rafmacalaba/datause-encoder-data huggingface dataset:
teachers in Nigeria earn only 90 percent of GDP per capita. In the OECD, teachers earn about 130 percent of GDP per capita (OECD, 2011). Plotting these numbers by countries’ GDP per capita (Figure 2), we find suggestive evidence of a negative correlation between teacher monthly earnings as a ratio of GDP per capita and a country’s income level. In other words, teachers’ earnings rise as national income per capita rises, but at a slower rate than national incomes per capita.<sup>28</sup> Teachers have higher monthly earnings than other wage workers with similar educational background in ten countries in our sample (Columns 1-4, Table 4). Teachers in all countries in our sample report working significantly fewer hours per week, about 36 hours among teachers compared to 49 hours among other workers (Columns 7-8, Table 4). After scaling monthly earnings by hours worked, the results show that teachers are paid more on an hourly basis than other workers in every country except the Democratic Republic of Congo and Niger. Furthermore, teachers are more likely to have a second job in almost every country, which suggests that teachers may want (or need) to work more to make higher earnings (Table 4, last two columns).<sup>29</sup> However, we do not observe an association between reported hours worked by teachers and the propensity to take a second job (corr = 0.08, p-value = 0.77, N = 14).

Who are these other wage workers? To better understand the relationship between teacher earnings and that of other secondary and post-secondary graduates, we summarize the broad occupations of these other workers and summarize their earnings, separated into those with secondary and those with post-secondary education. We classify occupations using the International Labor Organization codes from the household surveys (ILO 2012). While there is variation, across our study countries we see that among wage workers with secondary education, only one occupation – managers – earns more than teachers (Table 5A). Among wage workers with post-secondary education, there are just four occupations that earn more than teachers: managers, professionals, technicians, and skilled agricultural workers (Table 5B).<sup>30</sup>

> 28 While one might be tempted to conclude from this analysis that teachers in lower-income countries are relatively better off than those in higher-income countries, the poor living and working conditions in many low- and lower middle-income countries in our sample (not captured in our earnings data) may mean that even being paid several times the GDP per capita does not mean teachers enjoy the same quality of life as teachers in upper-middle- or highincome countries.

> 29 In their second jobs, teachers are mostly likely to work as small farmers, tutors, or vendors (Appendix Table A7, A8). See Appendix Table A9 for earnings of different teacher groups: teacher in public schools; primary school teachers; and secondary school teachers.

> 30 We also do this comparison by sector (Tables A13 and A14), using UN sector classification codes (UN 2007). With secondary education, teachers earn more than workers in any sector except mining. With post-secondary

16
# **5. References**

Asian Development Bank (2016), _Public Financial Management Assessment,_ Fiscal and Public Expenditure Program, Manila.

Armenakis, Achilles, and Stanley Harris (2009), “Reflections: Our Journey in Organizational Change Research and Practice,” _Journal of Change Management_ 9.2: 127-142.

Aspinall, Edward and Mark Berger (2002), “The Break-Up of Indonesia? Nationalisms after Decolonisation and the Limits of the Nation-State in Post-Cold War South East Asia,” _Third World Quarterly_ 22.6: 1003-1024.

Bryson, John (2007), “What to Do When Stakeholders Matter: Stakeholder Identification and Analysis Techniques,” _Public Management Review_ 6.1: 21-53.

Burnes, Bernard (2004), “Kurt Lewin and the Planned Approach to Change: a Reappraisal,” _Journal of Management Studies_ 41.6: 977-1002.

Butt, Simon (2012), “A Wolf in Sheep’s Clothing,” _Inside Indonesia_ 108. Apr-Jun 2012, online at <u>http://www.insideindonesia.org/a-wolf-in-sheeps-clothing-3, accessed 30 June 2016.</u> By, Rune Todnem (2005), “Organizational Change Management: a Critical Review,” _Journal of Change Management_ 5.4: 369-380.

Coca, Nithin (2016), “Indonesia’s Anti-Corruption Fight,” _The Diplomat,_ 8 Feb 2016, online at <u>http://thediplomat.com/2016/02/indonesias-anti-corruption-fight/, accessed 13 June 2016.</u> Cock, Andrew (2016), Governing Cambodia’s Forests: the International Politics of Policy Reform (Copenhagen: NIAS Press).

