rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7: Consumer Prices**

||**Base**<br>An|**Reform**<br>nual % Change 2|**Reform,**<br>**Prices**<br>013/14 - 2024/25|**Reform,**<br>**Prices &**<br>**Phase out**|
|---|---|---|---|---|
|**Total CPI**|5.7%|5.7%|6.2%|6.4%|
|**Energy**|5.4%|7.3%|7.2%|18.4%|
|**Agriculture**|5.9%|5.8%|6.3%|6.1%|
|**Food manufacturing**|5.8%|5.8%|6.3%|6.1%|
|**Other**|6.3%|6.1%|6.5%|6.7%|
|**Manufacturing**|||||
|**Water and**|4.8%|6.0%|6.3%|6.7%|
|**Sanitation**|||||
|**Housing**|5.5%|5.5%|5.7%|4.7%|
|**Other Services**|5.0%|5.2%|5.8%|6.2%|

_Source: Authors estimates_

# **Growth and Structure of Employment**

The structure and growth rates in employment from this analysis provide additional insights on the nature of the economic adjustments to be obtained with the reform of energy subsidies over time. It is worth noting that the energy sector employs only a relative small share of the labour force, 1.8% in the base year, and that with the reforms this would fall by only 0.6% of the labour force. The limitations on the input substitution in most of the other sectors constrain the employment impacts of these reforms.

The largest effects take place, as noted previously, in the construction sector, where the increases in employment would be substantial, with the share of total employment increasing by more than 50% under the more far-reaching reform efforts.

17
tax records is not without caveats. For example, we need to make assumptions about the functional form of the relationship between the house price and household income, and about the functional form of the upper tail of the house price distribution. In addition it is assumed that one house constitutes one household and that all houses are domestically owned. Therefore, in cases where tax record data are available these should undoubtedly be considered first. However, we certainly believe that our approach provides more reliable estimates of inequality than estimates obtained using survey data alone. The perfect should not be the enemy of the good.

This paper is related to a number of other studies which have tried to correct household surveys for the problem of missing or underreported top incomes.<sup>3</sup> Korinek et al. (2006) exploit geographic variation in response rates to correct for selective non-response in the United States. Lakner and Milanovic (2015) exploit the gap between household surveys and national accounts to adjust the top end of the income distribution.<sup>4</sup> This paper is structured as follows. The methodology is presented in Section 2. In Section 3 we introduce the data used in the empirical application to Egypt. The empirical application itself is presented in Section 4. Finally, Section 5 concludes.

# **2 Methodology**

## **2.1 Combining income survey with top income data**

The objective is to estimate the level of income inequality for a given population. We will refer to database 1 (DB-1) as the primary data source for the estimation of inequality. It is assumed that top incomes are mostly missing from this database. Database 2 (DB-2), which we will refer to as the secondary data source, primarily contains data on top incomes but generally not on lower incomes. Estimates of income inequality will be biased if computed using any single one of these databases. It takes a combination of the two to obtain consistent estimates of inequality. DB-1 commonly represents a household income or expenditure survey. For DB-2 researchers often look at tax record data, as is discussed in the introduction.

> 3Recently, the EU-SILC survey in some countries began using register-based information (including tax records) for some questions (J ̈antti et al, 2013). This is of course preferable to any ex-post combination of these different data sources, as we use in this paper. In the year after the introduction of the register data, the Gini index for France increased from 39 to 44, which is consistent with the previously used household data underestimating top incomes (Burricand, 2013).

> 4See also the study on global interpersonal inequality by Anand and Segal (2015) who append for every country the estimated top 1% share to the household survey distribution. The latter is assumed to represent the bottom 99%. For the majority of countries, the top 1% share is predicted from a cross-country regression using the top 10% share in the household survey.

4
the desk review, a field-test, a survey collecting complete information to construct full set of variables used in the two targeting systems, was carried out to further assess the targeting alignment.

