rafmacalaba/datause-encoder-data huggingface dataset:
investment in tractors, introduced at a subsidized rate in the country. The elasticities of all inputs except labor are increasing as indicated by positive trend coefficients. The return to scale (the inverse of the output elasticity) is 1.10 and increasing.

The elasticities of soil quality parameters were estimated at the regional mean values of input prices, output level, and technology variables. They are as expected. For example, organic matter and phosphorus have negative elasticities in all the systems, while total soluble salts, an indication of salinity, has a positive sign in the wheat-rice and wheat-mixed systems, confirming results of a recent farm-level study, that soil salinity is an important constraint on crop yields in northern Punjab (Siddiq, 1994). Of the two water-quality variables only electroconductivity produced a consistent and significant coefficient, indicating that soil salinity is in part due to the application of poor quality tubewell water.'<sup>2</sup>

> 12 The residual sodium carbonate of tubewell water was found to be highly correlated with electroconductivity and was deleted from the final equation.

18
has been neither uniform nor stable under the new globalization landscape [World Bank (2007)]. Although much more integrated, the new landscape remains skewed, partly as a result of a significant decline in the contribution of Sub-Saharan African countries to global output growth and exports over the last decades of sustained economic growth and boom in international trade.

In contrast, the remarkable gains achieved by emerging markets economies, particularly in Asia where capital accumulation was strong and economic growth rates were sustained, markedly increased the contribution of these economies to global trade [Yusuf and Nabashima (2007), IMF (2007)]. And in a zero sum game framework, the anemic growth and high volatility recorded in most countries in Sub-Saharan Africa, where investment rates fell dramatically, resulted in a significant decline of Sub-Saharan Africa’s contribution to global trade [Akyuz and Gore (2001)]. This declining contribution reached an all-time low level of 0.6 percent in 2007, and is in sharp contrast to the dramatic increase of East Asia’s overall share (see Table 1).<sup>13</sup> Although significant, the declining share of Sub-Saharan Africa is not an exception; a similar downward trend is also apparent in a number of other regions, albeit on a less pronounced basis. The regional contribution to global trade for a number of regional trading blocs over the last three decades also highlights the changing globalization landscape. In particular, the sustained output growth and total factor productivity gains achieved by emerging market economies dramatically altered the global distribution of trade, with the contribution of the East Asia region rising to over 20 percent.

**Table 1: Regional shares in global trade (in percentage of global trade)**

||**1975**|**1980**|**1985**|**1990**|**1995**|**2000**|**2005**|**2007**|
|---|---|---|---|---|---|---|---|---|
|**Euro Area**|31.3|31.3|30.2|35.5|32.7|28.5|29.3|29.3|
|**North America**|15.9|14.6|16.8|15.8|15.9|17.7|13.4|12.7|
|**USA**|12.3|11.4|12.5|12.4|12.4|13.5|10.1|9.7|
|**East Asia (incl. China)**|1.97|4.0|7.46|8.67|12.75|14.7|16.9|20.1|
|**Sub-Saharan Africa (SSA)**|**1.5**|**3.4**|**2.4**|**1.8**|**1.32**|**1.34**|**1.59**|**0.64**|
|**SSA (excl. South Africa)**|0.53|2.22|1.64|1.17|0.78|0.87|1.07|0.10|

Source: World Bank World Development Indicators.

Even the growing demands of primary commodities and raw materials from Sub-Saharan Africa in support of industrial output expansion in advanced and emerging market economies (in response to rising global demands) did not prevent the continued erosion and declining contribution of African countries to global trade. Worst of all, Sub-Saharan Africa’s declining share of global trade is mirrored by a concomitant drop in its contribution to overall economic growth in Figure 2. Although already abysmally low—less than 4 percent in 1985, this contribution declined even further.

> 13 And when South Africa is excluded, the Sub-Saharan Africa’s contribution to global trade is even lower, approximately 0.1% in 2007.

