rafmacalaba/datause-encoder-data 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 regions of the world, teaching was historically one of the few professions that women could access, although that has changed as other labor market opportunities for women have

> 9 The labor force is defined as people aged between 15 and 64 who worked, looked for a job in the past week, or had a job to return to.

> 10 The statistic of 2.2% for the US is derived from the May 2018 National Occupational Employment and Wage Estimates reported by the U.S. Bureau of Labor Statistics.  It is the share of all employment that is categorized as having the occupations of “Elementary and Middle School Teachers” or “Secondary School Teachers.”

> 11 Formal sector employees are people in the labor force who reported they earned a wage or salary in their primary jobs.

> 12 PASEC stands for Programme d’analyse des systèmes éducatifs de la Confemen.

7
statistics are not available, enrollment data from the national university show a massive increase in the number of university students in Comoros, seen in Chart 5.11. They are also only reflective of students who remain in Comoros, as many Comorians go abroad to study. The lack of economic growth limits the number of skilled jobs available in Comoros, meaning that teaching jobs are often an important employment option for university graduates.

**_Chart 5.11  Enrollment in the University of Comoros_**

<!-- Start of picture text --> 3,896<br>2,796 2,844<br>2,376<br>2,083<br>1,257<br><!-- End of picture text --> Source: Data from University of Comoros, 2010 As such, the analysis suggests that teachers generally hold advanced degrees, though fewer have received pedagogical training. If the previously described poor quality of education is related to teacher abilities, the focus of efforts should not be to increase the number of teachers with advanced diplomas, but rather increase the quality of education of teachers. Moreover, if pedagogical training indeed improves classroom performance of teachers, the downward trend should be of concern to policy makers, and suggests that the educational system should provide greater emphasis on this form of teacher-specific training.

# **Summary of Findings from the Civil Service Survey**

This section presented a quantitative analysis of an original database covering nearly 11,000 civil servants. Several clear trends in the data emerge, and are correlated with key events. First, the civil service has experienced a high level of recruitment in the past decade, with the majority of years exceeding a normal growth rate of around 3%. Such levels of spending on the civil service generally even exceed other comparable African countries. Second, the peak years of recruitment correlate with several events, suggestive of certain explanations. 2010, the year of the largest amount of recruitment, was also an electoral year; 2008 was the year the separation of the island of Anjouan ended, when government suddenly recruited a large number of civil servants; and in 2004, the island government of Ngazidja formed, filling numerous key positions. Finally, the data indicate that only a small percentage of teachers who were recruited from 20052011 did not have an advanced degree.

The above observations support different hypotheses to explain recruitment. First, patron-client relations are likely active in Comoros, given the large amount of recruitment during elections, but events in other years do not strongly point in this direction. Meanwhile, the dynamics of devolution appear to play an important role in explaining recruitment, given the spikes in the data that are correlated with major events in the islands, such as the creation of the Ngazidha Island Government and the re-integration of Anjouan in the Union Government. Finally, the surprisingly high level of education of teachers suggests that, if political clientelism is prominent, it does not take the form of hiring just any political supporters. Rather, educational standards are respected.

16
**Table 4** : Cross-section regression estimates (OLS) of the effect of per capita income, trade, FDI net inflows on the occupational gender wage gap by country income (standard errors in parentheses)

||**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|---|---|---|---|---|---|---|---|---|
||**Low and**|**lower mid**|**dle income c**|**ountries**|**High and**|**higher mid**|**dle income c**|**ountries**|
|Log GDP per capita|0.04|0.03|0.07|0.02|-0.01|-0.01|0.01|0.01|
||(0.01)|(0.01)|(0.05)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Trade (% GDP)|-0.0004||||-0.0004||||
||(0.0002)||||(0.0001)||||
|Trade (% GDP, constant LCU)||-0.0003<br>(0.0003)||||-0.0005<br>(0.0001)|||
|Sector Trade (% sector output)|||-0.00003<br>(0.0001)||||-0.00002<br>(0.00004)||
|FDI net inflows (%GDP)||||-0.003<br>(0.002)||||0.001<br>(0.002)|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Occupation dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dummies for|N/A|N/A|N/A|N/A|Yes|Yes|Yes|Yes|
|Cyprus, Japan and Korea|||||||||
|Dummy for (ex) Communist|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2246|2242|334|2244|7424|7236|2359|7187|
|Adjusted R<sup>2</sup>|0.16|0.16|0.13|0.16|0.55|0.56|0.71|0.56|

Note: standard errors are corrected for clustering within the same country/occupation observations. NA = not applicable.

