rafmacalaba/datause-encoder-data huggingface dataset:
Kolade, O., Kibreab, G., James, S., and Smith, R. (2019). _Picking up the pieces: social capital and entrepreneurship for livelihood recovery among displaced populations in Northeast Nigeria._ URL: <u>https://www.dora.dmu.ac.uk/handle/2086/18098</u> Lautze, S. (2006). _Violence and complex humanitarian emergencies: implications for livelihoods models._ In: Disasters Volume 30, Issue 4. URL: <u><mark>https://doi.org/10.1111/j.03613666.2006.00328.x</mark></u> Makama, G. A. (2013). Patriarchy and gender inequality in Nigeria: The way forward. European scientific journal, 9(17). URL: https://eujournal.org/index.php/esj/article/view/1161 Matfess, H. (2020): _Part and Parcel? Examining Al Shabaab and Boko Haram’s Violence Targeting Civilians and Violence Targeting Women,_ Studies in Conflict & Terrorism. URL: https://doi.org/ <u>10.1080/1057610X.2020.1759262</u> Maxwell, D., and Caldwell, R. (2008). _The Coping Strategies Index: Field Methods Manual Secondary Edition_ , WFP, Rome, Italy. URL: https://documents.wfp.org/stellent/groups/public/documents/ <u>manual_guide_proced/wfp211058.pdf</u> Nagarajan, C. (2017). Gender Assessment of Northeast Nigeria. Conducted for Managing Conflict in Northeast Nigeria. Managing Conflict in North East Nigeria (MCN). URL: <u>https://chitrasudhanagarajan.files.word-press.com/2018/03/gender-assessment-of-northeastnigeria.pdf</u> Nagarajan, C. et al. (2018). Climate-Fragility Profile: Lake Chad Basin. Berlin: adelphi.

<mark>Nguya, G., and Siddiqui, N., (2020).</mark> _<mark>Triple Nexus Implementation and Implications for Durable Solutions for Internal Displacement: On Paper and in Practice</mark>_ <mark>, Refugee Survey Quarterly, Volume 39, Issue 4, December 2020, Pages 466–480. URL:</mark> _<u><mark>https://doi.org/10.1093/rsq/hdaa018</mark></u>_ National Population Commission (NPC) and ICF (2014). _Nigeria Demographic and Health Survey 2013._ Abuja, Nigeria, and Rockville, Maryland, USA: NPC and ICF. URL: <u>https://dhsprogram.com/pubs/pdf/fr293/fr293.pdf</u> Nwoke, C. C., Becker, J., Popovych, S., Gabriel, M., and Cochrane, L. (2022). _Gender Transformation in Humanitarian Response: Insight from Northeast Nigeria_ . Journal of Humanitarian Affairs, 4(1), 36-47. URL: <u>https://doi.org/10.7227/JHA.080</u> Ogunlela, Y. I., & Mukhtar, A. A. (2009). Gender issues in agriculture and rural development in Nigeria: The role of women. Humanity & Social Sciences Journal, 4(1), 19-30. URL: <u>https://genderandsecurity.org/projects-resources/research/gender-issues-agriculture-and-ruraldevelopment-nigeria-role-women</u> Okpara, U. T., Stringer, L. C., & Dougill, A. J. (2016). Lake drying and livelihood dynamics in Lake Chad: Unravelling the mechanisms, contexts and responses. Ambio, 45(7), 781-795.

Pape, U., and Kwenin, H. (2018). _Profile of Internally Displaced Persons in North-east Nigeria 2018,_ NGA_2018_IDP_v01_M. URL: https://microdata.worldbank.org/index.php/catalog/3410 Pivovarova, M., & Swee, E. L. (2015). Quantifying the microeconomic effects of war using panel data: Evidence from Nepal. World Development, 66, 308-321. URL: <u>https://www.sciencedirect.com/science/article/abs/pii/S0305750X14002642</u> 20
distribution losses account for 8 percent of total output in South Africa, which outperforms both SSA (19 percent) and upper middle income countries (14 percent). Indeed, this aggregate performance is more comparable to the high-income group of OECD countries (6 percent). As a result of an number of supply-side management initiatives during the 1990s, Eskom’s generation plant performance improved substantially and by early part of this decade several of its technical performance metrics (e.g. Unit Capability Factor, Unplanned Capability Loss Factor, and Unplanned Automatic Grid Separations per 7000 operating hours) had reached world-class levels (in some instances well above the international best quartile). However, this aggregate picture must be qualified: for the 30 percent of the population not having access, this aggregate technical efficiency does not matter. In addition the increased prevalence of power outages in specific communities, even within Johannesburg, associated with the poor maintenance and the state of the distribution network under the control of local governments further qualifies the technical efficiency of the distribution network.

