramasubban , the reports of various Sanitary Conferences, and annual reports of the Sanitary Commissioner of Punjab Province.

> 8 In many provinces the quality of vital registration data was much higher than today (Dyson 1989).

3
Table 1: Summary Statistics

|Birth Cohorts|1968 t|o 1970|1988 t|o 1990|
|---|---|---|---|---|
||N|Mean|N|Mean|
|Years of Schooling|1,448|1.477|2,950|4.140|
|Literacy|1,430|0.164|2,857|0.427|
|Number of Births|1,448|5.578|2,950|0.941|
|Births by Age 20|1,448|1.317|1,654|0.813|
|Births by Age 25|1,448|2.741|674|1.749|
|Ideal Number of Children|1,448|7.704|2,950|4.527|
|Currently Working|1,446|0.321|2,945|0.333|
|Sector of Current|Work||||
|Skilled Manual or Professional|1,429|0.082|2,912|0.098|
|Service or Sales|1,429|0.100|2,912|0.138|
|Unskilled Manual or Agriculture|1,429|0.312|2,912|0.240|
|Read About Family Planning|1,448|0.063|2,947|0.115|
|Knowledge of Modern<br>Family Planning Method|1,448|0.920|2,950|0.911|

_Source_ : Author’s analysis based on data for women in the 2005, 2011, and 2016 rounds of the Ethiopian Demographic and Health Survey (DHS). _Note_ : Ideal number of children is censored at 20; no women in the DHS report having more than 18 children, and non-numerical responses are assigned the maximum value.
concentrated. This growing inequality over space in the poverty rate also featured in the rise over time in the coefficients of variation for the poverty statistics in Table 1.

The description of poverty patterns over time and space is completed by Figure 3, which shows the proportion of baseline poverty eliminated by 2011/12. The spatial patterns are especially clear when considering the poverty gap index in the right panel of Figure 3. The southern states of Kerala, Karnataka, TN, and AP, and all of Maharashtra except the Coastal region (where the poverty gap index increased) eliminated from 60-95 percent of the baseline poverty gap index in the rural sector and similar progress is seen in much of northwest India. However, in Assam, Bihar, Chhattisgarh, Jharkhand, Orissa, and parts of Madhya Pradesh, a much smaller proportion of rural poverty was eliminated. Thus, the rate of reduction in poverty is also spatially auto-correlated, with Moran statistics of 0.30 for the proportion of the rural poverty gap eliminated and 0.15 for the proportion of the rural headcount poverty that was eliminated. In other words, rural poverty started out as spatially concentrated, the poverty reduction process has also been spatially uneven, and so rural poverty at the end-line for our data is even more spatially concentrated than it was at the start.

# _3.2 Night Lights Data_

If India were China it would be fairly easy to examine how growth of different types of cities affects rural areas. China has annual GDP data for prefectures, a sub-provincial unit a little more disaggregated than NSS regions are with respect to India’s states, and also for each city and county. One could compare how rural poverty is affected by growth in big cities versus growth in smaller ones, such as county-level cities.<sup>11</sup> However, the available sub-national economic statistics are much more limited for India. For example, annual data on Gross District Domestic Product are reported by the Planning Commission, but exclude some big states like
Policy Research Working Paper 7733

# **Abstract**

Financial regulation affects government revenue whenever it imposes both the mandatory quantity and price of government bonds. This paper studies a banking regulation adopted by the National Bank of Ethiopia in April 2011, which forces all private banks to purchase a fixed negativeyield government bond in proportion to private sector lending. Having access to monthly bank balance sheets, a survey of branch costs and public finances documentation, the effect of the policy on government revenue can be tracked. This is compared to three plausible revenuegenerating alternatives: raising funds at competitive rates on international markets; distorting the private lending of the state-owned bank; and raising new deposits through additional branches of the state-owned bank. Three main results emerge: the government revenue gain is moderate (1.5–2.6 percent of the tax revenue); banks comply with the policy and amass more safe assets; banks’ profit growth slows without turning negative (from 10 percent to 2 percent).

This paper is a product of the Finance and Markets Global Practice Group. 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 authors may be contacted at fstrobbe@worldbank.org and N.Limodio1@lse.ac.uk.

_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
Y., Piotrowski, M., 2012. Migration and Health Selectivity in the Context of Internal Migration in China, 1997—2009. Population Research and Policy Review 31, 497–543.

- UNICEF, 2018. UNICEF Country Office Annual Report 2018.

- Wang, C., 2006. The Changing Situation of Migrant Labor. Social Research: An International Quarterly 73, 185–196.