39
<!-- Start of picture text --> 03 0.01<br>Protection Cycle Stabilization Cycle Commodities Cycle<br>02 .<br>H 0.005<br>~ :4 1] oO<br>oO :<br>SPSOWASP QTM SP2© gr PPSiNDbo FSSNF PLP fr SPSPoP SAESSSSoS SSo oF SHO°<br>-0.1 i -0.005<br>-0.2 i<br>i -0.01<br>-0.3 i /<br>-0.4 HH // -0.015<br>-0.5 : -0.02<br>eG oeTF P<br><!-- End of picture text -->
exposure to four types of disaster risk, one in each of the four categories: extreme rainfall, earthquakes, droughts, and floods. Subsequently, the survey module contains checklists designed to identify domains of household preparedness to deal with disasters that have been welldocumented in disaster research (see, e.g., Brown et al., 2021 and Levac et al., 2012). These checklists cover: supplies owned by the household to deal with a disaster or emergency, household discussion and plans of what to do if a disaster or emergency occurs, and whether the household lives in a community that has an early warning system to monitor natural hazards, detect risks, and inform community members about impending threats. The module also asks households, _inter alia_ ̧ about self-reported ability to cope with a disaster and the impacts of the COVID-19 pandemic on their ability to deal with a disaster. Appendix 1 displays the full disaster risk survey module.

We supplement the household survey data analysis with descriptive statistics based on EM-DAT, the International Disaster Database of the Centre for Research on the Epidemiology of Disasters. This database: _“defines disasters as situations or events which overwhelm local capacity, necessitating a request for external assistance at the national or international level. Disasters are unforeseen and often sudden events that cause significant damage, destruction, and human suffering. EM-DAT globally records at the country level human and economic losses for disasters with at least one of the following criteria:10 fatalities; 100 affected people; a declaration of state of emergency; a call for international assistance”_<sup>_7_</sup> The EM-DAT database covers a subset of the events captured in the survey: two types of meteorological events (tropical cyclones and severe storms), one type of geophysical event (earthquakes), two types of climatological events (cold waves and droughts) and three types of hydrological events (floods, landslides, and high sea-swells).

# **2.3 Methods**

We take a straightforward approach in analyzing the survey data. For each of the outcome domains (preparedness to deal with disasters, possession of emergency supplies,

> 7 See https://www.emdat.be/ (last accessed on April 13, 2024).

5
that the capital share ranges from α =0.4 to α =0.6, this implies that estimates of returns to scale must range from 1.7 to 2.5 in order for a poverty trap to exist. When the scale factor depends on total capital, Equation (15) tells us that the sum of the estimated coefficients on capital and labor is equal to 1 +γ . This in turn means that estimated returns to scale must range from 1.4 to 1.6 over some range of the production function.

These required levels of returns to scale ranging from 1.4 to 2.5 are much higher than what is typically found in the literature. We know of only one study that approaches the lower end of this range. Levinsohn and Petrin (1999) look at data from Chilean plants, and estimate scale returns from value-added production functions that are above 1.20, and in some cases reach up to 1.44. However, most other available studies only find constant to moderate increasing returns. Using Mexican plant-level data, Tybout and West-brook (1995) obtained almost no evidence of scale for large plants. For rich countries, Harrigan (1999) found no evidence of scale in cross-country regressions. Many other OECD country studies point clearly to the existence of moderate scale returns. Paul and Siegel (1999) estimated industry-level scale returns in the range of 1.30 for many U.S. manufacturing industries, and Fuss and Gupta (1981) and Griliches and Ringstad (1971) also estimate moderately increasing returns to scale for Canada and Norway respectively.

