rafmacalaba/datause-encoder-data huggingface dataset:
|Table 9. Monthly power b|ill for subsistence|consumption( <br>**Monthly bill**<br>**($)**|50 kWh)|**Share**<br>**can afford t**|**of households**<br>**he monthly bi **<br>Historic|**that**<br>**ll(%) (*)**|
|---|---|---|---|---|---|---|
||Effective tariff|Historic cost|LRMC|Effective tariff|cost|LRMC|
|Benin|6.31|9.92|9.50|95|68|72|
|Burkina Faso|10.29|7.53|12.50|51|69|36|
|Cameroon|4.30|8.56|3.50|100|100|100|
|Cape Verde|11.81|8.95||100|100|—|
|Congo,Dem. Rep. of|1.99|3.38|2.00|100|63|91|
|Côte d’Ivoire|4.81|5.47|7.50|100|99|98|
|Ethiopia|1.97|4.23|9.50|60|12|1|
|Ghana|4.36|6.18|5.00|97|93|96|
|Kenya|4.21|7.10|6.00|99|87|95|
|Madagascar|2.98|7.49||92|55|—|
|Malawi|2.39|4.54|2.50|92|43|91|
|Niger|7.25|16.07|12.50|55|7|19|
|Nigeria|1.25|4.84|6.50|97|84|74|
|Senegal|9.31|5.77|21.50|100|100|—|
|South Africa|—|2.98|3.00|100|100|100|
|Tanzania|1.60|7.04|5.00|99|59|84|
|Uganda|9.74|5.19|6.00|20|66|55|
|Zambia|2.09|3.26|4.00|100|97|96|
|Sub-Saharan African average|4.81|6.58|7.28|86.51|72.39|73.73|

_Source:_ Adapted from Vennemo and Rosnes 2008 and Africa Infrastructure Country Diagnostic Power Tariff Database.

_Notes:_ See Annex 12 for further details

- (*) it is assumed that a bill is affordable if it is below 5 percent of the household budget.

— Not available.

A frequent argument used for not raising tariffs to full cost recovery levels is the potential impact on poverty. However, empirical evidence suggests that the immediate poverty-related effect of raising tariffs to cost-recovery levels is generally quite small, although it may have second-order effects. Detailed analysis of the effect of significant tariff increases of the order of 40 percent for power and water services in Senegal and power services in Mali confirms that the immediate poverty-related effect on consumers is small, essentially because very few poor consumers are connected to the service (Boccanfuso, Estache, and Savard 2008a; 2008b; 2008c). As the consequences of higher power or water prices work their way through the economy, however, broader second-order effects on wages and prices of goods in the economy as a whole can have a more substantial impact on poverty (Boccanfuso, Estache, and Savard 2008a; 2008b; 2008c).

# **d. Are power tariffs equitable?**

Notwithstanding these findings, most African countries subsidize tariffs for power. On average, power tariffs recover only 80 percent of costs. The resulting implicit subsidies amount to as much as $2.3 billion a year on aggregate (or 0.4 percent of Africa’s GDP) (Foster and Briceño-Garmendia 2009). The aggregate burden of underpricing can be as much as 1–1.5 percent of a country’s GDP (as in Cameroon, the Democratic Republic of Congo, Ghana, Mali, Malawi, Nigeria, South Africa, Tanzania, Zambia, 31
for the environmental co-benefits from the sustainable management of the Brazil nut concessions, whose values are currently not attractive.

A similar study was carried out for the state of Acre, Brazil (Nunes et al., 2011). In this study, the authors used a spatially-explicit rent model of Brazil nut production to assess yields and potential rents from the major regions of Brazil nut collection in Acre, Brasil, under three scenarios of processing and management. They estimated potential annual production in this region to be 29,27±8,42 thousand tonnes of nuts with shell, which at 2008 regional sale prices corresponds to rents of between R$ 0,28±0,08 ha<sup>-1</sup> .year<sup>-1</sup> for nuts with shell to R$ 7,8±2,39 ha<sup>-</sup> 1.year-1 for shelled-certified nuts. Investments needed to scale-up certified production in the major collection regions, and boost the rents by more than 20 times, are in the order of approximately R$ 22 ha<sup>-1</sup> .