# **Investment Needs in South Africa’s Electricity Sector**

To put the discussion of the sector in the broader context of the infrastructure scale up and investment needs, in this section, we briefly present a panel-based projection of the large investment needs in the electricity sector for two growth scenario: (i) current growth scenario of 3.6 percent per annum and (ii) accelerated growth scenario of 6 percent per annum. We base our prediction of the required stocks and flows of electricity network infrastructure on an estimated long-run demand model using a 52country panel. The panel was limited to low- and middle-income countries, in order not to bias the estimated infrastructure demand upward through the inclusion of high-income countries.<sup>78</sup>

# **The Current Growth Scenario**

Our forecasts indicate that the electricity production would grow from 4,815.47 kwh per capita in 2002 to 6,453.19 kwh per capita in 2010. The average annual forecast growth rate for electricity production based on the estimated long-run relation during 2003-2010 is 3.7 percent.

Using the upper bound cost structure, we predict that total value of the electricity generating stock is going to increase from US$ 7.26 billion (R 47.17 billion) in 2002 to US$ 9.52 billion (R 61.88 billion at an exchange rate of R6.5/$1) in 2005, and then to US$ 14.64 billion (R 95.18 billion) in 2010. Therefore, required average annual 78 We estimated the following empirical specification of equation where I denotes the electricity infrastructural measure, P population, YPC per capita real GDP, YS sectoral shares in GDP, where we consider agriculture (YAG), manufacturing (YMAN), and services (YSERV), and Z denotes a vector of additional explanatory variables.

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indexes (both CPI and PPI) to deflate the series. Finally, all series are indexes whose base has been change, but not other transformation done (e.g. natural logarithm) and have been seasonally adjusted using X12.

# B Estimation

The model is estimated using a Gibbs sampling algorithm. We refer the reader to the Appendix of Gali and Gambetti (2014) for the details of the estimation. Here we discuss our priors specification and calibration.

Following Primiceri (2005), we make the following assumptions for the priors densities. First, the coeffi cients of the covariances of the log volatilities and the hyperparameters are assumed to be independent of each other. The priors for the initial states θ0, φ0 and log σ are assumed to be normally distributed. The priors for the hyperparameters, Ω, Ξ and Ψ are assumed to be distributed as independent inverse-Wishart. More precisely, we have the following priors:

- Time varying coeffi cients: P (θ0) = N (<sup>ˆ</sup> θ, V<sup>ˆ</sup> θ) and P (Ω<sup>−1</sup> ) = W (Ω<sup>−</sup> 0<sup>1, ρ</sup> 1<sup>);</sup>

- Diagonal elements: P (log σ0) = N (log ˆσ, In) and P (Ψ<sup>−</sup> i<sup>1) = W(Ψ−</sup> 0i<sup>1, ρ</sup> 3i<sup>);</sup>

- Off-diagonal elements: P (φi0) = N (φ<sup>ˆ</sup> i, V<sup>ˆ</sup> φi) and P (Ξ<sup>−1</sup> ) = W (Ξ<sup>−</sup> 0<sup>1, ρ</sup> 2<sup>);</sup>

