rafmacalaba/datause-encoder-data huggingface dataset:
# _Introduction:_

Development assistance is becoming increasingly community-driven but there are still more questions than clear answers on how community driven development (CDD) really works. Does it improve participation and targeting? What kind of impact does it have? Does it build the capacity for collective action'? One effective way on answering these important questions is by analyzing how the CDD process works inside communities, within their particular political, social and cultural systems. Previous research on CDD has tended to either use survey data from large samples or qualitative information from beneficiary assessments of communities that received projects. Both are limited in their ability to answer the questions posed above. Quantitative analysis can establish broad patterns of the impact of an intervention on poverty and well-being, but is less useful in understanding the institutional context within which CDD operates. Beneficiary assessments, on the other hand, can be very helpful in probing the social aspects of CDD but are subject to problems of selection bias, lack of generalizabilty, and are limited in their ability to establish the average causal impact of a project.

> This paper will attempt to provide some answers to these questions by integrating quantitative and

> qualitative methods to conduct an in-depth analysis of data from five pairs of randomly selected communities in Jamaica. In each pair, one community has received assistance from the Jamaica Social

> Investment Fund (JSIF) a community driven project assisted by the World Bank, while its very similar matched counterpart has not received any assistance. Qualitative evidence from focus group discussions and in-depth interviews will draw out the institutional and cultural context within which the Social Fund process operates. It will sketch the various agents in the community, how leaders interacted with the community, and how the community perceived its gains from the project. Quantitative data will then be analyzed to

> evaluate the impact of social funds on measures of participation and collective action capacity using propensity score matching methods and regression analysis. This mixed method approach will permit a

> nuanced and contextualized understanding of the social fund process in Jamaica, while providing some evidence of the causal impact of the social fund on the social, political and economic life of individuals within the community. The in-depth data on social fund communities, however, is traded off with the size

> and representativeness of the sample and our findings should not be read as an evaluation of the JSIF

> portfolio of projects, but as an in-depth case study.

Social Investment Funds (SIFs) are perhaps the most visible mechanisms of CDD assistance, and a fast growing part of the World Bank's portfolio. A Social Fund is both an organization and a process.

'The capacity for collective action is a widely used definition of social capital (Woolcock and Narayan, 2001). However, social capital has so many other definitions that, for the sake of clarity, we will avoid using the term in this paper.

3
# **Appendix Table 4. ECOWAS countries' Bound and Applied tariff rates in 2004**

<u>(In percent)</u>

|||WTO bound tariffs|||Applied tariffs||
|---|---|---|---|---|---|---|
||Minimum|Maximum|Average|Minimum|Maximum|Average|
|Total 1/|20|88|54|0|43|13|
|Benin|0|100|30|0|20|12|
|Burkina Faso|0|100|46|0|20|12|
|Cape Verde 2/|...|...|...|0|123|10|
|Cote d'Ivoire|0|25|11|0|20|12|
|Gambia|20|110|106|0|18|13|
|Ghana|30|99|95|0|110|13|
|Guinea|0|40|21|0|7|6|
|Guinea-Bissau|40|50|49|0|20|12|
|Liberia 2/|...|...|...|...|...|...|
|Mali|0|75|30|0|20|12|
|Niger|0|200|41|0|20|12|
|Nigeria|40|150|117|3|150|29|
|Senegal|15|30|30|0|20|12|
|Sierra Leone|30|80|47|0|30|14|
|Togo|80|80|80|0|20|12|
|Memorandum items:|||||||
|WAEMU countries 1/ 3/|17|83|39|0|20|12|
|Non-WAEMU countries 1/ 4/|24|96|77|1|63|15|
|Nigeria (after 2005 tariff reform)||||0|50|12|
|Guinea (after 2005 tariff reform)||||0|20|12|
|Non-WAEMU countries (after 2005 tariff re|forms) 1/ 4/|||0|46|13|

Sources: IMF Trade Policy Division's databases; and staff estimates.