Craig, David and Pak Kimchoeun (2011), “Party Financing of Local Investment Projects: From Elite to Mass Patronage,” in Caroline Hughes and Kheang Un eds, _Cambodia’s Economic Transformation_ (Copenhagen: NIAS Press), Dent, Eric, and Susan Galloway Goldberg (1999), “Challenging ‘Resistance to Change,’” _Journal of Applied Behavioral Science_ 35.1: 25-41.

Dolan, Shimon and Salvador Garcia (2002), “Managing By Values: Cultural Redesign for Strategic Organizational Change at the Dawn of the Twenty-First Century,” _Journal of Management Development_ 21.2: 101-117.

34
# **Measuring Skilled Migration Rates: The Case of Small States***

## **Frédéric Docquier**<sup>**a**</sup> **and Maurice Schiff**<sup>**b**</sup>

> a FNRS and IRES, Catholic University of Louvain b Corresponding author: DECRG (MC3-303), World Bank, 1818 H St, NW, Washington, DC 20433, USA. Tel: 202-473-7963; Fax: 202-522-1159; email: <u>mschiff@worldbank.org.</u>

> * This paper is a product of the Research Program on Economic Growth and Integration of Small States in the World Economy, PREMED, World Bank. It was presented at the World Bank Conference on “Small States, Growth Challenges and Development Solutions”, Washington, December 7-8, 2006. We thank David McKenzie and Edgardo Favaro for their comments. The views expressed here are those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors or the governments they represent.
# **Stronger Together: Intra-Household Cooperation and Household Welfare in Malawi**

Nancy McCarthy<sup>†</sup> and Talip Kilic<sup>‡1</sup> JEL Codes: C23, C72, J16, O12.

Keywords: Non-Cooperative Bargaining Model, Intra-Household Cooperation, Women’s Empowerment, Household Welfare, Malawi, Sub-Saharan Africa.

> 1 †Research Manager, LEAD Analytics, Inc., 5136 Nebraska Ave. NW, Washington, D.C, 20008. <u>nmccarthy@leadanalyticsinc.com.</u><sup>‡</sup> Corresponding Author. Senior Economist, Living Standards Measurement Study (LSMS), Survey Unit, Development Data Group, The World Bank, Via Labicana 110, 00179 Rome, Italy. <u>tkilic@worldbank.org. The authors thank Joshua Brubaker for excellent research assistance, and the participants of</u> the Seventh International Conference on Agricultural Statistics (October 26-28, 2016, Rome, Italy) for their comments. The findings, interpretations, and conclusions expressed in this work fully reflect the views of LEAD Analytics.
38 From the 254 permanent workers surveyed, 64 percent say they are provided with protective equipment, 80 percent of them have received a mask, six percent have received goggles, 36 percent have received gloves and 24 percent have received footwear. The frequency of use of the equipment is shown in Table 18 with 90 per cent of the respondents wearing their goggles, 72 per cent wearing their footwear (most wear their own), 46 per cent wearing their mask, and 66 percent wearing hair covering.

TABLE 17 ACCESS TO HEALTH AND SAFETY PROVISIONS (%)

|Drinking<br>water|96.5|3.5|
|---|---|---|
|First aid|91.3|8.7|
|Toilet|81.9|10.1|
|Training|76.4|23.6|
|Clinic|55.1|44.9|
|Doctor|53.9|46.1|
|Nurse|48|52|
|Fire safety|16.9|83.1|
|Safety Equipment|63.8|36.2|
|Mask|80.2|19.8|
|Hair coverings|43.8|56.2|
|Gloves|35.8|64.2|
|Others|31.5|68.5|
|Footwear|24.1|75.9|
|Hearing<br>protecton|11.1|88.9|
|Goggles|6.2|93.7|
Policy Research Working Paper 7042

