rafmacalaba/datause-encoder-data huggingface dataset:
The base year for the Ghana MAMS model is 2004. The data for the exercise come from a social accounting matrix (SAM) as well as detailed sector studies that describe the current situation in each of the human development (HD) sectors (education, health, water/sanitation) and quantify the progress required to reach the MDGs. The SAM was built by constructing a macroeconomic SAM from the national accounts data and then disaggregating it further by relying on public budget documents, income and consumption data from the fifth round of the Ghana Living Standards Survey (GLSS5), and a detailed Ghana SAM constructed by Ghana Statistical Services and the International Food Policy Research Institute (Ghana Statistical Services, 2006). Given that our primary focus is on government policies related to MDG achievement, we maintain a simple structure for the rest of the economy by aggregating all private activities into three sectors: agriculture, manufacturing, and services (see Table 2).

# **Table 2 Model dimensions**

**Activities/commodities** Private (3) Agriculture Manufacturing Services Public (7) Primary education Secondary education Tertiary education Health Water and sanitation Infrastructure Other government **Factors (11)** Unskilled labor--workers who have not completed secondary school Skilled labor--workers who have completed secondary school Tertiary-skilled labor--workers who have completed tertiary education Capital (8)--one stock for each model activity **Institutions (3)** Household Government Rest <u>of the world</u>

A major difference of the Ghana MAMS SAM from the one constructed by GSS and IFPRI is the disaggregation of public current expenditures and investments into the different activities covering education, health, water and sanitation, other infrastructure, and other public activities. This disaggregation is mostly based on public expenditure figures published in the Ghana External Review of Public Financial Management (World Bank, 2006) and the Report of the Auditor General on the Public Accounts of Ghana (Government of Ghana, 2004). Such breakdown of public expenditures in the model also allows a more systematic analysis of trade offs between various types of public expenditures.

6
**<u>Table 1: Household level descriptive statistics</u>**

|**Variable**|**Total**||**By land m**|**arket particip**|**ation status**||
|---|---|---|---|---|---|---|
|||**Lease**|**d-out**|**Autarky**|**Lea**|**sed-in**|
|**Household characteristics**|||||||
|Female head dummy (%)|15.37|39.95|***|11.82|3.29|***|
|Head literate (%)|41.31|29.05|***|41.27|51.14|***|
|Age of head in years|48.38|51.61|***|48.71|45.11|***|
|No. of rooms in dwellings|1.95|1.74|***|1.95|2.13|***|
|Owned area in ha|1.10|1.08||1.12|1.08||
|Number of adult males|1.49|1.04|***|1.57|1.70|***|
|Number of adult females|1.46|1.33|***|1.52|1.46|*|
|No. of dependents (<15 or >60)|2.72|2.26|***|2.74|3.05|***|
|**Agricultural production**|||||||
|Net land leased-in|0.01|-0.71|***|0.00|0.61|***|
|Cultivates any land (%)||63.2|||||
|Owned cultivated area in ha|0.95|0.37|***|1.12|1.07||
|Total cultivated area in ha|1.11|0.37|***|1.12|1.69|***|
|Share of good quality land (%)|38.42|36.68|*|39.42|37.70||
|Owns bulls or oxen (%)|67.31|23.65|***|72.42|91.32|***|
|No. of bulls/oxen (for owners)|1.19|0.39|***|1.21|1.79|***|
|Value of other livestock in (‘000) Birr|1.20|0.57|***|1.20|1.69|***|
|**Distribution over districts (****_Woredas_) **|||||||
|Woreda1 (Machakel), East Gojam (%)|15.44|13.09||12.55|23.38||
|Woreda2 (Gozamin), East Gojam (%)|15.91|16.88||16.64|13.61||
|Woreda3 (Enemay), East Gojam (%)|15.98|21.35||12.25|19.54||
|Woreda4 (Tehuledere), South Wollo (%)|20.57|18.94||23.20|16.35||
|Woreda5 (Tenta), South Wollo (%)|15.96|13.55||20.03|9.32||
|Woreda6(Habu),South Wollo(%)|16.14|16.19||15.33|17.81||
|Number of observations|4268|871||2302|1095||

**Source.** Own computation from AAU/UG Amhara Panel Survey **Note** _._ Unit of observation is a household in a given year. Except for negligible attrition (less than 3% between rounds), this implies that the same household contributes three observations. Stars indicate that means for the lease-out and lease in group, respectively, are significantly different from those for the group remaining in autarky. * significant at 10%; ** significant at 5%; *** significant at 1%.