1/Unweighted average.

2/ Not a member of WTO.

3/ Benin, Burkina Faso, Cote d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo 4/ Cape Verde, The Gambia, Ghana, Guinea, Liberia, Nigeria, and Sierra Leone NB: The average tariff estimate does not reflect the bans on imports and is therefore an underestimate, especially for Nigeria.

28
columns (5) and (6) imply increases in non-resident filings that exceed 35%. The positive and statistically significant coefficients on the post reform dummy interacted with the time trend in columns (7) and (8) imply faster growth over time in the number of non-resident filings after reforms. These findings provide additional evidence that MNEs increase the quantity of technology transferred after IPR reform.

# **5. Conclusion**

Understanding the effects of intellectual property rights on various types of international activity is an important agenda in economic research. This paper empirically tests the hypothesis that IPR reforms increase technology transfers between countries by analyzing how measures of U.S. affiliate activity and levels of foreign patenting change in a set of 12 countries that undergo reforms.

There are some significant advantages associated with this approach. By examining discrete reforms of IPR, the analysis includes country or affiliate fixed effects and therefore controls for time-invariant country and firm characteristics that are often correlated with factors that influence the decision to transfer technology. The firm-level data used in this paper cover affiliates of U.S. MNEs that report information in a consistent way across countries. The use of these firm-level data also permit tests that reveal whether IPR reforms have the largest consequences for firms that would be expected to value such reforms the most.

The results provide strong evidence that U.S. multinationals respond to changes in IPR regimes abroad by increasing technology transfers to reforming countries. In the wake of legal reforms that strengthen patent rights, intrafirm royalty payments made by 26
to 600 million more people could be at risk of hunger by the year 2080. In terms of combined ecological and social impacts, they identify eight regions of primary concern (see Box 2).

# **Box 2. Eight regions of concern identified by Warren and others (2006: 9), and estimates of population for each region by 2050 (from UN 2007)**

- Northern Africa, where impacts will include crop failures, desertification, and water resources stress (estimated population of 310 million by 2050)

- Southern and Western Africa where impacts will include maize crop failure, and increased famine risk (estimated population of 682 million by 2050)

- Central Asia where impacts will include crop failures (estimated population of 312 million by 2050)

- Coastal areas of South Asia where impacts will include flooding and salinisation (estimated population of 2,223 million by 2050)

- The islands of the Caribbean, through coral reef degradation, impacts on fisheries, and sea level rise, which may damage economies (estimated population of 50 million by 2050)

- The Arctic, due to impacts on infrastructure and ecosystems (current population is estimated to be 4 million, projections are not available [UNEP 2004])

- South America, where impacts will include water resource stress, falling crop yields, and biodiversity losses (estimated population of 517 million by 2050)

- Small islands in the Pacific and Indian Oceans, where impacts will include flooding and submergence due to sea-level rise (estimated population of 65 million by 2050)

Migration is one possible response to climate risks, and not everyone exposed to risk will respond by migrating (Adamo 2008, Black 2001). Others may adapt without recourse to migration. Others may not adapt, and may not be able to or may choose not to migrate, and so experience livelihood decline, increased health problems, and declining life expectancy (Kothari 2003). Indeed, the number of people who cannot migrate in response to climate change (for reasons of poverty, remoteness, ill-health, or age, for example) may far exceed the number that do, and so may pose a far larger humanitarian problem, even though this problem will be more spatially and temporally diffuse than events where displaced people are concentrated in specific locations.

These changes in climate and social-ecological systems will affect people’s perceptions of the risks and benefits associated with staying as compared to those associated with migrating. Where climate change exacerbates morbidity and mortality, reduces incomes, and decreases 13
number of households that consume tobacco, and the income lost is estimated as the average income per household. The effect of the tax increase is then estimated in relation to the income gains because of increased years of employment. It is expected that incomes will fall as the number of years lost increase because of higher numbers of premature deaths from tobacco consumption.