7
_of a financial nature offered by a financial service supplier_<sup>_14_</sup> _of a Member. Financial services include all insurance and insurance-related services, and all banking and other financial services (excluding insurance)_ ” (see Appendix I for a detailed description). The Annex specifically excludes “ _(i) activities conducted by a central bank or monetary authority or by any other public entity in pursuit of monetary or exchange rate policies; (ii) activities forming part of a statutory system of social security or public retirement plan; and (iii) other activities conducted by a public entity for the account or with the guarantee or using the financial resources of the Government”_<sup>15</sup> . This implies that macroeconomic management and a potentially significant part of the financial sector (e.g. development banks, mandatory pension funds that form part of the social security system in many LCR countries etc.) are not subject to WTO disciplines.

**Table 1: GATS Framework on Financial Services**

<!-- Start of picture text --> GATS agreement (general provisions on services)<br>Modes of supply<br>• Cross-border supply<br>• Consumption abroad<br>• Commercial presence<br>• Temporary presence of natural persons<br>General obligations and disciplines<br>• Most-favored-nation (MFN) treatment<br>• Transparency<br>• Recognition of services suppliers<br>• Restrictions to safeguard the balance of payments<br>Specific commitments<br>• Market access<br>• National treatment<br>• Additional commitments<br>Consultation, dispute settlement and enforcement<br>Financial Services Annex (FSA)<br>Coverage and definition of financial services<br>Prudential carve-out<br>Financial services expertise in dispute settlement<br>Recognition of prudential measures<br>Schedules of Commitments<br>Hybrid list approach<br>Scheduling by sector/sub-sector and by mode of supply<br>Market access and national treatment limitations<br><!-- End of picture text --> _Source: Adapted from Key S. (1997)._

> 14 A financial service supplier is defined as “ _any natural or juridical person of a Member wishing to supply or supplying financial services_ ”, excluding public entities.

15 If a Member allows any of activities (ii) or (iii) to be conducted by its financial service suppliers in competition with a public entity or a financial services supplier, “services” shall include these activities.

9
||Pre-Treatment: Talks<br>with Cz|Post Treatment|Diference|
|---|---|---|---|
|Slovakia|0.222346|0.261852|0.03951|
|Czech Republic|0.240419|0.264245|0.02383|
|Diference|0.018073|0.00239|-0.01568|

Table 1: Difference-in-Difference of GER at time _t_ (Treatment 1: The Opening of Accession Negotiations with the Czech Republic)

||Pre-Treatment:|Post Treatment|Diference|
|---|---|---|---|
||Talks with Slovakia|||
|Slovakia|0.261852|0.303742|0.03951|
|Czech Republic|0.264245|0.308823|0.04458|
|Diference|0.00239|0.00508|-0.00269|

Table 2: Difference-in-Difference of GER at time _t_ (Treatment 2: The Opening of Accession Negotiations with the Slovakia) Slovakia increases by nearly 280% of its pre-treatment difference (Table 3).

||Pre-Treatment:|Post Treatment|Diference|
|---|---|---|---|
||Talks with Cz (t+3)|||
|Slovakia|0.287256|0.323255|0.035999|
|Czech Republic|0.294225|0.349701|0.055476|
|Diference|0.006969|0.026446|0.019477|

Table 3: Difference-in-Difference of GER at time _t_ (Treatment 1: The Opening of Accession Negotiations with the Czech Republic (t+3)) Similarly it can be seen from Table 4 that in the last observations the Slovak GER begins to rise at a greater rate than the Czech GER thereby beginning to close the difference. This finding supports the hypothesis that the widening difference-in-difference was a result of the disparate timings of the opening of accession negotiations.

