rafmacalaba/datause-encoder-data huggingface dataset:
2.57 -

-

consequences. **Raising the** producer **prico** puts prosswe on COCOBOD to cut costs, and on the Government to reduce wasteful expenditure. The argument for taxing cocoa (or anything else) is that the revenue can be bettor or more productively spent by the government than by the taxpayer. This argument has been demonstrably false In the past, and without strong Incentives to hprove offlciency and cut waste, it will continue to be false. The lower Is the value of government expenditure, the lower should be the level of taxation, and the higher should be the producer prico of cocoa.

At this point it Is worth brnging out another hportant finding from the GLSS and AES survey data and the technical appendix. It appears that cocoa farmers are rather poorer that the typical consumer of **knported** or taxed goods, and that producers of non-traded foods are very considerably poorer than consumers of these foods. Given this, it Is, on distribuclonal grounds, desirable to transfer revenue from consumers of food to farmers, and also from consumers of taxed goods to cocoa farmers. Taxing cocoa farmers does the reverse on both scores, for It directly taxes the Income of cocoa farmers, and Indirectly lowers the Income of food producers and transfers Incomes from these farmers to food consumers.
- 19 -

# **C.   Total Fees**

42. Table 18 and Table 19 show total fees expressed as a share of contributions and assets, respectively, for each MPF over the 1998-2004 period. Average total fees as a share of contributions increased from 6 percent to about 10 percent in recent years, as shown in Table 19. This increase in total fees on contributions reflects primarily the increasing fees associated with asset management, and which results fundamentally from a fixed asset management fee per unit of assets and larger assets. Total fees charged by independent MPFs and MPFs sponsored by employers are on average significantly lower than the fees charged by MPFs sponsored by financial institutions, reflecting fundamentally their lower asset management fees. The differences in total fees over contributions charged by individual MPFs have increased over time, and in 2004 amounted frequently to 4 percent. Members comparing simply the operating fees (Table 15) would not be able to appreciate the differences across MPFs.

**Table 18: Total Fees as a Share of Contributions (%), 1998 – 2004**

|**Fund**|**Sponsor**|**1998**<br>**(%)**|**1999**<br>**(%)**|**2000**<br>**(%)**|**2001**<br>**(%)**|**2002**<br>**(%)**|**2003**<br>**(%)**|**2004**<br>**(%)**|
|---|---|---|---|---|---|---|---|---|
|Dimenzió|EMP|6.0|7.2|7.3|7.0|7.0|7.0|7.1|
|HONVÉD|EMP|6.1|6.6|6.8|8.5|8.8|8.2|8.7|
|Postás|EMP|5.9|5.5|5.6|6.0|6.2|6.2|6.3|
|Vasustas|EMP|8.0|8.0|6.8|7.8|7.7|7.6|7.8|
|VIT|EMP|6.0|6.0|6.1|6.0|7.0|7.0|7.1|
|AEGON|FIN|7.0|9.9|8.4|9.5|10.6|10.8|10.8|
|AH|FIN|5.4|6.0|6.8|8.9|10.6|9.4|10.5|
|Budapest|FIN|4.6|5.5|6.2|7.8|9.7|8.8|9.2|
|CS|FIN|5.9|6.0|7.0|8.2|10.1|11.0|10.4|
|ERSTE|FIN|5.4|6.1|7.9|7.5|8.7|10.6|10.9|
|ING|FIN|6.0|7.1|7.6|8.7|9.7|9.6|10.4|
|MKB|FIN|6.1|5.7|6.4|6.9|7.4|7.5|7.8|
|OTP|FIN|5.7|6.4|7.0|8.9|8.9|9.3|9.3|
|Quaestor|FIN|5.0|5.0|5.0|5.0|5.0|7.4|7.2|
|UNIQA|FIN|-|11.6|6.5|8.7|10.6|8.9|9.4|
|Aryankor|INDEP|5.8|5.8|5.8|9.1|10.7|8.7|8.6|
|Életút|INDEP|4.7|6.8|7.4|6.7|6.7|6.8|7.1|
|Évgyűrűk|INDEP|7.1|7.2|7.5|9.1|9.6|9.3|8.8|
|Weighted Average|MKT|6.1|7.1|7.2|8.8|9.7|9.6|9.8|
|Weighted Average|EMP|6.1|6.5|6.5|7.3|7.6|7.3|7.6|
|Weighted Average|FIN|6.1|7.2|7.3|8.8|9.7|9.7|10.0|
|Weighted Average|INDEP|6.0|6.7|7.0|8.4|9.1|8.4|8.2|