Lastly, total welfare gains are estimated for each income group by adding the results of the reduction of medical treatments, the gains in working years, and the increase in tobacco expenditures (see equation 1).

# **a. Elasticity parameters**

After the model is defined for the calculation of the impact on income of the tobacco taxes, the estimates in the literature on elasticities, disease prevalence, the cost of medical treatments, and mortality patterns are examined. Several studies have estimated the tobacco price elasticity in Ukraine and other European countries. Within this research, the work of Denisova and Kuznetsova (2014) and Krasovsky et al. (2002) stands out for their use of prices and quantities of tobacco in Ukraine. The parameters estimated by Krasovsky et al. (2002) are an average price elasticity of −0.24, with variations by income group and age. Denisova and Kuznetsova (2014) generate lower and upper bounds for this elasticity to simulate the impact of tobacco price rises.

Table 1 shows the elasticities estimated by Denisova and Kuznetsova (2014). Their elasticities are divided into 10 income deciles. The average elasticity is similar to Verguet et al. (2015) and to estimates on other countries.

**Table 1. Tobacco Price Elasticities, by Income Decile: Denisova and Kuznetsova (2014) (%)**

|_Price elasticity_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|_Average _|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Lower bound|−36|−34|−32|−27|−22|−15|−14|−10|−7|−3|−20.0|
|Medium bound|−44|−42|−40|−35|−30|−23|−22|−18|−15|−11|−28.0|
|Upper bound|−56|−54|−52|−47|−42|−35|−34|−30|−27|−23|−40.0|

_Sources:_ Denisova and Kuznetsova 2014;

# **b. Elasticity calculations**

To enhance the analysis, tobacco price elasticities are estimated using the household budget survey, 2010–13. The declared prices paid by households and the quantities of cigarettes purchased are used; this allows the calculation of a measure of effective price per cigarette, considering brand pricing variability. Figure 1 shows the variation in the price of cigarettes estimated using the household survey and the changes in prices using national statistical data on Ukraine. As expected, the variation in the prices paid by households reflect price variations between filtered and nonfiltered cigarettes, showing that households adjust consumption budgets as prices change.

9
<!-- Start of picture text --> Countries in 2nd and 3rd deciles have grown most since 2016.<br>Change in SPI Overall Score from 2016-19 by 2016 decile group<br>10.0<br>77<br>75 73<br>7<br>66<br>59<br>5.0 46 49 48 49<br>Change in Score<br>(2016-19)<br>25<br>1<br>0.0<br>25<br>1st Decile 2nd Decile 3rd Decile 4th Decile 5th Decile 6th Decile Tth Decile 8th Decile 9th Decile Top Decile<br>Decile in 2016<br>SPI Overall Score scale = 0 - 100 points.<br><!-- End of picture text --> <!-- Start of picture text --> SPI Overall<br>Data<br>Infrastructure<br>Correlation<br>Data Sources 1.0<br>~“~ 0.5<br>o<br>> 0.0<br>Data Products . -0.5-1.0<br>Data Services<br>Data Use<br>& @ »<br>° O 3D © RS > x &<br>9 SP Mee & es Ko<br>e & & & S<br>Var1<br><!-- End of picture text -->
Simplicity, objectivity, transparency and replicability all argue for weighting each variable (or each source, for sources that provide multiple indicators are provided by one source) equally in constructing a composite index. The TI index weights each of its sources equally, with a caveat: the three most recent WEF and IMD surveys are each included as a separate source. They each therefore receive triple the weight given to another source, such as the EIU or the WMRC.

