rafmacalaba/datause-encoder-data huggingface dataset:
Table A11: Results: Income per hour

||(1)<br>(2)<br>Earned Income/Hours Worked<br>Last 7 Days (USD)|
|---|---|
|Offered Training|0.10<br>0.28<br>(0.19)<br>(0.30)|
|Offered * Round 2|-0.06<br>(0.37)|
|Offered * Round 3|-0.47<br>(0.37)|
|Control Mean|1.82|
|Control Mean R1|1.71|
|Control Mean R2|1.79|
|Control Mean R3|1.94|
|Observations|2,093<br>2,093|
|p-value Round 2|0.42|
|p-value Round 3|0.51|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

Table A12: Impacts on Occupational Choice

||(1)<br>Empl|(2)<br>oyee|(3)<br>Apprenti|(4)<br>ce/Intern|(5)<br>Emp|(6)<br>loyer|(7)<br>Self-E|(8)<br>mployed|
|---|---|---|---|---|---|---|---|---|
|Offered Training|0.04|0.02|0.01|0.06<sup>_∗∗_</sup>|-0.00|-0.01|0.01|0.05<sup>_∗_</sup>|
||(0.02)|(0.03)|(0.02)|(0.03)|(0.01)|(0.01)|(0.02)|(0.03)|
|Offered * Round 2||0.03||-0.06<sup>_∗_</sup>||-0.00||-0.05|
|||(0.04)||(0.04)||(0.02)||(0.03)|
|Offered * Round 3||0.03||-0.08<sup>_∗∗_</sup>||0.02||-0.08<sup>_∗∗_</sup>|
|||(0.04)||(0.04)||(0.02)||(0.04)|
|Control Mean|0.36||0.22||0.03||0.18||
|Control Mean R1||0.27||0.20||0.03||0.18|
|Control Mean R2||0.39||0.23||0.04||0.15|
|Control Mean R3||0.41||0.22||0.02||0.21|
|Observations|2,597|2,597|2,597|2,597|2,597|2,597|2,597|2,597|
|p-value Round 2||0.20||0.90||0.40||0.94|
|p-value Round 3||0.16||0.37||0.28||0.20|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

35
65 uses a different mix of observable health inputs, or (2) that she uses these inputs more effectively, or (3) that her education **is** positively correlated with the use of unobserved health inputs and her education is credited with the effect of **these** unobserved inputs on child health (Schultz, 1984). Clearly, the mother is the most important health worker for her children. How well **she** performs this task appears to **depend** "on **her** schooling, which *quips her with **general** and specific knowledge, and **the** mans and confidence to seek new ideas' (Barrera, 1988b: p. 4). The challenge is to discover how education influences the use of health inputs to reduce **the** probability of child mortality. Because relatively few children die in middle and high income countries, a more widely distributed or continuous indicator of child health status is sought to extend and broaden the analysis. Weight of a child is often recorded at birth and provides a sensitive predictor of child mortality as well as lifetime health problem and mental and physical developmental handicaps. The self-selected health inputs of pregnant women Mst be analyzed as endogenous variables if one is to estimate the unbiased effect of these inputs on the production of child health (Schultz, 1984). Selection of some health inputs occurs in response to the mother's expectation of a goodl Or poor birth outcome, and consequently the simple correlation between these self-selected inputs and health outcomes may **be** misleading. Seeing a doctor early into the pregnancy **is** correlated with having a lower birthweight child, **whereas** administering the **same** health input to an average woman exerts a positive effect, holding constant the initial (but generally unobserved) health condition of **the** mother and child. In a U.S. study, the effect of mother's education on birthweight in 1967-69 and in 1980 appeared to be transmitted largely through the variation in the same four measured prenatal inputs
# **Figure 3. Approach with the best forecast performance**

# **A. 3-month horizon, by Diebold-Mariano test**

<!-- Start of picture text --> Percent of pairs<br>Consensus Futures Biv. corr.<br>60<br>BVAR OEM Mach. learn.<br>50<br>40<br>30<br>20<br>10<br>0<br>Oil Aluminum Copper Lead Nickel Tin Zinc<br><!-- End of picture text -->

# **C. 15-24-month horizon, by DieboldMariano test**

<!-- Start of picture text --> Percent of pairs<br>Consensus BVAR OEM Mach. learn.<br>50<br>40<br>30<br>20<br>10<br>0<br>Oil Aluminum Copper Lead Nickel Tin Zinc<br><!-- End of picture text -->

