rafmacalaba/datause-encoder-data huggingface dataset:
the business sector following complete elimination of outages and associated unreliability in electricity provision in the region. This is done by estimating the tax-revenue losses attributed to power outages over a two-year period should current challenges in the power sector of respective countries remain unresolved.

To begin with, we compare simulation results based on two measures of outage impact to enable us to ascertain whether there is any systematic difference in the simulated revenue losses/gains between the self-reported estimates and the econometric estimates from Mensah (2016) for 14 SSA countries. As shown in Figure 5, there are no systematic differences in the simulated tax-revenue losses between the two impact estimates. The largest gaps between the two estimates are observed in Nigeria (NIG), Ghana (GHA), and the Democratic Republic of Congo (COD), where the self-reported losses/gains are larger. A reason for an over-estimation of the estimates in some possible self-reported impact countries may be a heavy reliance of firms in these countries on electricity generators due to the frequent outages they experience. For example, data from the Enterprise Surveys reveal that 79 percent of firms in Nigeria rely on generators for electricity during power holidays. As stated before, however, for the most part the estimates of tax losses/gains are similar. Thus, we argue that the self-reported impact estimates provide a good measure of the associated impacts of power outages on firm’ revenue and profitability for countries that were not included in the impact estimate analysis. Therefore, we proceed to simulate the tax-revenue losses due to outages by relying on the self-reported impact estimate. All estimates are expressed in constant U.S. (2010) dollars.

Results on the annual loss in tax revenue due to reveal that South average outages Africa, Angola, Nigeria, Ghana, and Kenya record the highest losses – more than $300 million per annum. Nigeria and South Africa are the most prominent economies in SSA, with large industrial sectors, so one would expect the absolute impact of unreliable power supply on firms’ performance and consequently their tax payments to be high. Expressing these losses as percentages of total tax revenues, Angola (15.1%), Republic of Congo (8.9%), Nigeria (6.8%), Gabon (6.3%), and Mali (5.1%) are the top five losers (Figure 6). Cumulatively, the estimated losses in the study countries are in excess of $9.5 billion (4.3 % of total tax revenue). These losses represent tax-revenue gains that could be mobilized, other things being equal, if power outages in these countries could be eliminated. Considering the massive deficits in government revenue across countries in the region, the importance of tax-revenue savings from an improvement in the quality of electricity supply cannot be overemphasized. The enormity of potential tax-revenue savings underscores the economic of an sector to the of and benefits having efficient power propel engine growth development.

20
compared to the OLS specification. In this case, a change of 10 percentage points in the share of labor that uses computers is associated to a change of 0.02 in the ratio of white/blue-collar workers. This means that if an establishment changes from not using ICT to a 100% share of workers that use computers, this would translate into a change of 0.24 in the share of white/blue-collar workers, a value that is similar to the mean of this variable for 2013.

Once again, when we use wages of each type of worker as a dependent variable (Table 8), we find that there have been increases in wages for both groups as a result of the increasing adoption of ICT. Furthermore, the magnitudes for blue-collar workers appear to be slightly higher. Therefore, when we estimate the same equation using the wage gap between these two groups as a dependent variable (Table 9), we find that the coefficients are negative and significant in most of the specifications indicating a reduction in the wage difference between these two groups. In this case, a 10 percentage point increase in the share of labor that uses computers is associated to a reduction of this ratio in 0.24, which is equivalent to around 8% of the difference between the 10th and the 90th percentile of this variable.

Table 4: OLS estimates of the effect of ICT use on the Number of workers: Manufacturing

||(1)|(2)|
|---|---|---|
|**Dependent variable:** ∆**ln(Number of **|**white-collar workers)**||
|∆Share of labor that uses computers|0.0010***|0.0010***|
||(0.0002)|(0.0002)|
|∆Share of labor that uses Internet|0.0012<sup>_∗∗∗_</sup>|0.0013<sup>_∗∗∗_</sup>|
||(0.0002)|(0.0002)|
|∆ln(stock of computer equipment/worker)|-0.0246***|-0.0170***|
||(0.0056)|(0.0060)|
|**Dependent variable:** ∆**ln(Number of b**|**lue-collar workers)**||
|∆Share of labor that uses computers|-0.0002|-0.0001|
||(0.0002)|(0.0002)|
|∆Share of labor that uses Internet|-0.0003|-0.0001|
||(0.0002)|(0.0002)|
|∆ln(stock of computer equipment/worker)|-0.1179***|-0.0875***|
||(0.0056)|(0.0058)|
|**Controls**|||
|∆ln(capital/worker)|No|Yes|
|Observations|11,438|11,438|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

28
# **Abstract**

One of the alleged benefits of the recent global movement to strengthen intellectual property rights (IPR) is that such reforms accelerate transfers of technology between countries. This paper examines how technology transfer among U.S. multinational firms changes in response to a series of IPR reforms undertaken by 12 countries over the 198299 period. Analysis of detailed firm-level data reveals that royalty payments for intangibles transferred to affiliates increase at the time of reforms, as do affiliate research and development (R&D) expenditures and total levels of foreign patent applications. Increases in royalty payments and R&D expenditures are more than 20% larger among affiliates of parent companies that use U.S. patents more extensively prior to reform and therefore are expected to value IPR reform most.