The goal of accuracy could justify differential weighting, if there is good reason to believe that some sources are more informative than others. For this reason, the WBI index weights some sources more heavily than others. Specifically, the sources that tend to be more highly correlated with the other sources are given greater weight, with the precise weights determined objectively by (a variant of) principal components analysis. The assumption is that if sources are independent of each other, a source that agrees less with the others is a less accurate measure of corruption – whether due to pure measurement error (the source is deficient in measuring what it purports to measure) or due to extraneous content (e.g. if a source’s assessment criteria include factors other than corruption).<sup>22</sup> The rationale for such a procedure disappears however if measurement error is correlated among sources, i.e. if they are not independent. If high correlations among expert assessments are driven by the fact that they consult each other’s ratings – or even by experts all basing their ratings on the same information sources - agreement among them is a dubious proxy for their accuracy. In that case, any truly independent source will appear to be relatively inaccurate: using different information or a different

> 22 For example, the criteria for the corruption indicator from Business Environmental Risk Intelligence (BERI) include “xenophobia.”  The WBI index includes it, but TI excludes it based on this extraneous content.  The authors of the WBI index emphasize measurement error rather than conceptual mismatch as their justification for weighting more heavily the sources correlated more strongly with others.

24
# **Table 12a.  Tariff Rates, Export Tax Rates, Estimated Ad Valorem Equivalence of Barriers to FDI in Services Sectors and Estimated Improved Market Access** (ad-valorem in %) -- by sector

||||Estimated<br>change in<br>|Equivalen<br>to|t % barriers<br>FDI<br>|
|---|---|---|---|---|---|
||Tariff rates|Export tax<br>rates|world market<br>price|Base Year|Post-WTO<br>Accession|
|Electric industry|2.6|0.0|0.0|||
|Oil extraction|0.0|7.9|0.0|||
|Oil processing|4.5|4.6|0.0|||
|Gas|5.0|18.8|0.0|||
|Coalmining|2.2|0.0|0.0|||
|Other fuel industries|5.0|2.6|0.0|||
|Ferrous metallurgy|5.9|0.4|1.5|||
|Non-ferrous metallurgy|8.5|5.3|1.5|||
|Chemical & oil-chemical industry|7.5|1.6|1.5|||
|Mechanical engineering & metal-working|10.7|0.0|0.0|||
|Timber & woodworking & pulp & paper industry|13.5|6.9|0.0|||
|Construction materials industry|12.0|1.6|0.0|||
|Textiles and Apparel|16.8|4.1|0.5|||
|Food industry|14.1|3.1|0.5|||
|Other industries|12.4|0.0|0.5|||
|Agriculture & forestry|8.4|0.6|0.0|||
|Other goods-producing sectors|14.6|0.0|0.5|||
|Telecommunications||||33.0|0.0|
|Science & science servicing (market)||||33.0|0.0|
|Financial services||||36.0|0.0|
|Railway transportation||||33.0|0.0|
|Truck transportation||||33.0|0.0|
|Pipelines transportation||||33.0|0.0|
|Maritime transportation||||95.0|80.0|
|Air transportation||||90.0|75.0|
|Other transportation||||33.0|0.0|

Source: Tarr, Shepotylo and Koudoyarov (2005) for tariff rates; Kimura et al. (2004a,b,c) for barriers to FDI; Roskomstat for export tax rates; authors' estimates for change in world market prices.

39
Figure 1: CHILE <!-- Start of picture text --> (a) (b)<br>li t ! Money & Credit<br>2 ............... ...........................................<br>01973 / 61979 1982 198561988 1991 1G4 0.6<br>:-4 S5 34 04<br>- rfirst principa component | 1970 1973 1976 1979 1982 1985 1988 1991 1994<br>weightedaverageoi m 1-(M2/GNDI) t - - (pritale credftIGNDI) t<br>(c) (d)<br>r t (~~~~~~~~~~~~S/Y) t<br>50 0.25<br>__ __ l 0.2<br>A 1973 76 1979 1982 1985 1988 1991 1994 0.15 -<br>0.1<br>-100 \ I.0.05 ,1,,_T,.--,<br>1 3 5 7 9 11 13 15 17 19 21 23 25<br>-250~~~~ i ~~ ~~~~~~~ - adjusted -u nadjusted<br>5300<br><!-- End of picture text -->

