rafmacalaba/datause-encoder-data huggingface dataset:
|Table Cl. Comparison of esfimated deaths<sup>from certain specific causes in</sup><br>developing countries around 1985 with Walsh<sup>assessment ....</sup><br>...<br>Table C2. Expert assessments of mortality rates<sup>(per 100,000) and deaths (in</sup><br>thousands) for some specific causes, by age,<sup>sex, and region,</sup><br>1985 ..............................................|.. 68<br>70|
|---|---|
|List of Figures||
|Figure 1. Life expectancyby region ...............................<br>Figure 2. Wfant mortality by region<sup>...............................</sup>|6<br>6<br>|
|<br>Figure 3. Mortality by cause, world, 1970-85<sup>.........................</sup><br>Figure 4. Deaths by cause, developed countries,<sup>1970-2015....</sup><br>.....<br>Figure 5. Deaths by cause, developing<sup>countries, 1970-2015....</sup><br>.....|9<br>......<br>13<br>.....<br>14|

iii
As discussed, recent pipeline data already indicates that expected CER flows from pipeline projects exceed ex ante model predictions. From model parameters, it is possible to construct confidence intervals around each observation. Using this approach, we calculated the probability that the cumulative number of CERs identified in August 2009 already exceed the upper limit of 520 million tons of carbon dioxide equivalent (mtCO2e) predicted by the ex ante model. Based on parameters from the symmetric model, there is a 90 percent probability that the cumulative level of CERs in the August pipeline top the upper range of the model predictions for 2010. Using the better-fitting asymmetric model parameters, the probability exceeds 99 percent. Similarly, a test that the upper bound is really the 520 mtCO2e is rejected in both models.<sup>15</sup> It is likely that not all projects in the pipeline will succeed in producing the full complement of credits predicted in documents submitted to the CDM Board and some of the credits may arrive after the close of the first commitment period. Moreover, there remains some skepticism in the environmental community about whether leakage and inexactness in project baselines dilute the environmental integrity of the mitigation effects measured by the CDM. Even so, barring a significant departure from current trajectories, the CDM appears on track to match or exceed early expectations when the program was begun.

# **Summary and Conclusion**

There is a consensus view that, at reasonable prices for carbon, most of the global opportunities for mitigation reside in developing countries. And, to date, the Clean Development Mechanism is the only formal way developed countries can access mitigation opportunities in developing countries to meet their commitments under the Kyoto Protocol. Additionally, recent estimates by the IPCC suggest that a significant portion of the mitigation opportunities in developing countries are in agriculture, forestry and improving building efficiency. Yet, so far, project investments under the CDM are narrowly focused on the energy and industrial sectors, leaving these broad areas of potential untouched. This differs from earlier assumptions of how the program would work and raises the question of whether mitigation opportunities that the CDM currently taps will be sufficient to meet

> 15 The test is based on setting _m_ in (2) and (3) equal to 520.

12
<!-- Start of picture text --> Figure 7<br>Forecasted RE energy share in 2010 according to the year of publication Forecasted shares Forecasted share =<br> -0.02 x Publication Year + 47.95<br>Robust Tukey smoother<br>R 2 = 0.0061<br>Linear regression line<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br>RE share (%x100)<br><!-- End of picture text --> 14
relevant ranges. In the fairly small samples we work with (around 500 observations), the objective function is sufficiently flat and discontinuous that standard gradient-based minimization algorithms fail.

A natural alternative to gradient-based minimization is to simply do brute-force grid searches over the parameter space. This however becomes increasingly costly as the number of covariates increases, and particularly so for a reasonably fine grid of candidate parameter values. Instead, we rely on the Nelder and Mead (1965) method, which is a derivative-free heuristic minimization algorithm (available as an option within the Mata `optimize` module). Since its convergence properties are not known in general, we first verify that the Nelder-Mead algorithm finds a minimum that is close to the one identified by brute-force grid searches, for several examples with up to four covariates for which grid searches are not too computationally costly. To ensure robustness of the minimum identified by this algorithm to the choice of initial parameters, we minimize the prediction loss function for 100 randomly-chosen sets of initial conditions. The distribution of the initial parameters is centered on the probit weights for the linear classifier, and on the median value of each covariate in the conflict sample for the threshold classifier.

