rafmacalaba/datause-encoder-data huggingface dataset:
73

# Table 13

Government spending by function, 2002 (percent of GDP)

|Total|29.11|
|---|---|
|National|15.11|
|Provinces, Municipal|14.00|
|Government functions (a)|5.62|
|National|2.19|
|Provinces, Municipal|3.43|
|Social|19.48|
|National|10.37|
|Provinces, Municipal|9.11|
|Education|4.29|
|National|0.88|
|Provinces, Municipal|3.41|
|Health|4.44|
|National|2.22|
|Provinces, Municipal|2.22|
|Social security|7.03|
|National|5.54|
|Provinces, Municipal|1.48|
|Labor|1.52|
|National|1.44|
|Provinces, Municipal|0.08|
|Other|2.20|
|National|0.29|
|Provinces, Municipal|1.91|
|Economic services|1.39|
|National|0.43|
|Provincial, Municipal|0.96|
|Debt service|2.63|
|National|2.13|
|Provincial, Municipal|0.50|

a. Administation, justice, defense. Source: MECON (2003b) Table 13 shows the areas of specialization of the federal government and the provinces and local governments.<sup>70</sup> The bulk of educational spending is at the 70 An important caveat is in order concerning the spending estimates in table 13. Although these are the official government data, there appears to be a major gap between revenue data and spending data that is difficult to reconcile. Thus, as shown in table 10, federal revenue in 2002 was 17.8 percent of GDP, of which 16.1 percent of GDP was in taxes and social security contributions (table 11) and 1.9 percent was “other” (table 10). The Ministry of Economy reports the total cash deficit of the non-financial public sector at the federal level as 1.5 percent of GDP in 2002 (MECON, 2003d, p. 153). The provincial deficit in 2002 was 0.5 percent of GDP excluding interest (MECON, 2003c), and provincial and local interest payments (cash) were 0.5 percent of GDP (MECON, 2003d, p. 164). So the consolidated total deficit was 2.5 percent of GDP. Considering that total spending at all levels was 29.1 percent of GDP (table 13), with a total
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22
demonstrates the government’s interest in involving the leaders of local user groups in fisheries management; the program calls for the formation of state committees that include representatives of national, state, and local governments, as well as representatives of fisher groups.<sup>30</sup> Our results suggest that even greater devolution of authority to local users—by granting property rights—could be a viable management strategy in the right circumstances. By recognizing the influence that local institutions like Mexico’s fishing cooperatives may have on both ecological and economic outcomes, policymakers will be better able to craft proactive, ecosystem-based policies that sustain both marine resources and the human communities that rely on them.

# References

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- Acosta, C. A. 1999. “Benthic Dispersal of Caribbean Spiny Lobsters among Insular Habitats: Implications for the Conservation of Exploited Marine Species.” _Conservation Biology_ 13 (3), 603–612.

- Allison, E. H., and F. Ellis. 2001. “The Livelihoods Approach and Management of Small-Scale Fisheries.” _Marine Policy_ 25 (5), 377–88.

- Basurto, X., A. Bennett, A. Weaver, S. Rodriguez-Van Dyck, and J.-S. Aceves-Bueno. 2013. “Cooperative and Noncooperative Strategies for Small-Scale Fisheries’ Self-Governance in the Globalization Era: Implications for Conservation.” _Ecol. Soc._ 18 (38).

- Basurto, X., and E. Coleman. 2010. “Institutional and Ecological Interplay for Successful SelfGovernance of Community-Based Fisheries.” _Ecological Economics_ 69 (5), 1094–103.

- Branch, T. A., O. P. Jensen, D. Ricard, Y. Ye, and R. Hilborn. 2011. “Contrasting Global Trends in Marine Fishery Status Obtained from Catches and from Stock Assessments.” _Conservation Biology_ 25 (4), 777–86.

- Burgess, R., M. Hansen, B. A. Olken, P. Potapov, and S. Sieber. 2012. “The Political Economy of Deforestation in the Tropics.” _Quarterly Journal of Economics_ 1707–54.