The absence of empirical evidence of strong increasing returns casts doubt on these technology-based explanations for poverty traps. However, it is important to note that technology-based poverty traps could exist even in the absence of increasing returns in the aggregate production function. In the first example we discussed, we assumed that total factor productivity was a function of capital per capita. In this case, the aggregate production function has constant returns to scale, but a poverty trap could nevertheless exist. Moreover, in this case existing evidence on the degree of returns to scale is not helpful in assessing the empirical relevance of this particular mechanism. In the remainder of this section, we directly examine the relationship between estimates of total factor productivity and capital per worker using firm-level and cross-country data.

The three panels of **Figure 9** show the relation between (log) TFP and (log) capital per worker, total capital, and output across countries. Total factor productivity is obtained as residuals 29
LASSO Stability Selection identifies 18 common predictors for women and men and 12 gender-specific predictors. The list of variables for both gender groups shows interesting predictors related to **past migration or migration planning experience** ,<sup>14</sup> **social network’s migration experience** ,<sup>15</sup> **local labor market characteristics and experience** ,<sup>16</sup> and **knowledge about migration** .<sup>17</sup> The **gender-specific predictors** include social network and labor market characteristics variables for both women (e.g. having friends or acquaintances working abroad as domestic workers or having more than 10 friends or acquaintances working abroad) and men (e.g. number of male domestic migrants in the household).

> 14 Specific indicators under this category are worked or applied to work abroad in the past 5 years; reason why did not travel abroad despite applying: for family reasons other than caring for children; when last worked abroad; obtained information about migration opportunities through radio, newspaper, internet.

> 15 Specific indicators under this category are type of work friends/acquaintances are doing abroad: factory worker; duration of migration spell of the latest returning migrant in the household; no knowledge of number of friends/acquaintances working abroad.

> 16 Specific indicators under this category are average Male Labor Force Participation in the village; unemployment within past 12 months.

> 17 Specific indicators under this category are lack of knowledge about expected salaries in 6 main destination economies – Malaysia; Saudi Arabia; Taiwan, China; Singapore; Hong Kong SAR, China; and Brunei Darussalam; knowledge of documents required to work abroad.

21
# **Tables**

**<u>Table 1 Two-sample t test of specific fuel consumption, pooled data</u>**

|**Group**|**Obs**|**Mean**|**Std. Err.**|**t-value**|**Ha:**<sup>**a**</sup>|Pr(T>t)|
|---|---|---|---|---|---|---|
|Traditional|211|1104.676|59.68748||||
|_Mirt_|212|813.231|50.14642||||
|Combined|423|958.6091|39.55761||||
|Difference<sup>b</sup>||291.4453|77.92535|3.74|Difference > 0<br>t =|0.0001***|

a Ha alternative hypothesis b Difference = mean(traditional)-mean(mirt) ***, ** indicate significant at 1% and 5% levels, respectively

|**Table 2 Two-sample t test of specific fuel consumption by period**|
|---|

|**Group**|**Obs**|**Mean**|**Std. Err.**|t**-value**|Ha:|Pr(T >t)|
|---|---|---|---|---|---|---|
|**a) October/ N**|**ovember**|**2013**|||||
|Traditional|107|1134.524|82.09017||||
|_Mirt_|109|781.8441|76.1178||||
|Combined|216|956.5515|57.08871||||
|Difference||352.6804|111.8807|3.15|Difference > 0|0.0009***|
|**b) May/June**|**2013**||||||
|traditional|104|1073.967|87.08292||||
|_Mirt_|103|846.4463|64.75871||||
|Combined|207|960.7562|54.78199||||
|Difference||227.5208|108.6765|2.09|Difference > 0|0.0188**|

26 | P a g e
Page 78

- USAID (U.S. Agency for International Development). 1988. Office of Population. Reports on the Operations Research Project.

- U.S. Department of Commerce, Bureau of the Census and USAID Office of WID, 1985. "Women of the World: A Chartbook for Developing Regions." Washington, D.C.

- Venkatchalam, P., K. Shankar, and C. Copalan. 1960. "Changes in Body Weight and Body Composition During Pregnancy." Indian Journal of Medical Research, 48.

