rafmacalaba/datause-encoder-data huggingface dataset:
## <u>Protection contingent on circumstances as discipline</u>

In addition to the obligation to compensate and the ultimate threat of retaliation, Article XIX introduces a second source of discipline: conditions. Article XIX provides legal clearance only in instances where imports cause or threaten serious injury to domestic producers.

Some negotiators argued that the ‘injury’ criterion would provide little discipline. Import restrictions had been put in place to increase domestic production, removing them was indeed likely to displace it. Allowing new import restrictions when removal of old ones led to displacement of domestic production had the potential to undo a large part of the reductions. Negotiators consequently added the condition ‘as a result of unforeseen developments;’<sup>13</sup> so that the normal or expected results of a tariff reduction would not justify an increase.

## <u>Guidance for good policy</u>

Behind the inclusion of injury as a criterion for making an exception to a general policy of liberalization was a certain perception that the national economic interest would be advanced by applying this criterion. GATT Article XIX is almost word-for-word taken from the safeguard provision included in every pre-GATT trade agreement of the United States, and in US trade policy this safeguard clause was an expression of the ‘scientific tariff’ concept that for 150 years had dominated tariff thinking in the United States.<sup>14</sup> According to this thinking, the ‘optimal’ tariff rate on any product is one equal to the difference between the domestic and the foreign cost of production. In the US, safeguard investigations had replaced an older flexibility provision, ‘Section 336.’ Section 336 allowed the President to raise or to lower a tariff rate after an investigation by the US Tariff Commission to determine the domestic-foreign cost difference.<sup>15</sup> The early methodology for safeguard investigations was a close copy of the older Section 336 methodology. Non-injurious imports were, operationally speaking, imports that entered over a tariff equal to the difference between domestic and foreign costs.

Of course, modern economics does not recognize the cost-difference principle as identifying where a trade intervention would make economic sense.

# **Negotiated export restraints – reciprocity outside legal bounds**

By the mid 1970s, formal use of Article XIX and of the renegotiation process began to wane. Actions taken under the escape clause tended to involve

> 13 XIX.1(a)

> 14 Two good sources on historical US tariff policy and thinking are Stanwood (1903) and Hull (1946).

15 Over the instrument’s lifetime, 1931-1941, Section 336 investigations led to 29 tariff increases, 25 reductions and 47 decisions not to change the rate. (Finger and Harrison. 1996, 204).

9
Due to the high price of cocaine, by the late 1970s and beginning of the 1980s drug dealers discovered a new and cheaper alternative for low income users: crack, a rocky crystal that is obtained by mixing cocaine, baking soda, and water in a saucepan, and whose name derives from the crackling sound produced when the ingredients are being burned to smoke the resulting vapors (see Levitt and Dubner, 2005, and NIDA, 2005). Cocaine is the second most consumed illegal drug in the US (after marihuana) and the third in most European countries (after marihuana and heroin). Cocaine consumption triggers different physical effects. In moderate doses it causes disturbances in heart rates, elevated blood pressure, dilated pupils, decreased appetite, irritability and argumentative behavior, among others. In large doses it causes loss of coordination, collapse, blurred vision, dizziness, anxiety, heart attacks, chest pain, respiratory failure, strokes, seizures and headaches, abdominal pain, nausea, and paranoia.<sup>9</sup> The duration of the euphoric effect of cocaine (“the high”) depends on the route of administration. With faster absorption, the high is more intense, but does not last as long. When snorted, “the high” can last from 15 to 30 minutes; when smoked it can last from about 5 to 10 minutes (NIDA, 2006).

# **DATA SOURCES**

There are two main sources of data for illegal drug production, prices, extent of cultivation of illegal crops, seizures of drug shipments, etc. These are the United Nations Office on Drugs and Crime (UNODC) and the US government’s White House Office of Drug Control Policy (ONDCP). In

> 8 The stimulation produced by cocaine consumption comes from its interference with the reabsorption process of dopamine, which is a chemical messenger that is associated with pleasure and movement (National Institute of Drug Abuse – NIDA).

> 9 NIDA, 2006 and www.streetdrugs.org/cocaine.htm.

