rafmacalaba/datause-encoder-data huggingface dataset:
In 2014 the majority of educated women in Tunisia were from wealthier households, whereas only 15 percent of women who had a university degree belonged to households in the low wealth quintiles (Hanmer, Tebaldo, and Verner 2017). Tunisian educated women, especially married ones, report strong preferences for working in the public sector, where workdays are shorter, jobs are more secure, and maternity benefits are more generous. However, a decline in recruitment in the public sector most likely has resulted in a lack of job opportunities for educated women (Mouelhi and Goaied 2017). Women’s preferences for public sector employment also likely stem from the skills mismatch between their major area of study and what is demanded by employers in the private sector. Data on enrollment by major at universities in Tunisia (see the appendix) show a gender divide in which a larger share of women are concentrated in fields that tend to have lower employment and wage potential. In 2018, for example, 85 percent of students studying education were female, and 71 percent of students enrolled in the social services major were female. By contrast, only 39 percent of engineering students were female.

The lack of job opportunities is severer in Tunisia’s lagging regions than in its leading regions.<sup>5</sup> There, young women are often more geographically constrained than young men and are not able to relocate as easily to take advantage of economic opportunities. Assaad, Ghazouani, and Krafft (2017) argue that educated young women are more likely to be “trapped” by the opportunities available in their local labor markets, which may not correspond to their educational qualifications. These challenges are evident in the very high unemployment rates experienced by educated young women in rural areas and lagging regions. In 2014 more than one in three women were unemployed compared with approximately one in seven men in these lagging regions. In addition, rural females with a short-cycle university education<sup>6</sup> have an unemployment rate of close to 80 percent. The rate for long-cycle rural female university graduates is only slightly lower at almost 60 percent (Assaad, Ghazouani, and Krafft 2017).

Economic opportunities in the lagging regions are mostly concentrated in the low-productivity agriculture sector. Self-employment and unpaid family work alone provide more than a quarter of

> 5 Tunisia is organized into 24 governorates that can be aggregated into seven administrative regions; Greater Tunis, North East, and Centre East are considered leading regions and North West, Centre West, South East, and South West are considered lagging regions On average, lagging regions have higher poverty rates, lower consumption rates, and fewer economic opportunities than leading regions (World Bank 2015b).

> 6 In Tunisia, since the 2006/07 school year higher education is structured as follows. The first diploma _(license)_ is obtained after two to three years of higher education (short cycle). Students who continue their higher education studies for another two years (long cycle) obtain a master’s diploma (Assaad and Boughzala 2018).

11
**Table 1:** Data and sources

|**Country**|**Variable**|**Source**|
|---|---|---|
|China|Employment(%)|Ministryof Human Resources and Social Security/CEIC|
||GDP(2000prices,RM)|World Bank|
|Hong Kong SAR,<br>China|Employment (%)|IMF/ CEIC|
||GDP(2009 Chained HKD)|Haver|
|Korea, Rep.|Employment(%)|IMF/CEIC/Haver|
||_Agri. employment(%)_|Haver|
||GDP(2000 Prices,KRW)|IMF/CEIC|
|Malaysia|Employment(%)|IMF/CEIC|
||GDP(2000 Prices,MYR)|IMF/CEIC|
|Philippines|Employment(%)|IMF/CEIC/Haver|
||Agri. employment(%)|Haver|
||GDP(2000 Prices,PHP)|National Office of Statistics/World Bank|
|Singapore|Employment(%)|IMF/CEIC|
||GDP(2005 Prices,SGD)|Haver|
|Taiwan, China|Employment(%)|DG of Budget,Accountingand Statistics/CEIC/Haver|
||Agri. employment(%)|Haver|
||GDP(2006 Prices,NTD)|DG of Budget,Accountingand Statistics/CEIC|
|Thailand|Employment(%)|Haver|
||GDP(1988 Prices,BHT)|Haver|