# **3.  Measuring Conflict and Its Correlates**

In this section we briefly describe the cross-country panel conflict dataset that we use to evaluate our four prediction algorithms. The first subsection describes our definition of conflict, and the following subsection enumerates the explanatory variables we draw on as predictors of conflict.

## **3.1 Identifying Conflict Episodes**

Our starting point is a panel dataset of country-year observations covering 114 developing countries since 1977. As discussed in more detail below, the sample of countries and the time period are dictated by data availability. Our primary measure of conflict is a binary indicator of whether at least 25 battle-related deaths occur in a country in a given year.<sup>10</sup> This is a standard threshold for “minor armed conflicts” used in the literature to capture smaller conflicts, and naturally also includes much rarer civil war events which typically are measured using a higher threshold of 1,000 battle deaths. We supplement this measure with two additional indicators. The first is a binary indicator measuring whether a UN peacekeeping operation (UNPKO) is present in a given country year. This is intended to

> 10 Data comes from Uppsala Conflict Data Program/PRIO Armed Conflict Dataset. It defines armed conflict as “a contested incompatibility which concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths.”

17
# **References**

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- Atzberger, C. (2013). “Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs.” Remote Sensing, 5, pp. 949–981.

GoU (2013). “Uganda vision 2040.” Retrieved on May 11, 2017 from <u>https://goo.gl/DEZwNn.</u>

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Barrett, C.B., Bellemare, M.F., and Hou, J.Y. (2010). “Reconsidering conventional explanations of the inverse productivity-size relationship.” _World Development,_ 38, pp. 88-97.

- Bevis, L., and Barrett, C. B. (2017). “Close to the edge: do behavioral explanations account for the inverse productivity relationship?” Retrieved on August 2, 2017 from <u>https://goo.gl/j9dyJM.</u>

- Becker-Reshef, I., Vermote, E., Lindeman, M., and Justice, C. (2010). “A generalized regressionbased model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data.” _Remote Sensing of Environment_ , 114, pp. 1312–1323.

- Benjamin, D. (1995). “Can unobserved land quality explain the inverse productivity relationship?” _Journal of Development Economics_ , 46, pp. 51–84.

- Bhalla, S., and Roy, P. (1988). “Mis-specification in farm productivity analysis: the role of land quality.” _Oxford Economic Papers_ , 40, pp. 55–73.

- Carletto, G., Gourlay, S., Murray, S. and Zezza, A. (Forthcoming). “Cheaper, faster and more than good enough: is GPS the new gold standard in land area measurement?” _Survey Research Methods_ .

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27
statistical capacity and have higher values for the SPI and its components. The economic complexity index ( _ECIi_ ) measures the productive capabilities of an economic system (its integration into global value chains). More complex economies require more data to manage and operate successfully, which should increase both the demand for and supply of statistical services. Economies of scale in the production of statistical information are captured by the country’s population ( _Popi_ ). Economies of scale in large countries arise from the fixed costs of setting up statistical operations, which may vary little with the size of the country (e.g., De Vries 1999).

The demand for statistical information is linked to the level of education in the country ( _EDUi_ ). The ability to read and interpret the data published by national statistical offices depends on the levels of statistical literacy and numeracy in a county (Schield 2011). We, therefore, expect countries with larger shares of educated people to have higher SPI scores. The share of the urban population ( _SURi_ ) captures the structure of the economy, which could also affect demand for data. We use the voice and accountability indicator ( _VAi_ ) to control for the role of civil society as consumers of official statistics (Gray, Lämmerhirt, and Bounegru 2016). The fragility index ( _FRGi_ ,) accounts for the state of fragility and conflict in the country. The model also includes seven regional dummies to control for regional cooperation and knowledge transfer across NSOs.

# **_Data_**

We use several sources of data. The first is the SPI and its component. In our sample, it ranges from 0.21 (Papua New Guinea) to 096 for (Australia).