# **Abstract**

An accurate measurement of HIV incidence is a key for policy makers and HIV program managers directing national HIV response. However, there is no perfect method to measure or estimate the rate at which new HIV infections occur in a population. This review compiles and triangulates longitudinal HIV incidence and prevalence data from published studies and trials, national reports and surveys, and the Joint United Nations Programme on HIV/ AIDS estimates from the Spectrum model, focusing on 20 countries in Sub-Saharan Africa with generalized HIV epidemics. Three main points can be taken from this analysis of HIV incidence trends. First, modeled HIV incidence and nationally reported HIV prevalence levels in young females suggest that national HIV incidence has declined since 2000 in all except three countries analyzed (stable estimated HIV trends in Burkina Faso, Burundi, and Uganda), but trial and survey data suggest that in some demographics, HIV incidence remains critically high. Second, all modeled national HIV incidence curves and most empirically observed trends commenced a downward trajectory prior to the introduction of anti-retroviral therapy programs around 2004, suggesting the contribution of other factors, such as HIV prevention programs and natural epidemic dynamics, to this decline. Third, modeled HIV incidence estimates, including the incidence peaks in the past, exhibit much variation between Spectrum model versions and when new data are added, emphasizing the uncertainty of model outputs and the need to use incidence estimates with caution.

This paper is a product of the Health Nutrition and Population 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 mgorgens@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
# **Deconstructing the Missing Middle:**

**Informality and Growth of Firms in Sub-Saharan Africa**<sup>_∗_</sup>

Kaleb Girma Abreha<sup>_†_</sup> Xavier Cirera<sup>_†_</sup> Elwyn Davies<sup>_†_</sup> Roberto N. Fattal Jaef<sup>_†_</sup> Hibret Belete Maemir<sup>_†_</sup>

**JEL Classification:** L11, O11, O14, O17, O55

**Keywords:** Missing middle, firm size distribution, informality, market distortion, establishment censuses, manufacturing, sub-Saharan Africa

> _∗_ The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations or those of the Executive Directors of the World Bank or the governments they represent. All errors are ours. We would like to thank the infoDev Trust Fund for its financial support. The authors are grateful for the comments provided by Norman Loayza, Jorge Rodriguez Meza, and Sergio Schmukler, as well as the participants at the DECIG and DECRG Seminar Series.

> _†_ The World Bank, 1818 H Street, NW Washington, DC 20433, USA. Corresponding email addresses: kabreha1@worldbank.org (K.G. Abreha); xcirera@worldbank.org (X. Cirera); edavies@worldbank.org (E. Davies); rfattaljaef@worldbank.org (R.N. Fattal Jaef), and hmaemir@worldbank.org (H.B. Maemir).
# **Table 9.  Accounting for the log FDI per capita outcome**

|||M<br>|ean for<br>|∆�= 1.08<br><br><br><br><br>|
|---|---|---|---|---|
||Coeff|SDDC|EAP|_EX_<br>_EX_<br>_SADC_<br>_EAP_<br>_k_<br><br>)<br>(<br>|
|Independent variables:||2.09|3.17||
|Share of adult populationt-1|4.37|0.53|0.64|42.58|
|Mean real GDPpc growth rate in the<br>previous five years|0.08|0.59|5.17|31.81|
|ln(phone densityt-1)<br>|0.24|0.42|1.50|24.23|
|ln(GDPpct-1)|0.27|6.41|7.05|16.35|
|ln(schooling)|0.20|1.10|1.60|9.33|
|urbant-1|0.33|0.31|0.37|2.08|
|S.D. of real GDPpc growth rate in the<br>previous five years|-0.89|0.07|0.06|1.32|
|rulelawt-1|0.03|2.62|3.14|1.22|
|Relative exchange ratet-1|0.00|2.71|0.69|0.32|
|ln(population)|-0.12|1.91|4.83|-33.30|
|||SDDC<br>mean 2.09|similar income<br>countries:<br>mean 2.50|∆�= 0.41|
|ln(phone densityt-1)|0.24|0.42|1.15|43.04|
|Share of adult populationt-1|4.37|0.53|0.57|38.51|
|ln(GDPpct-1)|0.27|6.41|6.75|22.66|
|urbant-1|0.33|0.31|0.43|9.64|
|Mean real GDPpc growth rate in the<br>previous five years|0.08|0.59|0.98|7.09|
|ln(population)|-0.12|1.91|1.66|7.58|
|ln(schooling)|0.20|1.10|1.18|3.86|
|Relative exchange ratet-1|0.00|2.71|0.69|0.84|
|S.D. of real GDPpc growth rate in the<br>previous five years|<br>-0.89|0.07|0.08|-2.53|
|rulelawt-1|0.03|2.62|2.46|-1.01|

Note. The results for other variables are not reported.

