rafmacalaba/datause-encoder-data huggingface dataset:
# **_Domestic tax reform_**

Fourth, to alleviate the fiscal effects of the EPA, countries could implement domestic tax reforms, in particular the VAT, with a view to increasing tax yield. Countries that have already implemented a VAT (for example Ghana, Cape Verde, Senegal and other WAEMU countries), and have little scope for increasing the revenues from it, could consider strengthening other components of their tax and revenue systems (Hinkle and Newfarmer, 2005; Doe, 2006). There are precedents for successful replacement of tariff revenue losses even in weak administrative environments.

# **_Trade-induced growth_**

Fifth, our estimates of the impact of the EPA on government revenue excluded growth effects. Potential higher economic growth rates from the full implementation of the EPA would lead to an increase in imports from EU and non-EU countries, and generate additional tariff and domestic tax revenues. This revenue-enhancing effect of trade-induced growth could be captured in the analysis.

# **_Compensatory mechanisms_**

Finally, since the EPA studies show large country-by-country differences in fiscal revenue loss, ECOWAS could make more operational its compensatory mechanism among ECOWAS members to redistribute gains and losses in the context of the EPA, building on the experience of WAEMU countries which established in 1999 a mechanism for compensations for tariff revenue losses suffered because of the elimination of customs duties on intra-WAEMU trade<sup>24</sup> .

# **VIII.   CONCLUDING REMARKS**

The first three decades of ECOWAS’ existence was a turbulent political period for most of its members as stagnant economic output left social aspirations unfulfilled. In these trying circumstances, it was difficult to maintain meaningful momentum towards regional integration with the lack of regional integration in turn failing to provide much support to member countries’ economic development. After many false starts over the years, the ECOWAS member countries are currently engaged in an ambitious push towards significantly deeper integration.

24 The WAEMU commission (the administrative body of the Union) collected 85 billion CFA (130 million Euros) over 2001-2003 period, using a community solidarity tax of 1 percent, which is levied on all taxable imports. Compensation for shortfall of customs revenues only concerned the import of industrial products originating from WAEMU and was conditioned on the effective and full implementation of the common external tariff, certified by the Commission. 61 percent of the proceeds from PCS went for the compensation fund, 25 percent to the budget of the WAEMU Commission and 14 percent were used to finance the cost of eliminating regional disparities (Coulibaly, 2004).

15
**Figure 4: Impact of a 50% oil price increases on international trade in 2020 (% change from the baseline)**

<!-- Start of picture text --> 20.0<br>15.0<br>Imports Exports<br>10.0<br>5.0<br>0.0<br>-5.0<br>-10.0<br>World total High-income Aus-NZ Japan Canada United States France Germany Italy Spain UK Rest of EU & EFTA Middle & Low-income China Indonesia Malaysia Thailand Rest of EAP India Rest of SA Argentina Brazil Rest of LAC Russia Rest of ECA MENA South Africa Rest of SSA<br><!-- End of picture text -->

# **5. Conclusions**

Given its role as a critical input into so many production processes and its ubiquity in transportation systems, it is to be expected that an increase in the price of oil would have adverse impacts on the global economy. We apply a multi-country, multi-sector, recursive dynamic, global computable general equilibrium model to simulate various future oil price scenarios and assesses the corresponding impacts on the global economy.

