rafmacalaba/datause-encoder-data huggingface dataset:
Table D.23. Heterogeneity by Baseline Distances to Road Network (1980) and Nearest City (1945)

|_Panel A: Heterogeneity by Dista_|_nce to Road_|_Network in 1980_||
|---|---|---|---|
||Arcsine|Wealth Index|Years of Education|
||(1)|(2)|(3)|
|Guerrilla control|-0.177***|-0.0953**|-0.272**|
||(0.0272)|(0.0399)|(0.125)|
|Control_×_Distance to Road|0.00503|-0.0267|0.0410|
||(0.0212)|(0.0230)|(0.0737)|
|Dependent mean|3.536|-0.0160|6.573|
|Observations|3,652|3,630|3,637|
|Bandwidth (Km)|2.266|2.266|2.266|
|_Panel B: Heterogeneity by Distan_|_ce to Neare_<br>|_st City in 1945_<br>||
||Arcsine|Wealth Index|Years of Education|
||(1)|(2)|(3)|
|Guerrilla control|-0.225***|-0.109***|-0.295**|
||(0.0307)|(0.0412)|(0.116)|
|Control_×_Distance to City|0.0375**|-0.00881|0.0187|
||(0.0149)|(0.0223)|(0.0642)|
|Dependent mean|3.536|-0.0160|6.573|
|Observations|3,652|3,630|3,637|
|Bandwidth (Km)|2.266|2.266|2.266|

_Note:_ The table presents the results from the heterogeneity analysis at baseline for the main outcomes. Panel A shows how the results vary by distance to a road network in 1980. Panel B presents heterogeneity of results by distance to the nearest city in 1945. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

92
2 between type of regime and inequality is included in the base regression, its coefficient is insignificant. This indicates, in contrast to Alesina and Rodrik (1991), that inequality has a similar effect on both democracies and non democracies. The paper is set up as follows: the second section of the paper discusses properties of the inequality data, including Kuznets' Inverted U hypothesis and simple correlations with other variables. The third section shows preliminary results from cross country regressions with inequality measures included. In the fourth section the robustness of the correlation between inequality and growth is tested using a variant of extreme bounds analysis. The final section discusses results and makes final comments.

# H. Properties of the Inequality Data

In order to include "inequality" in cross country growth regressions, the abstract concept of inequality needs to be quantified. Since there is no single universally accepted measure of inequality, various measures are constructed to test that results are not dependent on inequality being measured in a particular way. The measures are the coefficient of variation (COEFFVAR), Theils' index (THEIL), and the Gini coefficient (GINI). Additionally, the ratio of the share of total income earned by the poorest forty percent of the population to the share of total income earned by the richest twenty percent of the population, the measure used in both Alesina and Rodrik (1991) and Persson and Tabellini (1990), is computed.

The Gini Coefficient, probably the most common inequality measure, is derived from the Lorenz curve, a graphical device which represents inequality in a society. The Lorenz curve plots F(z), the share of population with income less than z, against +(z) the share of total income of people with income less than z. It is important to note that this curve must lie below the 45 degree line. For example, suppose _1_ F(z) =<sup>_1_</sup> _/h_ and that 4(z) > _/z,_ this would imply that the poorer half of the population earned more than half of total income, which therefore is more than the richer half could earn. The Gini coefficient is
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21
in its construction in terms of the data, and it should not be understood as equivalent to the official PMT. The adjustments include use of different years of SUSENAS to the current set, corresponding to data available when this project was initiated, as well as the simplification of the dataset. We eliminate a number of household demographic and work status interaction terms that were too numerous for the sequential approach proposed below, while offering only a marginal predictive gain. Despite these differences, the targeting metrics are of broadly comparable magnitude<sup>5</sup> , and we would not expect a change in the qualitative conclusions if the current PMT were used instead.