Source: HFSA raw data 43. Average total fees expressed as a share of assets have decreased significantly, from 13 percent in 1998 to 3 percent in 2004. This sharp decrease is typical of young pension systems and reflects the rapid growth of assets relative to contributions. Again, total fees expressed as a share of assets were lower for the MPFs sponsored by employers (2.1 percent), followed by independent MPFs (2.6 percent) and MPFs sponsored by financial
Table 3: Share of Firms with Foreign Direct Investment, by Country and Manufacturing Sector

||Food &<br>Agroindustry|Textiles|Garments &<br>Leather|Metal &<br>Machinery|Electronics|Chemicals and<br>Plastics|<br>Wood & Paper|All<br>Manufacturing|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Cambodia|-|1.00|0.82|-|-|-|-|0.82|
|China|0.12|0.32|0.20|0.22|0.29|0.08|-|0.23|
|Indonesia|0.04|0.15|0.13|0.39|0.65|0.24|0.08|0.17|
|Malaysia|0.19|0.31|0.24|0.27|0.61|0.41|0.22|0.32|
|Philippines|0.07|0.14|0.28|-|0.67|-|-|0.25|
|SouthKorea|0.19|0.00|0.06|0.18|0.08|0.32|0.10|0.15|
|Thailand|0.21|0.20|0.18|0.33|0.61|0.14|0.11|0.26|
|Vietnam|0.12|0.22|0.18|0.12|0.32|0.10|0.06|0.12|
|All Countries|0.13|0.20|0.24|0.22|0.39|0.21|0.10|0.22|

Source: Author's calculations are based on the _Enterprise Suveys_ (World Bank) Note: Table reports the share of firms in the sample with at least 10% of foreign capital , by country and manufacturing sector. The last row reports the mean share of firms with FDI for all the countries. The latter is the weighted average of the mean share for each country, weighted by each country's sample size in the total sample. The last column reports the mean share of firms with FDI for all manufacturing sectors. Again, this is the weighted average of the mean shares in each sector, weighted by sector's size in the total country sample.
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 3.6 . Long-run marginal costs of power in the CAPP**

|(US cents/kWh)|Trade expansion|Trade stagnation|Absolute differential|Percentage differential|
|---|---|---|---|---|
|CAPP|7|9|–2|–22|
|EAPP|12|12|0|0|
|SAPP|6|7|–1|–14|
|WAPP|18|19|–1|–5|
|Cameroon|7|6|1|17|
|Central African Republic|11|11|0|0|
|Chad|7|11|–4|–36|
|Congo, Rep. of|6|8|–2|–25|
|Equatorial Guinea|8|10|–2|–20|
|Gabon|7|7|0|0|

_Source:_ Rosnes and Vennamo 2009 _Note:_ CAPP = Central Africa Power Pool; SAPP = Southern African Power Pool; WAPP = West African Power Pool; EAPP = East African Power Pool; kWh = kilowatt-hour.

Power trade expansion would reduce the long-run marginal cost (LRMC) of power in the CAPP by $0.02 per kilowatt-hour (kWh), or 22 percent overall. The savings accrued in the CAPP under trade expansion are greater than all other power pools and promise substantial economic benefits to the region.