However, whilst the lack of immediate impact can be justified by considering risk-averse agents, two concerns remain. Firstly, it is important to bear in mind that Slovakia does not represent a perfect counterfactual to the Czech Republic. From 1993 the two countries separated and became subject to separate policies 19
# **Table 1: Main regression results**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
||GE|RQ|RL|CC|CPI|ECI|
|Cabinet size|-0.143***|-0.103|-0.167***|-0.153***|-0.198**|-0.228|
||(0.051)|(0.063)|(0.053)|(0.053)|(0.081)|(0.179)|
|Polity|0.137|0.244|0.416**|0.039|-0.171||
||(0.200)|(0.176)|(0.164)|(0.140)|(0.179)||
|Electoral democracy||||||13.353<br>(11.674)|
|Left|-3.044**|-0.861|-2.441*|-3.256**|-2.162**|-1.601|
||(1.249)|(1.355)|(1.403)|(1.227)|(1.031)|(1.950)|
|Government fractionalization|-2.751|-1.724|-0.703|-1.500|-0.618|3.603|
||(2.553)|(1.973)|(1.746)|(1.772)|(1.772)|(4.748)|
|Programmatic parties|1.393|0.976|1.265|0.639|-0.478|0.883|
||(1.546)|(1.199)|(1.231)|(1.368)|(2.017)|(4.086)|
|GDP per capita|8.476*|12.307***|8.101**|6.881**|12.880***|6.882|
||(4.258)|(3.750)|(3.537)|(2.744)|(2.479)|(5.385)|
|Military|0.574|0.342|-0.202|0.484|-4.576***|-1.899|
||(1.218)|(1.146)|(1.069)|(0.735)|(1.024)|(3.729)|
|Defense minister|-0.099|0.633|1.559|0.503|-2.120*|1.306|
||(1.265)|(1.254)|(1.151)|(0.579)|(1.106)|(1.907)|
|Years in office|0.088|0.137**|0.075|-0.069|0.030|-0.200|
||(0.067)|(0.065)|(0.057)|(0.057)|(0.056)|(0.129)|
|Years to election|0.095|0.096|0.050|0.061|0.060|-0.025|
||(0.071)|(0.085)|(0.086)|(0.086)|(0.123)|(0.177)|
|Country FEs|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FEs|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|560|561|561|561|480|1,017|
|R-squared (within)|0.213|0.237|0.212|0.152|0.447|0.151|
|Countries|40|40|40|40|40|40|

Notes: Standard errors clustered by country in parentheses. Dependent variables are from the September 2018 update of the Worldwide Governance Indicators (estimates of GE = Government Effectiveness; RQ = Regulatory Quality; RL = Rule of Law; CC = Control of Corruption), various editions of the Corruption Perceptions Index (CPI), and the July 2018 version of the V-Dem dataset (ECI = Executive Corruption Index, reversed). See data appendix for full variable definitions and sources. *** p < 0.01, ** p < 0.05, * p < 0.1.

11
finding of Long and Leeds (2006) who claim that military alliances that are not linked to any specific issue should not be expected to increase cooperation in terms of that issue. (Long and Leeds considered economic issue linkage and impacts on dyadic trade.) Our results for the power and wealth asymmetries suggest that power asymmetries negatively affect cooperation. This is in line with some of the international negotiation literature, which suggests that power parity is more conducive to cooperation because it reduces mistrust and the fear of cheating, which is more common in asymmetric situations (Rubin and Brown 1975). Song and Whittington (2004), who explore the impacts of power asymmetries (measured as GDP) on cooperation, find that in situations of overwhelming power asymmetry cooperation is negatively affected. Differences in wealth did not seem to make a significant difference. Since wealth asymmetry proxies for the ability of richer states to provide financial incentives to poorer states so as to induce cooperation, the result may not be surprising. A country providing sidepayments may be regarded as a weak negotiator and may therefore prefer not to invite such a reputation (Maler 1990: 86). Furthermore, the anticipation of side-payments may provide incentives for strategic behavior by the party that can extract large compensation packages (Maler 1990: 85). Similarly, by providing payment, say, for incentivizing another country to abate pollution, the country providing compensation effectively admits some sort of responsibility for the pollution control even though it was not the main culprit. According to Dombrowsky (2007), side-payments are particularly problematic in international water negotiations as long as the underlying property rights regime is disputed.

# <u>Conclusions</u>

Our analysis is couched in recent empirical research, which investigates the general role of treaties in reducing conflict (Tir and Stinnett, 2012; Brochmann, 2012). Our investigation also complements studies that consider the effectiveness of treaties given their design (Dinar et al. 2015; Mitchell and Zawahri 2015; Tir and Stinnett 2011, 2012).

While most basins have no recorded instances of either cooperation or conflict in the BAR database, among those that do, the mere presence of a treaty seems to promote additional cooperation (or reduce conflict) beyond the signing of the treaty itself. Furthermore, the existence of several agreements signed between riparians in a given dyad seems to increase cooperation even 18
# **VII Discussion**

Peru’s temporary spatial closures policy attempts to reduce juvenile catch by temporarily banning fishing in the places with the highest relative abundance of juvenile anchoveta. While there is a (noisy) decrease in juvenile catch inside closed areas during closure periods, there are large increases in juvenile catch inside closed areas between the announcement and the beginning of closures, just outside closed areas during closures periods, and inside closed areas one and two days after closures end. On net, this policy worsens the target outcome, increasing total juvenile catch by 48%.