The study shows that global GDP contracts due to higher oil prices, and contracts more in the years close ahead, given the relatively inelastic demand for oil in the short run, and its reduction further out in the horizon is mitigated by adaptations to higher oil prices. Importantly, the role of biofuels in mitigating the effect of rising oil prices is limited because of its economic cost. Whereas developed countries/regions encounter modest reductions in GDP because of an increase in oil prices, the largest GDP losses are borne in emerging economies like China, India and Thailand, as they depend on imports for their oil supply and their economic structures are manufacturing based. With the increased prices, oil exporting countries in the 12
The stability of perception-based indicators is important given that several studies have opted to use perception-based indicators of the business environment from the WBES. We only sample a few studies here given the large literature. Perception-based indicators of access to finance have been used by Asiedu et al. (2013) to explore the influence of gender. The determinants of access to finance obstacles based on perceptions have been investigated by Beck et al. (2006). The influence of finance, legal and corruption obstacles (perception-based) on firm growth has been explored by Beck et al. (2005). Commander and Svejnar (2011) utilize the effects of nine business environment perception indicators on revenue efficiency and growth. The business environment perception-based indicators used in the study include access to finance, infrastructure, tax rates, business licensing and permits, customs, corruption, crime, anticompetitive practices, and macroeconomic instability. A number of studies have mixed both subjective (perceptions-based) and objective (experience-based) measures of the business environment in the WBES (Kinda et al., 2011; Fowowe, 2017; Reyes et al., 2017). There is some acknowledgement of potential bias in using perception-based measures of the business environment, and thus they should be used only if objective-based indicators are unavailable. A strand of the literature has highlighted the endogeneity of these perception-based measures and have warned against its direct use as covariates to evaluate binding constraints to growth (Carlin et al, 2007; Carlin, Wendy and Mark Schaffer, 2012a and 2012b).

There are several reasons why perception-based indicators are attractive to researchers. First, they tend to be simple to explain. It is much easier to claim that electricity is an obstacle because the manager of a firm indicated so, than to grapple through the experience of a firm in facing various dimensions of electricity challenges including power outages, electricity affordability, use of generators, and presence of rentseeking activity in the power utility sector. This leads to the second advantage of perception-based indicators: they tend to be holistic. Rating of a business environment obstacle by a firm can be used as a catchall indicator for that business environment element, obviating the concerns about the specific features of the business environment element that leads to its lower rating by the firm. Additionally, despite the fact that the business environment obstacle has a time frame by the insertion of the word “current,” it is likely 4
_Annex 1 Table 2: Varying trimming threshold on anthropometry data for 6-36-month-olds with above median saturation interaction_

||Height-for-<br>Age Z score|Stunting|Severe<br>stunting|Weight-for-<br>Age Z score|Underweight|
|---|---|---|---|---|---|
|_Trimming >-3 and <3 SD_||||||
|Program village|-0.077|0.022|0.001|0.042|-0.011|
||(0.178)|(0.074)|(0.002)|(0.178)|(0.049)|
|Ab di i|-0.077|0.024|-0.004|0.042|-0.094|
|ove mean saturaton|(0.225)|(0.083)|(0.004)|(0.226)|(0.061)|
|Program village*Above|-0.415|0.152|0.017|-0.488*|0.180**|
|median saturation|(0.273)|(0.107)|(0.017)|(0.294)|(0.079)|
|Control Observations|131|131|131|145|145|
|Treated Observations|142|142|142|151|151|
|Control Mean|-1.271|0.341|0.001|-0.821|0.160|
|_Trimming >-4 and <4 SD_||||||
|Program village|-0.226|0.056|0.043|0.128|-0.028|
||(0.211)|(0.077)|(0.031)|(0.203)|(0.052)|
||-0.362|0.069|0.053|0.000|-0.062|
|Above median saturation|(0.250)|(0.084)|(0.036)|(0.251)|(0.074)|
|Program village*Above|-0.253|0.129|0.007|-0.626*|0.208**|
|<br>median saturation|(0.306)|(0.110)|(0.055)|(0.330)|(0.087)|
|Control Observations|140|140|140|153|153|
|Treated Observations|156|156|156|172|172|
|Control Mean|-1.371|0.382|0.166|-0.947|0.206|
|_Trimming >-6 and <6 SD_||||||
|Program village|-0.333|0.063|0.047|0.257|-0.046|
||(0.253)|(0.075)|(0.046)|(0.216)|(0.053)|
||<br>-0.214|<br>0.054|<br>0.020|<br>0.066|<br>-0.075|
|Above median saturation|(0.306)|(0.082)|(0.050)|(0.261)|(0.074)|
|Program village*Above|-0.443|0.146|0.046|-0.798**|0.231***|
|<br>median saturation|(0.381)|(0.110)|(0.075)|(0.344)|(0.087)|
|Control Observations|147|147|147|156|156|
|Treated Observations|164|164|164|175|175|
|Control Mean|-1.452|0.402|0.106|-0.986|0.217|
|_Trimming >-7 and <7 SD_||||||
|Program village|-0.103|0.063|0.047|0.251|-0.046|
||(0.282)|(0.075)|(0.046)|(0.216)|(0.053)|
||<br>-0.076|<br>0.054|<br>0.020|<br>0.088|<br>-0.075|
|Above median saturation|(0.315)|(0.082)|(0.050)|(0.268)|(0.074)|
|Program village*Above|<br>-0.648|<br>0.146|<br>0.046|<br>-0.853**|<br>0.231***|
|<br>median saturation|(0.396)|(0.110)|(0.075)|(0.359)|(0.087)|
|Control Observations|149|149|149|156|156|
|Treated Observations|165|165|165|176|175|
|Control Mean|-1.457|0.402|0.106|-1.003|0.217|