- Ware, H. 1984. "Effects of Maternal Education, Women's Roles and Child Care on Child Mortality." Child Survival Strategies for Research. Mosely, W.H. and L.C. Chen, eds. New York: The Population Council.

- Ware, H. 1988. "Improving Statistics and Indicators on Women using Household Surveys." United Nations Statistical Office and INSTRAW, Studies in Methods, Series F., No. 48.

- Ware, H. 1988. "The Effects of Fertility, Family Organization, Sex Structure of the Labour Market and Technology on the Position of Women." Conference on Women's Position and Demographic Change in the Course of Development, Oslo, Norway, June 15-18.

- Wash, 1987, Report, Washington, D.C., USAID, Office of Health.

- Wheldon, A. 1986. "Representation of Girls in School Education in Swaziland." Bulletin of the Swaziland Institute for Educational Research 7.

- White, K., M. Otero, M. Lycette, and M. Buvinic. 1986. "Integretating Women into Development Programs: A Guide for Implementation for Latin America and the Caribbean." International Center for Research on Women, Washington, D.C.

- WHO. 1982. "The Prevalence of Nutritional Anemia in Women in Development Countries: A Critical Review of Available Information." World Health Statistics Quarterly, No. 2.

- WHO, Division of Family Health. 1985. "Prevention of Maternal Mortality." Report of a WHO Interregional Meeting, Geneva.

- WHO, Division of Family Health. 1986. "Maternal Mortality Rates." Geneva.

- WHO, Division of Family Health. 1986. "Essential Obstetrical Functions at First Referral Level." Report of a Technical Working Group, Geneva.

- Winikoff, B. 1983. "The Effects of Birth Spacing on Child and Maternal Health." Studies in Family Planning 14 (10).

- Wolfe, B., and J. Behrman. 1983. "Is Income Overrated in Determining Adequate Nutrition?" Economic DeveloDment and Cultural Change. 31 (3).
45 Table 2: Extreme weather events can have substantial negative effects on household welfare, immediately and through adaptation over time, with adaptation and disaster relief often mitigating the immediate losses

|Dependent variable|Log<br>exp/capita<br>(excl. health<br>expenditure)|Log (asset and<br>consumer<br>durable value)|Disaster<br>relief|Domestic<br>remittance<br>s|Remittance<br>s from<br>outside VN|Herfindahl<br>index|
|---|---|---|---|---|---|---|
||ML|OLS||Tobit||OLS|
||(1)|(2)|(3)|(4)|(5)|(6)|
||parameter/|parameter/|parameter/|parameter/|parameter/|parameter/|
||t‐value|t‐value|t‐value|t‐value|t‐value|t‐value|
|**_Extreme weather event in_**<br>**_community_**|||||||
|**Droughts**|||||||
|Drought this year|0.0245|0.193|‐667|423|‐13387*|‐0.0232|
||0.58|0.95|‐0.5|0.91|‐1.91|‐0.92|
|Drought this year*drought<br>frequency|0.0704|0.9981||||0.029|
||0.46|1.22||||0.36|
|Drought this year*ln(hours) from<br>500k city|‐0.0006|‐0.0923*|549|‐128|4192**|0.0058|
||‐0.06|‐1.95|1.56|‐1.07|2.28|1.03|
|Drought last year|‐0.0008|‐1.2525*|‐288|‐2208|17699|0.0538|
||‐0.01|‐1.68|‐0.05|‐1.49E+00|0.75|0.67|
|Drought last year*drought<br>frequency|‐0.1257|‐1.0411||||‐0.1084|
||‐0.45|‐0.85||||‐0.94|
|Drought last year*ln(hours) from<br>500k city|0.0111|0.3263**|‐388|313|‐4247|‐0.0053|
||0.31|2.24|‐0.28|0.87|‐0.72|‐0.3|
|Drought frequency (proportion)|‐1.2609***|‐3.4800**|11703|10822***|64532|‐0.3115**|
||‐4.25|‐2.26|1.11|2.98|1.18|‐2.15|
|Drought frequency*ln(hours) from<br>500k city|0.2845***|1.0100**|‐3830|‐2761***|‐19709|0.0738*|
||3.72|2.36|‐1.43|‐2.93|‐1.35|1.96|
|**Excess rain**|||||||
- 56 -