19
psychosocial support services. Finally, a limitation of our phone surveys is that we measure time spent on learning activities, but not how much learning has taken place. Continuing to monitor these students over time to measure learning effects is therefore needed.

# **References**

Bureau of Labor Statistics (2019) _American Time Use Survey Questionnaire 2011-18_ <u>https://www.bls.gov/tus/tuquestionnaire.pdf</u> [accessed 30 April, 2020] Cabrera, José María León and Anatoly Kurmanaev (2020) “Ecuador’s Death Toll During Outbreak Is Among the Worst in the World”, _New York Times_ , April 23, <u>https://www.nytimes.com/2020/04/23/world/americas/ecuador-deaths-coronavirus.html</u> [accessed 28 April, 2020].

El Comercio (2020) “Ministerio de Salud confirma primer caso de coronavirus en Ecuador”, <u>https://www.elcomercio.com/actualidad/salud-confirma-primer-caso-coronavirus.html</u> [accessed 29 April, 2020] Giannini, Stefania (2020) “Prioritize health and well-being now and when schools reopen”, UNESCO, April 30, <u>https://en.unesco.org/news/prioritize-health-and-well-being-now-andwhen-schools-reopen</u> [accessed 30 April, 2020].

Goldstein, Dana, Adam Popescu and Nikole Hannah-Jones (2020) “As School Moves Online, Many Student Stay Logged Out”, _New York Times_ April 6, <u>https://www.nytimes.com/2020/04/06/us/coronavirus-schools-attendance-absent.html</u> [accessed 28 April, 2020] Le Nestour, Alexis, Samba Mbaye, Justin Sandefur, and Laura Moscoviz (2020), “Phone survey on the Covid crisis in Senegal", April 23, "report_ENG_v1.pdf”, https://doi.org/10.7910/DVN/9XE95F/95RW9C, Harvard Dataverse, V3.

Ministerio de Salud Pública (2020) “Actualización de casos de coronavirus en Ecuador”, <u>https://www.salud.gob.ec/actualizacion-de-casos-de-coronavirus-en-ecuador/</u> [accessed May 4, 2020] Reuters (2020) “Ecuador confirma primera muerte por coronavirus”, March 13, <u>https://www.infobae.com/america/agencias/2020/03/13/ecuador-confirma-primera-muerte-porcoronavirus/</u> [accessed 29 April, 2020] Torres, Claudia, Patricia Otero, Byron Bustamante, Vanessa Blanco, Olga Diaz and Fernando Vázquez (2017) “Mental Health Problems and Related Factors in Ecuadorian College Students”, _International Journal of Environmental Research and Public Health_ 14(5): 530.

UNESCO (2020) “COVID-19 Educational Disruption and Response”, <u>https://en.unesco.org/covid19/educationresponse</u> [Data from April 4, 2020].

11
are presented in Table 7. Among natives with high parental education, estimated treatment effects are approximately -1 for mathematics and language, and statistically insignificant. Among native students with low parental education, estimated effects on language and mathematics test scores are both negative, and larger in magnitude compared to natives with high parental education. The estimated treatment effect is approximately 3.35 for Dutch language test scores, and significant at the 5% level. For mathematics, estimates are statistically insignificant. This indicates heterogeneity in treatment effects depending on the socio-economic background of native students receiving the treatment. While natives with high parental education are unaffected irrespective of the duration of stay of immigrant classmates, the scholastic achievement of natives with low parental education appears to be impacted by the presence of recent immigrants.