**Table 1. Rents of Rural Activities in Madre de Dios, Peru**

||**NPV**<br>**(US$/ha)**|**Reference**<br>**year **|**Source**|
|---|---|---|---|
|Ecotourism|440|<br>2005|Kirkby_et al_. 2010|
|Small landholder agriculture|294|<br>2006|Kirkby_et al_. 2010|
|Cattle ranching|395|<br>2006|Kirkby_et al_. 2010|
|Sustainable timber|544|2008-2009|Nunes et al. 2012|
|Improved, shelled-certified Brazil<br>nut|320|2005|Nunes et al. 2012|

NPV: Net Present Value over 25 years; discount rate of 7.35%. (Nunes et al., 2012) For açaí, there is a potential for such a product to capture enthusiasm, given the expressive growth in niche demand, whose production chain is estimated to generate over US$ 1 billion in net revenues annually (Brondízio, 2008). Nevertheless, Homma (2010) predicts the demise of NTFPs and their substitution with cultivated agroforestry products based on the same genetic material. This is the case of rubber tapping in Acre state, where the activity only survives due to economic subsidies from the government (Jaramillo et al., in preparation).

Thus, deforestation could inhibit sustainable harvesting but not necessarily growth in volume or value of such products, as they become fashionable in global markets. Agroforestry potential in forest buffer areas is associated with the use of NTFP as part of a diversified strategy for forest use.

Extractive activities have been progressively replaced by others more profitable per unit area, market intelligence and wider commercial networks. Their persistence is due to structural and situational factors: 1) continued existence of pockets of absolute poverty in rural areas; 2) 13 | P a g e
**Source:** Estimates calculated using data from UN Comtrade database, the World Integrated Trade Solutions (WITS), and the World trade Organization (WTO). Plotted estimates are incidental-biascorrected coefficients and standard errors following Correia et al. (2019).  The solid vertical line indicates the estimated value of the “average” agreement as described in equation 1. The estimates of the “average” agreement are basically identical across most agreements because Morocco’s individual agreements have little influence on the average estimate of the other 262 agreements (Morocco is not one of the largest trading countries).

Two main findings emerge from Figure 3.1. The first is that individual trade agreements are associated with a wide range of estimated changes in trade flows. This heterogeneity has been noted in several earlier studies, but few studies focus on the variation individual countries experience across agreements. Second, the changes in trade Morocco experienced after its trade agreements are slightly larger than, but not statistically different from, the change in trade following the “average” agreement. This suggests that Moroccan trade agreements are having positive effects, but are not necessarily more productive than the average agreement in terms of increasing trade flows.

Notably, the results for the Global System of Trade Preferences among Developing Countries (GSTP) are negative, meaning a decrease of around 9 percent in trade flows after entry into force relative to other agreements. The trade agreement between the European Union, which accounts for most of Moroccan exports share, is positive and has a positive effect of around 18 percent in trade flows, slightly larger than, but not statistically different from, the average effect of around 14 percent for all other agreements. It is also important that trade between trade agreement signatory countries geographically closest to Morocco has doubled (Agadir Agreement and PAFTA) and even tripled (Türkiye – Morocco) the tradeincreasing estimate of the “average” trade agreement.

Figure 3.2 provides a graphical benchmarking these results by illustrating the estimated effects of 35 other agreements including MENA countries.

# **Figure 3.2 PPML HDFE Gravity estimates of All Trade Agreements and Total Trade**

13 | P a g e
schooling. The fi ndings for non-manufacturing in table 5 are somewhat similar. In column (1) foreigners pay wages 14% higher for low educated, 41% higher for the medium educated and 33% higher for the high educated. Controlling for fi rm characteristics implies a substantial decrease in the coefficients. Differences across fi rms in the characteristics of the medium educated account for part of the wage difference. However, after controlling for fi rm and worker characteristics I still fi nd a wage differential of 8% for the low educated, 16% for the medium educated and of 11% for the high educated<sup>34</sup> . In sum, the results suggest that controlling for observable fi rm and worker characteristics explains part of the wage differential but fails to explain fully the foreign wage premium. At least part of these differences can re fl ect differences in the wage premiums of foreign fi rms across sectors of activity. Evidence in Table A6 in the Appendix shows that even within 2-digit sector of activity the cross-section differences between foreign and domestic fi rms remain statistically signi fi cant in several sectors.