- Calderon-Aguilera, L., S. Marinone, and E. Aragón-Noriega. 2003. “Influence of Oceanographic Processes on the Early Life Stages of the Blue Shrimp (Litopenaeus stylirostris) in the Upper Gulf of California.” _Journal of Marine Systems_ 39 (1), 117–28.

- Carson, R. T., C. Granger, J. Jackson, and W. Schlenker. 2009. “Fisheries Management under Cyclical Population Dynamics.” _Environmental and Resource Economics_ 42 (3), 379–410.

- Clark, C. W. 1990. _Mathematical Bioeconomics: Optimal Management of Renewable Resources_ , Hoboken, NJ: John Wiley and Sons Inc.

- Costello, C., S. D. Gaines, and J. Lynham. 2008. “Can Catch Shares Prevent Fisheries Collapse?” _Science_ 321 (5896), 1678–81.

- Costello, C. and D. T. Kaffine. 2010), “Marine protected areas in spatial property-rights fisheries.” _Australian Journal of Agricultural and Resource Economics_ 54 (3), 321–41.

30. For details, see http://www.conapesca.sagarpa.gob.mx/wb/cona/programa_nacional_de_inspeccion_vigilancia_ 3
47 Economic Parameters of Deforestation <!-- Start of picture text --> limber TaxILEB Case<br>Timber Price Path<br>Puce<br>...........<br>dCb<br>'~~~~~~~~Ln Llsei<br>Larid CLwLan 11 lL1 11~~~~~*11 Us<br>Thun<br>0 Agricurre Managed Forest U Unmanaged Forest<br>bck Iim idd ft bm cae ld urns rxcw.iuf<br><!-- End of picture text --> Figure 6
enhance national sample surveys. Annex 3 shows empowerment themes and strategies in selected Poverty Reduction Strategy Papers, and national-level empowerment indicators are presented in annex 4. A preliminary survey module comprises annex 5.

# 1. A Framework for Understanding and Measuring Empowerment

If a person or group is empowered, they possess the capacity to make _effective_ choices; that is, to translate their choices into desired actions and outcomes. In a five-country study on measuring empowerment currently overseen by the World Bank’s empowerment team, it is assumed that degrees of empowerment can be measured. As figure 1 illustrates, this capacity to make an effective choice is primarily influenced by two sets of factors: agency and opportunity structure.<sup>4</sup> Agency is defined as an actor’s ability to make meaningful choices; that is, the actor is able to envisage options and make a choice.<sup>5</sup> Opportunity structure is defined as the formal and informal contexts within which actors operate. Working together, these factors give rise to different degrees of empowerment. For example, a farmer in India chose to take out a Bank loan to finance a lift irrigation system, but the process for obtaining the loan required that he—an illiterate person—complete 20 forms, offer all his land as collateral, and obtain a lawyer to verify that he owned title to the land. The farmer’s choice was well informed and economically viable, but the opportunity structure—in this case the regulations concerning procurement—was an obstacle in his ability to make his choice effective.<sup>6</sup> Figure 1. The Relationship between Outcomes and Correlates of Empowerment <!-- Start of picture text --> A g e n c y<br>D e g re e o f D e v e lo p m e n t<br>E m p o w e rm e n t O u tc o m e s<br>O p p o rtu n ity<br>S tru c tu re<br><!-- End of picture text --> 4 Among other authors, this framework and its subsequent development owe much to the work of Bennett (2003); Kabeer (1999); Krishna (2003); Malhotra et al. (2002); Sen (1985) and (1992); and Smulovitz, Walton, and Petesch (2003). Readers are referred in particular to the Bennett and Smulovitz papers for a discussion of the theoretical underpinnings of this framework.

