rafmacalaba/datause-encoder-data huggingface dataset:
gols 4 and St Conr na for the Box gf the Pupils Model 4 examlnes the effect of sex on Mathematics and English achivement without lncludlng Grade 7 achievement. The model tests for boys, wlth girls as the reference point. The sex of the pupils was found to be a significant variable for Mathematics, accounting for not quite an extra half-polnt for boys. It **was** not found to be signifLcant in the case of English achievement.

Model 5 **is** constructed for Mathematics, alone, given the significance of sex **as** an explanatory variable. Both Grade 7 and sex are included. Whilst the cooffLeLant for **sex** is reduced, lt remains slgnificant. The inclusLon of this variable has a marginal effect in explalning the total variance and Lts different components, comparing the results for Model 5 with Model 2 for Mathematics.

# WHICH VARXABLES TO CHOOSE?

Testina glans and S **_Col_** Level V-arables for Possible inglui Due to aome of the practical problems experLenced in using ML3, and the time constraLnts of this report, lt was not possible to test all the variables using a multilevel **model.** Single level regressions were employed as a means of weeding out varLiables with less explanatory power than others. Although the standard errors are underestimated in the single level regressLons, it was thought that s*eo variables might reasonably be eliminated by puttlng the information **gained** from such an exercise together with the results of a similar study conducted in 1985 (Rlddell 1988).

26
# **V. Assessment of the Oil Wealth Governance and the Country’s PFM – Institutional Arrangements**

The phenomenon of Dutch Disease and boom and bust cycles are not the only mechanisms whereby economic growth is negatively affected by windfalls; probably, and almost equally important are the incentives that windfalls create for unproductive investments, rent-seeking and corruption. Many scholars and development practitioners argue that, because windfall rents are concentrated and easily obtained; they exert pressure to engage in rent-seeking, corruption and governments to share increased revenues with the public, often by investing in unproductive public work project or subsidizing food, fuel failing industries and even government jobs (Auty 1990); Gelb et al 1988) and (McMahon 1997). Therefore it is important to assess the oil wealth governance and PFM Institutional arrangements to increase transparency and accountability.

# **A - Assessment of the Oil Wealth Governance**

Since the onset of oil production in 1995, the hydrocarbon sector has been the engine of growth. The production has increased from 6,000 barrels a day to 282,000 barrels a day in 2003, supporting an average annual GDP growth of 31 percent. The production subsequently reached a milestone of 400,000 barrels a day in 2005 and accounted for over 90 percent of government revenues. This wealth is yet to be transformed in living conditions and poverty reduction, despite an increase in per capita GDP from US$346 in 1995 to about US$3391 in 2004, with a relative small population estimated at 506,000<sup>32</sup> inhabitants in 2004. A comprehensive medium-term strategy for managing the country’s rapidly rising oil wealth is required to ensure that the large oil endowment relatively to the size of the population is being translated into benefits for the poor. To promote good governance in the management of the country’s oil wealth, the government may wish to adhere to clear standards of accountability and transparency; especially by complying with the EITI.

Equatorial Guinea is currently the only African oil-producing country where oil revenue clearly exceeds the country’s absorptive capacity and where sizable foreign assets have been accumulated (Katz et al. 2004). The widespread poverty and low human indicators in Equatorial Guinea argue for upgrading domestic infrastructure and increasing public services so as to improve the quality and productivity of physical and human capital. Investment in financial assets would build the reserves needed to gradually increase capital investment as absorptive capacity grows. The current fiscal rule, which the government is trying to follow, is to use non-oil revenue to finance current expenditures and oil revenue to finance capital expenditure, and to save what is left.

> 32 World Development Indicator Report (WDI) - The World Bank 2005.

