rafmacalaba/data-use-mentions-extended huggingface dataset:
Republic of Congo, with 370 teachers on average per country. On average, teachers in these countries represent 2.2% of the overall labor force in each survey.<sup>9</sup> This share is comparable to that in high-income countries like the United States, where primary and secondary teachers make up about 2.2% of the labor force.<sup>10</sup> Across the surveys, African teachers on average constitute 13% of wage employment.<sup>11</sup> We also include supplementary analysis using data from a student test in grades 2 and 6 and a complementary teacher survey administered in francophone African countries in 2014, the PASEC (PASEC, 2015).<sup>12</sup> We include data from all available countries: Benin, Burkina Faso, Burundi, Cameroon, the Democratic Republic of Congo, Côte d'Ivoire, Niger, Senegal, Chad, and Togo. Finally, we present data on the total numbers of teachers and students from the UNESCO Institute for Statistics (UNESCO, 2020) and data on the labor force across countries from the International Labour Organization (ILO, 2020).

# **<u>2.2. Analytical strategy</u>**

This paper takes an exploratory approach to questions around teacher pay in Sub-Saharan Africa. Rather than establishing a theoretical framework—which has been done for teachers (Crawfurd and Pugatch, 2020) and for public sector workers more broadly (Finan et al., 2017)— we interrogate the data with the aim of establishing a series of stylized facts and exploring where there are empirical regularities across countries (and where there are not). Specifically, we structure our analysis around five main questions that we hope will help to motivate future work on teachers.

_Question 1: Who makes up the teaching workforce?_ This descriptive section explores the demographics of the teaching workforce. We explore various aspects, including gender and age. In some
Policy Research Working Paper 10666

# **Abstract**

The COVID-19 pandemic resulted in significant disruption in schooling worldwide. This paper uses global test score data to estimate learning losses. It models the effect of school closures on achievement by predicting the deviation of the most recent results from a linear trend using data from all rounds of the Programme for International Student Assessment. Scores declined by an average of 14 percent of a standard deviation, roughly equal to seven months of learning. Losses were greater for students in schools that faced relatively longer closures, boys, immigrants, and disadvantaged students. Educational losses may translate into significant national income losses over time.

This paper is a product of the Education 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 authors may be contacted at hpatrinos@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
sample survey and the predictive power of the satellite-based auxiliary data, among other factors. It is also likely that the predictive performance of geospatial and other big data will continue to improve as more and better indicators become available.

In addition, surveys can also be fielded in ways that better facilitate integration with geospatial data. For example, increasing the number of households interviewed in each enumeration area would likely further improve the predictive performance of the model and the accuracy of the estimates. Likewise, given the better performance of all small area estimates when predicting in-

> 24 This is partly because the model used to generate the official 2015 estimates of municipal poverty rates utilized data from the 2010 census as predictor variables.

33
shares are correspondingly higher. Figure A.1 shows that the cost shares also vary by firm size (turnover) and profit margin. Profitability is increasing in firm size. The labor cost share and the fixed cost share are decreasing across firm-size deciles, while the share of material inputs is increasing.<sup>8</sup> This means that larger firms can better adjust to the demand shock, somewhat mitigating the shock’s aggregate impact.

To confirm the accuracy of the cost share information, we compare our data to the cost shares observed in the World Bank Enterprise Survey, which contains the breakdown of costs into material, labor and fixed costs for the manufacturing sector. As Figure A.3 shows, the cost shares for the manufacturing sector in the two datasets are similar in levels and in slopes across firm-size deciles.

> 6 We implicitly assume that the sectors gaining from the pandemic, e.g. retailers of cleaning products, refrigerators and teleconferencing equipment are sufficiently small and specific so that there is no sector group that experiences an overall revenue increase.

> 7The Albania data do not have the breakdown of costs into the different components, and the Ethiopia data do not feature a distinction between labor costs and material costs. We hence impute the cost shares in these cases with the average from the

> 8Within the top decile, the profit margin continues to increase, but the cost shares are fairly stable (Figure A.2).