An indicator of voice and accountability comes from the World Governance Indicators (WGI) database, produced by the World Bank annually since 1996 for over 200 countries (Kaufmann et al. 2010). This indicator ranges from –2.5 (lowest) to 2.5 (highest).

We control for a country’s secondary enrollment rates and GDP per capita (in constant 2011 purchasing power parity dollars) using data from the World Development Indicators database (World Bank 2020a) and information from other sources. We use the economic complexity index at the MIT Lab and Harvard University (Hausman and Hidalgo 2012). We include the country fragility index produced by the Center for Systemic Peace in the State Fragility Index and Matrix (2018) data set (Marshall and Cole 2018). It ranges from 0 to 24, with higher values indicating greater state fragility. Table 1 displays the descriptive statistics for our main variables.

12
where _X h_ is a vector of household and individual characteristics, _Ch_ is a vector of control variables, and the term in parentheses is a i.i.d error term with states fixed effect. Specifically, education and gender of the household head, household size, and a dummy for urban areas are included as regressors. Since it has been shown that income is a strong determinant of the percentage gains in welfare, the logarithm of household income is included as a control variable. The estimation uses a fixed effect approach to control for idiosyncratic components at the state level.

The results, shown in table 5.1, illustrate which groups of households or individuals are relatively advantaged in reaping the benefits of trade liberalization. The results indicate that while the effect is no different between male- and female-headed households, education is a strong determinant of the distribution of the gains from trade liberalization. Each additional year of education of the household head corresponds to an average increase in the gains of about 0.21 percent. Urban households also have substantial advantage over rural households (0.35 percent) while the effects for age and households size are minimal.

Finally, I explore the extent to which trade liberalization has affected poverty levels. To do so, figure 5.4 illustrates the density function of the log of per capita expenditure as observed in the year 2000 and as it would be if Mexico had kept its tariffs at the level of 1989.

The fact that the observed density lies to the right of the estimated one is evidence of first order stochastic dominance. In other words, poverty levels have declined for any poverty line. Since the integral below the density and to the left of the poverty line corresponds to the poverty headcount index, density functions are useful to illustrate poverty effects. In 2000 about 43 percent of the Mexican population lived in poverty (poverty line about 880 pesos per month or about 100 US dollars).<sup>34</sup> If Mexico had not liberalized its markets

> 34 COMISIÓN ECONÓMICA para AMÉRICA LATINA y el CARIBE (CEPAL), 2001, _Panorama social de América Latina 2000-2001_ , Santiago de Chile.

29
Shleifer 2010). Both involve the construction of meaning of which the perceiver is generally unaware. He imagines that he is responding objectively to the situation.

The interpersonal and intergenerational transmission of cultural models gives them life beyond the circumstances that originally gave rise to them. This can explain why past structures of power, by shaping social constructions, can have persistent effects on perception.

As Loury (2002, 37) asks, if a representation attributed to a group, such as a race, gender, caste, or class, is a human product—a social construction—then shouldn’t humans be able to control it? Can humans intervene to give people new mental models or to alter which mental models are activated in a particular context, in such a way that individuals judge each other more fairly and act more fully on their opportunities? This paper considers three interventions that have had some success in developing countries. It elaborates on themes of behavioral development economics in the _World Development Report 2015_ (World Bank Group 2015) _._ But first a brief primer on cognition is in order.

# **A primer on cognition**

Psychologists, neuroscientists, and economists have been converging on a new understanding of the brain’s functioning.<sup>1</sup> Humans use a dual process to make decisions—fast, intuitive thinking as well as slow, controlled thinking. Psychologists describe the two modes metaphorically as two distinct systems in the mind: System 1 is the _automatic system,_ and System 2 is the _deliberative system_ (see Kahneman 2003). In most situations, people use automatic thinking reflexively unless provoked to check their thoughts deliberatively.