In line with other research on this topic, the OLC will be estimated as a function of the output gap and the employment gap (e.g. Perman and Tavera 2005). These variables are calculated as the difference between observed values and potential GDP and the ‘natural rate of employment’ (mirroring the more commonly used natural rate of unemployment) respectively. These two unobserved measures have to be estimated. One common method is using the Hodrick and Prescott filter which decomposes integrated series (such as GDP and employment) into a stochastic trend and a cyclical component (Hodrick and Prescott 1980, 1997). This method is applied here, using a smoothing parameter of 1600 which is recommended for quarterly data (Rven and Uhlig 2002). The output gap is then calculated by subtracting the logged GDP trend from the logged GDP values. Similarly, the employment gap is calculated – for total employment and agricultural/non-agricultural employment where data are available – by subtracting the estimated employment trend from the observed employment data. For illustration, Figures 1 and 2 plot the output and employment gaps for Hong Kong SAR, China and Malaysia respectively.

4
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34
|Head of hh ethnicity: Lao|0.4799|
|---|---|
|Woman was never married|0.4797|
|Kitchen type: outside unroofed|0.4779|
|Crop loss shock|0.4775|
|Woman's conversations with village head|0.4767|
|Natural disaster shock|0.4739|
|Num. of days woman eats meat (weekly)|0.4692|
|Business or unemployment shock|0.4681|
|Kitchen type: inside the house|0.4593|
|Dry season water: river/lake|0.4590|
|Num. of days kids eat dairy (weekly)|0.4579|
|Num. of TVs|0.4574|
|Dwelling walls: brick|0.4565|
|Toilet: flush|0.4551|
|Dry season water: protected well|0.4531|
|Lighting source: electric network|0.4514|
|Rainy season water: piped water|0.4499|
|Num. of computers|0.4467|
|Toilet: no facility|0.4458|
|Woman works in non-hh business|0.4425|
|Someone in hh is a friend/relative with village head|0.4425|
|Num. of days woman eats dairy (weekly)|0.4419|
|Dwelling roof: wood|0.4403|
|Daily num. of balls of glut. rice for kids|0.4398|
|Daily num. of balls of ord. rice for women|0.4392|
|Robbery shock|0.4358|
|Num. of ducks|0.4339|
|Kg of fruits per hh member (weekly)|0.4298|

41
MULTILINGUAL CHILD ASSESSMENT

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27
this bias is constant over the sample period to calculate the average annual bias presented in column 4. The correction factor, that is the CPI multiplier to estimate true inflation, is shown in column 5.

The CPI bias is positive and statistically significant for the sample periods in 12 of the 16 countries in our study, namely, Côte d’Ivoire, the Democratic Republic of Congo, Ethiopia, Madagascar, Mauritius, Mozambique, Nigeria, Rwanda, Senegal, South Africa, Tanzania, and Togo. In these countries, households allocated their budget share on food as if their true cost of living was increasing more slowly than the rate indicated by the CPI. The upward bias ranges from 2.8 percent a year in Ethiopia between 2004 and 2010 to 43.9 percent a year in Nigeria between 2011 and 2013. Meanwhile, two countries experienced the opposite trend in the CPI bias, ranging from -7.5 percent a year in Uganda between 2009 and 2012 to -8.9 percent a year in Burkina Faso between 1998 and 2003. The bias is not statistically significant in Cameroon and Ghana.

# **VI. Implications of CPI bias for poverty trends in Africa**

Consumer price indexes (CPIs) play a pivotal role for the measurement of poverty trends in SubSaharan Africa. The World Bank’s international poverty estimates currently use an international poverty line of $1.90 per person per day at 2011 international prices. This poverty line is affected by CPI bias, because CPIs are used to deflate the poverty line between the year of the household survey and 2011 (the benchmark year of the purchasing power parity exchange rates). In addition, some countries in Sub-Saharan Africa use the CPI to update their national poverty lines. National poverty lines are usually based on the Cost of Basic Needs (CBN) approach which aims to compute the cost of maintaining some basic living standard. A typical CBN poverty line is calculated from the cost of a food basket that meets certain food energy requirements – for example 2,100 calories per person per day – and observed spending on non-food essentials such as clothing and household items (Ravallion 1998). Anyone living below this poverty line is considered poor. Some countries – for example Uganda and Mauritania – use the CPI to update their national poverty lines over time.<sup>14</sup>

> 14 Other countries have used the CPI in the past, for example Ethiopia (before the 2010 update) and Zambia (before the 2009 revision of 1996-2006 poverty estimates).