6
# **1. Introduction**

In the past decade, mobile phones have become a ubiquitous feature of life in developing countries. Between 2005 and 2011 mobile cellular subscriptions nearly tripled in the developing world, increasing from 1.2 to 4.5 billion. In Africa, the region with the fastest mobile subscription growth rate, mobile cellular subscriptions increased from 87 million in 2005 to 433 million in 2011. A number of factors contributed to the luxury-to-commodity transformation of mobile phones. Technological advances along with economies of scale in network equipment and handsets in the developed world led to price declines. Additionally, the introduction of pre-paid billing systems lowered service costs by eliminating the need for operators to send bills and collect debts, thus making mobile phones accessible to lower income consumers. Finally, deregulation created a competitive marketplace, further reducing prices and bolstering mobile phone adoption (Economist 2009). Accompanying the growth in handset ownership has been growth in mobile applications, including digital money transfers and payments, citizen polling station surveillance, remote health care consultation and diagnosis, and transmission of timely market information to farmers.

One area in which mobile technology holds promise is for implementing surveys. Despite recent advances, in many developing countries there is a lack of reliable and timely socioeconomic data. Today, there is an opportunity to leverage mobile phones for routine data collection to help fill this gap and support policy decisions, strengthen civil society, and facilitate program management (Croke et al. 2012). This paper examines the opportunities and challenges of using mobile phones as a research tool, based on the experience with the South Sudan Experimental Phone Survey (SSEPS).

The paper is organized as follows. Section 2 of the paper describes options for data collection via mobile phones, Section 3 describes the South Sudan survey, Section 4 assesses patterns of non-response and survey results, and Section 5 concludes.

# **2. Mobiles for Data Collection**

For mobile phones to be useful as a survey device, they need to satisfy a number
is a significant non-linear effect. The linear effect remains positive and significant. Past a threshold, however, the continued robotization is associated with a statistically significant decline in the rate of change in outbound FDI from HICs to LMICs. At the sample mean of robots per 1,000 workers (7.6), approximately one-third of the linear effect of automation is offset. At just over 24 robots per 1,000 employees is where the positive impact of automation is greatest. After that, continued robotization is associated with an increase in the growth of FDI but at a diminishing rate. However, less than 10 percent of the sample is above this threshold level of the intensity of robot use. The threshold level beyond which the marginal impact of the negative quadratic term outweighs the positive linear one to imply that further automation (578 robots per 1,000 employees) leads to an actual decline in outbound FDI from HICs to LMICs is not within the current range of data. Therefore, the increased intensity of robot use in HICs is associated with growth in outbound FDI from HICs to LMICs, which is increasing at an increasing rate for approximately 90 percent of the sample.

These results are robust to business cycle effects associated with the recent financial crisis, the exclusion of the transportation equipment sector, the inclusion of the stock of ICT capital per 1,000 workers engaged, and the inclusion and exclusion, respectively, of China as a source and destination country. Their robustness is also illustrated through an analysis of bilateral FDI flows where arm’s length offshoring, market size effects, FDI restrictions and bilateral trade or investment agreements, respectively, were controlled by the inclusion of bilateral exports from

> 2 The positive impact of robotization in the North on imports from the South is mainly driven by exchanges of parts and components.

> 3 Beyond hearing aids, the authors find that 35 products that are increasingly being 3D printed have also experienced faster trade growth relative to other similar goods.

3
associated with higher variability of consumption in all four regressions. One possible explanation for this counter-intuitive result is that the elements of corruption that create volatility manifest themselves in enforceability of contracts and in the strength of accounting standards. Thus, the positive relationship we find between corruption and stability is only a partial effect after controlling for the most damaging effects of corruption. This conjecture is supported by the fact that the raw correlations between corruption and volatility indicate that more corrupt economies have greater volatility. A possible interpretation of our finding is that, after controlling for these variables, consistent and predictable corruption has a stabilizing component. Finally, _STRUCTURE,_ the reliance of the economy on the stock market relative to banks, enters positively and significantly in two of the four consumption regressions. These results would be consistent with a wealth effect that stimulated consumption through appreciation in stock market wealth. However, it is doubtful that this effect would be identifiable in aggregate data for most of the countries in our sample.

# 4. **Conclusion**

There exist at least three different but related strands in the economic literature which assert or imply that financial development should reduce macroeconomic volatility. Panel data from 70 countries covering the years between 1956 through 1998 reveal evidence in support of this hypothesis.