The failure of this policy to achieve its objective is not due to a failure of targeting. Even though the regulator does not use length distribution data to determine closures, they still succeed in closing areas where juveniles are abundant. In the 9 to 12 hours before the beginning of a closure, 47% of individuals caught inside the soon-to-be closed area are juveniles, higher than in any other treatment bin and much higher than the 9% juvenile caught outside the treatment window (Figure C2). Furthermore, within the support of the data, larger and longer closures offer no improvement (Appendix D.1).<sup>34</sup> I illustrate the short- and long-run costs of the temporary spatial closures policy with two simulations. First, I reproduce the method of Salvatteci and Mendo (2005), who estimate the cost of juvenile catch by comparing status quo landings to a counterfactual where juveniles make up a smaller fraction of individuals landed. I similarly project forward the length distribution of individuals caught during my study period until counterfactual juvenile catch is 32% lower than status quo juvenile catch (equivalently, until status quo juvenile catch is 48% higher than counterfactual juvenile catch). Status quo tons landed are 2.1% lower than in my counterfactual projection because anchoveta growth exceeds natural mortality within the support of the data. FOB export revenues were $1.79 billion in 2017 (PRODUCE, 2018 _a_ ). I therefore calculate that the closures policy reduces exports by $75 million per year

> outcome variable and control variables using third-party inspector data.

> 34Larger closures could cause larger increases in juvenile catch because congestion costs are lower or if larger closures are a stronger positive signal of fishing productivity.

44
**IV. PREDICTING ECONOMIC ACTIVITY**

First, we use the specification of Henderson et al. (2012) to estimate the elasticity of GDP with respect to nightlight intensity in the world and in South Asia: where ln(GDPc,t) is the natural logarithm of GDP of country _c_ in year _t_ measured in constant local currency, ln(lintensityc,t) is the natural logarithm of light brightness per km<sup>2</sup> , bc is a country fixed effect and ct is a year fixed effect. Country fixed effects control for cultural differences across countries, e.g. varying nightlight intensity for the same economic activity, different population densities, and economic factors like the composition of output or electricity generation capacity. Year fixed effects control for changes in light intensity of economic activity that could originate from changing technology, aging of satellites, different sensitivity across satellites, changes in economic conditions worldwide, or energy costs. Elasticity of true GDP with respect to light intensity is obtained from within-country relative variation in light and income over time by relating annual changes in light intensity within countries to annual changes in measured income. The coefficient δ maps the growth in nightlight intensity into a proxy for economic activity. This allows us to analyze whether nightlight intensity is in fact a good indicator for economic activity in South Asia. It also gives us an annualized monthly indicator of economic activity at the country level if monthly nightlight data is used instead of annual series.

Second, we use a spatial approach to arrive at a measure of district level economic activity. The spatial approach uses nightlight and rural population to distribute predicted (or measured) national GDP across districts. To account for the weak relationship between nightlight intensity and economic activity in the agricultural sector, which we will explore below, we first split aggregate GDP into 7
|Cambodia|
|---|
|Lao PDR|
|Lesotho|
|Ethiopia|
|Sri Lanka|
|Tanzania|
|Southeast Asia|
|Rest of Africa|
|Rest of the world|

# **_Baseline_**

The GTAP database is benchmarked to 2011. We run the model to 2018, replicating the key macroeconomic aggregates from the World Bank’s _Global Economic Prospects_ (GEP 2016)<sup>[3]</sup> report. Population growth is based on the medium fertility variant of the 2012 UN’s population projections. Labor force growth follows the growth of the working age population – defined here as the demographic cohort between 15 and 64 years of age. The evolution of supply of skilled and unskilled workers is consistent with the IIASA constant educational trends (CET) scenario, where growth rates of the supply of skilled workers exceed that of unskilled. Capital accumulation is equated to the previous period’s (depreciated) capital stock plus investment. Productivity growth in the baseline is “calibrated” to achieve the growth rates for the baseline scenario (as in the GEP (2016)) up to 2018, then we fix the productivity growth for 2018-2030 to be equal to its 2017 value. These productivity growth rates remain fixed in the counterfactual scenarios. The baseline scenario also incorporates tariff and NTM reductions in existing FTAs. These are based on the data set provided by the International Trade Center, including all TPP members’ FTA commitments up to 2046 (MacMaps, 2016).