# 4.1. The Sample of Foreign Acquisitions

In this subsection I use the sample of foreign acquisitions to control for those fi rm unobservable characteristics correlated with higher wages and also with the foreign ownership. The model estimated is the following: where all the notation is as above.

The results of estimating equation (4.2), including the fi rm fi xed effects, by weighted least squares are presented in columns (4) and (5) of table 4 for manufacturing and in table 5 for non-manufacturing. In manufacturing, low, medium and high educated workers have increases in wages following the acquisition of 3%, 5% and 13%, respectively. These values are substantially smaller than the ones reported in the cross section. Therefore, part of the

> 34For non-manufacturing, wages are higher in fi rms with more than 100 workers and where the workforce has more experience in the fi rm. Contrary to manufacturing, worker characteristics like age and tenure on the fi rm are important in explaining wage differences. Controlling for the age of the fi rm has a small effect on the wage differentials of non-manufacturing (3 percent increase) and no effect in manufacturing.

16
# **4.3 Scenario 2:  The Urbanization Scenario**

In the early 1950s in Turkey, internal or rural-to-urban migration and urbanization gained momentum with accelerating development and industrialization trends. Migration is defined as ‘residency shifts across geographical regions and/or administrative areas’ (Ünalan, 1998). Such residency shifts may be due to natural, social, economic, or political requisites (Pazarlıoğlu, 1997). According to Akşit (1998), Turkey has experienced internal migration most prominently during the period 1950-1985. Table 4.8 shows that in 1975 the share of the urban population in total population was 42 percent, jumping to 52 percent in 1985, and reaching 65 percent in 2000 (World Bank, World Development Indicators Online). Akşit (ibid.) states that the contribution of internal migration in such population movements is above 50 percent.

**<u>Table 4.8.Urban population dynamics in Turkey</u>**

||**1975 **|**1980 **|**1985 **|**1990 **|**1995**|**2000**|**2003**|
|---|---|---|---|---|---|---|---|
|**Population growth (annual, %)**|2|2|2|2|2|2|2|
|**Urban population (% of total)**|42|44|52|59|62|65|66|
|**Urban population growth (annual, %)**|4|3|6|5|3|2|2|
|_Source: World Bank WDI Online_||||||||

According to latest figures from TurkSTAT, migration across different centers has reached 6,662,263 individuals within the period 1995-2000. This implies that during this time frame 11 percent of the total population in Turkey has been on the move from one residential area to another. Figure 4.1 displays the break-down of the migrating population across different centers for this period. One important aspect to mention is that when compared to previous periods, urban-to-rural (city-to-village) migration is gaining relative importance. Urban to rural migration consists of 20 percent of the total during 1995-2000, while this ratio used to be only 13 percent on average during 1980-1990. Rural to rural migration is observed to lose importance progressively from year to year, while urban to urban migration remains to be the principal form of migration, nevertheless showing a slight drop when compared to previous periods.

42
Health Education Consultation during pregnancy Papanicolau and Colposcopy Antitetanic Vaccine High-risk Pregnancy consultation Human immunodeficiency virus (HIV) care during pregnancy Delivery Cesarean Measles immunization <u>Puerperium counseling</u> Immunization of the new-born children Incubator up for a period of 48 hours Immediate treatment in case of HIV vertical transmission Ophthalmologic consultation Follow-up consultation <u>Dental care counseling</u> Pregnancy test Colposcopy in pregnant control Blood extraction Blood test Thoracic XR <u>Ecography</u> Detection of pregnant women in their first quarter of pregnancy Round of sanitary agent in rural area Socio — Epidemiologic diagnosis of population at risk Reunions for feeding guidelines promotion <u>Infant development promotion meetings</u>
|_Table 5-10 Jordan: La_|_bor Market Imp_<br>Net Jobs|_acts of COVID-_<br>Experienced in|_19 on Core Sect_<br>the Past 60 Day|_or "PFPS" W_<br>s due to Coro|_orkers_<br>navirus or Rela|ted Restrictions?|
|---|---|---|---|---|---|---|
||Lost Due to<br>COVID since<br>March 2020<br>(percentage<br>of pre COVID<br>jobs|Temporarily<br>laid off/<br>suspended<br>without pay<br>(Gross<br>change)|Permanentl<br>y laid off/<br>Suspended<br>(Gross<br>change)|Working<br>hours<br>reduced|Wages<br>reduced|Delay in<br>wage|
|By sector|||||||
|Manufacturing<br>Construction or|39.1|10.8|29.7|7.4|4.5|9.8|
|utilities|0.0|0.0|0.0|33.2|0.0|56.2|
|Retail or Wholesale|17.3|3.1|14.7|11.0|12.6|18.9|
|Transportation and|||||||
|storage|16.2|8.2|16.2|23.8|15.5|20.0|
|Accommodation and|||||||
|food services<br>Information and|38.8|3.6|19.0|5.0|7.9|26.0|
|communication|0.0|10.4|3.5|18.8|7.5|7.5|
|Other services|15.1|3.8|15.1|0.0|0.0|0.0|
|Total<br>Source: CMMHH 2021.|23.3|5.8|16.7|11.6|8.0|18.7|