The the scale matrices are parameterized as follows Ω<sup>−</sup> 0<sup>1</sup> = λ1ρ1V<sup>ˆ</sup> θ, Ψ0i = λ3iρ3iV<sup>ˆ</sup> φi and Ξ0 = λ2ρ2In. The hyper-parameters are calibrated using a time invariant recursive VAR estimated using a sub-sample consisting of the first T0 = 32 observations. For the initial states θ0 and the contemporaneous relations φi0, we set the means,<sup>ˆ</sup> θ and φ<sup>ˆ</sup> i, and the variances, V<sup>ˆ</sup> θ and V<sup>ˆ</sup> φi, to be the maximum likelihood point estimates and four times its variance. For the initial states of the log volatilities, log σ0, the mean of the distribution is chosen to be the logarithm of the point estimates of the standard errors of the residuals of the estimated time invariant VAR. The degrees of freedom for the covariance matrix of the drifting coeffi cient’s innovations are set to be equal to T0, the size of the initial-sample. The degrees of freedom for the priors on the covariance of the stochastic volatilities’innovations, are set to be equal to the minimum necessary to 26
**<mark>Table 4. Sovereign selective default episodes and spreads on the foreign-currency bond markets</mark>**

|Country|Secondary market<br>default spread (bps)|Selective default date|Emergence date|Time in selective<br>default|
|---|---|---|---|---|
|Argentina|5,320|November 6, 2001|June 1, 2005|43 months|
|Dominican Rep.|616|February 1, 2005|June 29, 2005|5 months|
|Ecuador|3,654|December 15, 2008|June 15, 2009|6 months|
|Russia|2,537|January 27, 1999|December 8, 2000|22 months|
|Uruguay|929|May 16, 2003|June 2, 2003|1 month|
|Venezuela*|446|January18,2005|March 3,2005|11⁄2 month|
|Average|2,250|||13 months|

_Source: Default information is based on "Sovereign Defaults And Rating Transition Data, 2009 Update", Standard and Poors, March 17, 2010; sovereign spreads from J.P. Morgan EMBI Global_ (*) In the case of Venezuela, there was a debate at the time among credit agencies; evidently investors did not react to S&P downgrade We run a set of regressions with interactions between the systematic component of sovereign spreads (estimated from equation 5) and our country-specific crisis dummies using: ��,��� ���,� ���<sup>�</sup> ��� ���<sup>�</sup> �� ���<sup>�</sup> �� ���<sup>�</sup> ���<sup>�</sup> ����� ��<sup>�</sup> ������ ���� (7) where: ����� ����,� ��<sup>�</sup> �<sup>�</sup> ���, and the results are reported in Table 5.

As seen from Table 5, the estimated coefficient SSR is positive and statically significant, even with the presence of domestic macroeconomic variables in the equation explaining the determinants of private corporate bond market spreads in emerging economies. Interacting SSR with the country crisis dummy variable provides a measure of the degree to which sovereign risk affects private external borrowing capital costs during times of sovereign debt distress and financial crises. In all equations reported in Table 5, the estimated coefficient is also positive and significant.

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particular sectors in their efforts to attract FDI, they adopt a DID approach and compare FDI inflows into targeted sectors, before and after targeting, to FDI inflows into non-targeted sectors during the same period. In addition, the changes in the host country business environment is controlled by including country-year fixed effects, heterogeneity of sectors in different locations by country-sector fixed effects, and shocks to FDI supply in particular sectors by sector-time fixed effects.

This study finds that investment promotion appears to be more effective in countries where English is not an official language and in countries which are more culturally distant from the U.S. It works better in countries with less effective governments, higher corruption, and a longer time period required to start a business or to obtain a construction permit. IPA tends to have a positive impact on FDI inflows in the developing world but not in industrialized countries. Even within the subsample of developing countries, it works better in places with higher information asymmetries and more red tape. The back-of-the-envelope cost-benefit calculation suggests that targeted sectors receive more than twice as much FDI than non-targeted sectors in developing countries. As for the costs, the 2004 budget figures from the Census indicate that an average IPA spent $90,000 per sector targeted. Combining the benefit and the cost figures, it seems that a dollar spent on investment promotion leads to $189 of FDI inflows. Using a data set on employment in U.S. affiliates abroad, the study also shows that priority sectors experience a 68 percent increase in affiliate employment when compared to non-targeted sectors. This implies an additional 1,159 jobs for the average sector, or $78 per job created.