23
agricultural profits (economy-wide) rise by 20% and connectivity costs decline through the economy by 15%, while there is a reduction of carrying capacity of 20%. Simulations of other scenarios are in the Appendix and broadly reflect the finding of this particular illustration. To guard against exaggeration of impacts the assumed benefits from the proposed changes in the Serengeti are considerably higher than suggested gains, while assumed impacts on carrying capacity is lower than suggested by recent demographic models.<sup>22</sup> These changes would result in a reduction of proceeds from international tourists of $552 million per year (tourist numbers go from 750,000 to 515,000, expenditure per day is $200 with 10 days average stay). To guard against exaggerate tourist expenditures are significantly underestimated. Data reported in the World Travel and Tourist Council suggest expenditures of about $500 per day in Tanzania.

As the table shows, in all the simulations, the effects appear to be diffused through the economy. However impacts are especially large among (poor) rural households. Even in a case when there is a very large positive shock on agriculture, to compensate for a loss of bushmeat, there is a net loss registered in the rural sector as a result of economic contraction following the reduction of foreign exchange flows. Value added (a proxy for GDP) , on the other hand, changes by more than the flow of tourists revenue due to changes in the exchange rate. These exchange rate changes imply that the effects of tourism are spread across the economy. In short the simulations suggest that policies that lower or increase revenues and benefits from the Serengeti have wide ranging impacts that spill over to other sector of the economy. Understanding the direction and magnitude of these spillovers is crucial to policy analysis.

# **6. Conclusions**

This paper has developed a bioeconomic model linked to a CGE model to assess the economy-wide consequences of alternative policies. The bioeconomic model characterizes optimal policies and suggests how actual responses may depart from optimal policies. Simulations in a partial equilibrium

> 22 The assumed are far above what is suggested by proponents and considerably higher than what estimates suggest might eventuate (see GoT 2011 and Holdo et al 2011)

22
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Lucas, Robert E. (1988). On the Mechanics of Economic Development, Journal of Monetary Economics 22, 3–42.

Lucas, Robert E.B. 2005. _International Migration and Economic Development: Lessons from Low-income Countries_ . Cheltenham, Edward Elgar Publishing.

Massey, Douglas S., Joaquín Arango, Graeme Hugo, Ali Kouaouci, Adela Pellegrino, and J. Edward Taylor. 1993. “Theories of International Migration: A Review and Appraisal.” _Population and Development Review_ 19: 431–66.

Mayer, Thierry, and Soledad Zignago. 2006. “A Note on CEPII's Distances Measures, Explanatory note, CEPII, Paris.

Mountford, Andrew. 1997. “Can a Brain Drain be Good for Growth in the Source Economy?” _Journal of Development Economics_ 53: 287–303.

Müller, Tobias, and Silvio H.T. Tai. 2010. “Individual attitudes towards migration: a reexamination of the evidence.” University of Geneva, mimeo.

Stark, Oded, Christian Helmenstein, and Alexia Prskawetz. 1997. “A Brain Gain with a Brain Drain.” _Economics Letters_ 55: 227–34.

# _NOTES_

1 The number of tertiary-educated immigrants living in OECD countries increased from 12.5 million in 1990 to 20.4 million in 2000 (Docquier and Marfouk, 2006). Half of the skilled migrants resided in the United States, and approximately one-quarter resided in other Anglo-Saxon countries.

28
- Palmer, Margaret A, Catherine A Reidy Liermann, Christer Nilsson, Martina Flörke, Joseph Alcamo, P Sam Lake and Nick Bond. 2008. ―Climate change and the world‘s river basins: anticipating management options,‖ _Frontiers in Ecology and the Environment_ , 6(2): 81-89.

- Papa, F., Prigent, C., and Rossow, W. B. 2007.  ―Ob' River flood inundations from satellite observations: A relationship with winter snow parameters and river runoff,‖ _Journal of Geophysical Research_ , 112(D18): 1-11. doi: 10.1029/2007JD008451.

- Peck Dannele E. and Richard M. Adams. 2012. Farm-level impacts of climate change: alternative approaches for modeling uncertainty. In: Dinar, A. And R. Mendelsohn, Handbook of Climate Change and Agriculture.  Chenlenham: Edward Elgar Publishing (In Press).