# **3 Methods**

We already identified accuracy, cost and verifiability as the three major design considerations of a statistical targeting method. An additional consideration for methods that require on-the-fly prediction in the resourceconstrained, often geographically remote setting of PMT deployment is that they need to be computationally feasible. The approaches proposed here meet this criterion by virtue of being relatively simple to implement and deploy on hardware with modest storage, memory and computational power at inference time. If we consider verifiability to be embedded in the selection of variables that can be queried reliably, accuracy and cost remain as desirable outcomes. A more extensive variable set improves the predictive power of household data, as long as the additional variables contain additional information about its consumption level, but it also requires higher collection cost. Consequently, statistical targeting requires a choice along the length-accuracy trade-off in a resource-constrained setting.

The standard PMT approach described above has become widespread since its publication by Grosh and Baker [1995] to design models that use a moderate number of explanatory variables that also achieve a suitable level of targeting accuracy. In its pure form of automated variable selection, it selects the point on the accuracy-vs-enumeration-cost spectrum that maximizes model accuracy. This section revolves around the idea that other points – and especially earlier points – along the variable sequence should also be considered. A shorter questionnaire may be less accurate, yet it may result in lower overall exclusion due to the consideration of more households. We suggest methods which replace the single point with a range of options that allow policymakers to select a length-accuracy profile that is suitable for their conditions.

> 5Klasen et al. [2016] estimated exclusion errors for a forward stepwise regression model of 27% for a simulated 50% program coverage. The forward stepwise model constructed mirrors closely the approach taken in generating PMT rankings in the update of the UDB/DTKS in 2015. Appendix A shows a PMT exclusion error rate of 21% at 50% coverage.

13
for example. Further, many services–such as power and telecommunications, or even toll roads— are excludable, and thus suitable for financing through user fees (and for private provision).<sup>4</sup> The literature has considered two forms of infrastructure congestion. Under absolute congestion, services received by an individual user depend negatively on aggregate usage. Under relative congestion, they depend positively on the individual’s usage relative to aggregate usage. Analytical details vary depending on the chosen option as well as the production technology (Barro and Sala-i-Martin 1992, Eicher and Turnovsky 2000). Nevertheless, some basic results from the models without congestion continue to hold. For example, in an endogenous-growth setting with infrastructure modeled as a service flow, the welfare-maximizing level of public infrastructure spending is still dictated by the Barro rule. If infrastructure is viewed instead as a stock, such rule leads to excessive accumulation, just like in the absence of congestion (Turnovsky 1997). However, in an exogenous growth setting, the crowding-in effect of infrastructure on non-infrastructure capital tends to be diminished, or even reversed, especially when the financing is done through distortionary taxes (Fisher and Turnovsky 1998).

Another important feature of infrastructure is the presence of network effects, which can lead to strong nonlinearities in its marginal productivity. For example, road construction may have limited effects until the road network is minimally developed, at which point the marginal output contribution of additional roads may rise sharply. Once the entire network has been completed, however, additional road building is likely to have rapidly declining output effects (see Fernald 1999). Under appropriate conditions, these nonlinearities may lead to multiple equilibria and to an enhanced role of infrastructure development policy: with a poor infrastructure endowment, the marginal productivity of infrastructure is low, and only a low-growth equilibrium may be attainable by the economy; however, a sufficient expansion of infrastructure networks would raise the productivity of infrastructure and permit reaching the high-growth equilibrium (Agenor 2013).

_2.2 Empirical studies_

> 4 Ott and Turnovsky (2006) examine the implications of congestion and excludability for the optimal financing of infrastructure services in a macroeconomic setting. Absent excludability, distortionary taxation reduces overutilization of infrastructure and thus congestion. With full excludability, user fees provide the optimal form of infrastructure financing.

6
million; 72 percent), and general purpose machinery (US$782 million; 51 percent). These sectors are thus likely to be important in determining the overall impact of the EPA.