Similar to other quasi-experimental studies, a key concern, however, is the identification assumption of this study, i.e., to what extent can one treat the sector targeting strategy of an IPA as an exogenous event? An IPA’s sector targeting decision could be a response to earlier FDI experience in the sector or in anticipation/prediction of the emerging trend of U.S. or global FDI inflows in different sectors. To address this concern, the study excludes the countries that reported in the IPA census that targeting decision was based on the past success or failure in attracting FDI to the sector, and the above results seem to hold. In addition, the authors did not find any evidence suggesting that targeting took place in sectors with relatively high or low FDI inflows in the years preceding targeting. While these are important checks, these do not 30
based on the Brazilian Labor Force Survey “Pesquisa Mensal de Emprego” for our sample years indicate that the shares of formal employment, gender, age, and industry distribution in Belo Horizonte (the capital of Minas Gerais) is very similar to those in other urban areas (see table 1).<sup>2</sup> Our Uruguayan data consist of social security records from the Banco de Previsión Social (BPS). After the 1995 social security reform, the BPS started keeping employer and employee administrative records on a monthly basis. We exploit a random sample of the BPS records, which, like the Brazilian RAIS, contains information on establishments (number of employees, sector, region), workers (age, sex) and their jobs (occupation, weekly hours of work, monthly wages). Establishments and workers are uniquely tracked by an individual identifier and an establishment identifier. Each job within a workerestablishment pair is uniquely identified as well, which allows us to track year-to-year job stayers who have been neither promoted nor demoted. This job identifier, however, is not available in the Brazilian RAIS, so for the sake of comparability in our empirical analysis we define stayers as workers who are continuously employed in the same establishment for a year or more.<sup>3</sup> Regarding the compensation measures used in our analysis, the Brazilian RAIS reports monthly wages earned in December, which include extraordinary additions, supplements and bonuses, tips and gratuities, commissions and fees, contracted premia, overtime compensation for contracted extra hours, and, in general, all forms of payment that are taxable income or are subject to Brazilian social security contributions. The “thirteenth salary” -the special December payment that is made in some sectorsas well as severance payments for layoffs and indemnity payments are not considered wage components. Therefore, we construct a comparable monthly wage measure from Uruguay’s BPS that excludes severance payments and the special December payment. The number of hours worked per week is reported in both data sets. There are some

> 2 The Pesquisa Mensal de Emprego is representative of urban areas in 8 districts of Brazil. We restrict the comparison to Belo Horizonte, where, according to our data, 28 percent of formal employees in Minas Gerais are employed.

> 3 However, it is worth noting that our results are very similar when we exclude promoted or demoted employees from our Uruguayan sample of stayers. These resuls are available upon request from the authors.

7
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# **2. The observed rate of return of public programs and projects**

Obtaining historical information on the rate of return of public spending is a difficult task, but the data limitation may be partially overcome by considering a sample of public projects financed by the World Bank in recent decades. In spite of the limitations of this sample, derived from its small size in relation to public spending in each country and possible selection bias discussed below, there are interesting facts of the rate of return during the period 1980-2004. The observed rate of return of public spending changes through time and falls within the ranges estimated in the previous section. Naturally, this refers to the median of the sample for each year, and there is much variability across sectors and regions (Herrera, 2005). While the economic rate of return fluctuated between 12 and 14 % during the eighties, there is an upward trend during the nineties to settle around 18 or 20%.

# **Figure 13**

<!-- Start of picture text --> Median Economic Rate of Return of World Bank Projects<br> 1972-2004<br>26<br>%<br>24<br>22<br>20<br>18<br>16<br>14<br>12<br>10<br>1975 1980 1985 1990 1995 2000<br><!-- End of picture text --> Source: Calculation based on OED database The upward trend in the rate of return of public spending is associated with a more stable macroeconomic environment. During the nineties, there was a generalized opening of trade and improvement of fiscal positions that are associated with better project performance. Different studies show that there is a statistically significant relationship between the macro environment and project performance (Isham and Kaufmann, 1999; Herrera 2005). This fact reinforces the point that fiscal policy does not act in a vacuum, and better project outcomes will be achieved in settings in which markets are left to operate so that prices are effective signals of fundamentals.
regressions, the standard errors are corrected for heteroskedasticity and clustering at the commune/survey round level.