The coefficients are based on the same specification in Column (1) of Table 7--except that we do not control for SADC dummy here, since it is not statistically significant.

31
free cost transportation to the training location, and a video CD with positive testimonials about the training. We find that the latter two incentives did not increase take-up, but both current and deferred monetary payments boosted attendance rates by about 10 percentage points (from 18 to between 27 and 33 percent, depending on the payment size). There was no significant difference in attendance between those who got the payment immediately and those who received a deferred payment, suggesting that time-inconsistency or high discount rates are not the main constraint to participation.

A follow-up survey conducted six months after the course is then used to measure the impacts of the training on financial knowledge, behaviors and outcomes. We find attending training results in a 9 percentage point increase in financial knowledge, and a 9 percentage point increase in saving outcomes, but no impact on credit card behavior, retirement savings, or borrowing. Moreover, administrative data suggests that the savings impact is relatively shortlived. Data on credit card balances and repayment rates show no systematic differences across the treatment and control groups related to the course. In addition to limited overall impacts of training, we find relatively little heterogeneity in impacts. We do not find significantly different effects by gender or whether the individual is a customer of our partner financial institution, but we do find stronger savings impacts for individuals with a bachelor’s degree than those without.

Our results therefore suggest that the answer to the question of why voluntary financial literacy programs are so unpopular is that they do not offer that much in the way of benefits to those who are not currently attending them. That is, it appears individuals may be making rational decisions not to attend such financial literacy training programs. We cannot rule out that such programs offer benefits to the individuals who voluntarily choose to go without being given any additional information or incentives, but our results suggest there are limited gains to trying to encourage more people to attend.

There is a growing literature attempting to assess the causal impacts of financial literacy trainings, which we review in the next section. Much of the existing literature has looked at training provided in a compulsory manner, such as to students (Bernheim et al, 2001; Cole and Shastry, 2008; Lührmann et al, 2012; Bruhn et al, 2013), or otherwise has focused on specialized content given to specific populations such as migrants and their families (Gibson et al, 2012; Seshan and Yang 2012; Doi et al, 2012), and farmers (Cai, 2012). We contribute to this literature by focusing on a large-scale voluntary financial literacy program offered to the general population in a large urban city – precisely the type of effort that is being increasingly adopted in a number of countries around the world. As well as evaluating the impact of such a 4
**Table 1. Percent Underemployed by Gender and Location**

||**Male**|**Female**|**Total**|**Pop. Share**|
|---|---|---|---|---|
|Urban|7.9|8.9|8.3|18.1|
|Rural|12.6|13.8|13.1|81.9|
|Total|11.7|13.0|12.2|100|
|**Pop. Share**|58.3|41.7|100|I|

Note: Unless otherwise indicated, figures in tables are based on the population of workers in the informal sector, excluding both those with formal sector jobs and those not wanting more work. The source for all data described in this paper is the 1993 SAKERNAS survey.

A breakdown of the underemployed by gender and age describes a similar patterm for men and women, with the highest rates of underemployment occur among young workers (see Table 2). For both men and women, workers under 35 years of age experience higher than average rates of underemployment. The rate of underemployment for women is above that for men except among the very youngest and the two oldest age categories.

**Table 2. Percent Underemployed by Gender and Age**

|**AgeRange**|**Male**|**Female**|**Total**|
|---|---|---|---|
|**10-14**|17.8|16.5|18.3|
|**15-19**|26.4|27.0|26.7|
|**20-24**|20.7|22.1|21.3|
|**25-29**|13.7|16.5|14.9|
|**30-34**|11.5|13.4|12.4|
|**35-39**|9.8|12.2|10.8|
|**40-44**|8.5|8.7|8.6|
|**45-49**|7.2|8.6|7.8|
|**50-54**|6.0|6.2|6.1|
|**55-59**|5.9|5.2|5.6|
|**60 +**|3.6|3.6|3.6|
|**Total**|11.7|13.0|12.2|

The concentration of younger workers among the underemployed is displayed graphically in Figure 1 below. Workers under the age of 30 account for more than half of s
in either signal are due to the timing of their measurement, and are inherently not shared. If this is so, and labeling the other signal not _x_ as _w_ , we have an additional 3 + _G_ th moment from estimating Equation 10 by instrumental variables to identify the growth elasticity, _β_ .