Figure 4

# SECONDARY VrEW/ PRIM EDUC

<!-- Start of picture text --> lo<br>140-<br>130-<br>120-<br>10 0<br><!-- End of picture text --> SECONDARY VrE W/ LOWER SEC EDUC <!-- Start of picture text --> 20 .<br>Go e<br>140Doo<br>1 10<br>go-<br>170<br>-<br>no oT<br>10 10-03<br>MO -VT<br>+ - 3 years of secondary VTE<br>O - 3 years of secondary VTE + diploma<br><!-- End of picture text -->
# Enterprise Restructuring in Eastern Europe How Much? How Fast? Where? Preliminary Evidence from Trade Data'

## Bernard Hoekman and Gerhard Pohl

## The World Bank

We are very grateful to Ying Lin for his excellent research assistance in compiling the database used in this paper; to Alexander Italianer, Jerzy Rozanski, Zhen Kun Wang and Alan Winters for their help in obtaining and understanding the raw data; to Faten Hatab for report processing and preparation; and to Roland Eggerer and participants in a World Bank seminar for helpful comments.
# **FAMILY FIRMS AND CONTRACTUAL INSTITUTIONS**<sup>_∗_</sup>

Leonardo Iacovone William F. Maloney Nick Tsivanidis _JEL_ Codes: L2, M2, M5.

> _∗_ Iacovone: World Bank. Maloney: World Bank. Tsivanidis (corresponding author): Dartmouth College and UC Berkeley. Email: John.N.Tsivanidis@Dartmouth.edu; Address: 6106 Rockefeller Hall Hanover, NH 03755; Tel: 773-957-5617. We thank Marianne Bertrand for helpful comments, as well as seminar participants at the Stanford 2015 Empirical Management Conference. First version: June 2014.
Cammack, M. and R. M. Feener (2007) “ _Islamic Law in Contemporary Indonesia: Ideas and Institutions_ ”. Harvard University Press.

Chamie, J. (1981) “ _Religion and Fertility: Arab Christian-Muslim Differentials_ ”. Cambridge University Press.

Chaudhry, M. S. (1996) “ _Family Planning in Islam_ ”. Impact Publications International.

Chowdhury, M. and R. Bairagi (1990) “Son Preference and Fertility in Bangladesh”. _Population and Development Review_ 16: 749–57.

Clark, S. (2000) “Son Preference and Sex Composition of Children: Evidence From India”. _Demography_ . 37 (1): 95–108.

Das, N. (1987) “Sex Preference and Fertility Behavior: A Study of Recent Indian Data”. _Demography_ . 24: 517–30.

Dharmalingam, A., and S. P. Morgan (2004) “Pervasive Muslim-Hindu Fertility Differences in India”. _Demography_ . 41 (3): 529-545.

Filmer, D., J. Friedman and N. Schady (2009) “Development, Modernization, and Childbearing: The Role of Family Sex Composition”. _The World Bank Economic Review_ . 23 (3): 371–398.

Hafidz, W., A. Taslim, and S. Aripurnami (1992) “Family Planning in Indonesia: The Case for Policy Reorientation”. _Inside Indonesia_ . 1992: 19–22.

Hull, T. and V. Hull (1997) “Politics, Culture and Fertility: Transitions in Indonesia”. In: Jones, G.W. et al. (Eds.). _The Continuing Demographic Transition_ . Claredon Press. Oxford.

Hull, T. and H. Mosley (2008) “Revitalization of Family Planning in Indonesia”. _Mimeo_ . Bapenas, BKKBN Indonesia and UNFPA.

Jeffery, P. and R. Jeffery (2000). “Religion and Fertility in India”. _Economic and Political Weekly_ . 35(35–36): 3253–3259.

Jeffery, P. and R. Jeffery (2002) “A Population Out of Control? Myths About Muslim Fertility in Contemporary India” _. World Development_ . 30 (10): 1805-1822.

Jensen, E. (1996) “The Fertility Impact of Alternative Family Planning Distribution Channels in Indonesia _”. Demography_ . 33: 153-165.

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