The study presents findings from the field-test. Specifically, it examines each household’s eligibility for the two programs and estimates the share of humanitarian caseload that would qualify for the MoLSA cash transfer program under different expansion scenarios. Then it evaluates the performances of the PPMT models presented in Phadera et al. (2022) in correctly identifying cross-model eligible households. Since it uses only a subset of variables from the MoLSA PMT model (full PMT), the PPMT cannot assess eligibility for the MoLSA cash transfer program with certainty. However, it can be applied directly to the existing humanitarian database and avoid the time and resources in collecting new data. It could prove to be a useful tool, particularly, when lack of budget or other circumstances (e.g., insecurity) prevent collecting new data with full PMT variables that the MoLSA uses. On the other hand, the probabilistic nature of the PPMT means some eligible households will not be referred, and some ineligible households will be. Rereferral of ineligible households could raise expectations but can create disappointment among those that are eligible but are not referred. Similarly, there is a trade-off between the probability of success and the number of referrals. Policy makers would be left with a choice between increasing the confidence level for a greater referral success rate but with leaving-out a greater share of eligible households (exclusion error) and decreasing the confidence level for a lesser exclusion error but with including a greater share of non-eligible households (inclusion error), raising budget concerns.

Consistent with the desk review, the field-test analysis finds a significant number of households that qualify for the humanitarian MPCA assistance to be eligible for the government’s cash transfer program. While the referral accuracy of the PPMT models are high (low inclusion error), they are also likely to leave out a number of eligible households (high exclusion error). Following Phadera et al. (2022), the crossreferrals are examined under two vulnerability assessment models used by the MPCA program - the legacy Vulnerable Assessment model (Legacy -VA; referred to as VM in Phadera et al. (2022)) and the updated 2019 model called the Socio-Economic Vulnerability Assessment Tool (SEVAT). The analysis shows that 40 percent of the households identified as <mark>extremely vulnerable by the SEVAT model would also be considered as poor by the MoLSA PMT scoring and, thus, would be eligible for the government’s cash transfer program under a scenario where the program expansion is limited to those below the national poverty line of IQD 110,000 per capita consumption per month (a medium-small program). For the same expansion, the referral rate for the Legacy-VA extreme vulnerable group would be about 20 percent. Again, as per the desk review recommendation,</mark> the <mark>PPMT performance is evaluated under two</mark> 5
C, and 35 to 41 C. Controlling for degree days rather than temperature polynomials better accounts for the effect of temperature on agricultural yields, which is important because agricultural yields could have direct effects on some of our outcome variables, such as asset wealth and those related to conflict [101].

In addition to calculating weather control variables for each protected area-year, we follow the same procedure to calculate the same weather control variables for each protected area’s year and 25 km, 50 km, and 75 buffers. We use this second set of weather control variables in the primary asset wealth and conflict regressions because those regressions include data inside and within 25 km of protected areas. We use the 50 km and 75 km weather control variables in Figure S18.

We detail all outcome variables and specific regressions in the SI.

# **References**

- [1] Newbold, T. _et al._ Global effects of land use on local terrestrial biodiversity. _Nature_ **520** , 45–50 (2015).

- [2] Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. Biodiversity: The ravages of guns, nets and bulldozers. _Nature_ **536** , 143–145 (2016).

- [3] Young, H. S., McCauley, D. J., Galetti, M. & Dirzo, R. Patterns, causes, and consequences of anthropocene defaunation. _Annual Review of Ecology, Evolution, and Systematics_ **47** , 333–358 (2016).

- [4] Ceballos, G. _et al._ Accelerated modern human–induced species losses: Entering the sixth mass extinction. _Science Advances_ **1** , e1400253 (2015).

- [5] Myers, S. S. _et al._ Human health impacts of ecosystem alteration. _Proceedings of the National Academy of Sciences_ **110** , 18753–18760 (2013).

- [6] Keesing, F. & Ostfeld, R. S. Impacts of biodiversity and biodiversity loss on zoonotic diseases. _Proceedings of the National Academy of Sciences_ **118** , e2023540118 (2021).

- [7] Frank, E. & Sudarshan, A. The Social Costs of Keystone Species Collapse: Evidence From The Decline of Vultures in India. _Becker Friedman Institute Working Paper_ (2023).