_Table 5-11 Georgia: Percentage of Permanent Formal Private Sector Jobs Lost by Sector, Model Predicted and Survey-Measured Projections, CFUWBES1 (July 2020)_

||Econometric<br>Model Projections|Survey<br>Measured|No. Of<br>observations|
|---|---|---|---|
|1. Chemicals|6.0|30.3|7|
|2. Food, drink, and tobacco|7.7|13.2|98|
|3. Garments<br>4. Hotel, restaurants, and|33.4|44.4|8|
|transportation<br>5. Machinery, electronics, and|27.4|39.9|99|
|construction|23.9|18.9|54|
|6. Metals and non-metallic minerals;||||
|Plastic and rubber|8.4|5.2|40|
|7. Wholesale, retail, and other<br>services|15.0|8.2|164|
|8. Wood, paper, publishing, and<br>printing|26.8|17.5|14|
|Total|19.0|18.1|484|

Sources: LFS 2019, CFUWBES1, WBES 2013 and 2019. Notes: Model-predicted estimates are based on projected jobs lost using a multi-stage model and the employment structure in LMPS. Survey-measured jobs lost are based on directly computed sector-average job losses. In both cases, to estimate the allocation of jobs lost to production and non-production workers, sector-specific Tobit polynomial models are estimated using the 2013 and 2019 WBES. All calculations are survey-weighted.

have faced fewer job losses (6 percent of workers). In almost all cases, non- production workers experienced lower job losses in the summer than production workers in all the sectors except for garments (See Appendix 34
sufficiently powerful to be able to identify the dynamic profile of the response of output to government spending.

A third potential objection to the identifying assumption has to do with possibility that commitments and/or disbursements on loans trigger changes in policy performance that themselves have direct effects on economic growth. If, for example, the approval of a loan from an official creditor such as the World Bank is conditional on specific policy reforms that themselves might affect growth over the next several years, this would violate my exclusion restriction that loan commitments are uncorrelated with future shocks to growth. Another possibility might be that loan approvals are a response to policy reforms which themselves are persistent over time.<sup>25</sup> To the extent that the associated reforms are growth-enhancing, this would contribute to a positive correlation between my instrument and the error term in Equation (1), which would in turn imply an upward bias in my 2SLS estimates of the multiplier.

I investigate this possibility in columns (7)-(9) of Table 7, by controlling for contemporaneous changes in policy performance, as measured by the World Bank's Country Policy and Institutional Assessment (CPIA) ratings. These ratings have been produced annually since 1978 by World Bank country economists for all World Bank borrowers. The ratings are on a six-point scale, based on an underlying checklist of various policy areas, with higher values corresponding to better policy performance. Annual changes in policy performance are significantly positively correlated with growth in the full sample and high disbursements sample in the 2SLS specification. However, controlling for policy does not significantly change the 2SLS estimates multipliers relative to the benchmark specification, with estimates that fall between 0.29 and 0.44. In summary, while short-run changes in policy performance do appear to matter for short-run fluctuations in growth, these changes in policy performance are not very strongly correlated with past loan commitment decisions, and so do not significantly impact my estimates of the multiplier.<sup>26</sup> To sum up, in this section I have considered a range of alternative specifications designed to check the robustness of my results to a variety of potential objections to the validity of the exclusion

> 25 Note however that, since I rely on predicted rather than actual disbursements, I do not need to be concerned with the possibility that _disbursements_ on loans are triggered by policy reforms.  This is because any countryspecific variation in disbursement rates has been eliminated from the predicted disbursements instrument. 26 There is however some weak evidence that loan approvals might be correlated with lagged improvements in policy.  Adding the first lag of changes in the CPIA reduces the estimates of the multiplier somewhat relative to the benchmark, with 2SLS estimates ranging from 0.11 to 0.31 across the three samples (as compared with 0.38 to 0.42 in the benchmark specification).