- Phillips, David, Marwa Daoudy, Stephen McCaffrey, Joakim Öjendal, and Anthony Turton.

2006. _Trans-boundary Water Co-operation as a Tool for Conflict Prevention and Broader Benefit Sharing_ . Global Development Studies, 4, Ministry of Foreign Affairs, Sweden

- R Development Core Team. 2008. R: A language and environment for statistical computing, reference index version 2.7.2. Copyright (C) 2008 R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org. ISBN 3-90005107-0.

- Reuveny, Rafael. 2007. ―Climate Change-Induced Migration and Violent Conflict,‖ _Political Geography_ , 26: 656-673.

- Salehyan, Idean. 2008. ―From Climate Change to Conflict? No Consensus Yet,‖ _Journal of Peace Research_ , 45(3): 315-326.

- Schultz. 1994. ―Meso-scale modelling of runoff and water balances using remote sensing and other GIS data,‖ _Hydrological Sciences -Journal- des Sciences Hydrologiques_ , 39(2): 121-142.

- Scipal, K., C. Scheffler, and W. Wagner. 2005. ―Soil moisture-runoff relation at the catchment scale as observed with coarse resolution microwave remote sensing,‖ _Hydrological Earth System Science Discussion_ , 2: 417–448.

29
(TFPR), conflate true firm efficiency with firm price effects that capture input costs, product quality, and market power (Cusolito & Maloney, 2018). Hence, our finding that firm shocks result in a decline in firm productivity does not necessarily imply a decline in firm efficiency, which might appear at odds with the evidence on the shocks also leading to occupational restructuring towards high-skilled workers, but may simply reflect a decline in firm market power. The decline in firm profits in Table 5 corroborates this interpretation.

# **V. Mediating Role of Informality and Lack of Competition**

The long-run severity of crisis-induced employment and earnings losses may vary with local labor market characteristics. For developing countries, a first-order issue is informality. A high degree of informality may lead to substantial misallocation of resources and hamper employment growth, but it may also provide de facto flexibility for firms and workers to cope with adverse shocks and more alternatives for displaced workers. We examine this issue exploiting the wide heterogeneity in local conditions across Brazil’s vast territory and using a widely-used definition of informality. We estimate a modified version of Equation (5) replacing worker categories by indicator variables for higher than median (high) versus lower than median (low) informality rate in the worker’s 2007 employing firm’s municipality. The results in Table 8 point to a significantly smaller decline in formal worker employment due to GFC firm shocks in Brazilian localities with a higher informality rate. In highly informal localities, even formal firms are likely to employ many informal workers and it is for those workers (rather than for formal ones that we capture in our RAIS employer-employee database) that the firm adjusts the months worked and the wages when facing a negative shock. This finding supports the idea that informality provides de facto flexibility for firms and workers to cope with negative demand shocks, in line with the findings on the effects of trade liberalization in Brazil by Dix-Carneiro & Kovak (2019); Dix-Carneiro et al. (2021) and Ulyssea & Ponczek (2022).

The evidence in Section IV points to potential cleansing resulting from the GFC-induced foreign shocks as larger and more productive firms are more likely to exit in Brazil. But low-productivity firms may have a “way out” moving to the informal sector, in which case the competition effects behind cleansing effects would not be present. For example, if protected firms, defined as those facing less competition, adjust less during a crisis (as is shown below) and gain more market share, they may trap additional resources that could be used more efficiently elsewhere. The trade literature also shows the importance of reallocation across sectors for the productivity and inequality effects of trade liberalization (McCaig & Pavcnik, 21
To tackle the identification challenge, researchers have used the regression discontinuity (RD)-in-time, difference-in-differences (DID), and event study (ES) approaches, which rely on different identification assumptions. The key assumption behind the RD in time and ES approaches is that no unobservables exhibit discrete changes at the time of treatment (e.g., subway opening), so as not to confound the impact of the treatment.<sup>9</sup> The key identification assumption behind DID is the parallel trend assumption, i.e., unobservables do not affect the treatment and control groups differently in the absence of the treatment.