5
PPP and non-PPP, those differences mainly are confined to levels and do not affect growth trajectories.<sup>1</sup> All data are from _World Development Indicators_ , unless otherwise specified. The time-series spans the years 1975 to 2005. Our sample contains all sub-Saharan African countries for which PPP GDP data exist. For Liberia, San Tomé and Principe, and Somalia, there are no GDP per capita PPP data.<sup>2</sup> We have an unbalanced panel of data with 44 countries and 31 periods. The mean GDP per capita between 1975 and 2005, using unweighted data, was $2,306 for the 44 countries in our sample. The mean GDP using weighted data was $1,702. Unless stated otherwise, we used unweighted country data because we are interested in examining the representative country.<sup>3</sup> Along with descriptive statistics and kernel distributions, we analyzed the GDP data using bivariate and multivariate cross-country and pooled ordinary least squares (OLS) regression models and multivariate logit models. Because we are interested in identifying the long-run association between growth and other variables, rather than in modeling the determinants of growth, we are not concerned about omitted variables and problems associated with direction of causality, reverse causality, endogeneities, nonlinearities, and other potential econometric issues that usually plague growth regressions. We interpret our econometric results descriptively only.

# **3. Characteristics of Africa’s long run growth**

Figure 1a shows that mean GDP per capita had a slow, positive long-term trend, consisting of about 20 years of virtual stagnation with a point of inflexion in the early 1990s.<sup>4</sup> Since the mid-1990s, the variance of mean income per capita also appears to have declined. Weighting by GDP (figure 1b) gives a U-shaped pattern of GDP per capita, reaching a minimum in the mid-1990s. By 2005, GDP per capita had not yet recovered to the levels observed in mid-1970s.

> 1 Figure A1 and Table A1 in the Appendix show that PPP and non-PPP growth data share similar statistical properties.

> 2 Our sample accounted for 98.4% of population and 99% of regional GDP as of 2005. 3 Although Equatorial Guinea is in our sample, we removed the country from all charts, tables, econometrics, and aggregate descriptive statistics because its extremely high growth rates in recent years distort the results.

> 4 We employ the Hodrick-Prescott filter in Figures 1 and 2 to smooth the estimate of the long term trend component of the GDP series.

3
- 35 -

||(0.113)|(0.138)|(0.127)|
|---|---|---|---|
|ln_TV_|0.0096 ***|0.0094 **|0.0106 ***|
||(0.0036)|(0.0046)|(0.0036)|
|ln_Video_|0.0033 ***|0.0031 *|0.0035 **|
||(0.0010)|(0.0018)|(0.0015)|
|ln_Refrigerator_|0.0049 *|0.0055|0.0056 *|
||(0.0025)|(0.0038)|(0.0030)|
|ln_Freezer_|0.0022|0.0060|0.0047|
||(0.0045)|(0.0068)|(0.0060)|
|ln_Washer_|0.0065 ***|0.0061 **|0.0074 ***|
||(0.0015)|(0.0029)|(0.0022)|
|ln_Dishwasher_|0.0105 ***|0.0114|0.0113 **|
||(0.0033)|(0.0069)|(0.0048)|
|ln_Stove_|0.0046 ***|0.0043 *|0.0048 **|
||(0.0012)|(0.0024)|(0.0019)|
|ln_AirConditioner_|0.0120 ***|0.0119 **|0.0121 ***|
||(0.0018)|(0.0060)|(0.0028)|
|ln_Water Boiler_|0.0002|0.0012|0.0007|
||(0.0013)|(0.0024)|(0.0019)|
|ln_Computer_|0.0049 ***|0.0051|0.0052 **|
||(0.0016)|(0.0033)|(0.0026)|
|ln_PowerOutages_|–0.0019 *|–0.0017|–0.0022|
||(0.0011)|(0.0015)|(0.0018)|
|D(_Urban_)|0.203 ***|0.122 ***|0.201 ***|
||(0.027)|(0.051)|(0.034)|
|Constant|6.303 ***|4.592 ***|6.816 ***|
||(0.459)|(1.105)|(0.634)|
|Obs.|4678|4678|4678|
|Adj R-squared|0.396|0.204||
|_F_-statistics|54.20|||
|Pseud R-squared|||0.213|
|LR chi2|||1888.26|
|_σ_1<br>2||0.086||
|_σ_12<br>||–0.011||
|_σ_2<br>2|||0.518|
|Number of district dummies|35|35|35|

1/ In the Nelson-Olsen estimator, ln _MP_ and ln _D_ are replaced with the predicted values from the 1st stage regressions of the IV estimator. The block rates are dropped due to multicollinearity. 2/ ln _MP_ is dropped due to multicollinearity. Note: The dependent variable is the log of observed electricity consumption (kWh). The standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively.