Lee G. Branstetter Columbia Business School and NBER Uris 815 3022 Broadway New York, NY 10027 TEL 212-854-2722 <u>lgb2001@columbia.edu</u> Raymond Fisman Columbia Business School and NBER Uris 823 3022 Broadway New York, NY 10027 TEL 212-854-9157 <u>rf250@columbia.edu</u> C. Fritz Foley University of Michigan Business School 701 Tappan St. Ann Arbor, MI 48109 TEL (734) 763-1436 <u>ffoley@umich.edu</u>
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16

# ENDNOTES

1. This paper is based on research for the UNDP/World Bank Trade Expansion Program reports for Mongolia and Romania. The author thanks Jaime de Melo and Arvind Panagariya for useful comments on a previous draft. The views are those of the author, not necessarily those of the United Nations or of the World Bank.

2. In England Elizabeth I banned the exportation of vwool fleeces in the 16th century to encourage the textile industry. Other modern examples include Uruguayan prohibitions on exports of raw hides. Many countries currently undertaking the transition from central planning to a market economy also control raw material exports.

3. For Romanian wood products this assumption is reasonable. In the case of cashmere, however, Mongolia may have the ability to influence world market prices. Published estimates of Mongolia's share of world raw cashmere production range between 10 percent (Browne, 1990) and 2025 percent (Economist Intelligence Unit, 1991). Mongolia exports both raw cashmere and knitted cashmere products, so higher world market prices for raw cashmere increase export earnings directly and also indirectly by increasing the costs of competing firms in the market for knitted cashmere products. For an analysis of the potential welfare gains from exploiting market power in world raw material markets when a country also exports the final product, see Jones and Spencer (1989). Including market power in world markets in the model would be a useful extension of this paper.

4. This approach is based on Corden (1973, pp. 30-35). Suppose that the processing industry uses et inputs to produce one unit of output. Then each "package" of cm inputs woid be a "unit" of inputs. For example, if 10 ounces of raw cashmere were used to produce a sweater, then a 10ounce ball of raw cashmere would be one "unit" of input. If the price of an ounce of cashmere were $1.00, then the price of a "unit" of input would $10.00.

_5._ Income is omitted as a determinant of demand because of the partial equilibrium assumption.

6. The limit case of a complete ban on exports of the raw material would be analyzed as a zero quota.

7. The increase in exports may be underestimated in the partial equilibrium framework used here if income feedback effects are important. Lower real incomes due to the efficiency losses could reduce domestic demand for cashmere products, which are presumably luxury goods. On the other hand, lower income may reduce imports and therefore exports through balance of payments or exchange rate effects.

8. This list is based on information from the License Bureau, Ministry of Trade and Industry. Exports of other products also were subject to license: scrap copper, scrap iron, scrap aluminum, scrap aluminum alloy, scrap steel, meat, wheat, children's clothes and shoes, and imported
**Table 1a: Household characteristics**

||N|mean|Min|max|
|---|---|---|---|---|
|Household size<br>|1,762<br>|8.09<br>|1<br>|44<br>|
|number of cells|1,762|2.49|1|12|
|single cell household|1,762|17.70|0|100|
|hh with 3 cells or more|1,762|37.82|0|100|
|hh with 2 couples of more|1,762|15.76|0|100|
|nb of children 0 - 4 y.o.|1,762|1.23|0|10|
|nb of children 5 - 14 y.o.|1,762|2.15|0|13|
|nb of elderly over 66 y.o.|1,762|0.30|0|4|
|nuclear household|1,762|40.55|0|100|
|hh include vertical extensions|1,762|35.12|0|100|
|hh includes horizontal extensions|1,762|27.41|0|100|
|hh includes vertical and/or horizontal ext.|1,762|48.93|0|100|
|hh includes other types of member|1,762|30.31|0|100|
|share of non nuclear hh members|1,762|28.22|0|96|
|hh includes non nuclear hh members|1,762|66.07|0|100|
|Dakar|1,762|28.13|0|100|
|Other urban area|1,762|19.78|0|100|
|Rural|1,762|52.09|0|100|
|Female hh head|1,762|20.59|0|100|
|hh head is polygamous|1,762|29.44|0|100|
|hh head is polygamous man|1,762|23.51|0|100|
|hh head is polygamous woman|1,762|5.93|0|100|
|hh head has no formal schooling|1,762|64.68|0|100|
|age of hh head|1,761|49.85|18|93|

Source: “Pauvreté et Structure Familiale” (PSF) survey, authors’ calculations using sampling weights.