In particular, we have shown above that countries with more developed financial sectors experience less fluctuations in real per capita output, consumption and investment growth. However, we have also demonstrated that the manner in which the financial sector develops matters. The importance of banks in the financial system is the most robust in explaining consumption and investment volatility, whereas the relative amount of credit supplied to the private sector has the most explanatory power for the volatility of consumption and output. These results appear to be robust to different estimation techniques and data selection strategies. Although our data and methodology do not allow us to distinguish between the different mechanisms through which financial development 17
. Benston, Hunter and Wall (1995) similarly find that bank mergers and acquisitions are in part motivated by enhancing the deposit insurance put option. Penas and Unal (2004) consider the returns to bond holders around US bank mergers, finding that adjusted returns on merging banks’ bonds are positive across pre-merger and announcement months

> 6 We compile time series information on ownership relationships using Bankscope’s yearly CDs from 1999 to 2008, while ownership data for 2009 are gathered from Bankscope as available through the Wharton Research Data Services (WRDS).

7
# **(7.2) Effects of Trade Cost Shock on the Employment Pattern**

We utilize two rounds of census data to explore the impacts of trade/transport shock on the employment structure along the Abidjan and non-Abidjan routes. In addition to employment in manufacturing and agriculture, we also consider employment in trade and other services. During the pre-crisis period, 91 percent of workers were engaged in agriculture in the study countries. Among those employed in non-agriculture, most were traders (5 percent), followed by manufacturing (2 percent) and rest in services (2 percent). The second round of census data corresponds to 2006 for Burkina Faso and to 2008 for Mali, both years are at around the time of the truce and conclusion of peace treaty in Cˆote d’Ivoire in 2007. The estimates for employment shares should thus be considered conservative as they relate to the immediate post-crisis years.

# **(7.2.1) Identifying the Comparison Autarkic Hinterland**

An important insight from proposition (2 _.a_ ) is that the areas with very poor market access are essentially isolated hinterland, and their employment structure is not likely to be significantly affected by the trade shock. The empirical analysis in section (7.1) above provides suggestive evidence that, during the pre-CIV crisis period, the isolated hinterland consists of the areas in the lower 60 percent of the market access distribution, i.e., _σ ≤_ 0 _._ 60. Thus the average effects over a route is a weighted average of the zero effect in the autarkic hinterland and the effect of the trade shock on the integrated areas. Since the theory suggest that the trade shock to the Abidjan route will shift the SMI outward in the alternate nonAbidjan routes, we expect the extent of the isolated region to shrink after the shock. If an area remains isolated after the shock, the employment structure will not be affected by the CIV trade shock, and any changes would reflect only the long-term factors
phone or face-to-face surveys, these internet surveys do not capture any information about respondents. In such a setting, it is possible that respondents might worry less about the social desirability of their answers, being targeted for their opinions, or about any of the other issues that might limit respondents’ willingness to respond to sensitive questions.

Although the internet surveys used here are indeed anonymous, we investigate whether

> 1Specifically, the surveys are performed by a company named RIWI, which purchases the domain names of old websites and those of spellings that are close to popular websites. A proprietary algorithm then determines which of the internet users that reach RIWI-owned websites are offered the chance to participate in an RDIT survey.

2
in consumer credit is combined with low levels of financial literacy among borrowers. The share of bad consumer loans was a sizeable 12.25% in 2010 (Central Bank of Russia, 2011). It is within this rather unique context that our survey instrument was designed.

# **4. Data and Summary Statistics**

We use a panel dataset of Russian individuals in May/June 2008 and in June 2009. The 2008 sample was designed to be nationally representative at the individual level, and weighted by gender, age, education, 46 oblasts (i.e., administrative regions), and 7 federal region<sup>4</sup> for a total of 1,600 individuals interviewed face-to-face.<sup>5</sup> This is one of the very few panel surveys measuring financial literacy and other key variables over time. In 2009, 22% of individuals from the original sample either no longer resided at the same location or refused to answer the follow-up survey. However, analysis of the data between the two years does not show evidence of significant selection bias across the covariates used in the empirical work (available upon request).

These surveys collected information on individual levels of financial literacy (i.e., numeracy, knowledge of interest compounding, understanding of inflation), as well as use of financial services (e.g., the use of bank accounts and formal credit). The dataset also provides rich demographic and socioeconomic information, and measures of financial vulnerability. The primary respondent was the household head, without an age limit.