17
Earlier in this paper, we mentioned that the housing finance system (HFS) extended loans to about 27 percent of 23.7 million requirements between 1964 and 1996 (World Bank 2002). In the same report it has also been reported how the housing deficit increased from 1991 to 1998. We are interested in putting these pieces together and use our analytic framework to find out whether credit market imperfections influence the functioning of the land market.

In our model we assume that developers use bank loans to fund new land development and purchase building material. As a result, credit rationing hinders land development and construction by developers.

**_Proposition 3:_** _Stringent credit rationing distorts other resource markets._

**Proof** : From the developers optimization problem we get that Lt and Bt are inversely related to the bank loan rate, rt.

Also, from bank’s optimization we get the loan rate is inversely related to fraction of total demand for credit supplied, ω _r_ as _rt_ = _d_ , _t_ + _rc_ . ω Now, given rdt and rc, as lim π→ 0 ⇒ _rt_ ↑⇒ L t , _Bt_ ↓ QED Proposition 3 shows that stringent credit rationing leads to distortions in the land market. In other words, land or housing supply decisions are constrained due to scarcity 24
**-40 .**

Figure 17. Ghana's price incentives and agricultural exports <!-- Start of picture text --> Index 1970 = 100<br>130<br>110<br>90<br>70-<br>50-<br>300 I , I I<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>- -<br>producer price export volume<br><!-- End of picture text --> Figure 18. Zaire's price incentives and agricultural exports <!-- Start of picture text --> Index 1970 = 100<br>170-<br>145-<br>120-<br>95<br>70-<br>45-<br>20<br>1970 1972 1974 1976 1978 1980 1982 1984 1988<br>- -<br>producer price export volume<br><!-- End of picture text -->
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 54

# **Appendix 3. Unit costs of sea dikes in M euros km**<sup>**-1**</sup> **m**<sup>**-1**</sup> **, reference year 2014**

|**Country**|**DIVA unit**<br>**cost**|**Country**<br>**unit cost**<br>**factor**|**New unit**<br>**cost**<br>**estimate -**<br>**Low**|**New unit**<br>**cost**<br>**estimate -**<br>**High**|
|---|---|---|---|---|
|Aruba|1.6|0.4|1.8|5.0|
|Afghanistan|0.0|0.0|0.0|0.0|
|Angola|2.6|0.6|3.0|8.3|
|Anguilla|4.7|1.1|5.5|15.1|
|Aaland|2.1|0.5|2.5|6.8|
|Albania|2.0|0.5|2.3|6.2|
|Andorra|0.0|0.0|0.0|0.0|
|United Arab Emirates|3.3|0.8|3.8|10.4|
|Argentina|8.3|2.0|9.6|26.5|
|Armenia|4.2|1.0|4.9|13.5|
|American Samoa|3.8|0.9|4.3|11.9|
|French Southern Territories|2.8|0.7|3.2|8.8|
|Antigua and Barbuda|2.6|0.6|3.0|8.3|
|Australia|3.8|0.9|4.3|11.9|
|Austria|0.0|0.0|0.0|0.0|
|Azerbaijan|4.2|1.0|4.9|13.5|
|Burundi|0.0|0.0|0.0|0.0|
|Belgium|4.2|1.0|4.9|13.5|
|Benin|2.8|0.7|3.2|8.8|
|Bonaire, Saba and Saint Eustatius|1.6|0.4|1.8|5.0|
|Burkina Faso|0.0|0.0|0.0|0.0|
|Bangladesh|1.3|0.3|1.5|4.1|
|Bulgaria|2.0|0.5|2.3|6.2|
|Bahrain|3.3|0.8|3.8|10.4|
|Bahamas|2.8|0.7|3.2|8.8|
|Bosnia and Herzegovina|1.4|0.3|1.6|4.4|
|Saint Barthelemy|2.8|0.7|3.2|8.8|
|Belarus|4.2|1.0|4.9|13.5|
|Belize|2.9|0.7|3.4|9.4|
|Bermuda|1.6|0.4|1.8|5.0|
|Bolivia|0.0|0.0|0.0|0.0|
24