# **VII. DISCUSSION**

One unexpected result is that weather conditions have no significant impact on the electricity demand. The weather-related variables tend to have insignificant coefficients, regardless of model and specification. A possible reason is that home appliance holdings might have
# **4. IMPLICATIONS FOR APPLYING THE MODEL**

Results in the previous section offer some guidance for applying the framework outlined in this paper to real world statistics. It was found that initial estimates of regional commodity trade flows based on survey data with very high statistical variability are highly preferable (in the experiments) to a widely used non-survey approach for producing initial estimates.<sup>11</sup> This finding holds promise for opportunities to use other survey data to recover unobserved regional economic accounts. It was also found that solving an aggregate account (e.g., a MRIO or MR-SAM) as an intermediate step is at least as accurate (in the experiments) as producing a direct solution to an extended account (e.g., IRIO or IR-SAM) when superior data unique to the later are not widely available. This finding is useful when working with regional economic accounts of considerable sector and region details. Results also support the _product mix_ approach, whereby the most feasible sector detail for regional gross output estimates are used to derive weighted average national technical coefficients for more aggregated regional sectors.

Statistical systems vary by nation and no one-size fits all rules exist that tell us how to seamlessly employ every data-system to best advantage.<sup>12</sup> However, there are general guidelines for implementing the optimization framework presented in this paper to a large dimension multiregional account. To facilitate discussions of implementation, we assume that a detailed national account always exists and regional sector statistics are also available in a variety of details. Then the implementation process may be classified into three broad phases as discussed below.

# **_Develop Independent Estimates for Major Components of a Multi-regional Account_**

It has been stressed as far back as Wilson (1970) that information used to produce parameters and initial estimates of a regional economic system should be estimated independently. While this produces unbalanced initial accounts, it avoids introducing spurious information that can lead to biased estimates (McDougall, 1999). A useful approach is to partition the multi-regional account into components that coincide or are related to known statistical survey series published regularly in the nation under study.

20
have different opportunities to generate income, it is unlikely that IDPs choose not to smooth their resources to balance between food and non‐food consumption in a way that endangers their life.<sup>4</sup> If it is the case that survey respondents misreport, the inaccuracies it generates in the data are highly problematic. At best, it makes the data spurious and unusable. At worst, it could lead to misallocations of aid, from more vulnerable areas to less vulnerable areas, or from solutions emphasizing sustainability to immediate relief where immediate relief is unnecessary. Due to the dangerous environment in South Sudan and Somalia, it is not currently possible to do use alternative data collection methods, for example ethnographic research, to investigate this puzzle in the data. The validity of alternative investigative methods such as food diaries is vulnerable to the same incentive to game as surveys.

One way to investigate whether people misreport is to test whether consumption rates change in response to nudges. If these primes are effective, they would be expected to particularly affect potentially underreporting, hence, poor households. Moreover, as vulnerable populations would have higher incentives to underreport, priming should be stronger for IDPs than for comparable non‐IDP populations. We find the primes induce higher reporting in lower quintiles of reported consumption. This treatment pattern is driven by aid reliant IDPs and vanishes when considering the comparison group of non‐IDPs. The results are especially strong for consumption quantities (items and kilograms), which are most easily subject to intentional misreporting. This suggests that IDPs are indeed misreporting. The paper has two main limitations. First, it can only compare the treated group against an estimate of the “true” consumption rates. Second, the intervention is bundled. For this reason, it is impossible to isolate the causal mechanism affecting the observed changes in reporting. Further work is needed to identify an estimate of the true level of consumption against which to compare the primed individuals and to isolate the causal mechanisms by which people are changing their behavior.

The paper proceeds as follows. Section 2 provides an overview about the underlying context and the compiled data. Section 3 provides an overview about the underlying methods, while Section 4 introduces the empirical approach, which builds the foundation for the results in Section 5. This is complemented by an assessment of robustness and potential channels in Section 6. Finally, findings are discussed and summarized in Section 7.