Automatic thinking makes implicit use of mental models to process information. Shared mental models may include concepts, identities, prototypes, stereotypes, causal narratives, and worldviews. The cultural understandings within an individual may rest on neuronal connections

> 1Surveys are Nisbett and Norenzayan (2002) and Camerer, Loewenstein, and Prelec (2005).  Demeritt and Hoff (2015) consider the applications to behavioral development economics.

4
After selecting variables by applying Lasso to a single imputation, we estimate the post-lasso OLS regressions across all 100 imputations, take the average of the point estimates, and estimate the variance using Rubin’s rule, as is standard when using multiple imputations.<sup>11</sup> To examine the predictions in the first wave, we estimate the following equation in the HFPS wave 1 data: wave 1 data: 0 1R , where indicates the subnational region. ii and R ii denote the lasso-selected set of HFPS cc WW = αα + αα RR + εε well-being and real-time indicators, respectively. ii is a random disturbance term, clustered on ii ii WW RR country. cc εε For the long-term analysis, we consider the following OLS model using the demeaned variables, 0 1R� , where and R� denote the set of demeaned HFPS well-being and real-time indicators iiww iiww ccww WW<sup>�</sup> = ββ + ββ RR + εε respectively for wave w in region i. iiww iiww is a random disturbance term, clustered on countryWW<sup>�</sup> RR wave. Using the demeaned variables is equivalent, at least in terms of the coefficient estimates, ccww εε as a model in levels that includes regional fixed effects in the estimation. The resulting R<sup>2</sup> measures provide a measure of how well the selected real-time indicators explain the variation in outcomes, within regions across time.

# **3. Trend Analysis**

# 3.1 Trend of HFPS Well-Being Measures

Figure 1 presents the trend of five well-being measures in 2020, averaging over countries in the sample. Not all measures are available for each country in each wave, so the composition of countries varies by measure and month. In addition, the first wave of the survey asks about changes since the pandemic began, while subsequent months ask about changes since the previous survey wave for that country. As the surveys start in April, we cannot report changes in the first quarter. The figure shows that the share of respondents reporting income loss is

> 11 We use the mi estimate command in Stata for this.

12
**Table S24:** Association of changes in market access on changes in outcome variables using a long difference, OLS results [Woredas]

||NT|L|Urb|an|Crop|land|NT|L|Ur|ban|Cropl|and|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._01|_−_0_._004|_−_0_._02|_−_0_._02|_−_0_._004|_−_0_._003|_−_0_._05|_−_0_._05|0_._03|0_._03|_−_0_._002|_−_0_._002|
||(0_._02)|(0_._02)|(0_._01)|(0_._01)|(0_._004)|(0_._004)|(0_._04)|(0_._04)|(0_._03)|(0_._03)|(0_._01)|(0_._01)|
|MA_×NTL_96 Lit||0_._41<sup>_∗∗∗_</sup>||0_._18<sup>_∗∗_</sup>||0_._03||0_._46<sup>_∗∗∗_</sup>||0_._29<sup>_∗∗∗_</sup>||0_._03<sup>_∗_</sup>|
|||(0_._09)||(0_._07)||(0_._02)||(0_._07)||(0_._09)||(0_._02)|
|MA, 1996|0_._02<sup>_∗∗∗_</sup>|0_._01|0_._01<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗_</sup>|0_._001|0_._0004|0_._04<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗_</sup>|0_._01|_−_0_._001|_−_0_._001|_−_0_._002|
||(0_._004)|(0_._01)|(0_._003)|(0_._002)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._01)|(0_._01)|(0_._002)|(0_._002)|
|Log mean light, 1996|_−_0_._15<sup>_∗_</sup>|_−_0_._24<sup>_∗∗∗_</sup>|0_._29|0_._26|_−_0_._12|_−_0_._12|_−_0_._11|_−_0_._23<sup>_∗∗∗_</sup>|0_._32<sup>_∗_</sup>|0_._25|_−_0_._11|_−_0_._12|
||(0_._08)|(0_._07)|(0_._19)|(0_._18)|(0_._10)|(0_._10)|(0_._07)|(0_._07)|(0_._19)|(0_._18)|(0_._10)|(0_._10)|
|Pre-trend: log mean light|0_._11|0_._08|_−_0_._20|_−_0_._22|0_._12|0_._12|0_._11|0_._07|_−_0_._20|_−_0_._22|0_._13|0_._12|
||(0_._13)|(0_._12)|(0_._23)|(0_._22)|(0_._08)|(0_._08)|(0_._13)|(0_._11)|(0_._23)|(0_._22)|(0_._08)|(0_._07)|
|Pre-trend: log N urban pixels|_−_0_._24<sup>_∗∗_</sup>|_−_0_._20<sup>_∗∗_</sup>|0_._33<sup>_∗_</sup>|0_._34<sup>_∗_</sup>|_−_0_._05<sup>_∗∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._26<sup>_∗∗_</sup>|_−_0_._22<sup>_∗∗_</sup>|0_._36<sup>_∗_</sup>|0_._38<sup>_∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._04<sup>_∗_</sup>|
||(0_._10)|(0_._09)|(0_._19)|(0_._18)|(0_._02)|(0_._02)|(0_._10)|(0_._09)|(0_._21)|(0_._20)|(0_._02)|(0_._02)|
|Model|_OLS_|_OLS_|_OLS_|_OLS_<br>|_OLS_|_OLS_|_IV_|_IV_|_IV_|_IV_|_IV_|_IV_|
|Observations<br>|780|780|780|780|780|780|780|780|780|780|780|780|
|Adjusted R<sup>2</sup>|0.33|0.37|0.28|0.29|0.37|0.37|0.33|0.36|0.27|0.28|0.37|0.37|