Chen & Whalley (2012) estimate the effects of the opening of one subway line in Taipei on air pollution based on the RD in time framework. They find that the opening of the subway line reduced carbon monoxide (CO) emissions by 5–15 percent. Employing an approach similar to that used by Chen & Whalley (2012), Goel & Gupta (2017) use the RD method to examine the impact of the Delhi Metro expansion on air quality. They find a 34 percent localized reduction in CO in the short run. Using an ES design, Gendron-Carrier et al. (2018) examine 43 cities across the world that opened new subway systems between 2000 and 2014. They find that particulate concentrations dropped by 4 percent on average following the opening of a new subway system. Zheng et al. (2019) use the DID method to estimate the impacts of the opening of the first subway line in Changsha, China. They find an 18 percent reduction in CO in areas close to subway stations.

A strategy to deal with the endogenous location concern of the public transit is the instrumental variable (IV) method. The instrument should provide variation that affects location choices but is exogenous to contemporaneous shocks to air pollution and other outcome variables. Baum-Snow (2007) uses planned routes (many of which were not built) as the IV for US highways to examine the trend of suburbanization; Faber (2014) constructs a hypothetical highway system in China based on historical planning maps using a minimum spanning tree (MST) approach and topographic variation to examine trade integration and industrialization. Li et al. (2019) follow a similar strategy to examine the impact of Beijing’s subway system, using the original planning routes as the IV.

> 9 The RD-in-time method uses time as the running variable and assumes that the impact of unobservables on air quality can be captured by flexible functions of the time trend. The identification relies mainly on time-series variation, different from the traditional RD in cross-sectional settings. The RD in time, in essence, is the same as the event study method which explicitly uses pre- and post- event data for identification. Hausman and Rapson (2018) discuss the pitfalls and recommendations for addressing them when applying the RD-in-time method.

12
- 44 -

# Table 3.6

# Characterization of Generic Options Based on Private Sector Development (Ratings are H=High, M=Medium, or **L=Low.)**

|Option<br>|Raing|**Discussion**|
|---|---|---|
|(1) Monolithic railway (status quo)|L|The monolithic railway, by definition, is not to be sold off<br>piecemeal, although some contracting is possible.|
|(2) Lines-of-business organization|M|Although still subsidiary to the monolith, the lines of<br>business may lend themselves at least to joint-venture or<br>other creative arrangements with the private sector.|
|(3) Competitive access|M|This option is still conducted by monolithic carriers,<br>although some private involvement in the reshaping is<br>possible.|
|(4) "Wholesaler"|H|Presumably the "retailers" would come from the private<br>sector.|
|(5) "Toll rail" enterprise|H|The splitting of functions could easily lend itself to private<br>sector participation.|

Table 3.7 Characterization of Generic Options Based on Startup/Transaction Costs (tatings are H=High, M=Mediun, or L=Low.)

|Option<br>|Rating|Discussion|
|---|---|---|
|(1) Monolithic railway (status quo)|L|By definition, a single entity in a status quo situation must<br>have zero "startup" costs and low transaction costs.|
|(2) Lines-of-business organization|M|Startup costs would vary with the scope of the changes made;<br>transaction costs would have to increase as more precise<br>accounting is introduced for internal transactions.|
|(3) Competitive access|M|WiU vary with the precise nature of the operation. Trackage<br>rights operations will, at the very least, lead to new<br>transactions among the competitors.|
|(4) "Wholesaler"|M|Although the nature of the tansactions wil be more complex<br>than under the status quo, the number of transactions should<br>decrease because of the relatively small number of "retail"<br>customers of the railway "wholesaler".|
|(5) "Toll rail" enterprise|H|The complete separation of functions envisioned in this option<br>should lead to the highest startup and transaction costs.|
products in EU markets. In this scenario we have assumed that the price of Turkish exports will increase by 1% in EU markets. This results in an increase in welfare of 0.1%.