The official data summarized here do not capture unrecorded informal trade (smuggling)—an important characteristic of the Nigerian economy. Smuggled imports generally enter Nigeria through three routes: from bordering ECOWAS countries (Benin and Niger), from bordering CEMAC countries (Cameroon and Chad), and directly through customs fraud at Nigeria's own ports and airports. The available evidence indicates that the ECOWAS route is the most important one.<sup>5</sup> Raballand and Mjekiqi (2008) estimate that up to US$4 billion of cargo enters Nigeria’s market unofficially from the Cotonou port alone, which would represent a substantial portion of Nigeria’s total imports. Smuggling activities tend to be concentrated on a limited number of products which are highly protected or banned in Nigeria: cigarettes, textiles and clothing, used goods (cars, tires and clothes), bulk food items (rice, wheat, sugar), and processed foods (tomato paste, condensed milk) (World Bank, forthcoming). Given that smuggling in a sector is determined primarily by the degree of Nigeria’s trade restrictiveness<sup>6</sup> , an EPA may significantly alter the incentives to smuggle. A preferential tariff reduction on imports from the EU would reduce the arbitrage opportunity currently available to import EU products into lowtariff Benin (for example) before re-exporting them to Nigeria. The removal of import bans and a non discriminatory “most-favored-nation” (MFN) reduction of tariffs - decreasing the tariff differential between Nigeria and its neighbors – would go even further in reducing smuggling.

# **3. Nigerian Trade Policy**

One of the key characteristics of the Nigerian import regime is the frequency of NTBs, including outright prohibitions (or import bans). While tariffs have declined somewhat since 2002, the number of products subject to import bans has gone up significantly, with increases in 2001,

> 5  Recent comparison of wholesale prices in Nigeria and three neighbouring countries (Benin, Niger, and Togo) reveal that, for a number of highly protected commodities (rice, sugar, cigarette, prepared/preserved tomatoes, and concentrated unsweetened milk), the difference in prices is considerably less than one might expect from the difference in tariff protection.  This suggests that there is a significant level of smuggling in those commodities, and that the smuggling is effectively eroding the actual protective power of the high tariff rate.

> 6 For example, about 90 percent of Benin’s poultry imports are re-exported informally to Nigeria, while almost all frozen fish imports are intended for the domestic market.  There are no restrictions on fish imports into Nigeria whereas frozen poultry is banned.  Thus, Benin tends to re-export goods that are most heavily protected in Nigeria. Source: Chapter 3, World Bank (Forthcoming)

7
If we perform the same regressions as _desa_ averages we roughly reconfirm the above results, but also demonstrate the potential losses from focusing exclusively on _desa_ aggregated data, even in examining community impacts. Table 11 shows OLS regressions of _desa_ averages of the three reported voice variables on _desa_ averages of the social activity and control variables<sup>2 2</sup> . In each case the sign of average social organizational membership is associated with higher expressions of voice. In contrast, average participation in the _desa_ government organizations is associated with _less_ voice. The magnitudes are roughly comparable with the sum of the two effects reported in tables 9 and 10 (see "Total HH" column in Table I _1) -desa_ government organizations are associated with 51 percent less protest in the averages while the household data suggest a 42 percent decrease. No expression of discontent in spite of problems is 28 percent more likely when estimated with the averages, 39 percent more likely from the household data. While the household data suggest only a modest decline in the probability of being in the most effective voice category, the aggregates suggest an 18 percent reduction in "effective voice" (although aggregating to _desa_ averages requires treating the categories as cardinal).

There are two large advantages of using the household data over the _desa_ averages. First, without the household level data one cannot see that the _desa_ aggregate impact is a combination of private and community effects. For the _desa_ government organizations a strong positive private effects is generally offset by a more than compensating negative community impact. Second, when using _desa_ averages none of 22 With the two binary variables the average is just the fraction of households answering "yes" but with the "effective voice" variable we have to assume (as we did not before) that the categories can be treated as cardinal numbers so they can be averaged.

33
This section briefly discusses on historical and future CO2 emissions as well as CO2 emission intensities of various countries and geopolitical regions<sup>8</sup> .