Given that power is a key production input to the economy, any reduction in the reference level of power costs would have an important and positive effect in terms of productivity and competitiveness. For the CAPP as a whole, trade expansion would reduce the LRMC of power from $0.09 to $0.07 per kWh, a reduction of 22 percent (table 3.6).

The magnitude of power cost savings, however, varies across individual countries in the CAPP area (table 3.6). Small countries that have traditionally relied on very expensive small-scale oil-based generation have the most to gain from switching to the import of hydropower from Cameroon. In particular, Chad, Equatorial Guinea, and Republic of Congo could save between $0.02 and $0.04 per kWh, a percentage reduction in power costs of between 20 and 36 percent. Chad, which currently produces power domestically at high costs, could save $0.04 per kWh and reduce costs of generating power by as much as 36 percent under trade expansion. The Central African Republic and Gabon do not accrue significant savings under trade. The Central African Republic continues to rely on domestic sources for power. Cameroon, the major power exporter under trade expansion, would face an increase in LRMCs due to the need to develop a much larger amount of power, and hence more expensive schemes than those that would be strictly necessary to meet domestic demand alone.

The costs related to transmission grid expansion are about $310 million higher (since there are additional investments in cross-border transmission lines). On the other hand, the annual costs of the enlarged system (variable costs) in 2015 are $255 million lower.

42
# **Annex A: Supplementary tables and figures**

**Table A.1: Labor income regression (Heckman’s selection model)**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|_Demographic variables_|||||
|Age|0.109<sup>***</sup>|0.109<sup>***</sup>|0.110<sup>***</sup>|0.111<sup>***</sup>|
||(0.026)|(0.026)|(0.030)|(0.030)|
|Age squared|-0.001<sup>***</sup>|-0.001<sup>***</sup>|-0.001<sup>***</sup>|-0.001<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Male|0.579<sup>***</sup>|0.600<sup>***</sup>|0.454<sup>***</sup>|0.466<sup>***</sup>|
||(0.080)|(0.082)|(0.092)|(0.092)|
|Secondary|0.308<sup>***</sup>|0.275<sup>***</sup>|0.241<sup>**</sup>|0.198|
||(0.104)|(0.106)|(0.123)|(0.128)|
|University|0.957<sup>***</sup>|0.888<sup>***</sup>|0.850<sup>***</sup>|0.772<sup>***</sup>|
||(0.119)|(0.123)|(0.152)|(0.158)|
|Other|-0.182|-0.224|-0.197|-0.339|
||(0.279)|(0.276)|(0.262)|(0.255)|
|_Job characteristics_|||||
|Industry||0.670<sup>***</sup>|0.487<sup>**</sup>|0.332|
|||(0.210)|(0.220)|(0.254)|
|Services||0.739<sup>***</sup>|0.375<sup>*</sup>|0.221|
|||(0.195)|(0.198)|(0.236)|
|Other||0.680<sup>***</sup>|0.306|0.172|
|||(0.202)|(0.220)|(0.256)|
|Private||-0.057|||
|||(0.103)|||
|International organization||1.301<sup>***</sup>|||
|||(0.328)|||
|Informal|||-0.447<sup>***</sup>|-0.393<sup>***</sup>|
||||(0.094)|(0.093)|
|Hours of work last week|||0.006<sup>***</sup>|0.006<sup>***</sup>|
||||(0.002)|(0.002)|
|_Geographic characteristics_|||||
|Distance to CBD||||-0.041<sup>**</sup><br>(0.018)|
|Distance to CBD squared||||0.001<br>(0.001)|
|Obs.|6,759|6,755|6,041|5,908|

_Note:_ Standard errors in parentheses.* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01. Other control variables include marital status. The results of the first-stage selection models are not reported.

25
However, she does not investigate the effect of the program on collaboration. Also, although the matching technique produces a comparison group of firms with similar observable characteristics, the results could potentially be driven by differences in unobservable characteristics. Lööf and Broström (2008) use a similar matching technique to estimate the impact of university collaboration on innovation in Sweden. They find that large manufacturing firms that do collaborate with universities have higher innovation sales (share of sales from new or improved products) and a greater number of patent applications, with again the same concerns about selection on unobservables.