Threats to identification include the facts that obstructions like clouds affect both lights and NO2. In the analysis, we explain at length the rationale behind our identifying assumption and provide tests of overidentifying restrictions.

# **5 Baseline Results**

Figure 6 sets the stage for our baseline results by simply plotting the relationship between log GDP and log NO2 per area, net of country and year fixed effects. As indicated by the statistically significant best-fit line, there is a positive relationship in the data, and a linear model captures the relationship well. We now utilize the empirical framework described in the previous section to formally solidify the relationship.

## **5.1 Proxy**

Table 2 presents results from regressions of log GDP on a log signal. Thus, the reported coefficient is _ψ_<sup>�</sup> , and the fitted values are proxies. Column 1 uses NO2 as a signal. There are 178 countries in the unbalanced panel over 2005-2020, resulting in a sample of 2,752 observations. The difference between this regression and Figure 6 is that we now include a country-specific time trend (Equation 4). Thus, we seek explanatory power for off-trend annual fluctuations. Robust standard errors, clustered by-country, indicate NO2 is strongly statistically significant. The within-country adjusted R-squared of 0.805 indicates substantial explanatory power.

Column 2 repeats the same exercise, now using night lights as a signal, for the same sample.<sup>12</sup> While night lights do have some explanatory power for cyclical fluctuations, statistical significance is less than when using NO2. For context, in Columns 3-5 we repeat the exercise again, but now using ground level signals which have less broad availability than the satellite signals. Column 3 uses ground station measured CO2 emissions from combustion sources, as reported by the International Energy Agency. As previously noted, while satellite data is not subject to politically-motivated manipulation, this data may be. Moreover, at the time of writing, it was available for a slightly smaller sample of countries, and only until 2019. Column 4 contains log electricity consumption (units are kWh). Column 5 uses household survey response means from the World Bank’s PovcalNet database. These means aggregate income or

> 12See Online Appendix B.1 for details, and correspondence with the baseline estimates in Henderson et al. (2012).

15
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE however, establishing the necessary institutional framework and institutions such as a Road Fund, Road Maintenance Executing Agency, and Road Agency usually increases the likelihood of better mobilizing and protecting maintenance funding and therefore delivering better maintenance. Countries that have adopted a road fund and a road agency in Africa have the best track record of roads in good and fair condition—82 percent—in contrast to those countries that have created only a road fund (70 percent) or a road agency (62 percent ) (table 4).

**Table 4. Mali is on track to secure road maintenance resources**

Percentage of main road network in good or fair condition by country <u>groupings</u>

|**Country catego**|**ry**|**Institutions**||**Geography**<br>**Financing**||
|---|---|---|---|---|---|
|Middle-income|81|Road fund and agency|82|Flat and arid<br>77<br>High fuel levy|79|
|Low-income,aid|75|Road fund only|70|Rollingand humid<br>70<br>Low fuel levy|70|
|Low-income, oil<br>producer|70|Road agencyonly|62|No fuel levy|75|

Source: Gwillliam and others 2009.

Despite its inability to maintain the existing network, Mali continues to expand the road network. There is that pressure to connect the north to the rest of the country for national unity and security reasons with standards that sometimes defy economic justification, for instance paved roads in tranches that don’t carry enough traffic to justify it, and provide connectivity thought corridors that might not be the ones providing market access to the most productive areas. This leads to an over spend on capital investments in general at the expense of maintenance, and within capital spending, a bias to allocate resources for expansions rather than rehabilitations. Network expansion has clearly come at the expense of maintaining the existing network (figure 7).

**Figure 7. Mali is underspending on maintenance and overspending on expansion**

<!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>-80<br>-100<br>Senegal Burkina Cote Liberia Niger Mali Ghana<br>Faso d'Ivoire<br>maintenance rehabilitation<br>spending as % of requirements<br><!-- End of picture text --> Source: Gwillliam and others 2009.

Traffic over most of the paved road network in Mali averages 550 vehicles per day, which is well below the average of 1,000 vehicles per day in Africa’s low-income countries. Close to 50 percent of the 14