*** p<0.01, ** p<0.05, * p<0.1. All specifications include linear controls for child age (in months). Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey

66
<!-- Start of picture text --> Percent of<br>16 GDP<br>. 1.38<br>14<br>1.2<br>1.0<br>0.8<br>0.6<br>04 0.31<br>><br>TM Subsidy phaseout only<br>CEPR<br> (US$25/ton CO2e carbon tax + subsidy phaseout)<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP Subsidy phaseout<br>m US$25/ton CO2e carbon tax<br>40<br>30<br>20<br>00 eellla a<br>x } m 5 N ° > 0<br>& Fe SS & eS SF<br>& eS 2 ww Q & os<br>RSss xv<br><!-- End of picture text --> <!-- Start of picture text --> Economy<br>Bangladesh India Nepal Pakistan = Sri Lanka<br>F 0<br>oe -1<br>ao<br>of<br>= 63<br>Os<br>£o Es-<br>De<br>S 8-6 mUS$25/ton CO2e carbon tax<br>2 —<br>5 8-7 Subsidy. phaseout<br>y -8<br>2 9 @ CEPR (US$25/ton CO2e carbon tax + subsidy<br>phaseout)<br><!-- End of picture text -->
# 3.2 Comparison with previous study

Carrington and Detragiache’s study clearly initiated new debates on the size and the distribution of the brain drain. Our purpose is to re...ne their method by incorporating additional statistical sources. By collecting Census, Register and Survey data from all OECD countries, we eliminate two sources of bias:

- 2 Relying on OECD statistics on immigration brings on several problems. First, in 1990, these data only provided information on the country of origin for the top-10 or top-5 sending countries. Hence, small sending countries are usually not identi...ed, at least in the majority of receiving countries. Second, immigration in EU countries is based on the concept of citizenship rather than on country of birth. Third, immigration data are missing for a few OECD countries (Greece, Iceland, Mexico, Slovakia, Turkey). Finally, the OECD provides data on the total immigration stock rather than on the adult immigration stock (which can be compared with the labor force in sending countries). Compared to national Censuses, we estimate that relying on OECD statistics implies an average underestimation in skilled workers migration rates by 11.3% in 1990 and 10.6% in 2000. This is the major source of bias, especially for small sized countries which are usually not identi...ed as important sending countries;

- 2 Transposing the US educational structure on other OECD countries induces an average overestimation in skilled workers migration rates by 7.25% in 1990 and 6.22% in 2000. The bias is obviously strong in countries sending a minor part of their emigrants to the US.

On average, Carrington and Detragiache’s method underestimates the emigration rates of skilled workers by 4% in 1990 and 4.4% in 2000. This appears in ...gure 3 (...g 3.A and 3.B) which gives skilled migration rates evaluated under three measurement methods: (i) a method fully based on Censuses and administrative data (our method), (ii) a method based on OECD statistics and US educational attainment data (Carrington and Detragriache), (iii) an intermediate method based on Census/administrative data and US educational attainment data. In comparison to the Census method, the second method clearly underestimates the brain drain for a large majority of countries. On the contrary, the third method overestimates the brain drain.