The unit of analysis is Woredas. Standard errors are clustered on Zones and all models include Zone fixed effects. MA is the logged difference of MA from 1996 to 2016. IV indicates that MA is instrumented with a 50km MA Doughnut variable; that is, in calculating market access, Woredas within 50km are excluded. _NTL_ 96 Lit indicates that the Woredas had some positive nighttime lights at baseline. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within Woredas, Urban is the total urban area within Woredas, and Cropland is the total cropland area within Woredas; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S35
**4 |** P a g e

world exports increased indicating better than average performance.<sup>1</sup> In all, exports displayed huge volatility during the post‐war recovery, stability during slow down and steady growth during the current expansionary phase, although at falling—but still double‐digit—growth rates: both phases had one‐year surges in exports growth of 67 percent in 1997 and 58 percent in 2004. Viewed in the regional perspective of other SEE‐5 exports performance, the share of BiH in total regional exports increased in 1997‐99, was stable in 1999‐2001, increased in 2002, contracted in 2003, and rebounded in2004‐06, only to fall slightly in 2007 (Figure 1).

**Figure 1: BiH export growth performance in regional perspective: annual growth rates and share in SEE‐5 exports in 1997‐ 2006 (in percent)**

<!-- Start of picture text --> 70 18<br>16<br>60<br>14<br>50 BiH growth<br>12 rate of<br>40 10 exports in<br>goods<br>30 8<br>6<br>20 BiH share in<br>4 SEE‐5<br>10 2 exports of<br>goods<br>0 0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Growth rate (in percent)<br>Share (in percent)<br><!-- End of picture text --> Note: No regional total exports are available, because as of July 2007, not all SEE-5 countries reported their trade data to the UN COMTRADE database.

Sources: own calculations from data reported to the UN COMTRADE database.

While high double‐digit growth rates during the post‐war recovery do not come as surprise, the current expansion points to the progress achieved in industrial restructuring. The current phase has been particularly impressive in the pace of exports growth. BiH has displayed a very strong export performance during the current expansion phase relative to other SEE‐economies. Its share in aggregate SEE‐6 exports of goods rose from 2 percent in 1997 to 12 percent during the last year of post‐war recovery in 2000. It fell to 10 percent by the end of the stagnation phase in 2002. Despite the value of

> 1 Its share increased from 0.014 percent in 2000 to 0.015 percent in 2001, and 0.017 percent in 2002 (derived from the UN COMTRADE database).