# **E. 6-12-month horizon, by RMSE**

<!-- Start of picture text --> Percent of pairs<br>Consensus Futures Biv. corr.<br>BVAR OEM Mach. learn.<br>100<br>80<br>60<br>40<br>20<br>0<br>Oil Aluminum Copper Lead Nickel Tin Zinc<br><!-- End of picture text -->

# **B. 6-12-month horizon, by DieboldMariano test**

<!-- Start of picture text --> Percent of pairs<br>Consensus Futures Biv. corr.<br>50<br>BVAR OEM Mach. learn.<br>40<br>30<br>20<br>10<br>0<br>Oil Aluminum Copper Lead Nickel Tin Zinc<br><!-- End of picture text -->

# **D. 3-month horizon, by RMSE**

<!-- Start of picture text --> Percent of pairs<br>Consensus Futures Biv. corr.<br>BVAR OEM Mach. learn.<br>100<br>80<br>60<br>40<br>20<br>0<br>Oil Aluminum Copper Lead Nickel Tin Zinc<br><!-- End of picture text -->

# **F. 15-24-month horizon, by RMSE**

<!-- Start of picture text --> Percent of pairs<br>Consensus BVAR OEM Mach. learn.<br>100<br>80<br>60<br>40<br>20<br>0<br>Oil Aluminum Copper Lead Nickel Tin Zinc<br><!-- End of picture text --> _Source_ : World Bank.

_Note_ : “Biv. corr.” stands for bivariate correlations, “Mach. Learn.” stands for machine learning approach. Charts show the percent of comparisons between all pairs of the six approaches in which the Diebold-Mariano statistic (A-C) or a lower RMSE (D-F) indicates that the approach shown on the x-axis has statistically significantly better forecast performance. Futures prices and bilateral correlations are not evaluated beyond the 12-month horizon, as discussed in the text.

33
|West Star MiningCompanyLtd.|Ankobra|50.0|2007|2017|
|---|---|---|---|---|
|Blue River MiningCompanyLtd.|Ankobra|40.4|2007|2017|
|First Canadian Goldfields Ltd.|Benso<br>Hwini-Butre|20.38<br>40.38|2007<br>2008|2011<br>2012|
|Kibi Goldfields International|Pusupusu|49.95|1995|2015|
|Ghana Bauxite Company|Nsuta|29.39|1997|2022|
|Savanna Cement CompanyLtd.|Buipe|50.0|2000|2030|
|Chacem Ltd.|Yongwa|0.64|2004|2014|

_Source:_ Ghana, Republic of 2008a, 205.

# **c. Representation of Mining Communities**

34. Democratic representation for districts and communities is essential to ensure political attention is given to their welfare. Significant tension exists between mining communities and the mining industry—and many NGOs seek to support the communities in what appears to be a power imbalance between poor farmers and international multinationals. Mining communities have maintained that the government does not work in their interest.

35. Policy making is centralized in national institutions. Some institutions, such as the Minerals Commission (MC), are accountable to Parliament only indirectly through their supervising ministries. The state has not developed a culture of community engagement—especially on resource issues (World Bank 2008b). Several studies find that Parliament provides little support for communities affected by mining (Akabzaa 2000; Akabzaa, Seyire, and Afriyie 2007; Dalberg/World Bank Group 2007). One possible explanation might be that the MPs who have been appointed by successive governments to represent the state in company boards usually have been selected among those who represent mining communities. These appointments may be devised to ensure that mining community interests are represented in all bodies in which decisions influencing their welfare are made. To the contrary, however, the appointment of MPs from mining communities to company boards has strengthened the Parliamentarians’ support for the companies. This support may have created a conflict of interests, perceived to be in favor of the mining industry.

# **d. Party Loyalty and Central Control on District Governance**

36. Although the country has adopted decentralization as a political and development strategy since the advent of constitutional rule in 1993, little progress has been made to devolving real and genuine power to the district assemblies. The president appoints all district chief executives. These chief local representatives of the central government are responsible for the day-to-day performance of the executive and administrative functions of their District Assembly (DA). As a result, these district chief executives are viewed as part of a bigger patronage system (Ayee 2000, 2002, 2006, 2007).