53
# **Figure 20. Number of Dimensional Deprivations, 2010–15, Percent**

<!-- Start of picture text --> 2010 20.0 36.7 29.3 11.6 2.4<br>2011 25.5 38.3 24.5 10.1 1.7<br>2012 27.3 38.0 25.2 8.4 1.2<br>2013 27.8 38.9 23.7 8.2 1.4<br>2014 26.3 38.6 26.3 7.5 1.3<br>2015 27.8 41.1 23.4 6.6 1.1<br>0 20 40 60 80 100<br>0 1 2 3 4 and more<br><!-- End of picture text --> _Source_ : ILCS data.

The overlap of deprivations highlights the association between several dimensions of nonmonetary poverty: (1) A large number of households is deprived in at least one dimension. However, deprivations in one dimension do not imply that households are automatically deprived in all poverty dimensions. (2) There is a close association between deprivations in one dimension and those in other dimensions captured by the measure of multidimensional poverty (see Table 7). Assuming a random distribution of household deprivations, 2.39 percent of the population would have been deprived in basic needs and housing simultaneously. As the actual number is 3.5 percent (something also true for any pairwise comparison), deprivations in one dimension increase the probability of being deprived in another.

**Table 7. Two-dimensional Overlap of Deprivations, 2015, Percent**

||**Basic**<br>**Needs**|**Housing**|**Education**|**Labor**|**Health**|**_Population_**<br>**_Deprived in_**<br>**_Dimension_**|
|---|---|---|---|---|---|---|
|Basicneeds|100.0|||||_6.4 _|
|Housing|3.5|100.0||||_37.3_|
|Education|0.2|1.1|100.0|||_1.7_|
|Labor|4.4|16.5|0.7|100.0||_42.7_|
|Health|1.8|10.1|0.5|11.1|100.0|_24.0 _|
|_Population Deprived_<br>_in dimension_|_6.4 _|_37.3 _|_1.7_|_42.7_|_24.0 _||

_Source_ : ILCS 2015 data.

23
**Policy, Research, and External Affairs**

# Public Economics

# WPS 651

rhis paper - a product ol thic Public ILconIomics l)iViSiOI1, Country<sup>FcoIomiCs D)Cpartrilcit-is part</sup><sup>**01o**</sup> a larger efforl in PlRI to rcfonr taxes in developing<sup>Countrics. COI)ieS</sup><sup>**_are_**</sup><sup>_aiklih_lrcc</sup> _11iI_ t.cefro the World Bank, 1818 II Street _NW,_ Washington, DC 2(433. Please conta2t Anin<sup>Bhlillal</sup> aooion N 10 **055.** ecltision _379()(_ (62 pages, with ligur-es and tables<sup>.</sup> In developing CouflItrics. iTmost financial asseis in formal markets are dcposits at linanci al institoilions. Tlis potentially important tlax hase coul(d be taxed at a low administrative cost.

Wlien revenucs of financial taIxes are significant, implicit taxes dwa :xplicit taxes. Chamlcv focuses ontilc implic;! taxation ol financial assets through scigniorage, reserve rcquirenictnts, Icnding tiargets, and interest ceilings combined wi tih inlatlioni, 'T'hc last instrumiienit hlas oftcn been overlookcdL, but **it hIs** gencralted more than **a** tlhiird of imPplicI **t** evenues in somc cases (Nigeria.) **hb** lowening IelC cost of' government borrowine.

Tax revenues at-c (hifficult to measute because of rcgulations that prevent thc<sup>Ltse</sup> ol market prices oibm computation aIln **d** istolr thie micaning of solll (se lefinitiois. I-or somlie couIntries, **Ilic** standard mtchiod of scigniorage grossl underestimates thc rt-evene flomil financial taxation.