5 This understanding of agency is similar to Appudarai’s (2001) concept of the “capacity to aspire,” defined as the ability to express preferences and make choices that are associated with leading a good life. Nussbaum (2001) argues that people’s preferences can be manipulated by tradition or intimidation. Sen (1992) also argues that the consciousness of the less powerful can be manipulated to the extent that they “accept the legitimacy of the unequal order.” 6 Srijan (1999).

6
# **Diversity and quality of place**

In addition to technology and talent, existing creative centers rank high on quality of place, which refers to a unique set of local characteristics – attractive natural and built environments, diverse range of people, and vibrant street life. These places do not just provide one thing, but a range of options. In particular, cities rank high on diversity (or multiculturalism) and tolerance tend to attract more creative people who have always gravitated to certain kinds of places (Florida 2002).<sup>24</sup> Creative cities are places where outsiders can enter and feel a certain state of ambiguity. They ought to neither be excluded nor be so easily embraced that their creative drive is lost. Creative cities also tend be places in flux, where new socioeconomic and ethnic groups are defining and asserting themselves.<sup>25</sup> The social environment is stable enough to allow continuity, yet diverse enough to nourish creativity in all forms (Florida 2002, Hall 2000).<sup>26</sup> Surveys in the U.S. find that knowledge workers want urban amenities such as outdoor dining, walking streets, vibrant night life, and river walks combined with outdoor recreation activities including urban kayaking, rock climbing and bike trails (CEOs for Cities 2000, IntelliQuest 1999). Within cities the focus of the recent wave of creative firms is often on the downtown, as opposed to more typical suburban office park locations. This cuts across clusters, including software in Austin, biotech R&D in San Diego and Seattle, publishing and fashion in New York, and video games in Tokyo. Typically, the downtown offers historic as well as modern structures, excellent public transportation, a mix of restaurants, a vibrant music/art/entertainment scene, and diverse residential neighborhoods. There is a variety of foot traffic, wide sidewalks, and different types of buildings (Florida 2002, Sommers and Calson 2000).<sup>27</sup>

> 24 Florida (2002) shows that the key values of creative people include individuality, meritocracy, diversity, and openness.

> 25 There is evidence that highly conservative, very stable societies are not likely to be creative places; neither will be places in which all sense of order has disappeared.  See Hall (2000).  Historically it appears that highly creative cities have been those in which an old, established order was being challenged or overthrown, such as Vienna in 1900 and Berlin in 1920.

> 26 To measure diversity, Florida (2002) has developed a composite index that adds together the Gay Index (concentration of gay people), Melting Pot Index (relative percentage of foreign-born people), and Bohemian Index (concentration of artistically creative people).

> 27 These life-style preferences also are influencing the way architects and developers design and build the next generation of offices for creative and high-tech firms.  Sidewalks, pedestrian streets, and entertainment facilities are being emphasized.  See Sommers and Calson 2000.

17
|TableA5. Gender Pr|ovisions, GVCs andFem<br>(a) All<br>|ale Non-Produ<br>regions<br>|ctionWorker<br>(b) M<br>|s<br>ENA<br>|
|---|---|---|---|---|
||GVC1|GVC4|GVC1|GVC4|
|(1)GVC|0.229***|0.448***|0.358***|0.096|
||(0.0296)|(0.0541)|(0.088)|(0.229)|
|(2)Gender Provisions|-0.121***|-0.113***|0.026|0.010|
||(0.0250)|(0.0253)|(0.045)|(0.051)|
|GVC*Gender Prov.|2.24e-05|-4.01e-05|-0.020|0.074*|
||(0.000429)|(0.000829)|(0.022)|(0.056)|
|Country dummies|Yes|Yes|No|No|
|Year dummies|Yes|Yes|Yes|Yes|
|Sector dummies|Yes|Yes|Yes|Yes|
|Observations|41,356|41,356|6,864|6,864|
|R-squared|0.538|0.537|0.469|0.466|

Robust standard errors in parentheses, clustered by country and year, *** p<0.01, ** p<0.05, * p<0.1 Note: - We control for firm’s age, size, main city and the share of government ownership. - The intercept is included.