Note: Summary statistics of households’ characteristics. Nuclear households are households containing only the head, his wife (wives) and their children (biological, adopted or fostered). Vertical extensions designate parents, grand-parents, grand-children. Horizontal extensions include sibling, nephew and uncles of the household head. “Households with two couples or more” indicates that the household contain at least two married men accompanied by at least one wife each.

36
|**Table 5. OLS Regressions across countries for decision-making over large purc**<br>**interview the husband**<br>**Wife says she is the**<br>**main decision**<br>**k/diiki**|**hases when decided to**<br>**Husband says wife is the**<br>**main decision**<br>**k/diiki i**|
|---|---|
|**maerecson-mang**<br>**is joint**|**maerecson-mang s**<br>**joint**|
|<br>(1)|<br>(2)|
|**_Women's Status & Empowerment Proxies_**||
|Difference in years of schooling: husband-wife<br>-0.002***<br>|0.001<br>|
|<br>[0.00]<br>***|[0.00]<br>***|
|Wife not working<br>-0.080<br>[0.01]<br>|-0.034<br>[0.01]<br>|
|Wife works off farm<br>0.021***<br>|-0.015***<br>|
|[0.01]|[0.01]|
|Wife says she earns more than her husband<br>0.107***<br>[0.01]|0.019*<br>[0.01]|
|<br>008***|<br>0023***|
|Wife says she owns land<br>.5<br>[0.01]<br>|.<br>[0.01]<br>|
|Woman condones a husband beating his wife<br>-0.061***<br>[0.00]|-0.027***<br>[0.00]|
|Wife is aged 15-19<sup>(b)</sup><br>-0.110***<br>|-0.094***<br>|
|<br>[0.01]<br>-0047***|[0.01]<br>-0044***|
|Wife is aged 20-34<sup>(b)</sup><br>.<br>[0.01]<br>|.<br>[0.01]<br>|
|Age difference: husband-wife<br>-0.003***<br>[0.00]|0<br>[0.00]|
|Wife was married before age 20<br>0.015***<br>|0.013***<br>|
|<br>[0.00]<br>0001***|[0.00]<br>0|
|Years of marriage<br>.||
|<br>[0.00]|[0.00]|
|<br>l b f hild h<br>-0.005***|<br>-0.002*|
|Tota numer o cren woman as<br>[0.00]<br>|[0.00]<br>|
|Total number of children who died<br>-0.010***<br>[0.00]|-0.004*<br>[0.00]|
|Couple in a polygamous marriage<br>-0.093***<br>|-0.082***<br>|
|<br>[0.00]|[0.00]|
|**_Individual/ Household-level characteristics_**||
|Husband works in agriculture<br>0.004<br>|-0.004<br>|
|<br>[0.01]|[0.01]|
|<br> <br>-0.019***|<br>-0.013**|
|Rural area<br>[0.01]<br>|[0.01]<br>|
|Log time (mins) to the nearest water source<br>0<br>[0.00]|-0.002***<br>[0.00]|
|Household has electricity<br>0.016***<br>|0.032***<br>|
|<br>[0.01]|[0.01]|
|<br>Wlth itil btt 20%<br>-0.052***|<br>-0.036***|
|ea qune: oom<br>[0.01]<br>|[0.01]<br>|
|Wealth quintile: next-to-bottom 20%<br>-0.026***<br>[0.01]|-0.008<br>[0.01]|
|Wealth quintile: second highest 20%<br>0.019***<br>|0.016***<br>|
|<br>[0.01]<br>0082***|[0.01]<br>0051***|
|Wealth quintile: top 20%<br>.<br>[0.01]|.<br>[0.01]|
|Observations<br>61,839|61,839|
|Adjusted R-squared<br>0.201|0.181|
|(a) Robust t-statistics in brackets. *** p<0.01, ** p<0.05, * p<0.1 .||
|<br>(b) Country fixed effects are included.||

38
Figure 1). In 1990, CO2 emission intensity of Annex I countries was 11.83 tonnes CO2 per capita, this has been decreased by 14 percent to 10.15 tonnes CO2 per capita in 2009. During the same period (i.e., 1990-2009), Non-Annex countries emissions increased by 73 percent from 1.58 tonnes CO2 per capita to 2.73 tonnes CO2 per capita. The emission intensity gap between the Annex I countries and Non-Annex I countries has decreased by around 20 percent from 10.25 tonnes CO2 per capita in 1990 to 7.42 tonnes CO2 per capita in 2009.