- Wang, C., Zhang, C., Ni, J., Zhang, H., Zhang, J., 2019. Family migration in China: Do migrant children affect parental settlement intention? Journal of Comparative Economics 47, 416–428. https://doi.org/10.1016/j.jce.2019.01.002

- Wang, S.X., 2019. Timing and duration of paternal migration and the educational attainment of leftbehind children: Evidence from rural China. Review of Development Economics 23, 727–744. https://doi.org/10.1111/rode.12572

- Wang, Y.-Y., Xiao, L., Rao, W.-W., Chai, J.-X., Zhang, S.-F., Ng, C.H., Ungvari, G.S., Zhu, H., Xiang, Y.-T.,

2019. The prevalence of depressive symptoms in “left-behind children” in China: a meta-analysis of comparative studies and epidemiological surveys. J Affect Disord 244, 209–216. https://doi.org/10.1016/j.jad.2018.09.066

- Wen, M., Lin, D., 2012. Child Development in Rural China: Children Left Behind by Their Migrant Parents and Children of Nonmigrant Families. Child Development 83, 120–136. https://doi.org/10.1111/j.1467-8624.2011.01698.x

-
areas are younger than firms in regions with a moderate conflict. Interestingly, the opposite holds for urban firms in conflict areas – these are older than urban firms in non-conflict areas.

**Table 5: Firm growth in urban and rural areas in moderate and severe conflict environments**

|**Intensity of**<br>**conflict**|**Urban**|**Employment**<br>**(median)**|**Share of**<br>**investing**<br>**firms**|**Age**<br>**(median)**|**Annual**<br>**relative**<br>**employment**<br>**growth**<br>**(median)**|**Annual**<br>**relative**<br>**employment**<br>**growth**<br>**(mean)**|
|---|---|---|---|---|---|---|
|Moderate<br>conflict|Non-<br>urban|16|36%|15|0%|9%|
|Moderate<br>conflict|Urban|25|47%|13|4%|13%|
|Moderate<br>conflict|All|22|42%|14|2%|11%|
|Severe conflict|Non-<br>urban|12|53%|6|0%|9%|
|Severe conflict|Urban|15|42%|15|0%|5%|
|Severe conflict|All|12|46%|8|0%|7%|

_Source: Interim Enterprise Survey, 2009/10; Own
Policy Research Working Paper 9764

# **Abstract**

This paper proposes an approach to guide statistical capacity building in developing countries using an analysis based on components of the World Bank’s Statistical Performance Indicator on a sample of 215 countries. The approach demonstrates the importance of expanding traditional capacity-building activities to include programs to strengthen and better monitor user demand for data. Based on this analysis, the paper recommends a two-step strategy for building and enhancing the sustainable statistical capacity of national statistical systems in developing countries. The strategy creates a sustainable trajectory for developing national statistical systems that meet the growing demands of local and global data users. The paper emphasizes the importance of donor coordination and South-South learning initiatives for international capacity-building efforts.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia 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 author may be contacted at mlokshin@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
shocks affect households by identifying the households most affected by shocks, the extent to which various indicators of household welfare are affected by various shocks, the duration of the such impacts, and the effectiveness of coping strategies in mitigating the impact of shocks. Using consumption as a measure of welfare, several studies have shown a negative effect of shocks. For instance, Jacoby and Skoufias (1998) as well as Dercon and Krishnan (2000), among others, observed that poor weather may affect household welfare by lowering household income, which also lowers consumption. Other studies such as Skoufias and Quisumbing (2005) examine the impact of shocks by disaggregating household consumption into food and non-food consumption. They showed that food consumption is better ensured than non-food consumption, particularly against idiosyncratic shocks. The reduction in non-food consumption, they argued, can be interpreted as a strategy to better ensure food consumption. Aside from consumption/income, shocks may also significantly affect other measures of household welfare such as assets, health, and vulnerability to future poverty (Hoddinott 2006); Hoddinott and Kinsey (2001); and Porter and Hill (2015). In addition to various indicators of welfare, the impact of shocks may also vary across shocks—idiosyncratic shocks (those affecting individual households such as death of a household member, theft of livestock or crops, and so on) are often better insured than covariate shocks (those affecting all households in a community such as floods and droughts) (Harrower and Hoddinott 2005).