The congressional AGOA Extension and Enhancement Act of 2015 has created greater uncertainty for Lesotho’s exporters and their trading partners. The Act provides the United States with greater flexibility in reviewing countries on an ongoing basis, allowing the privileges to be either withdrawn or suspended throughout the calendar year. The possibility of quarterly reviews of Lesotho’s compliance with AGOA requirements could lead to lower orders from the United States depending on investors’ assessment of the likelihood of Lesotho retaining the AGOA privileges.

Potential changes to Lesotho’s preferential margin have informed our analysis of select scenarios. The first hypothetical scenario analyzes the potential impacts of the sudden loss of AGOA privileges by Lesotho in 2018. Further, Lesotho’s export competitiveness could be eroded by potential free trade agreements between the United States and Lesotho’s major competitors. As documented in Section 3.3, Lesotho enjoys a significant preferential margin in the US market without which it might not be able to compete. The second scenario looks at the impacts of a potential US-Vietnam FTA based on the tariff and NTM liberalization as negotiated under the Trans Pacific Partnership (TPP). Under this scenario, Vietnam is gaining duty-free access to the US market. Both events, the suspension of AGOA privileges and a USVietnam FTA, would result in increased competitive pressures on Lesotho. Both scenarios serve only illustrative purposes and should not be treated as indications of future developments. Finally, the last

> [3] For China, we replicate the growth projections of World Bank (2014).

14
# **Comment**

In the event of a political agreement that would harness the above contributions from developing countries, the operational challenge the regime will have to face is how to track and monitor the mitigation contribution of countries that operate with a variety of measurements. The issue is absent in the first commitment period of the Kyoto Protocol where the only metric is national absolute emissions with respect to 1990 levels. As can be seen, the various mitigation efforts intended by these countries for the post 2012 period have different metrics and different reference dates, making comparability a difficult task. And yet, measurement, reporting and verification (MRV) are critical elements of the regime, and have also been agreed to under the Bali Action Plan.

# **REFERENCES**

Baumert and Winkler 2005: Baumert, Kevin / Winkler, Harald, SD-PAMs and international climate agreements, in Bradley, R. / Baumert, K. / Pershing, J. (eds.), Growing in the Greenhouse: Protecting the Climate by Putting Development First, World Resource Institute, Washington (2005). Bodansky 2008: Bodanksy, Daniel, International Sectoral Agreements in a Post-2012 Climate Framework, Pew Center, accessed June 22, 2009. http://www.pewclimate.com/docUploads/International%20Sectoral%20Aggreements% 20in%20a%20Post-2012%20Climate%20Framework.pdf (2007). Bosi and Ellis 2005: Bosi, Martina / Jane Ellis, Exploring options for “sectoral crediting mechanisms”. Paris: Organization for Economic Co-operation and Develop-ment (OECD)/International Energy Agency (IEA), COM/ENV/EPOC/IEA/SLT (2005) 1 (2005). Brown et al 2004: Brown, Katrina / Adger, WN / Boyd, E. / Corbera-Elizalde, E. / Shackley, S., How do CDM projects contribute to sustainable development? Tyndall Centre for Climate Change Research, June 22, 2009, <u>http://www.tyndall.ac.uk/research/theme2/final_reports/it1_13.pdf</u> (2004) Cosbey et al 2005): Cosbey, Aaron / Parry, JE / Browne, J. / Babu,YD / Bhandari, P. / Drexhage, J. / Murphy, D.. Realizing the Development Dividend: Making the CDM Work for Developing Countries. Winnipeg, Manitoba: International Institute for Sustainable Development (2005). Egenhofer and Fujiwara 2008: Egenhofer, Christian / Fujiwara, N.. Global Sectoral Industry Approaches to Climate Change. The Way Forward, Center for European Policy Studies, Brussels (2008). Eliasch Review 2008: Eliasch, Johan, Climate Change: Financing Global Forests (2008).

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