- [8] Damania, R. _et al. Nature’s Frontiers: Achieving Sustainability, Efficiency, and Prosperity with Natural Capital_ (World Bank Publications, 2023).

- [9] Murphy, J. T., Breeze, T. D., Willcox, B., Kavanagh, S. & Stout, J. C. Globalisation and pollinators: Pollinator declines are an economic threat to global food systems. _People and Nature_ **4** , 773–785 (2022).

- [10] Gourevitch, J. D. _et al._ Projected losses of ecosystem services in the US disproportionately affect non-white and lower-income populations. _Nature Communications_ **12** , 3511 (2021).

- [11] Brader, C. COP15: Global biodiversity framework. https://lordslibrary. parliament.uk/cop15-global-biodiversity-framework/ (2023).

- [12] Wilkie, D., Painter, M. & Jacob, A. Decision adopted by the Conference of Parties to the Convention on Biological Diversity (2022).

- [13] Bruner, A. G., Gullison, R. E., Rice, R. E. & Da Fonseca, G. A. Effectiveness of parks in protecting tropical biodiversity. _Science_ **291** , 125–128 (2001).

17
is comparable to the full model, indicating that these two variables account for most of the predictive power of the model.

**Table 3:  Predictive Power of the Prediction Model**

|||**In-Sample**|**Out-of-Sample**|
|---|---|---|---|
|**Unsatisfactory by IEG:**||(1561projects,1995 - 2005)|(1168projects 2005-2012)|
||_Number_|413|324|
||_Fraction_|26%|28%|
|**Correctly predicted as unsat**|**isfactory **|**by:**||
|Full Prediction Model||||
||_Number_|191|130|
||_Fraction_|46%|40%|
|Prediction Model with CPIA|Only|||
||_Number_|148|116|
||_Fraction_|36%|36%|
|Prediction Model with TTL O|nly|||
||_Number_|174|123|
||_Fraction_|42%|38%|
|Prediction Model with CPIA|and TTL|||
||_Number_|186|135|
||_Fraction_|45%|42%|

How impressed should we be with this predictive performance? Ideally we would like to perfectly predict both successful and unsuccessful outcomes, but this is not possible. A first benchmark against which to assess the predictive performance of the model is the opposite extreme, in which we just randomly predict that 26 percent of projects are unsuccessful. Under this benchmark, we would correctly predict successful projects 74 percent of the time, and failed projects 26 percent of the time.<sup>4</sup> The prediction model clearly does better than this benchmark, correctly predicting unsuccessful projects 46 percent of the time, and out-of-sample correctly 40 percent of the time.

A more relevant benchmark is how well this empirical model does relative to early-warning indicators of project outcomes that are available over the course of project implementation. Specifically, can this model do a better job of predicting outcomes than TTLs’ own assessments of the project's interim performance, as measured by the ISR-DO ratings? To answer this question, we construct dummy variables capturing the ISR-DO ratings during the first, second, third and fourth

> 4 If predictions are made at random, the probability that a project is successful and is predicted to be successful is p<sup>2</sup> , where p is the success rate of projects.  Since only a fraction p of projects actually are successful, the rate of p<sup>2</sup> correct predictions of successful projects is = p .  The same argument applies for unsuccessful projects. p

9
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE financing major track rehabilitation, with the limited investments made being confined to purchases of rolling stock. The basic reason for this is that the traffic volumes on almost all African railways are well below the 1 to 1.5 million net tonnes of traffic needed per year needed to generate sufficient cash flow for investment finance, particularly given that rail tariffs are constrained by intense intermodal competition from the trucking sector. As a result, track upgrades have ultimately been funded by government borrowing from international financial institutions. In the case of Ghana, traffic is not much more than 0.2 million tonnes, making it improbable that track improvements could be funded by the private sector.