**Figure 1: Historical emission intensity gap between the Annex I and Non-Annex countries (tons CO2 per capita)**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>Annex I<br>6 Non-Annex I<br>4<br>2<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text -->

**Source:** IEA (2011a)

The objective of this analysis is to examine the costs to the global economy of moving forward in the direction to converse emission intensities between Annex I and Non-Annex I countries. A range of uniform carbon taxes are introduced in Annex I countries as a policy instrument while non-Annex I countries are exempted from the carbon taxes. The impacts of the policy instruments are simulated using a global, dynamic, multi-sector computable general equilibrium model. Our study finds that the CO2 intensity gap between the Annex I and NonAnnex I countries would decrease by 65 percent in 2030 from the level in the baseline if a relatively high carbon tax of $250/tCO2 is imposed to Annex I countries. However, the CO2 intensity of Annex I countries would still remain almost twice as high as that of Non-Annex I countries although CO2 emissions in the former decreases by 57 percent from the baseline. This 3
<!-- Start of picture text --> 2003-2013<br>6%<br>2 4%<br>g<br>€ 2%<br>So<br>oh<br>a 0%<br>5<br>2%<br>i<br>4%<br>Tradabl es Non-tradables low- Non-Tradables Tradables Non-tradables low- Non-Tradables<br>wage high-wage wage high-wage<br>Countries without commodity boom Countries with commodity boom<br>SUnskilled eLow-skilled oe Skilled<br><!-- End of picture text --> <!-- Start of picture text --> 1 | a a<br><!-- End of picture text -->
- Figure 8: Net Borrowing by Source 1 990

Table2(con't):Net Boffowing from Commercial Banks (US$miiion)

||22|19<br>9|I22|2(e)|
|---|---|---|---|---|
|Htmgary|529|-1,670|-1,150|-935<br>|
|CSFR|469|-271|-364|-176|
|Bulgaria|1,114|497|685'|7001|
|Poland|n.a.|796<br>|850-<br>|855-<br>|
|Romania|-278|-6|200|125|

**_(.)_** Includes IMF, IBRD, and other multilateral creditors and official bilateral sources.

# ( Includes rescheduling of interest and principal.

In 1989 Romania was the only country making major e negative transfers from the region, and this was based on deliberate government policy. The negative transfer totalled US$1.7 billion, of which US$1.5 billion was to official creditors. Other private creditors were an important source of funds for Hungary and Bulgaria in 1989. Commercial banks lent more to Bulgaria than to any other country in the region that year, amounting to over US$ 1.1 billion.

The following charts illustrate the developments since 1989: <!-- Start of picture text --> FIlure 7: Net Borrowing by Source -<br>1 9 8 9<br>2000 - I<br>o 150 ooo li t # Sl rLobligations<br>2 -1000 ll<br>a 1500 1|<br>-2000<br>,,, 85 - |<br>I 3 tr<br>a Net borrowing from Net borrowig t fg frm o<br>ctewcIai btas otherprivate offidcial 1w0<br>credit n<br>Sou cc Waf esum ateas osn a IotryFeporta M .<br><!-- End of picture text -->

Figure 7 shows Romania and Bulgaria making large nettransferstoofficialsources. Also,forBulgariaitisthe last major bofrowing episode from OECD commercial banks. The situation changed dramatically in 1990, as shown in Figure 8. Commercial banks retreated massively from Hungary (net negative transfer of US$1.8 billion) and to a lesser extent from CSFR. Other private creditors were the main source of funds for Hungary. Bulgaria's net <!-- Start of picture text --> 40M<br>3000_<br>r<br>i 1000 llD<br>0<br>a -l0J<br>-2000X<br>ONettorrowingfrom *sNetborrowlngtrom ENetbowrowingfrom<br>comiwlcl banks other p. - te official wrca<br>cedites<br>s o-cC btf estimates 0 1nty D1ew IIF NOWTS<br><!-- End of picture text --> borrowing from commercial banks reflects unpaid obligations, and the figures for Poland mainly result from the official debt reduction deal.