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**Table 1. Evolution of the DSM approach (2011 - 2019)**

|**Year**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|**2018**|**2019**|
|---|---|---|---|---|---|---|---|---|---|
|No. of districts|2|7|33|54|83|100|132|228|290|
|No. of seed|NA|29|124|294|456|650|800|1163|1400|
|providers/agents||||||||||
|No. of crops|1|1|1|4|6|6|7|8|10|

_Source_ : ATA (2020). _Note_ : NA = not available.

DSM aims to develop a dynamic, effective, and well-regulated seed sector that provides farmers with timely access to varied, sufficient, affordable, and high-quality seed at competitive prices (Benson, Spielman, and Kasa 2014). Under DSM, both public and private seed producers are authorized to market seed directly to farmers through multiple channels such as primary cooperatives, individual or private agents, and their own trading outlets.

The model distinguishes itself from the conventional seed marketing system in many ways. First, it allows both public and private seed producers to conduct their own demand assessments and then multiply and sell seed in accordance with their assessment of the market. This is intended to address demand and supply mismatches and costly inventory carryovers and shortfalls. Second, DSM aims to considerably shorten the seed distribution chain by allowing seed producers to market directly to farmers, thereby reducing transaction costs associated with the public distribution system. Third, DSM seeks to create a competitive seed market at the final stage of distribution by allowing seed producers to promote their seed and to compete with other seed producers based on price, traits, quality, timeliness of delivery, after-sales service, and other attributes. Fourth, DSM potentially improves seed traceability, providing an accountability mechanism that renders seed producers (instead of extension agents) directly responsible to farmers for seed quality. This accountability mechanism is as important for extension agents as it is for farmers and seed providers: by removing extension agents from the seed exchange equation, extension agents reduce their exposure to reputational risks that accompany the distribution of poor-quality seed to farmers under the conventional state-led distribution system (Benson, Spielman, and Kasa 2014; Mekonen et al. 2019).

7
<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Community health workers Nurses Doctors<br>No condition One condition Both conditions<br>Share of providers<br><!-- End of picture text -->

**Figure 4: Diagnostic and treatment accuracy across cadres**

Share of providers among doctors, nurses, and community health workers correctly diagnosing the two main killer diseases for children (diarrhea, pneumonia). The estimates are (unweighted) mean outcomes across countries, with the country means calculated using country-specific sampling weights. The error bars represent the 95-percent confidence interval. Data are from clinical vignettes from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Sierra Leone (2018) Tanzania (2016), Togo (2014), and Uganda (2013).

34
Chinese exports. In short, with some exaggeration one might say that the MFN tariff of countries is really a China tariff, or the China tariff is really the ―least-favored-nation‖ tariff.

<!-- Start of picture text --> Figure 1 China's share in industrial imports of 10 largest<br>import share in importers,<br>percent 2001 and 2009<br>40<br>2001<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> Source: Mattoo, Ng and Subramanian (2011) <!-- Start of picture text --> Figure 2 China's share in imports of 10 most protected<br>sectors in 10 largest importers, 2001 and 2009<br>China's import applied tariff in<br>share in percent percent<br>80 30<br>70<br>25<br>60<br>20<br>50<br>40 15<br>30<br>10<br>20<br>5<br>10<br>0 0<br>Import Share in 2001 Import Share in 2009<br><!-- End of picture text --> Source: Mattoo, Ng and Subramanian (2011) 5