InI Subh-Saharanl;t11 cOulni`it'`. Ie,t ilvlcl>t **01** taxation is smnall and harud to detc.I,t<sup>**\**</sup> **ien** tIlc liinancial burdcn is lo". In CountLriS \\ iti repeated experiences of high tat viion, tIhe impact has beetl substalintil (Iamore t1-an 50 percent ol revenues o0t the a **n** tain). III cou1ntriCS \N itl1 _111i1rC_ developed financial markelts such as I Thailand olr Indonicsiai, the excess hurden of tIatIion is<sup>**\**</sup> er\ large evein lor smzall values ol **thIL** (implicit) tax ratcs.

Thelc aUI1tOr discusscs \ ar-ious SOuICes () (distortion1 but ignores poictntial impacts oti tile lCvCI ot **5I \** iS n and tlhC **tro\\ II** I;tlC.

Althouoh taxes oni (11iancial asse'ts have a low administrative cost, thlc \cess burdei that stens Irom lthe misallocation ol rcs,ourccs is probably a m iliAer fraction ol rew<litius tlhnIlLUtwl tha;lt ol' otlhr ta\s.

Thc **PRE** W(rIkinj2 **P,'ipi SeIh'. di'' -,,Ci1 tc-** **_1_** t<sup>**_1_**</sup> **_w_ 1n1dingp** of \%onk **i,itdcT \%i** 'I **ihc Ban,mk'. Pu)l** \ **k<c,oX,t1, .11.' 1\1dJll Affair,Xonipltex AnI** o1lQIi\C o ofthCTriC' . 1 n't **_lh7.-L_ !inJiiids** t **kh** . **c' cn** ii prc'cnl:1t ll c **ti;. tl\ .-b<** KI **'Fhic finding,s, inerpTl.1alIjons, and c-ont In'inn** in **lthsC** rlap.lS<sup>**do not**</sup> _TelaTC_ **p** _IC 1_ **.'** _1I_ **t .il It .tn,,** .1 . .,.

**PIodw, cd th tbc** P'RF **DI)innaflioi** Ii. **Comtel'n**
Montreal Protocol and ODS commitments to trade policy under RTAs introduces the potential for additional punitive measures for non-compliance in the form of retaliatory trade policy allowed for under RTA dispute settlement mechanisms. Because these expanded punitive measures fall under the RTA rather than the Montreal Protocol, they potentially cover a broad set of goods and services rather than the narrow focus on ODS-related goods under the Montreal Protocol.

We consider two aspects of issue linkages between trade policy and environmental policy using detailed data on trade agreement content from the World Bank’s Deep Trade Agreement database. First, we empirically investigate the use of trade agreements to induce accession to international environmental agreements—a phenomenon referred to as “participation linkage” in the literature. Because the Montreal Protocol limits trade in ODS-related goods with non-member countries, member countries may lobby to include Montreal Protocol accession as a requirement in trade agreements with non-members in order to more effectively open trade while avoiding any barriers under the Montreal Protocol. Second, we consider “enforcement linkage” between trade agreements and reductions in use of ozone depleting substances. In contrast to participation linkages—in which commitments bind via the Montreal Protocol—we also consider ODS commitments directly enumerated in trade agreements themselves that bind via RTA dispute settlement mechanisms. In particular, we study whether such enforcement linkages are effective at mitigating ODS emissions. This is a critical question in light of recent evidence that China has increased production and consumption of CFC-11 in violation of its Montreal Protocol commitments (Rigby _et al._ , 2019).

We also examine the role of substitution in the apparent success of the Montreal Protocol. We study whether reductions in regulated ODS were, in part, accomplished via substitution to other unregulated or less stringently regulated substances. While policy linkage in trade agreements can support commitments prescribed by international environmental agreements, exposure to potential trade remedies may exacerbate or accelerate policy spillovers such as substitution away from regulated substances to potentially harmful alternatives.

We find evidence that RTAs and ODS provisions are effective in promoting participation linkage. While the Montreal Protocol and its amendments have achieved near universal ratification (besides the most recent Kigali Amendment in 2016), many countries—both developed and developing— 3