# **6.5 Effect sizes**

When statistically significant, the negative effects of having a higher concentration of recent immigrants in the classroom are quite small. To give the reader a sense of the magnitude of the effects, it is useful to compare them to well-known educational interventions. One of the most studied educational inputs is class size. The cleanest available evidence so far comes from the Tennessee STAR experiment studied by Krueger (1999) as well as later contributions, summarized in Hanushek (2006). The STAR sample consists of primary school students of similar age as in our sample. Krueger (1999) reports a negative effect size for an increase in class size by one student of about 0.03 standard deviation in test scores. The effect size found by Angrist and Lavy (1999) for Israel using quasi-experimental methods is similar in magnitude, ranging from 0.02 for fourth graders to 0.036 for fifth graders. Our estimates suggest that an increase in the concentration 29
|**Zone**|**1970**|**P**<br>**2000**|**opulation**<br>**Annual**<br>**Growt**<br>**h**<br>**1970**–<br>**2000**<br>**(%)**|**Per**<br>**Capita**<br>**GDP**<br>**1996**<br>**(R$)**|**Poverty**<br>**% Earning**<br>**<One**<br>**minimum**<br>**salary**<br>**2000**|**_Real GD_**<br>**1980–**<br>**90**|**_P Growt_**<br>**1990–**<br>**96**|**_h(%)_**<br>**1970–**<br>**96**|
|---|---|---|---|---|---|---|---|---|
|Serras<br>Centrais|71,123|90,667|0.81|776.2|88.3|-1.51|8.02|3.93|
|Alto do<br>Apodi|176,653|223,969|0.79|892.7|75.0|-1.67|6.37|4.4|
|Agreste|219,550|285,166|0.88|830.5|67.3|-1.05|3.74|2.84|
|Caicó|88,901|120,562|1.02|1353.8|25.9|0.73|3.08|3.49|
|Litoral<br>Oriental|505,794|1,271,156|3.12|3334.1|44.0|6.3|2.23|7.33|
|Litoral<br>Norte|125,013|168,980|1.01|1783.2|51.7|1.83|1.35|2.84|
|Currais<br>Novos|71,783|94,333|0.91|863.1|83.3|-2.14|0.84|2.53|
|Mossoroens<br>e|291,427|521,949|1.96|1775.3|54.1|2.56|0.09|5.71|
|TOTAL RN|1,550,244|2,771,000|1.95|2,291|51.9|4.25|2.07|6.09|
|_Source:_Calculate|d with municipal|GDP data from I|PEA and Cens|us 1970 and 2|000.||||

The large economic and population growth in the capital zone contrasts sharply with the developments in the zones that are mainly in the Sertão and where income opportunities are limited, droughts are frequent, and social protection programs are few. A large share of the population in the five zones that are almost completely in the Sertão migrated to the metropolitan area, which resulted in a population growth in these zones of less than a third of the average population growth in the state of 79 percent, and a sixth of the capital zone’s. Income growth followed the same path, but it was steeper because in three zones (Serras Centrais, Caicó, and Currais Novos) income contracted rather than grew during the last 30 years. Low-growth agriculture played a key role, and the main causes were the steep decline in cotton production and the end of traditional mineral extraction.

Poverty is rampant among families left in the zones from which there has been strong migration. More than 80 percent of the household heads had incomes less than one minimum salary in Serras Centrais and Currais Novos, compared to the zone average of 51 percent, or the capital zone’s 44 percent. Carió, also in the Sertão, is the exception to this stark picture, but still did not have high economic growth or low migration. Caicó had the lowest proportion of people earning less than a minimum salary (26 percent) in RN, but this cannot be explained by economic development, because the zone was just as hard hit as the other zones by factors such as the collapse of cotton cultivation. The difference may have been due to social development, including participation (see Chapter 4). This indicates that changes are more complex than just migration, and an alternative livelihood is needed in the rural areas.