**Table 3. Railway indicators for Ghana and selected other countries, 2000–05**

||**GRC (Ghana)**|OCBN (Benin)|SITARAIL (Cote d’Ivoire –<br>Burkina Faso)|NRC (Nigeria)|TRANSRAIL (Senegal - Mali)|CAMRAIL (Cameroon)|SPOORNET (South Africa)|
|---|---|---|---|---|---|---|---|
|Concessioned (1)/ state-run (0)|**1**|0|1|1|1|1|0|
|Traffic density, freight, 1,000 tonne-km/km|**242**|148|494|15|318|1,091|5,319|
|**EFFICIENCY:**||||||||
|Staff: 1000 UT per staff|**84**|40|481|37||603|3,037|
|Coaches: 1000 passenger-km per coach|**416**|900|862|737||4,738|596|
|Cars: 1000 ton-km per wagon|**458**|74|1020|59|804|868|925|
|Locomotive availability in %|**7**|3|35|13|40|26|—|
|**TARIFFS:**||||||||
|Average unit tariff, freight, US cents/tonne-km|**2.4**|2.0|3.3|—|2.2|2.2|—|
|Average unit tariff per passenger<br>US cents/passenger-km|**4.4**|5.8|5.5|—|3.3|5.2|—|

Source: Bullock 2009.

Derived from AICD rail operator database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available.

# Ports

## **Achievements**

Ghana has made significant progress in modernizing its ports sector and is committed to making further improvements. It is one of the few African countries in the process of adopting the landlord model, which has become the preferred institutional framework for the sector around the world. At the national level, stevedoring operations have been privatized. The Port of Tema awarded a container terminal concession in 2006, and dry-bulk handling operations are fully privatized. Further concessions are envisaged at the Port of Takoradi, where major new terminal developments are planned.

Ghana’s two major ports, at Tema and Takoradi, are large relative to other West African ports (table 4). Demand for both container and general cargo services has more than doubled over the period 19982006. Under the Ghana Gateway Program, the ports sector is being developed within an explicit 13
respectively). The higher use in these services is likely reflecting the relatively higher level of sophistication of chemicals and automotive exports, but also the higher level of sophistication of business services. This is because business services themselves create higher value‐added.

Figure 5: Services share of value added in manufacturing sectors gross exports (2011) <!-- Start of picture text --> Agribusiness 15.4 2.7 3.50.6 5.7<br>Chemicals 10.7 2.2 4.70.9 8.7<br>Automotives 8.1 2.1 5.80.9 8.8<br>Metals 7.7 2.4 4.40.8 4.8<br>0 10 20 30<br>Wholesale & Reta il Transport & Storag e Finance<br>Computer R&D & Business<br><!-- End of picture text --> Source: Authors’ calculations using OECD/WTO TiVA.

Compared to peer countries, Brazil shows stronger forward linkages in services from sectors such as distribution and R&D/business services. Across all sectors and compared to most of its comparator countries, Brazil shows high levels of domestic value added from distribution and business services to GVC sectors (Figure 6). In agribusiness and in chemicals, Brazil has the highest share in domestic value‐added from distribution services across its peer countries. Transport services on the other hand have low contributions to GVC value‐added exports in each of these sectors in Brazil compared to peer countries. Interestingly, the forward linkages of business services in value‐added in these four sectors in Brazil does relatively well. For instance, although the share is still relatively low in agriculture, it is nonetheless highest compared to other countries, including Canada. This is also true for metals, automotives and chemicals in which the services value‐added from business services is highest compared to other countries. By similar token, the use of financial services by all GVC sectors also performs relatively well compared to other countries.

10
|Povertyand Conflict Program|Claus Pram Astrup|
|---|---|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|Nutrition and Malaria Control for Child Survival|Evelyn Awittor|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|The AO-Local Development Project|Philippe Auffret|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
|Social Protection Development|Marina Petrovic|
**<u>Table 6: Coastal subdivisions in BDHS 2011 and 2014 with the infant and neonatal death caused by salinity</u>**