The two sources of bias cancel each other in a couple of cases. However, the brain drain is particularly overestimated in countries such as Algeria, Tunisia, Morocco, Turkey, Somalia. By transposing the educational structure observed in the US, Carrington and Detragiache and Adams obtain high emigration rates of tertiary education workers for these countries (between 55 and 65 percent for North Africa and Turkey). Taking into account the low level of education observed among emigrants to Europe (where the large majority of these migrants live), we obtain much lower skilled emigration rates (between 5 and 10 percent).

26
_CHAPTER 4. AREA LEVEL MODELS_ 21 estimated variances _ψ_<sup>ˆ</sup> _d_ = _Sd_<sup>2</sup><sup>_/nd_,where</sup><sup>_S_</sup> _d_<sup>2=(</sup><sup>_nd−_1)</sup><sup>_−_1 �</sup> _i∈sd_<sup>(</sup><sup>_ydi−_</sup> _y_ ̄ _d_ )<sup>2</sup> , Rivest and Vandal (2003) proposed an estimator of the MSE that accounts for the uncertainty due to the estimation of these variances. Wang and Fuller (2003) gave another estimator of the MSE when **_β_** and _σu_<sup>2areestimated,usingcertainmomentestimatorsoftheseparameters.</sup> However, more general results for other fitting methods and estimators ˆ _ψd_ of _ψd_ are lacking.

The Generalized variance function (GVF) proposed by Vaillant (1987), or a more general preliminary regression model for the estimated variances � var _π_ ( _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> ), is often applied to smooth these estimated variances. The smoothed variances obtained as predicted values from this model are then treated as the true variances _ψd_ . However, the number of variables (or number of parameters) in that model, which tunes the level of smoothing of these variances, is not clear. Note that the aim of the preliminary model is not to reproduce the direct variance estimates. Moreover, when assessing the gains of FH estimators compared to direct estimators in applications, it is unclear whether the comparison should be done based on the raw estimated sampling errors or on the smoothed versions.

The SAIPE project (see e.g. Bell, 1997) within the US Census Bureau regularly employs the FH model. Ericksen and Kadane (1985) and Cressie (1989) used the FH model to estimate the decennial census undercounts in each US state, and Dick (1995) used it to estimate Canadian census undercounts. A historical application of FH model is given by You and Chapman (2006), who used the same data set as in Arora and Lahiri (1997) to estimate small-area average expenditure on fresh milk.

The FH model has been utilized by various authors to estimate welfare indicators. Let us mention just a few examples. Molina and Morales (2009) estimated poverty rates and gaps in Spanish provinces by gender. Jedrzejczak and Kubacki (2013) estimated income inequality and poverty rates by regions and family type in Poland. Casas-Cordero Valencia, Encina and Lahiri (2015) estimated poverty rates in Chilean comunas
Compared to media reports, academic studies provide a much more nuanced picture of how parental migration on left-behind children. The impacts on child outcomes are quite heterogenous, depending on the specific measures examined, child age (de Brauw and Mu, 2011) and gender (Zhang et al. 2015).

Studies of child health, nutrition, and education outcomes yield mixed results. Some studies find parental migration may benefit child nutrition, with lower risk of stunting, underweight, and wasting for young children of migrants (Shi et al., 2020). The timing of parental migration matters; children left behind during early childhood have slower growth rates in height and weight, especially boys (Zhang et al. 2015). Parental migration may also have positive impact on child academic performance, measured by standardized Chinese, Math, and English test scores (Bai et al., 2018; Chen et al., 2014). These studies argue that even though the positive findings might reflect selection if parents do not migrate when they expect their children’s education to suffer, the findings still suggest fewer costs attributable to parental migration. In addition, (Zhou et al., 2015) show left-behind children perform as well as or better than children living with both parents on nine health, nutrition, and education indicators, arguing that all children in rural China are vulnerable and the extra resources accessed by left-behind children should be expanded to all.