# Table A6. IV Approach – First <u>Stage</u>

||(a) All<br>|regions<br>|(b) MEN<br>|A region<br>|
|---|---|---|---|---|
|Variables|GVC1|GVC4|GVC1|GVC4|
|IV:Shift_Share_GVC|0.481***|0.227***|0.404***|0.168***|
||(0.008)|(0.010)|(0.022)|(0.026)|
|Ln(Age)|0.015***|-0.001|0.001|-0.007***|
||(0.002)|(0.001)|(0.006)|(0.002)|
|Medium|0.084***|0.012***|0.092***|0.007**|
||(0.003)|(0.001)|(0.007)|(0.003)|
|Large|0.251***|0.073***|0.306***|0.056***|
||(0.004)|(0.001)|(0.010)|(0.003)|
|Main city|0.010***|-0.0001|0.015**|0.002|
||(0.003)|(0.001)|(0.008)|(0.003)|
|Ln(Gov own)|0.013***|0.002|0.012|0.005|
||(0.003)|(0.001)|(0.010)|(0.004)|
|Country dummies|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|
|Sector dummies|Yes|Yes|Yes|Yes|
|Observations|82,937|82,937|11,610|11,610|
|Underidentification test P-Val|0.000|0.000|0.000|0.000|
|Cragg-DonaldWald F-statistic|3849.44|518.03|334.91|41.36|

Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: - The intercept is included.

- The minimum Eigenvalue is higher than all the critical values at 10%.

36
This instrumented _θ_ elasticity is at the lower end of the estimates provided in the literature. Costinot et al. (2012) is the closest comparison given their use of industry-level regressions of productivity data and trade. Using a cross-sectional analysis of producer price data for OECD countries in 1997, they derive their preferred estimate of 6.5, which is substantially larger than this study’s estimate of about two. On the other hand, Costinot et al. (2012) derive a quite similar elasticity of 2.7 when they consider labor productivity metrics, the metric considered here. They too have IV estimates that are considerably larger than OLS estimates. More broadly, Eaton and Kortum (2002) provide larger initial estimates of the θ elasticity, with a preferred estimate in the range of eight. Simonovska and Waugh (2014a) revisit these results with a new estimator and come to a preferred estimate of about four. Overall, this study’s estimates are again lower than this connected work. The robustness checks described below find _θ_ estimates that continue in this ballpark, never exceeding four. Thus, this empirical approach consistently derives _θ_ estimates that are among the lowest in the literature, with some part of this difference due to methodology but an important part being substantive in interpretation.

It is important to identify the dual meaning of the higher IV results compared to OLS with respect to the _θ_ parameter. In the Ricardian model, a higher _θ_ parameter corresponds to a reduced scope for intraindustry trade due to comparative advantages across varieties. IV estimations thus suggest that OLS specifications overestimate the scope for intraindustry trade because they understate the link between country-industry productivity improvements and their associated export volumes. Both impetuses can be connected to Ricardian theories of comparative advantage for trade, but the role of the structural _θ_ parameter needs to be carefully delineated. This partitioning can also have important consequences for views of development and export success. Compared to OLS, the IV results shift more emphasis toward fundamental country-industry productivity improvements rather than intraindustry varieties; yet, on the whole, the overall work in this paper with emerging economies provides more support for intraindustry varieties than typically found in the Ricardian literature that has focused mostly on OECD trade flows (as evidenced by the lower _θ_ estimates compared to prior studies).