10
**TABLE 10: Investment financial performance and macroeconomic variables recorded in the year before the first cash flow.** The dependent variable is the investment-level PME vs. S&P 500. Explanatory variables are the log difference in the variable between the year of first cash flow and the previous year times 100, so coefficients may be interpreted as the association of the PME with a 1 percentage point change in the variable prior to the first cash flow. Standard errors are clustered at the country-year. *** p<0.01, ** p<0.05, * p<0.1

||(1)|(2)|(3)<br>Qua|(4)<br>ntile Regre|(5)<br>ssion|(6)|
|---|---|---|---|---|---|---|
||OLS|0.1|0.25|0.5|0.75|0.9|
||PME|PME|PME|PME|PME|PME|
|Panel A) Direct investments, vintage pr|e-2010||||||
|Real GDP|0.093|0.000|0.011***|0.012|0.013|0.032|
||(0.129)|(0.003)|(0.004)|(0.011)|(0.015)|(0.059)|
|Local currency GDP defator|-0.042|0.000|-0.002|-0.005|-0.013**|-0.022|
||(0.027)|(0.001)|(0.002)|(0.004)|(0.006)|(0.014)|
|Local currency units per U.S. dollar|0.054|0.000|0.003*|0.005|0.015*|0.029*|
||(0.034)|(0.001)|(0.002)|(0.004)|(0.008)|(0.017)|
|Constant|2.338***|0.002|0.161***|0.714***|1.536***|3.007***|
||(0.454)|(0.015)|(0.018)|(0.040)|(0.057)|(0.349)|
|R-squared|0.002|0.001|0.002|0.002|0.002|0.002|
|Number of observations|1,242|1,242|1,242|1,242|1,242|1,242|
|Panel B) Fund investments, vintage pre|-2010||||||
|Real GDP|0.139|0.002|-0.000|0.018|0.027|0.166***|
||(0.084)|(0.012)|(0.010)|(0.016)|(0.031)|(0.056)|
|Local currency GDP defator|-0.032*|-0.007*|-0.005|-0.010|-0.030|-0.053***|
||(0.019)|(0.004)|(0.004)|(0.010)|(0.034)|(0.003)|
|Local currency units per U.S. dollar|0.035*|0.008*|0.005|0.010|0.033|0.058***|
||(0.019)|(0.004)|(0.004)|(0.011)|(0.034)|(0.003)|
|Constant|1.380***|0.146***|0.306***|0.629***|1.066***|2.411***|
||(0.491)|(0.047)|(0.046)|(0.063)|(0.122)|(0.461)|
|R-squared|0.012|0.002|0.002|0.009|0.004|0.011|
|Number of observations|148|148|148|148|148|148|
|Country fxed efects|No|No|No|No|No|No|

41
workforce.<sup>15</sup> This is low by any standard: comparable numbers for professional and technical staff in Gambia (in 2007) were 26% and 14.3% in Sudan (in 2004).<sup>16</sup> Low remuneration appears to be one of the major reasons why Sierra Leone’s civil service has not been able to attract and retain suitably qualified professional and technical staff. One indication that low remuneration is a key constraint to attracting qualified staff is that entry level personnel recently recruited for certain technical positions had to be paid more than 400% above regular civil service salaries to accept the appointments and the plethora of “coping” arrangements that circumvent the low public pay scale (World Bank 2012b, pp. 2).

The quality of staff in the middle and upper grades is also low. As the civil service atrophied during the civil war, it was generally the most qualified and competent people with marketable skills who left. With some exceptions, the civil servants remaining are those who lack the skills and competencies to find jobs overseas or in the private sector.

The third problem is that Sierra Leone’s civil service is demoralized. Unfair pay is an important demotivating factor, as many civil servants are concerned about how they are remunerated in relation to others. The government has failed to introduce a pay structure in which relativities are based on the relative worth of the job. Instead, ad hoc pay adjustments for a select few have created significant anomalies and distortions which are resented by the majority of civil servants.<sup>17</sup> This has undermined esprit de corps within the service, which has had an adverse effect on professional cooperation both within and between functions and departments. The vested interests established in the process have made reforms harder to implement.

# **B. Institutional fragmentation**<sup>**18**</sup>

While the responsibility for public service reform is fragmented in many countries (by its very nature), in Sierra Leone this fragmentation is greater and, in practice, exacerbates the collective action problem in a government that is characterized by weak coordination and poor information sharing. Coordination between a large number of offices, ministries and agencies is required for implementing these reforms. These include, at a minimum, the Secretary to the

> 15 According to a recent Public Expenditure Review (PER) (World Bank 2010), the comparable figure was 7% in 2008. The “civil service” in Sierra Leone includes health workers but not teachers who are included in the “public service” count but not in the “civil service”.