Contrary to these positive findings, some studies have found negative impacts on other health and education measures. For example, boys’ academic performance may suffer when both parents migrate (Zhou et al., 2014). Moreover, studies relying on school survey data do not consider school enrollment as an outcome, but evidence suggests that fathers’ migration timing and duration affect children’s probability of enrollment, with rural children of migrant fathers having lower enrollment rate (Wang, 2019). Considering both the positive remittance effect and the negative time effect, parental migration has an overall negative impact on children’s timely enrollment (Yang and Bansak, 2020). For high school enrollment specifically, (Hu, 2012) finds that remittances only partially compensate for the negative impact of an absent parent, with a particularly salient net negative effect for girls and children from poor households. One reason for this may be that middle school graduates with migrant parents have lower migration costs and better migration opportunities, making them more likely to forfeit high school education (de Brauw and Giles, 2017).

Most studies estimate the effect of _contemporaneous_ parental migration. However, (Meng and Yamauchi, 2017) examine the impact of _cumulative_ exposure to parental migration over a child’s lifetime, revealing adverse effects on test scores and weight-for-age z-scores after accounting for migration selection. Studies measuring parental absence by contemporaneous migration may underestimate the total impact of lifetime exposure to parental migration.

22
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- Martin, R., et al. (2014). Industry Compensation Under Relocation Risk: A Firm-Level Analysis of the EU Emissions Trading Scheme. _American Economic Review_ , 104(8), 2482–2508.

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- Pindyck, R. S. (1981). The Economic Effects of a Decline in Energy Price Volatility. _The Energy Journal_ , 2(1), 1–19.

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14
Adjusting the distribution of regional output wedges offers a way to evaluate the hypothetical effects of changes in the distribution of regional intersectoral productivity gaps on national labor productivity. Note however that our model independently maximizes output in each region. Thus, the model does not itself provide a unique mechanism to adjust the distribution of regional output wedges. The same level of disparity in regional intersectoral productivity gaps and the consequent level of national labor productivity can arise from different distributions of regional output wedge sizes. Moreover, equalizing regional output wedges at different wedge sizes can produce different levels of national labor productivity.

B. _Effect of the disparity in regional output wedges on national labor productivity_ We calculate regional output wedges using Equation 18. Actual regional output wedges are calibrated based on sectoral labor productivities and labor allocation in a region, and the base model parameter values.

Using the calculated values of regional output wedges, the ratio of the productivity level in the informal sector relative to that in the formal sector, averaged across the regions, is 0.19.<sup>17</sup> This figure is smaller than the corresponding average of 0.26 estimated solely using the IBES. The correlation between the actual ratio and that obtained from applying the calculated values of regional output wedges is .92. Additionally, the correlation between regional output wedges and regional intersectoral productivity gaps is .81, and the correlation between regional output wedges and regional productivity losses due to the intersectoral misallocation of labor is .93. All the correlations are statistically significant at the 1 percent level. These patterns provide support for the use of regional output wedges as a proxy for regional intersectoral productivity gaps to assess the effect of hypothetical adjustments in the disparity of regional intersectoral productivity gaps on national productivity.

To assess the effect of the distribution of regional output wedges on national labor productivity, we perform a series of simulations. Table 1 presents the results regarding the effects on national nonfarm labor productivity from three types of simulations: uniform percentage changes in actual regional output wedges in all regions (panel a), in the five poorest regions (panel b), and in the five richest regions (panel c). The incremental adjustments we examine lie in the range from 10 percent to 75 percent of actual regional output wedges. Under the actual distribution of regional output wedges, we obtain a national nonfarm productivity level of 211.9 thousand cedis per worker. The standard deviation of regional output wedges

> 17 The average is weighted, with regional shares of total value-added serving as the weights.

18