# **3.1 CO2 Emissions**

Table 1 and Figure 1 present historical and future CO2 emissions from energy related activities<sup>9</sup> , country/regional mix in global CO2 emissions and average annual growth rates of emissions experienced over the last 25 years and expected over the next 25 years. There is a slight difference between the historical emissions and the projected emissions; while the historical emissions account for emissions from oil and natural gas flaring, the projected emissions do not. The difference, however, is negligible and hence ignored.

- 8 Ideally, study like this should have discussed GHG emissions and GHG intensities instead of only CO2 emissions and intensities, however, data limitations, particularly the lack of detailed data for non-CO2 GHGs constrained in so doing. Although IPCC (2000) provides forecasts of all types of GHG under 40 scenarios, these forecasts are presented in globally aggregated level and not available at the national or regional levels. Moreover, the focus of IPCC scenarios is for long-term forecasts (i.e., for 100 years) instead of short-term ones this study is dealing with.

- 9  Other activities such as land use, land use change and forestry (LULUCF) also release CO2 emissions. At the same time, these activities also act as sink of CO2; net release of CO2 differs country to country. For example, LULUCF has been acting as a net sink of CO2 historically in the United States (USEPA, 2006). The Initial National Communication of China to the UNFCCC reports that the LULUCF has acted as a net sink of CO2 in China (PRC, 2004).

-9-
- 24 -

Turnbull (1972) describes the complete social breakdown that was caused by the exclusion of the Ik from their hunting grounds in Kidepo National Park, Uganda. The tribe was told to take up farming and provided with land that was not or.ly entirely unsuitable but overlooked the park land - allowing the Ik a prospect of their lost animals as they slowly starved.

In Kenya, the Liangulu were redefined as poachers overnight when Tsavo National Park was established. For some time,they had already been subjected to regulations on elephant hunting, which they had ignored. When the ivory trade boomed during the 1950's, many of the Liangulu were jailed. But, when the ecological crisis reversed itself and there was a danger of over-population, 400 were released to participate in the Galana elephant cropping scheme (Poole 1968). Gomm (1974) has suggested that the banning of Liangulu hunting itself contributed to the elephant population explosion.

Exclusions of indigenous people from parks and reserves appears to have been more common throughout the former British colonies than, for example, South America. Maasai were initially excluded from six parks and reserves in East Africa (Deihl 1985). San were removed from the Gemsbok National Park, Botswana (Hitchcock 1985) and it has quite recently been proposed that the Central Kalahari Game Reserve be redefined as a national park as a step towards evicting the San now living there (Volkman 1986).

A cooperative project between the Indian Government and the WWF/IUCN to preserve the tiger population has had serious effects upon tribal communities. A 1972 census yielded a national estimate of only 2,000 tigers. Project Tiger was launched in 1973 with the establishment of eight tiger reserves. Each consisted of a buffer surrounding a core zone in which all human activity was banned. In the Kanha reserve, 40,000 people living in 20 communities were relocated from the core zone. Villagers in the Betla reserve have lost much of their livelihood while the area now attracts 20,000 tourists each year (Earthscan 1981). The 40,000 ha Chenchu Reserve had been established in 1940 but its Chenchu inhabitants had remained undisturbed until 1979, when the reserve was re-designated a Tiger Sanctuary and people were prohibited from using the resources of the core area (Survival International 1987a).

Marshall (1984) describes a proposal to set up a nature reserve of about 6,000 sq km in Bushmanland. Once this happens, the Ju/Wasi living there will no longer be able to keep their small herds of cattle which combine with gardening, hunting and some wage-earning to provide a selfsupporting local economy. The Ju/Wasi will be obliged to move from the natural water holes in their present lands to expensive boreholes in the arid lands to the west. Writing about the same proposal, Volkman (1986) refers to the plans for eco-tourism in the reserve, which will include JulWasi on the agenda. Local objections, once the reserve plan was made public, led to a qualified modification: Ju/Wasi would be allowed to keep a limited amount of cattle, use some of their waterholes and hunt in the traditional manner (Marshall 1984).
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