# 2. Context and data

On July 9 2011 South Sudan became the 55th African independent state after seceding peacefully from the Republic of Sudan. Facing a history of a 50 year lasting conflict South Sudan slid back to instability

> 4 The underlying survey data of this study discussed at a later stage actually indicates that IDPs have a more calorie intensive food consumption profile (refer

Figure 8).

4
using the generalized mixed logit GMNL estimator created by Hole (2013).

# **5. Results and Discussion**

Table 2 presents results for the main effects specifications analyzed using a conditional logit model (column 1), the main effects specification analyzed using an MMNL (column 2), the attribute interactions effects specification analyzed using an MMNL (column 3), the geographical interaction effects specification analyzed using an MMNL (column 4), and the respondent beliefs interaction effects analyzed using an MMNL (column 5). Table 3 identifies the presence of significant individual heterogeneity in the multinomial-logit setting (MMNL). As can be seen many of the variables exhibit individual heterogeneity and therefore it is necessary to account for this in the analysis by using an MMNL model.

The overall results from the five specifications indicate that the percentage of the payment going to the community, the duration of the contract, the required grazing reduction and the payment are all significant variables in determining the respondent s’ willingness to adopt REDD+ contracts. The significant coefficient results are robust across the econometric specifications and have expected signs. The significant results indicate the following:

1. As the percentage of the payment going to the household increases, respondents are more likely to choose that option;

2. As the duration of the contract increases, respondents are more likely to choose that option; 3. As the required grazing reduction increases, respondents are less likely to choose that option; 4. As the payment values (amount) increase, respondents are more likely to choose that option The results that include attribute interaction terms (column 3) indicate that the interaction term for the _firewood reduction_ variable and the _payment to the community_ is significant. Since 13
We amplify on these four models in more detailed analysis of the channels and robustness tests. Specifically, we expand model two by also relating cereal yield, a specific productivity measure, to undernourishment. And, we expand model three by relating financial sector development to other productivity measures, livestock production and crop and cereal yield.

The results in the basic four models could all be affected by endogeneity problems. At least in theory, in the first model, a reduction in undernourishment either directly or as a proxy for, say, a reduction in poverty, may stimulate demand for financial services, leading to reverse causality. In the second model, less undernourishment could translate into healthier, more productive workers, raising agricultural productivity. In the third model, higher productivity of workers could raise demand for financial services. And in the fourth model, use of productivity enhancing inputs can again lead to demand for financial services.

To alleviate these problems, we use an instrumental variables approach. We need two sets of instruments: one for private credit to GDP and one for agricultural productivity. To instrument for Private credit to GDP, we rely on the law and finance literature. This literature widely uses the legal origin of countries as an exogenous source of variation which is highly correlated with financial development measures, but not necessarily with undernourishment. This literature finds that property rights are better established in British common law countries and less so in Civil law countries (French, German, and Scandinavian origin) (see, for example, La Porta et al. 1997, 1998). These superior property rights facilitate financial contracting and translate into improved financial development. To instrument for agricultural productivity, we relay on fertilizer use in 100 grams per hectare of arable land and the number of tractors per agricultural worker. Arguably, these two variables are highly correlated with productivity but only indirectly with undernourishment.

We use two tests to validate our instruments: first we use the Hansen over-identifying restrictions test. This tests whether the instruments are associated with undernourishment 15
Bobonis, Gustavo, Edward Miguel and Charu Puri Sharma (2006) “Iron Deficiency Anemia and School Participation”, _Journal of Human Resources_ , 41(4), 692-721

- Box, George E.P., J. Stuart Hunter and William G. Hunter (2005) _Statistics for Experimenters: Design, Innovation and Discovery, Second Edition._ WileyInterscience, New Jersey.

- Burtless, Gary (1995) “The Case for Randomized Field Trials in Economic and Policy Research”, _The Journal of Economic Perspectives_ , 9(2): 63-84.

- Committee for Proprietary Medicinal Products (2003) “Points to consider on adjustment for baseline covariates” CPMP/EWP/2863/99 (http://www.emea.europa.eu/pdfs/human/ewp/286399en.pdf [accessed February 6, 2008]

- De Mel, Suresh, David McKenzie and Christopher Woodruff (2008) “Returns to capital: Results from a randomized experiment”, _Quarterly Journal of Economics_ , forthcoming.