This paper adds to this literature by studying the effect of the In-Tech program in Poland on science-industry collaboration, research and innovation, and product commercialization. The InTech program provides grants to consortia of research entities and firms for proposed research projects. Applications receive a score based on peer reviewer ratings and those with a score above a threshold are offered funding. Based on this funding rule, we use a regression discontinuity (RD) design to estimate the effects of receiving In-Tech funding for applicants to the 2012 and 2013 calls for proposals. We use data from In-Tech application forms to show that applicants above and below the cutoff have similar characteristics, suggesting that the RD approach is valid.

Follow-up information on projects and consortia outcomes comes from a 2016 survey of 400 applicants both above and below the funding cutoff that was specifically designed to measure the impact of In-Tech. The consortium leaders in our sample have a mean of 100 and median of 28 research employees and a mean of 140 and median of 41 technical and administrative staff members. Most projects are in a field of engineering, with the largest field being electrical, mechanical, or materials engineering.

Our findings show that receiving In-Tech funding increases the probability of a project being completed by almost 60 percentage points (from about 20% completed to close to 80% completed). The survey responses regarding collaboration reveal that most consortia had already collaborated before applying to the program (about 85 percent of applications). However, the impact estimates show a 14 to 18 percentage point increase in the likelihood of a new collaboration taking place within the consortia since applying. We also examine whether In-Tech funding crowds out collaboration with entities outside the consortia and do not find this to be the case, suggesting that In-Tech led to more science-industry collaboration overall.

3
**Figure 24: Returns to Education**

<!-- Start of picture text --> A. Real Monthly Wages by Education of Wage B. Real Monthly Wages by Education of Monthly<br>Earners, 1991 – 2004, LFS Wage Earners, 1991 – 2004, LFS<br>15,000 15,000<br>14,000 14,000<br>13,000 13,000<br>12,000 12,000<br>11,000 11,000<br>10,000 10,000<br>9,000 9,000<br>8,000 8,000<br>7,000<br>7,000<br>6,000<br>6,000<br>5,000<br>4,000 5,000<br>3,000 4,000<br>2,000 3,000<br>1,000 2,000<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04 1,000<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br>None Less than Primary Primary Lower Secondary<br>Upper Secondary Vocational University Overall None Less than Primary Primary Lower Secondary<br>Upper Secondary Vocational University Overall<br>C. Real Monthly Wages by Education of Wage D. Returns to Education of Wage Workers, Hourly<br>Earners, 1986 – 2002, SES Wages, Rel. to Less than Primary, 1991 - 2004, LFS<br>22,000 700 14<br>20,000<br>18,000 600 12<br>16,000<br>500 10<br>14,000<br>12,000 400 8<br>10,000<br>300 6<br>8,000<br>6,000 200 4<br>4,000<br>2,000 100 2<br>0<br>1986 1988 1990 1992 1994 1996 1998 2000 2002 0 0<br>1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Overall None Less than Primary Primary Lower Secondary Upper Secondary Vocational University<br>Primary Lower Secondary<br>Upper Secondary Vocational<br>University Years of Schooling (RHS)<br>E. Ret’s to Educ. of Monthly Wage Workers, Hourly F. Returns to Education of Wage Workers, Monthly<br>Wages, Rel. to Less than Primary, 1991 - 2004, LFS Wages, 1986 – 2002, SES<br>400<br>400<br>300<br>300<br>200<br>200<br>100<br>100<br>0 0<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04 F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br>Primary Lower Secondary Upper Secondary Vocational University Primary Lower Secondary Upper Secondary Vocational University<br><!-- End of picture text --> 58
The paper posits three main hypotheses to reconcile the mixed results discussed so far. First, the relationship between FLFP and inequality follows an inverted U-shaped pattern if developing countries are also included; this can also allow us to identify a “turning point” at which the disequalizing factors, such as the assortative mating, attenuate depending on the FLFP level. Second, along this curve, it is possible to identify groups of countries (Gornick et al., 2019) with common features and a similar FLFP to inequality gradient. Third, by means of an unconditional quantile regression (Firpo et al., 2009) we can replicate the macro-level results using micro data, and show the asymmetric impact of FLFP on different quintiles, as well as confirm whether a pro-inequality or pro-equality impact exists in a given country.