> 4 Since March 1, 2008, the Russian Federation consists of 83 federal subjects. Six types of federal subjects are distinguished: 21 republics, 9 krais, 46 oblasts, 2 federal cities, 1 autonomous oblast, and 4 autonomous okrugs. We exclude the North-Caucasian (Chechnya) district because civil unrest prohibited surveying. 5 Summary statistics by gender, age, and education (% with secondary degrees) are very
data collection with a classroom observation survey conducted in all the preschools within the sample villages.

The household survey included information about family structure, household wealth, and other socioeconomic background characteristics.The caregiver survey included questions regarding the child’s preschool enrollment and socio-emotional development, as well as 25 questions about parenting practices. The latter measure three key dimensions of parenting: “cognitive parenting”, “emotional parenting”, “and negative parenting”. More cognitive parenting means parents are more likely to engage in activities that contribute to the development of their child’s cognitive competencies (e.g. by playing games, reading books, or playing with toys). More socioemotional parenting means parents interact more in ways that provide emotional support and responsiveness (e.g. comforting, encouraging or complimenting the child). More negative parenting means parents are more likely to use harsh or punitive approaches to discipline their child. Last, the survey included information about home visits, D2D activities, and participation in the HBP, as well as the caregiver’s perceived returns to education, and a short test of the respondent’s nonverbal reasoning ability (based on the Raven’s Progressive Matrices Test).

The approximately 45-minute child assessment included a comprehensive battery of cognitive tests (executive function, language, early numeracy, fine motor development and, at baseline and midline only, gross motor development) as well as anthropometric measures (height and weight). Most of the child tests were based on the Measuring Early Learning Quality and Outcomes (MELQO) toolkit. (See UNESCO (2017) for a description of the measures-of-development process and Raikes et al. (2019) for evidence of validity).<sup>22</sup> Additional child tests were added to the MELQO items to increase the sensitivity and breadth of the child assessment. The additional tests included the following: the Dimensional Change Card Sort (Zelazo, 2006)<sup>23</sup> ; a receptive vocabulary test based on
year’s demand matches supply or, in other words, a price such that inventory variations are zero. Moreover, it is even possible to assume that inventories could reach zero at the end of the year, given that, in practice, in the last quarter it usually reaches very low values with respect to harvest size. It proceeds to compare these theoretical prices with observed ones and estimate the di¤erence, and ...nd that after 1996, di¤erences are small and do not last more than a year or two. Additionally, this document ...nds that the increase in price levels in the 2000s seems to be explained mostly by the presence of an increasing demand for bioethanol.

In the quarterly model, it follows a market structure that mimics the typical seasonality seen in agriculture markets ( with production occurring only during one quarter). Given that the time lapse between decisions is shorter, it also allows for information shocks to occur by introducing them to the agent’s expectations through private and public signals. Results show that around 70% of price volatility can be explained through the shocks cited above, leaving a 30% measured space for other non fundamental sources of variation.

Regarding model speci...cs, the long run case assumes isoelastic forms Dt = Ztp<sup>�</sup> t<sup>fordemand</sup> and St = Atp<sup>�</sup> t<sup>forsupply,the...rsttermrepresents(exogenous)levelsandthesecondtermthe</sup> endogenous response to price with the respective elasticity. Therefore, shocks are captured as changes in Zt; At. These will be de...ned as the current fundamental source of price changes. Time series for these variables can be obtained using USDA corn usage and production data. They are then integrated into a market clearing equation with no storage decisions. The short run case considers demand as a whole aggregate and assume identical isoelastic functions for both demand and
**Figure 9. The long-run impact of indexing minimum wage by inflation (Scenario 1) across sectors of the economy.**

<!-- Start of picture text --> 50 6<br>45<br>5<br>40<br>35<br>4<br>30<br>25 3<br>20<br>2<br>15<br>10<br>1<br>5<br>0 0<br>Impacted (%) Job losses (%) Wage increase (%)<br>Impacted (%)<br>Job Losses and Wage Increase (%)<br><!-- End of picture text --> Source: Own estimates based on administrative tax data and microsimulation model Note: Sample is restricted to wage earners, assuming all dismissed workers move to unemployment or inactivity.

45