These estimates are significant in terms of their potential economic importance and explanatory power. Using an elasticity of two, the interquartile range of country-level
**Table 5: Best and Worst Typology Description for Selected Countries**

|**Country**|**Type**|**Description**|**Sample**<br>**Share**|**Per-Cap**<br>**Consumptio**<br>|
|---|---|---|---|---|
|||||**n**|
|**Nii**|Best|Birth region = South South, South West, Other Country<br>Father Education = Tertiary<br>Mother Education = Tertiary|.02|7.6|
|**gera**|Worst|Birth region = North East, North West<br>Religion = Islam, Traditional, Other<br>Birth urban-rural = Rural|.29|2.5|
||Best|Father education = Tertiary<br>Birth Region = Addis, Amhara, Dire Dawa, Harari,<br>Oromia, Other Country|.03|8|
|**Ethiopia**|Worst|Father education = None<br>Birth Region = Afar, Gambela, Somali<br>Father Industry = Agriculture<br>Religion = Catholic, Protestant, Traditional|.03|2.6|
||Best|Birth region = Dar es Salaam, Arusha, Kilimanjaro, Other<br>Country<br>Mother education = Secondaryor more|.01|11.9|
|**Tanzania**|Worst|Birth region = Geita, Katavi, Kigoma, Lindi, Mwanza,<br>Rukwa, Shinyanga, Simiyu, Songwe, Tabora<br>Father education = Primary or less<br>Mother education = None|.23|2.4|
||Best|Race = White<br>Mother Education = Tertiary|.01|80.7|
|**South**<br>**Africa**|Worst|Race = African, Colored<br>Mother education = Secondary or less<br>Birth Region = Eastern Cape, KwaZulu-Natal,<br>Mpumalanga|.45|6.3|
||Best|Mother education = Secondary or more<br>Father education = Tertiary|.02|14.2|
|**Uganda**|Worst|Mother education = Primary or less<br>Ethnicity = Bagisu, Bakiga, Iteso, Langbi, Lugbara, Other<br>Birth Region = Eastern, Northern|.37|2.8|

Per-capita consumption is measured in USD (2017 PPP)

# Section 5: Comparison with Other Regions

In this paper, we have shown that IOp in SSA is higher than previously thought. In this section we place those findings in the global context. It is difficult to compare IOp in SSA with IOp other regions because of differences in methodology and circumstances used in existing global estimates. Nevertheless, Figure 4 shows that IOp in SSA is broadly in the same range as in Latin America and South Asia, and notably higher than in Europe and other high-income economies.

16
This model treats the farmer as though he is an income maximizing entity. Although this paradigm clearly fits large farms that act as firms, there are two issues that must be addressed for the model to fit smaller household farms. First, households use their own labor and there is no observed wage for household labor. Although one might at first assume household labor and hired labor can easily substitute for each other, empirical evidence suggests that the implicit wage rate for household labor is less than the wage for hired workers. It is not obvious what wage rate to assign to household labor. Second, households often consume a large fraction of their output. In this study, we assume that own consumption should be valued at the prevailing market prices. When households consume only a fraction of their output, this is not a strong assumption. However, when households consume all of their output, one cannot be certain that they actually valued the output at market price. For example, a remote household far from markets may place a higher price on the output than what they would get at the market.

The model is precise in the vicinity of the sample data, for example, valuing marginal changes. However, if climate changes a great deal and this leads to a dramatic change in the livestock sector, prices will be likely to change (Cline 1996). The Ricardian model will overstate welfare changes if there are large price changes.

# **4. Data and empirical model**

This study is based on a survey of farmers in ten countries across Africa: Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South Africa, and Zambia (Dinar et al. 2006). Data from Zimbabwe, the eleventh country in the survey, had to be dropped because of erratic responses from farmers due to tumultuous conditions in that country in the year of the survey. We define a region called ‘West Africa’ that includes Burkina Faso, Cameroon, Ghana, Niger and Senegal.

Districts were selected to obtain a wide range of climates within each country. Districts that could not support any agriculture (such as deserts) were not surveyed. In each country, 15–30 districts were selected. Within each district, 20–30 households were interviewed in clusters. Cluster sampling was done to control the cost of the survey.

The survey records information on the number of livestock, transactions, livestock products and related costs. The eight major livestock sold are cattle for meat, cattle for milk, breeding bulls, 9