37. In addition, the president appoints one-third of the DA’s membership. This rule is made to guarantee that each DA has some level of technical capacity, for example, in law, accounting, and management. Party loyalty, rather than technical competence, frequently has been the main criterion for these appointments. These informal party structures combined with the president’s formal authority have ensured that the DAs operate with direct influence from 17
- 40 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

Pandey, B; Joshi, PK; Seto, KC (2013). Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data. _International Journal of Applied Earth Observation and Geoinformation_ , 23: 49–61. https://doi.org/10.1016/j.jag.2012.11.005

- Partridge, MD; Rickman, DS; Kamar, A; Olfert, MR (2008). The geographic diversity of U.S. nonmetropolitan growth dynamics: A geographically weighted regression approach. _Land Economics,_ 84: 241–266

- Parr, JB (1979). Regional economic change and regional spatial structure: some interrelationships, _Environment and Planning A,_ 11(7): 825 – 837.

- Pesaresi, Martino; Ehrilch, Daniele; Florczyk, Aneta J.; Freire, Sergio; Julea, Andreea; Kemper, Thomas; Soille, Pierre; Syrris, Vasileios (2015):  GHS built-up grid, derived from Landsat, multitemporal (1975, 1990, 2000, 2014). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_built_ldsmt_globe_r2015b

- Pesaresi, M; Ehrlich, D; Ferri, S; Florczyk, A.J; Freire, S; Halkia, M; Syrris, V (2016). Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014; JRC Technical Report EUR 27741 EN. Ispra (VA), Italy. https://doi.org/10.2788/253582

- Pinkovskiy, M (2013). Economic Discontinuities at Borders: Evidence from Satellite Data on Lights at Night. Working paper, Massachusetts Institute of Technology, Boston MA.

- Quigley, J (1998). Urban Diversity and Economic Growth. _Journal of Economic Perspectives_ , 12: 127-138.

Rauch, J (1993a), Productivity gains from geographic concentration of human capital: Evidence from the cities. _Journal of Urban Economics_ , 34: 380-400.
Table **7**

||In|vestmentieturns||
|---|---|---|---|
||Rateof<br>Return<br>**(%**assets)|Investment<br>Income<br>(%**ofGNP)**|Investment<br>Income<br>(%ofNetChange)|
|**1982**|**28.5**|**0.71**|**26.8**|
|**1983**|**21.3**|**1.96**|**5S.5**|
|1984|**3.6**|**1.65**|50.2|
|**1985**|13.4|3.04|**65.2**|
|**1986**|**12.3**|**2.95**|**63.2**|
|**1987**|5.4|**3.30**|**65.0**|
|**1988**|**6.5**|**2.29**|**51.3**|
|**1989**|**6.9**|4.25|**65.0**|
|**1990**|**15.6**|**7.32**|**67.5**|

Source: Habitat **(1991)** In _fact,_ the sharp rise in investment returns in **1990** is attributed to the strong performance of equities, which has been boosted **by** large capital gains due to the revaluation of assets. Thus, in 1990, investment income amounted to over **7%** in relation to **GDP** and accounted for over **67%** of the increase in pension fund assets. The strong performance of the stockmarket caused a further substantial increase in investment returns in the first half of **1991.**

## **_3.6_** Pensions and Other Benefits

In **1990** pensions paid **by** AFPs represented 0.2% of **GDP** or 20% of the net change in total pension iund assets, while transfers to life insurance companies for the pdrchase of life annuities for old age as well as disability and survivorship pensions amounted to **0.6%** of **GDP** or **6%** of the net change in pension fund assets. The latter represent transfers of capital values.

The number of pensioners increased from less than **5,000** in **1982** to over **87,000** in **1990.** Initially, all pensions were either disability or survivorship pensions but the number of old age pensions reached nearly **30,000 by 1990.** The average value of pensions taken in the form of life annuities is **_2.5_** times greater than that of pensions based on scheduled withdrawals. There are two reasons, for this. First, because life annuities are not inherited, they can pay a higher pension than scheduled withdrawals. And, second, retired persons cannot use the life annuity option if their account balance is not adequate to cover the minimum pension requirement. Many pensioners have made contributions to the new system for a rather short period and this may expiain the number of people with inadequate balances.

# **_3.7_** Commissions

AFPs are entitled to levy a number of different commissions. These include: a flat fee per collection; a percentage fee calculated on pensionable salary; a fee for opening new accounts; a fee per pension payment; and fees for voluntary savings accounts. No APP has used the last two types of fees and only one has imposed fees on new accounts. As noted earlier, AFPs used to charge a management fee calculated on the outstanding account balance, but this was disallowed in September 22
self-interest of middle income countries to assist poorer countries in their quest for economic development.