Access to social protection and safety net programs and households’ capacity to effectively cope with shocks are likely to influence the impact of shocks on household welfare. Most households in developing countries, where credit, asset, and insurance markets are missing/incomplete and/or social protection and safety net programs are unavailable, cope with shocks by using strategies that may trap them into

> 5 The most recent data on these indicators were provided by the 2014/15 NBHS, which is the main dataset used in this paper.
compared to BAU. In 2009 (according to our analysis using annual data for forecasting the BAU point estimate—under the assumption that the fall in the first 4 months of 2009 would continue for the rest of the year), the share of the rural labor force employed offfarm would drop to 60 percent, more than 9 percentage points (9.1%) less than BAU. If the annual projections were correct, the difference between BAU and the actual, postfinancial off-farm employment would have been 48 million laborers (that is 0.091 x 520 million).

The same analysis conducted with data from south China (based on data from the samples in Zhejiang, Hubei and Sichuan) and north China (based on data from the samples in Liaoning, Hebei and Shaanxi) show that the financial crisis had a more severe impact on off-farm employment in the south (Figure 2, Panel B). Using the same forecasting methods and assumptions (and relying on annual data), the difference in the share of the rural force that was employed off-farm between the 2009 BAU projection and the 2009 year-end (extrapolated) actual share is 9.7 percentage points. The difference in the north is estimated to be only 7.8 percent. This is consistent with the observation that the most severely hit industries were those associated with the export sector, which has a larger presence in south China.

As discussed above, the monthly-based analyses are likely to be more helpful for analyzing the impacts of financial crisis on China’s rural off-farm employment, and so we next use the extrapolated growth rates to predict a BAU estimate on a monthly basis after September 2008. To predict growth rates in monthly off-farm employment from September 2008 to April 2009 relative to the same month in the previous year, we used the average annual 2005-2007 growth rates.

10
Policy Research Working Paper 6466

# **Abstract**

The paper shows how Labor Force Surveys can be used effectively to estimate poverty rates using Household Expenditure Surveys and cross-survey imputation methods. With only two rounds of Household Expenditure Survey data for Morocco (2001 and 2007), the paper estimates quarterly poverty rates for the period 2001–2010 by imputing household expenditures into the Labor Force Surveys. The results are encouraging. The methodology is able to accurately reproduce official poverty statistics by combining current Labor Force Surveys with previous period Household Expenditure Surveys, and vice versa. Although the focus is on headcount poverty, the method can be applied to any welfare indicator that is a function of household income or expenditure, such as the poverty gap or the Gini index of inequality. The newly produced time-series of poverty rates can help researchers and policy makers to: (a) study the determinants of poverty reduction or use poverty as an explanatory factor in cross-section and panel models; (b) forecast poverty rates based on a time-series model fitted to the data; and (c) explore the linkages between labor market conditions and poverty and simulate the effects of policy reforms or economic shocks. This is a promising research agenda that can expand significantly the tool-kit of the welfare economist.

This paper is a product of the Poverty Reduction and Economic Management Department, Middle East and North 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 authors may be contacted at pverme@worldbank.org or rvanderweide@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
their costs by 0.5%. This effect can be expected to be significantly larger in developing countries with less reliable water infrastructure.

Indeed, Islam and Hyland (2018) use Enterprise Survey data for 103 countries and analyze the effects of water supply disruptions on firms separately for low and lower‐middle income and for upper‐middle and higher income countries. They find a significant effect only for the poorer of the two groups; here, an additional water outage incident in a day would lead to sales losses of about 8.2% for the average manufacturing firm. Described as an elasticity, a 1% increase in daily water shortages causes a sales decline of 0.002%. While the ES includes only formally registered firms, similar results also hold in a survey of informal firms in 12 developing countries. For these, an increase of the monthly duration of water shortages by one standard deviation causes annual losses of about 15% of the monthly sales per worker (Islam, 2018).

Such clear relationships, however, are not present in all studies. Using multivariate analyses on ES data for firms from 100 countries, Bhat (2015) does not find a link between water scarcity problems and firms’ labor productivity. This result, however, does not imply that there is no causal link. Instead it may also indicate that firms account for unreliable water supply and choose less water intensive activities.

# **Traffic congestion disturbs economic activity in metropolitan areas**

In addition, inefficient transport infrastructure can further affect firm productivity – especially through traffic congestion in urban areas. The negative impacts of congestion on economic growth have been shown in a number of case studies, which have been reviewed by Sweet (2011). The evidence suggests that firms that are reliant on high‐skilled labor, specialized inputs, and geographically distributed markets are especially sensitive to congestion, as it obstructs their access to these factors (Weisbrod, Vary and Treyz, 2003). Sweet (2013) uses panel data from