The coefficient on rice price β 1 indicates the effect of rice prices on female participation (or hours worked) in various activities. The coefficient on the interaction of rice prices and male indicator β 2 indicates an additional association between rice prices (if any) and the participation (or hours worked) of men in the activity. Thus, the total observed effect of rice prices on men is the sum of the coefficient on rice prices and the coefficient on the interaction between rice prices and the male dummy, β 1 + β 2 .

Finally, section 3 indicated that in Vietnam people participate in a wide range of work. In addition, people can participate in more than one activity. This could occur for several reasons. First, if the value of time is equalized across activities, the choice of which activity to participate in is arbitrary. Second, individuals that desire to work for wages could face binding constraints on the availability wage work because of efficiency wage related issues. Third, businesses and households might face rationing constraints on the pool of workers they can hire, especially when mobility restrictions limit the free flow of labor as in Vietnam. This could preclude households from hiring labor to perform activities within the household. As a result, household members might engage in work within the household in addition to a job outside the household and might also perform multiple tasks within the household. Fourth, Paxson and Mammen (2002) emphasize that in some cultures there is a stigma associated with female work outside of the household or with male work in some types of activities within the household. In this case, then, individuals may have to perform multiple tasks where their productivity differs across tasks.

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# Table 1: Correlations between Legal Disparities and Gender Indices

||**Legal disparities: Overall m**|**easure (Higher value**|**s, greater gender disparity)**|
|---|---|---|---|
||Correlation coefficient|Significance|Number of observations|
|CIRI Women's Social Rights 2004 (Higher values greater gender equality)|-0.50|***|147|
|CIRI Women's Economic Rights 2011 (Higher value greater gender equality)|-0.56|***|164|
|CIRI Women's Political Rights 2011 (Higher values greater gender equality)|-0.41|***|164|
|WEF Gender Gap Index 2015 (Higher values greater gender equality)|-0.66|***|136|
|Social Watch Gender equity index 2012 (Higher values greater gender equality)|-0.66|***|141|
|Ratio of female to male human development index 2013 (Higher values greater gender equality)|-0.57|***|136|
|UN gender inequality index 2013 (Higher values worse gender equality)|0.53|***|142|
|OECD Social Institutions and Gender Index 2014(Higher values worsegender equality)|0.70|***|98|

# Table 2: <u>Summary Statistics for Regressions Analysis</u>

||Obs|Mean|Std. Dev.|Min|Max|Maximum possible<br>disparities|
|---|---|---|---|---|---|---|
|Legal disparities: Overall measure|167|17.4|9.1|2|43.9|71|
|Legal disparities: Accessing Institutions|167|2.7|3.3|0|18|30|
|Legal disparities: Using Property|167|1.0|1.6|0|7|7|
|Legal disparities: Getting a Job|167|9.6|3.9|0|19.9|22|
|Legal disparities: Going to Court|167|0.2|0.5|0|2|2|
|Legal disparities: Providing Incentives to Work|167|0.2|0.5|0|2|2|
|Legal disparities: Building Credit|167|2.3|1.2|0|3|3|
|Legal disparities: Violence Protection|167|1.5|1.6|0|5|5|
|Log of GDP per capita (constant 2005 US$)|157|11,175|15,727|155.25|79,509||
|Rule of law|167|0.0|1.0|-1.79|1.97||
|Years of education, women relative to men ages 25 plus|163|0.8|0.2|0.2|1.1||
|Labor force participation rate, women relative to men|159|0.7|0.2|0.2|1.0||
|Proportion of seats held by women in national parliaments (%)|157|20.6|11.3|0|63.8||
|Proportion of women top managers (%)|113|17.3|8.7|1.2|38.6||
|Borrowed from a financial institution, women relative to men (% age 15+)|136|0.8|0.3|0.1|2.2||
|Mortalityrate, under-5(per 1,000 live births)|162|30.9|33.1|2|167.4||

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