**Table 5:  Emerging MIC Donors, Net ODA, $ millions**

||**Lower Estimate**|**Upper Estimate**|**Year**|
|---|---|---|---|
|Brazil|437|437|2008|
|China|1,800|3,000|2007|
|India|610|610|2008/2009|
|Russia|200|200|2008|
|South Africa|109|109|2008/2009|
|Latvia||22*|2008|
|Lithuania||48*|2008|
|Romania||123*|2008|
|Turkey||780*|2008|
|Thailand||179*|2008|
|**Total**|4,308|5,508||

Source: OECD, 2010.

*Reported directly to the DAC, so not based on lower/upper estimate range.

In summary, we see how the pattern of global economic progress has been transformed in recent years in many dimensions. The old growth poles of earlier decades that comprised twothirds of global GDP<sup>21</sup> have ceded space to emerging market economies. The old patterns of trade and finance with primary goods flowing ―north‖ and financial flows heading ―south‖ has been replaced by much more complex patterns of trade and finance across countries. Even in the technological realm, new emerging markets are moving toward the frontier of innovation. This increasing flow of goods, capital and ideas within the developing world needs to continue in order for the convergence of the new millennium to be sustained in the coming decades. Demographic factors, fiscal problems and other ―hangover‖ elements from the Great Recession are likely to keep growth subdued in the high income countries and perhaps below the growth rates of the last century. Thus, the need for new growth poles to strengthen economic linkages among themselves becomes even more important as a source of future growth.

The international governance of the multi-polar global economy is also gradually changing: the G20 is becoming the main platform for multilateral economic policy coordination, replacing the G7 after the global financial crisis erupted in 2008. The Bretton Woods institutions have made progress in changing voting shares on their respective Boards of Directors to

> 21 At Market exchange rates.  The figure would be about 50 percent using PPP exchange rates.

25
||**% GDP**|**[6]**<br>|1.566*<br>(0.870)<br>0.597***<br>(0.030)<br>0.168***<br>-0.021<br>-0.01<br>(0.007)<br>0.04<br>(0.157)<br>0.181***<br>(0.028)<br>0.264***<br>(0.086)<br>1524 / 85<br>0.833<br>20.933<br>6.156|
|---|---|---|---|
|_s a share of GDP_|**ross capital formation as**|**[4]**<br>**[5]**<br>**(t+1)**|1.729***<br>(0.258)<br>2.576***<br>(0.939)<br>0.641***<br>0.600***<br>(0.029)<br>(0.030)<br>0.162***<br>-0.021<br>0.00<br>-0.01<br>(0.007)<br>(0.007)<br>0.05<br>0.01<br>(0.142)<br>(0.150)<br>0.138***<br>0.156***<br>(0.025)<br>(0.028)<br>0.231***<br>0.246***<br>(0.087)<br>(0.084)<br>1525 / 85<br>1445 / 81<br>0.82<br>0.84<br>20.933<br>20.95<br>6.156<br>5.988<br>ured.|
|_l formation a_|**Real G**|**[3]**|1.968***<br>(0.264)<br>0.641***<br>(0.029)<br>0.00<br>(0.007)<br>0.01<br>(0.134)<br>0.108***<br>(0.025)<br>0.210**<br>(0.085)<br>1446 / 81<br>0.828<br>20.95<br>5.988<br>variable is meas|
|**Table 7**<br>_Sample of 1974-2004_<br>_Dependent Variable: Nominal Gross domestic savings, Real Gross capita_|**Nominal Gross domestic savings as**<br>**% GDP**|**Variables**<br>**[1]**<br>**[2]**<br>**(t+1)**|int1. Index (t)<br>1.058**<br>(0.477)<br>int2. Index (t)<br>5.027***<br>(1.340)<br>_Control Variables_<br>Lagged dep. var.<br>∆%GDP (t)<br>0.158***<br>0.162***<br>(0.039)<br>(0.038)<br>Savings/GDP<br>∆Log(ToT)<br>0.059***<br>0.062***<br>(0.011)<br>(0.010)<br>Population growth<br>-0.02<br>0.03<br>(0.187)<br>(0.183)<br>Financial account to GDP<br>-0.171***<br>-0.142***<br>(0.036)<br>(0.033)<br>Trading partners growth<br>-0.10<br>-0.08<br>(0.137)<br>(0.132)<br>Observations / Countries<br>1467 / 84<br>1544 / 88<br>R-squared<br>0.805<br>0.81<br>Mean<br>16.064<br>16.369<br>St Dev<br>10.805<br>10.877<br>Note:_y_(t, t+s) corresponds to the average over the period t to t+s of the variable_y_.<br>Except otherwise indicated, all controls are averages for the period over which the dependent<br>All regressions included country fixed effects and Time dummies<br>Robust standard errors in parentheses<br>* significant at 10%; ** significant at 5%; *** significant at 1%|
variable are used. Second, the procedure uses a common variance-covariance of moment conditions across periods. This results from substituting the assumption that the average (across periods) of moment conditions for a particular instrument be equal to zero for the conventional, but more restrictive, assumption that each of the period moment conditions be equal to zero.<sup>9</sup> At the cost of reduced efficiency, these two steps decrease over-fitting bias in the presence of small samples by accommodating cases where the unrestricted variance-covariance is too large for estimation and inversion given both a large number of explanatory variables and the presence of several time-series periods.