||**rank**|**subdivision**|**number of live**<br>**births**|**number of**<br>**infant deaths**|**number of**<br>**infant deaths**<br>**due to salinity**|**% infant**<br>**deaths due to**<br>**salinity**|**number of**<br>**neonatal deaths**|**number of**<br>**neonatal deaths**<br>**due to salinity**|**% neonatal**<br>**deaths due to**<br>**salinity**|
|---|---|---|---|---|---|---|---|---|---|
||1|Khulna|332,191|5,866.4|700.0|11.93%|5,866.4|552.7|9.42%|
||2|Patuakhali|83,005|4,111.5|343.8|8.36%|2,594.7|270.2|10.41%|
|**11**|3|Barisal|383,495|23,894.3|1,280.3|5.36%|21,308.5|995.6|4.67%|
|**20**|4|Noakhali|531,940|21,841.8|1,018.6|4.66%|14,699.1|795.1|5.41%|
|**HS**|5|Bogra|53,546|5,989.8|233.2|3.89%|4,908.3|187.4|3.82%|
|**BD**|6|Chittagong|693,222|23,238.6|572.3|2.46%|17,375.1|448.8|2.58%|
||7|Faridpur|394,901|24,611.4|195.6|0.79%|24,611.4|145.7|0.59%|
||8|Comilla|859,255|22,338.4|12.2|0.05%|15,091.6|0.0|0.00%|
|||subtotal|3,331,556|131,892.0|4,356.0|3.30%|106,455.0|3,395.4|3.19%|
|||others<sup>*</sup>|3,126,269|142,833.5|0.0|0.00%|133,583.0|0.0|0.00%|
|||Total|6,457,825|274,725.5|4,356.0|1.59%|240,038.0|3,395.4|1.41%|
||**rank**|**subdivision**|**number of live**<br>**births**|**number of**<br>**infant deaths**|**number of**<br>**infant deaths**<br>**due to salinity**|**% infant**<br>**deaths due to**<br>**salinity**|**number of**<br>**neonatal deaths**|**number of**<br>**neonatal deaths**<br>**due to salinity**|**% neonatal**<br>**deaths due to**<br>**salinity**|
||1|Barisal|404,238|6,852.7|1,354.0|19.76%|6,852.7|1,053.8|15.38%|
|**14**|2|Patuakhali|157,576|6,318.7|740.2|11.71%|3,617.4|590.7|16.33%|
|**20**|3|Noakhali|482,958|20,594.7|770.5|3.74%|18,434.3|592.6|3.21%|
|**DHS**|4|Khulna|293,990|19,046.6|449.8|2.36%|17,735.4|347.8|1.96%|
|**B**|5|Faridpur|713,118|36,234.3|311.0|0.86%|36,234.3|233.0|0.64%|
||6|Chittagong|763,435|43,159.0|301.9|0.70%|27,483.7|233.3|0.85%|
|||subtotal|2,815,316|132,206.0|3,927.5|2.97%|110,357.8|3,051.2|2.76%|
|||others<sup>*</sup>|4,142,029|183,832.5|0.0|0.00%|162,701.9|0.0|0.00%|
|||Total|6,957,345|316,038.5|3,927.5|1.24%|273,059.7|3,051.2|1.12%|

Note: * coastal subdivisions that had no salinity induced infant or neonatal mortality 29
data, which may bias our sample towards including the more established and possibly larger and more capital intensive firms.

Consistency in the definition of variables is important for the rural-urban comparison to be accurate. Here, we briefly discuss the construction of the most important variables. See the Appendix for more details and more variables. To start with factors of production, we measure labor inputs in terms of the “full-time equivalent” number of employees since the high seasonality of rural enterprise activity renders the total number of workers a misleading indicator of total labor input. We use the replacement value of equipment as our measure of the capital stock. For urban manufacturing microenterprises this variable was imputed on the basis of expenditure data on rented capital. Of course, inaccurate imputations may bias the regression results. Fortunately, our estimates turned out to be rather robust to using different imputations of the capital stock.<sup>4</sup>

# **_3.2 A Bird’s-eye view of rural and urban enterprise activity_**

This section provides an overview of enterprise activity in rural and urban areas in Ethiopia. We first discuss salient enterprise characteristics, before proceeding to assess differences in the investment climate.

# **_3.2.1 Salient enterprise characteristics_**

Table 1 documents summary statistics on key enterprise characteristics, and reveals large differences in size, factor usage, and productivity across space. These differences are further illustrated by graphs 1, 2, 3 and 4 which plot kernel density estimates of the sample

> 4 Results are omitted to conserve space, but available from the authors upon request.

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