AsshowninFigure9,theofficialsectoristhepredominantsourceoffundsineverycountryintheregionin 1991. CSFR is the largest recipient in dollar terms, with<sup>net</sup> borrowing of almost US$2.5 billion. Again, the commercialbankpresenceforPolandandBulgariareflectsunpaidl 1S11 * g rather than new lending. Other pnvate credi<!-- Start of picture text --> Figure 9: Net Borrowing by Source -<br>1991 (Estimate)<br>~~~~~~~~~2000- ;<br>c<br>500 l<br>00<br>-1 000 -<br>-15 t. ,*<br>a'<br>'<br>aNetborrowirngfrom U Netborrowingfrom Ut tborrowingfrom<br>coofwrciul bet otherprivate officialsource<br>IrnF in<br>Source taf tif 00 an Itfy K*W%G<br><!-- End of picture text -->

**_5_**
**Appendix Table 1. Person who usually decides on large household purchases**

||Variable||(1)|(2)|t-test|
|---|---|---|---|---|---|
|||N|_Wife _|_Husband_|_(1)-(2)_|
||Husband/partner alone||0.27|0.283|-0.013|
||Respondent alone||0.027|0.015|0.012*|
|Senegal|Respondent and husband/partner|937|0.199|0.296|-0.097***|
||Husband/partner alone||0.775|0.689|0.085***|
||Respondent alone||0.048|0.17|-0.123***|
|Sierra Leone|Respondent and husband/partner|3502|0.503|0.377|0.126***|
||Husband/partner alone||0.449|0.452|-0.003|
||Respondent alone||0.13|0.122|0.008|
|Uganda|Respondent and husband/partner|2370|0.506|0.427|0.080***|
||Husband/partner alone||0.364|0.451|-0.087***|
||Respondent alone||0.102|0.081|0.021***|
|Zambia|Respondent and husband/partner|6756|0.565|0.583|-0.018**|
||Husband/partner alone||0.332|0.336|-0.003|
||Respondent alone||0.243|0.196|0.047***|
|Zimbabwe|Respondent and husband/partner|3379|0.635|0.723|-0.088***|
||Husband/partner alone||0.123|0.081|0.042***|

39
# **Figure 4.   Structure of Production for Increasing Returns to Scale Russian Firms: Representative Good or Service in a  Representative  Regional Market (RM) m**

**Goods: to all Russia**

<!-- Start of picture text --> Services to own Regional Market<br>Gross output<br>σ = etad = 7.5<br>CES<br>Exports to Rest of World Variable inputs from RMm Specific factor (fixed supply)<br>σ = 5 = etadx<br>CET<br>Leontief σ = 0 a /<br>Value-added and Business Services<br>Intermediate Goods<br>Other Services<br>CES σ = 1.25 = esubs<br>Leontief Leontief<br>σ = 0 a / σ = 0 a /<br>Business Services<br>.... Value-added<br>Cobb-Douglas σ = 1 Cobb-Douglas σ = 1 b / IntermediateIRTS good i Composite c / .... CRTS good j IntermediateComposite d / Services 1Other .... Services 5Other<br>Business .... Business Sector Skilled CES CES<br>Service 1 Service 9 Specific Capital Unskilled Labor Labor CES σ =3 σ =3<br>Resources σ = 3= esub CES<br>σ = sigmadm<br>CES CES Services Cross<br>σ = 1.5 σ = 1.5 Intermediates Intermediates from Imported Intermediates Imported from RMm Intermediates from other Russia Services Border from RMServices m Intermediates from other Russia Services Border Cross<br>Domestic Cross Border Domestic Cross Border in RMm σ=3 other Russia σ=3 Intermediate σ =3 e / from Russia σ =1.5 sigmadm Rest of worldIntermediates<br>Presence Services Presence Services<br>Services Services<br>σ =3=sub σ =3=sub Firms in RMm Firms from Foreign-based Own Other<br>other Russia Firms Regional Russian Imports<br>Market<br>Multinational Russian Multinational Russian CES<br>Services Services in RMm Services Services in RMm σ = 2 * sigmadm<br>Notes:<br>CES σ =3 e / CES σ =3 e / CES σ =3 CES σ =3=sub Imports fromRegional Imports fromRegional Imports fromRegional ab/ / σσ = “esubintermediate”= “esubprimary”<br>Market 1 Market m Market q c/ i = l ,..., g where g is the number of IRTS goods<br>d/ j = l ,..., l where l is the number of CRTS goods<br>Multinational Russian Multinational Russian 55 e/ σ = “esub”<br>Service Service Service Service<br>Providers<br>Providers Providers in RMm Providers in RMm<br><!-- End of picture text -->