The consistency of the GMM estimators depends on whether lagged values of the explanatory variables are valid instruments in the growth regression. Two specification tests are run to verify this. The first is the Hansen test of overidentifying restrictions, which tests the validity of the instruments by analyzing the sample analog of the moment conditions used in the estimation process. Failure to reject the null hypothesis gives support to the model. The second test examines whether the original error term (that is,  _i_ , _t_ in equation (3.2)) is serially correlated. The model is supported when the null hypothesis is not rejected.<sup>10</sup>

# **4. Growth Determinants and Natural Disasters**

To perform the estimations, a pooled cross-country and time-series data panel is compiled covering 94 developing and developed countries over the period 1961-2005. The data are organized in non-overlapping five-year periods, with each country having at most 9 observations. The panel is unbalanced, with some countries having more observations than others. Appendices 1 and 2 provide summary statistics of the variables both for the pooled sample and developing countries only. Appendix 3 presents a matrix

> 9 The “collapse” option of xtabond2 for STATA is used to do so.

> 10 In the system specification, it is in fact tested whether the first-differenced error term (that is, the residual of the equation in differences) is second-order serially correlated.  First-order serial correlation of the differenced error term is expected even if the original error term (in levels) is uncorrelated, unless the latter follows a random walk.  Second-order serial correlation of the differenced residual indicates that the original error term is serially correlated and follows a moving average process of at least order one.

13
struggle. _It appears that a positive expectation about the future feeds into the propensity to armed rebellion in communities where the geographic characteristics increase the probability of success_ . While the effect of a variation in income level on the propensity to armed struggle is not significantly different in the _“geographically-risky”_ sample compared to the overall sample (even though it is slightly higher in the former), there is a remarkable difference in the effect of variations in educational attainment. While a standard deviation increase in educational attainment (other variables remaining the same) reduces the propensity to armed rebellion by 2.06 standard deviations in the “ _geographically risky_ ” sample, the effect on the propensity to armed rebellion in the overall sample is -0.92 standard deviations. In terms of odds, a one unit increase in educational attainment reduces the odds of participation in armed struggle by 53 percent in the _“geographically-risky”_ sample compared to 28 percent in the overall sample.

Finally the mean predicted probability of participation is higher in the _“high-risk”_ and _“geographically-risky”_ samples compared to the overall sample, and highest in the _“geographically-risky”_ sample at 0.78 (Table 10).

# **5. CONCLUSION**

This paper attempts to explain the determinants of the propensity to armed struggle and the probability of participation by individuals in the Niger Delta region of Nigeria using primary (micro) data. About 36 percent of the sampled population revealed a likelihood of participating in an armed struggle, implying the existence of a potential rebel army size of about 24 percent of the overall male population in the three states that constitute the heart of the Delta. This is a formidable size and indicates that the risk of an armed struggle in the region is not illusory.

While grievance appears to be pervasive among individuals and communities and is systematically explained by the data, neither grievance level nor its commonly-cited causal factors, appear to be strong enough to create a disposition toward armed rebellion. Rather, factors that reduce the opportunity cost and risk of participation or increase the perceived benefits appear to be more important. Three of these factors (income level, educational attainment, and government presence) are amenable to policy makers’ control.

25