To sum up, increasing FLFP is widely considered an important societal target with numerous positive developmental outcomes; it also increases the level of resilience, boosting productivity and accelerating economic diversification (IMF, 2018). Increasing FLFP is also part of a broader women’s empowerment agenda being actively promoted by many international institutions and governments (UNHLP, 2016). Moreover, it clearly represents a priority in those countries where FLFP is low and women are burdened with a disproportionate proportion of unpaid care work, arising from dominant social norms regarding gendered division of roles (Chopra and Meenakshi, 2019). However, while the consensus on this issue is almost unanimous, the picture becomes a bit fuzzier when it comes to translating the principles into active policies. As pointed out in this section, neither the direction nor the shape of the nexus between FLFP and inequality is clearly established. However, as we will discuss in the concluding section, policy makers should take this important piece of information into account when formulating policies aimed at promoting FLFP.

# 3. Data and methods

Different data sources are used for this paper. The macro analysis (first part of Section 4) is conducted using the World Bank’s PovcalNet<sup>3</sup> database. The analysis was limited to observations after 2002, as well as to countries with data on GDP and other basic socio-economic variables. This reduces the sample to about 127 countries, encompassing a range of low-to-high income

> 3 The PovCal database covers the period since 1978 and includes low-income, lower middle-income, upper-middleincome countries and high-income countries. Most of the Gini observations are calculated from direct access to household surveys but clearly countries do not collected data in the same years and the time gap varies across countries. The panel is therefore an unbalanced one.

6
local rainfall and vegetative conditions, thereby offering a good proxy for agricultural suitability, and help to cluster and compare relatively similar areas within and across countries. One of the most important things to note from this table is that, with a few exceptions, there are few obvious patterns by agro-ecological zone. In all countries households in the cool/sub-humid zone are more likely to apply inorganic fertilizer, while the incidence of irrigation is highest in the warmarid areas, likely because the payoff to irrigation is higher where rainfall is lower (BinswangerMkhize and Savastano in review). Given the ease of cross-country comparability, we will return to these categorizations, in addition to a range of other geo-referenced agro-climatic variables, later in this paper.

Table 12: Differences in input use between households across standard agro-ecological zones

||No. of<br>households<br>(weighted)|% using<br>organic<br>fertilizer|% using<br>inorganic<br>fertilizer|Avg total<br>inorganic<br>fertilizer<br>(kg/ha)|% using<br>any<br>improved<br>seed|% using<br>agro-<br>chemicals|% using<br>irrigation|% owning<br>any ag<br>equipment|
|---|---|---|---|---|---|---|---|---|
|Warm-arid|||||||||
|Ethiopia|15|0|0|0|7|0|64|51|
|Niger|256|15|9|20|2|18|13|65|
|Warm/semiarid|||||||||
|Ethiopia|102|56|24|27|7|34|7|80|
|Malawi|5,165|19|73|118|-|4|<1|1|
|Niger|1,993|60|18|3|3|6|6|79|
|Nigeria|1,113|9|63|197|-|42|6|19|
|Tanzania|220|23|8|5|-|5|12|13|
|Warm/humid|||||||||
|Nigeria|247|0|14|50|-|5|0|1|
|Tanzania|40|18|0|0|-|0|0|6|
|Uganda|1,061|4|4|1|-|13|3|18|
|Warm/subhumid|||||||||
|Ethiopia|37|17|27|8|19|5|0|35|
|Malawi|3,081|13|79|185|-|1|<1|<1|
|Nigeria|1,553|1|29|84|-|32|3|4|
|Tanzania|1,084|10|12|9|-|11|3|12|
|Uganda|64|10|2|8|-|4|2|2|
|Cool/semiarid|||||||||
|Ethiopia|726|66|42|33|10|16|17|87|
|Malawi|1,293|23|84|137|-|1|<1|1|
|Tanzania|124|33|15|14|-|12|0|31|
|Cool/humid|||||||||
|Ethiopia|527|67|61|51|25|43|5|73|
|Tanzania|52|13|0|0|-|3|1|7|
|Uganda|576|15|3|2|-|11|7|12|
|Cool/subhumid|||||||||
|Ethiopia|1,650|69|62|50|27|34|7|72|
|Malawi|547|14|89|210|-|4|<1|5|
|Nigeria|28|0|98|475|-|25|10|7|
|Tanzania|831|31|28|31|-|18|2|22|
|Uganda|384|32|1|<1|-|4|3|5|