- Duflo, Esther (2005) “Evaluating the Impact of Development Aid Programmes: The Role of Randomised Evaluations”, pp. 205-247 in _Development Aid: Why and How? Towards Strategies for Effectiveness_ . Proceedings of the AFD-EUDN Conference, 2004, Agence Française de Développement.

- Duflo, Esther, Rachel Glennerster, and Michael Kremer (2007) “Using Randomization in Development Economics: A Toolkit”, CEPR Working Paper No. 6059.

- Duflo, Esther, Rema Hanna and Stephen Ryan (2007) “Monitoring Works: Getting Teachers to Come to School”, Mimeo. MIT.

- Duflo, Esther and Michael Kremer (2004) “Use of Randomization in the Evaluation of Development Effectiveness” pp. 205-232 in Osvaldo Feinstein, Gregory K. Ingram and George K. Pitman, editors _Evaluating Development Effectiveness_ (World Bank Series on Evaluation and Development, Vol 7) Transaction Publishers: New Brunswick, NJ

- Dupas, Pascaline (2006) “Relative Risks and the Market for Sex: Teenagers, Sugar Daddies, and HIV in Kenya”, Mimeo. Dartmouth College

- Field, Erica and Rohini Pande (2008) “Repayment Frequency and Default in MicroFinance: Evidence from India”, _Journal of European Economic Association Papers and Proceedings_ , forthcoming.

- Glewwe, Paul, Albert Park and Meng Zhao (2006) “The Impact of Eyeglasses on the Academic Performance of Primary School Students: Evidence from a Randomized Trial in Rural China”, Mimeo. University of Minnesota.

- Glewwe, Paul, Michael Kremer, Sylvie Moulin and Eric Zitzewitz (2004) “Retrospective vs. prospective analyses of school inputs: the case of flip charts in Kenya”, _Journal of Development Economics_ 74: 251– 268

- Greevy, Robert, Bo Lu, Jeffrey H. Silver, and Paul Rosenbaum (2004). “Optimal multivariate matching before randomization”, _Biostatistics_ , 5: 263–275.

- He, Fang, Leigh Linden and Margaret MacLeod (2007) “Teaching What Teachers Don’t Know: An Assessment of the Pratham English Language Program”, Mimeo. Columbia University.

- Imai, Kosuke, Gary King and Clayton Nall (2007) “The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation”, Mimeo. Harvard.

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For any variable Y in the specification above (X, GDP and REER), growth rates are calculated as in Davis, Haltiwanger and Schuh (1996) based on the following: The data on exports come the U.S. Census Bureau Related Party Database as described above and cover U.S. goods exports for the period 2002-2014. Export values were deflated using data on the importer’s consumer price index (CPI) from the IMF’s International Financial Statistics. Data on real GDP comes from the World Bank’s World Development Indicators while bilateral real exchange rates were constructed using the importing country’s exchange rate against the U.S. dollar, deflated by the ratio between U.S. and importing country’s CPI.

The results of the econometric estimation are presented in Table 1 for the sample as a whole (1) as well as sub-samples that include exports of manufactures (2), exports to advanced economies (3) and to emerging markets (4). They suggest that arm’s-length exports are more sensitive to changes in demand and in real exchange rates.

The income elasticity of arm’s-length U.S. exports is significantly (about one-fifth) higher than that of intra-firm exports. This finding is robust to the use of different sub-samples. For instance, a 1 percentage point increase in the real GDP growth of the importing county is associated with 1.5 percentage point higher growth in arm’s length exports, but only 1.2 percentage point higher growth in intra-firm exports.

Similarly, a real exchange rate appreciation of 1 percentage point is associated with 3.1 percentage point lower growth in arm’s-length exports but only 2.8 percentage point lower growth in intra-firm export growth. This result is consistent with findings of Ahmed, Appendino and Ruta (2015) who show that rising participation in global value chains explains on average 40 percent of the fall in the real exchange rate elasticity of exports.

These result suggest that the higher elasticity of arm’s length trade compounded compositional effects that hindered arm’s-length trade over intra-firm trade. As a result of the greater income elasticity or arm’s-length trade, post-crisis weakness in global growth and, especially, the growth slowdown in EMDEs weighed more on arm’s-length trade than on trade.

14