24
degree of latitude is 110.6 kilometers at the equator and 111.7 kilometers at the poles. A degree of longitude is rather more elastic, being 111.3 kilometers at the equator, 55.8 kilometers at 60 degrees latitude and only 16.9 kilometers at 80 degrees latitude. Consequently Cartesian geometry cannot be used to measure either distances or areas when working with latitude and longitude coordinates.<sup>15</sup> Projected coordinate systems convert latitude and longitude coordinates from the earth’s three dimensional surface onto a two dimensional map. For example, a widely used projected coordinate system for measuring distance is the Universal Transverse Mercator (UTM) which divides the earth into 60 zones, each spanning six degrees of longitude and ranging from latitude 80 degrees South to 84 degrees North. Each zone has its own central meridian and locations within a zone are measured in meters eastward from the central meridian (which is given a value of 500,000 meters so that even the furthest west point in the zone does not get a negative coordinate) and meters northward from the equator.<sup>16</sup> For example, the coordinates of Lima, Peru translate from decimal degrees of 12.05S, 77.05W to an Easting of 276,836 and a Northing of 8,667,084 within the UTM zone 18S.<sup>17</sup> Consequently if location data from a GPS (which are for a three dimensional surface and so use a geographic coordinate system) are combined with two dimensional map data (in a projected coordinate system), a conversion has to be made to line up the various data layers. This is a common and easily done task in a GIS, and building up different layers of data for households, villages, and features of interest like roads, coastlines, rivers, or public services adds value to the information in each layer. However, the data layers will not line up if different coordinate systems are used for each layer (for example, using the wrong UTM zone number could place the same features several hundred kilometers apart). It is surprisingly easy to incorrectly have unmatched coordinate system because metadata, which should tell users about the coordinate system and datum used, are not always included with existing geographical data. For example, one of the authors digitized a road around the edge of the island of Tongatapu in

> 15 Instead the formula for great circle distance should be used, which can be found at: <u>http://mathforum.org/library/drmath/view/51711.html. Stata code that implements this formula is available in the</u> “globdist” ado written by Ken Simons. 16 For locations in the Southern Hemisphere, the Northing is calculated as 10,000,000 minus the meters south of the equator.

> 17 This zone ranges from 72 to 78 degrees west with a central meridian of 75 degrees. The Easting of 276,836 indicates that Lima is approximately 223 kilometers west of this meridian.  The Northing of 8,667,094 indicates that it is approximately 1300 kilometers south of the equator.

- 20 -
preferential trade relations with the EU, such as the countries of South Eastern Europe. Romania is still part of CEFTA, whose expansion in Eastern Europe it actively encourages.

64. However, Romania did not make similar progress in multilateral trade liberalization. Romania’s tariff levels, measured here as MFN tariffs on industrial goods, remained higher in 2006 than both in comparator middle income countries and in all the pre-accession OECD CEE (Figure 15). In particular, Romania entered the EU with substantially higher tariff levels than Hungary, Poland, the Czech Republic and the Slovak Republic in 2004. However, as of January 1, 2007, tariff levels have been lowered to equal those of the EU Common Market.

**Figure 15. Tariffs**

<!-- Start of picture text --> 5.0<br>5.0<br>4.3<br>4.0<br>4.0<br>3.0<br>3.0<br>2.0<br>2.0<br>1.4<br>1.0 1.0 1.0<br>1.0<br>0.0<br>Slovak EU15 Czech OECD Bulgaria Hungary Poland MICs Romania<br>Republic Rep.<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, and Turkey. Values refer to 2006 for Romania and Bulgaria, 2004 for Brazil, and 2003 for all other countries. Romania’s 2002 score was calculated using a different methodology so is not strictly comparable. For full data set see appendix I.

65. In contrast, Romania compares well with both high income EU countries and with pre-accession CEE countries and other MICs with respect to foreign ownership barriers (Figure 16). Substantial progress has been achieved in this regard compared to 2002.

24
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