Note: All of the above fall into the “tropics” AEZ category, which is why the distinction is dropped. The AEZ categories are georefernced to data from Harvest Choice at IFPRI and created using WorldClim climate data. A set of geovariables specific to the household location in Uganda 2010/11 does not yet exist, so we use the variables from 2009/10, meaning households that move or split between the two survey rounds are not included. In Ethiopia, the warm/humid and cool/arid zones are omitted since less than 5 households appear in either.

19
<!-- Start of picture text --> Other Food Electronic Equipment<br>BRA 20012011 BRA 20012011<br>CHL 20012011 CHL 20012011<br>CHN 20012011 CHN 20012011<br>COL 20012011 COL 20012011<br>IND 20012011 IND 20012011<br>PER 20012011 PER 20012011<br>RUS 20012011 RUS 20012011<br>THA 20012011 THA 20012011<br>TUR 20012011 TUR 20012011<br>ZAF 20012011 ZAF 20012011<br>0 10 20 30 40 50 0 20 40 60<br>Total LVAX share (%) Total LVAX share (%)<br>Total LVAX Unskilled Total LVAX Skilled Total LVAX Unskilled Total LVAX Skilled<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

# **Figure 21: Skilled and unskilled labor content of exports in GVC-intensive sectors 2001 vs. 2011**

<!-- Start of picture text --> Wearing Apparel Motor Vehicles<br>BRA 20012011 BRA 20012011<br>CHL 20012011 CHL 20012011<br>COL 20012011 COL 20012011<br>IND 20012011 IND 20012011<br>PER 20012011 PER 20012011<br>RUS 20012011 RUS 20012011<br>THA 20012011 THA 20012011<br>TUR 20012011 TUR 20012011<br>ZAF 20012011 ZAF 20012011<br>0 1,000 2,000 3,000 4,000 5,000 0 2,000 4,000 6,000 8,000<br>US$ mil. US$ mil.<br>Total LVAX Unskilled Total LVAX Skilled Total LVAX Unskilled Total LVAX Skilled<br>Other Food Electronic Equipment<br>BRA 20012011 BRA 20012011<br>CHL 20012011 CHL 20012011<br>COL 20012011 COL 20012011<br>IND 20012011 IND 20012011<br>PER 20012011 PER 20012011<br>RUS 20012011 RUS 20012011<br>THA 20012011 THA 20012011<br>TUR 20012011 TUR 20012011<br>ZAF 20012011 ZAF 20012011<br>0 1,000 2,000 3,000 0 1,000 2,000 3,000 4,000<br>US$ mil. US$ mil.<br>Total LVAX Unskilled Total LVAX Skilled Total LVAX Unskilled Total LVAX Skilled<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

# **6. Conclusions**

This paper has used the newly developed World Bank’s Labor Content of Exports database to provide some novel evidence on the wages and jobs in South Africa’s exports. As have other large commodity-dependent 32
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