In the scenario "TAR" we simulate the impact of Turkey lowering its non-agricultural tariffs against the manufactured products of the EU and implementing the CET of the EU, including the preferential access agreements. We take as our initial equilibrium the situation that prevailed in 1993 (based on actual collections of tariffs, as opposed to MFN rates). By 1993, Turkey has already implemented a large portion of the tariff changes by phasing in the tariff changes over a 12 year or 22 year period, depending on the product. We take as our counterfactual scenario, the tariffs that will prevail in 2001, after preferential agreements have been negotiated with those countries that the EU has preferential agreements.' This scenario results in an improvement of welfare equal to 0.1% of GDP. The benefits from this aspect of the customs union are perhaps smaller than some would anticipate. This follows from a number of reasons: First, in our base year data, although the EU was the major trading partner of Turkey (43.6% of imports and 48.3% of exports), the trade intensity of Turkey was not very high--imports were 18.6% of GDP and exports were 14.6% of GDP. Thus, the total value of all EU imports was only about 9% of GDP, and the tariff reductions induce 'triangles" of consumption efficiency gains that are only a proportion of the value of the imports<sup>9</sup> . Additional gains come from

> 8 Tariffs in the initial equilibrium are based on actually collected rates, and are taken from Arslan (1995). Tariffs in the counterfactual are taken from data provided by Professor Subidey Togan (see appendix for precise rates). These latter data are based on nominal tariffs expected to prevail in 2001. It is assumed with these latter data that actual and nominal tariffs will be equal. Reasons for differences between collected and nominal rates of tariffs should be significantly reduced if not totally eliminated due to CU regulations. That is, there will be little scope for the investment code and duty drawback exemptions.

9In partial equilibrium, the static consumption deadweight loss from an ad valorem tariff equal to t is:

# CDWL = 0.5 [t/(1 +t)]<sup>2</sup> e V

where e is the elasticity of demand, and V is the initial value of imports (see Morkre and Tarr, 1980, chapter 2). That is, the gains from eliminating tariffs are proportional to the value of the imports, where 11
3. Normalize the resulting vector of welfare using Ordered Quantile Normalization, as described in Peterson and Cavanaugh (2020) and implemented in the R bestNormalize package.

4. Set the poverty line as the minimum value of normalized welfare for non-poor households, as defined in step one. Since we are only interested in estimating headcount poverty, we do not need to back transform to recover the original welfare measure. Instead, we can simply compare a household’s predicted location in the normalized distribution to the location of the poverty line.

5. Select a model from the pool of candidate variables using the “plug-in LASSO” estimator implemented in Stata 16, allowing for heteroscedasticity, with the dependent variable equal to normalized welfare (Belloni and Chernozhukov, 2013). We use the “plug-in” lasso to be sure to avoid overfitting, since it selects more parsimonious models than those selected using cross-validation (Statacorp, 2021). The weights in the LASSO regression are the sample population weights (household weight times household size), normalized to sum to the number of observations in each municipality. The candidate set of variables include the rates of moderate and extreme poverty predicted by the CNN at both the AGEB and municipality level, predicted land classifications at both the AGEB and municipal level, a dummy for whether the household’s AGEB is urban or rural, the share of the population in the municipality that are rural, and state dummies.

6. Using the model selected in step 5 and the poverty line calculated in step 4, estimate headcount poverty rates for each municipality using a modified version of the EMDI software package in R. The modification allows for the use of sample and population weights, as described in Appendix C. The “population” file is based on the synthetic census, constructed as described in the data section.

7. Benchmark the estimates by multiplying the estimated headcount rates and the estimated root mean squared error by a scaling factor calculated for each state. The scaling factor is calculated as a ratio, where the numerator is the official state headcount estimate taken from the survey data. The denominator, meanwhile, is the average, across each state, of the small area municipalities that are contained in the MCS-ENIGH survey, weighted by the sum of the original sample weights for each municipality. These scaling factors are calculated based on the small area estimates from municipalities included in the survey 17