> 16 Figures for Gambia are from the payroll for 2007; figures for Sudan are from Background Paper for ‘Sudan: Joint Assessment Mission’, unpublished mimeo, World Bank, 2004.

> 17 “Rapid Assessment of Dynamics of Public Sector Reform in Sierra Leone,” report prepared by Sidi Koroma, Positive Change Human Resource Consultancy Services, Unpublished, Sierra Leone, May 2011.

> 18 See previous footnote.

10
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|P088252|2007|2010|11/27/2009|5 Closed|Romania|
|---|---|---|---|---|---|
|P088252|2007|2010|3/29/2010|6 Closed|Romania|
|P088252|2007|2010|5/24/2010|7 Closed|Romania|
|P088252|2007|2011|2/9/2011|8 Closed|Romania|
|P088252|2007|2011|5/28/2011|9 Closed|Romania|
|P088252|2007|2012|1/3/2012|10 Closed|Romania|
|P088252|2007|2012|3/26/2012|11 Closed|Romania|
|P088256|2005|2008|12/4/2007|1 Closed|Honduras|
|P088256|2005|2010|1/24/2010|2 Closed|Honduras|
|P088256|2005|2010|6/25/2010|3 Closed|Honduras|
|P088256|2005|2012|8/20/2011|5 Closed|Honduras|
|P088256|2005|2015|11/14/2014|4 Closed|Honduras|
|P088292|2004|2008|9/12/2007|1 Closed|Tonga|
|P088292|2004|2009|8/15/2008|2 Closed|Tonga|
|P088298|2004|2008|8/13/2007|1 Closed|Cambodia|
|P088299|2004|2008|8/13/2007|1 Closed|Indonesia|
|P088319|2006|2006|3/7/2006|1 Closed|Honduras|
|P088319|2006|2006|6/19/2006|2 Closed|Honduras|
|P088319|2006|2007|10/4/2006|3 Closed|Honduras|
|P088319|2006|2007|2/27/2007|4 Closed|Honduras|
|P088319|2006|2008|10/1/2007|5 Closed|Honduras|
|P088319|2006|2008|3/27/2008|6 Closed|Honduras|
|P088319|2006|2009|10/16/2008|7 Closed|Honduras|
|P088319|2006|2009|4/7/2009|8 Closed|Honduras|
|P088319|2006|2010|7/29/2009|9 Closed|Honduras|
|P088319|2006|2010|2/1/2010|10 Closed|Honduras|
|P088319|2006|2010|6/10/2010|11 Closed|Honduras|
|P088319|2006|2011|2/13/2011|12 Closed|Honduras|
|P088319|2006|2011|6/15/2011|13 Closed|Honduras|
|P088319|2006|2012|12/5/2011|14 Closed|Honduras|
|P088319|2006|2012|6/9/2012|15 Closed|Honduras|
|P088319|2006|2013|1/23/2013|16 Closed|Honduras|
|P088319|2006|2014|7/5/2013|17 Closed|Honduras|
|P088362|2005|2005|6/21/2005|2 Closed|Vietnam|
|P088362|2005|2006|5/30/2006|3 Closed|Vietnam|
|P088362|2005|2007|12/5/2006|4 Closed|Vietnam|
|P088435|2010|2010||0 Closed|Yemen,Republic of|
|P088435|2010|2011|3/11/2011|1 Closed|Yemen,Republic of|
|P088435|2010|2012|11/27/2011|2 Closed|Yemen,Republic of|
|P088435|2010|2012|6/24/2012|3 Closed|Yemen,Republic of|
|P088435|2010|2013|1/15/2013|4 Closed|Yemen,Republic of|
|P088435|2010|2014|7/27/2013|5 Closed|Yemen,Republic of|
|P088435|2010|2014|1/7/2014|6 Closed|Yemen,Republic of|
|P088435|2010|2014|5/23/2014|7 Closed|Yemen,Republic of|
|P088435|2010|2015|12/29/2014|8 Closed|Yemen,Republic of|
|P088435|2010|2015|5/14/2015|9 Closed|Yemen,Republic of|
|P088435|2010|2015|6/29/2015|10 Closed|Yemen,Republic of|
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35