rafmacalaba/datause-encoder-data huggingface dataset:
# 1. **INTRODUCTION**

**Infrastructure privatization.** A wave of infrastructure privatization activity is currently sweeping the globe affecting about 100 countries and amounting annually to over US$60 billion of business on average over the past decade. It has become increasingly clear to policymakers and firms alike that a major challenge is to ensure that such privatization activity will yield clear benefits. Existing empirical studies suggest that ownership change per se will often yield benefits particularly where it leads to reduced noncommercial government interference. However, regulation that is required in areas with natural monopoly features may become overly intrusive and undermine the progress made. To generate lasting and sizable welfare improvements the introduction of real competition is required.' Effective competition requires that firms can fail. This in turn tends to require private ownership as public firms may more easily count on being bailed out. It is in this sense that private ownership may be most clearly necessary for achieving lasting efficiency gains.

**Questions policymakers pose.** Many reforming governments want to employ competitive solutions. By way of example consider recent World Bank experience with reforming client governments. Key questions were:

- Should we allow completely free entry into all telecommunications services or is there reason to fear uneconomic duplication of investment and services?

- Should we introduce competition in power generation by unbundling generation and allow trade in transmission capacity rights such that decentralized bargaining over such rights determines dispatch?

- Should we provide an exclusivity period for gas distribution systems or allow free entry?

- Should we separate rail track from rail service operations and let the latter be competitively supplied or should we grant monopoly franchises combining track and service operations?

- Should we introduce auctions for landing slots at airports?

- Should we require that port concessionaires not be controlled by shipping lines so as not to bias access opportunities for other shippers ?

- Should we provide a measure of bankruptcy protection for private competing airlines to ensure essential service ?

A background paper summarizing the results of existing empirical work is being prepared.
the month of June since it is the critical window during the main agricultural season for cereal crops that are most commonly cultivated in Niger. As in other Sahelian regions, rain deficits in the planting season are strongly correlated with crop failures.<sup>23</sup> Table A1 shows that the exogenous shock variable is strongly associated with self-reported measures of drought shocks, but also with agricultural outputs, welfare and food security in the control group. Specifically, drought shocks are associated with a lower production of millet and sorghum by 148 kg, with a lower consumption per capita per year by 17.7%, and a lower food consumption score by 10.4 points. Figure A1 shows that the 20<sup>th</sup> percentile of the historical distribution corresponds to the most severe shocks related to agricultural production and consumption, and correlates well with self-declared shocks.<sup>24</sup> These results are consistent with studies that emphasize the negative effects of low rainfall on agricultural and household outcomes in Niger (Gao and Mills, 2017; Asfaw et al., 2016).

Table A2 documents balance between the treatment and control groups for households in areas exposed to exogenous shocks. There are only minor differences between treatment and control households exposed to shocks, and the two groups are overall well-balanced, including the sample of households below the PMT threshold.

# _3.3 Measuring resilience_

In light of policy makers’ increased focus on resilience, several measurement approaches have been proposed in recent years (Constas et al., 2013; Serfilippi and Ramnath, 2018). Barrett and Constas (2014) recommend building specific resilience measures (such as one based on the FGT poverty indices). Cissé and Barrett (2018) follow their conceptual framework to construct a

> 23 For planting season dates, see for instance FAO country briefs for Niger:

> <u>http://www.fao.org/giews/countrybrief/country.jsp?code=NER. See also Marteau et al. (2011) for the importance</u> of the early rainy season (the month of June) for crop failure in Niger. Our results are consistent with Asfaw et al. (2016) who show that a late onset of the rainy season has a large and significant negative impact on agricultural productivity and income in Niger. For a similar focus on the planting season for identifying exogenous shocks in agriculture in another context, see Macours et al. (2012). Early season rainfall events are often critical for agricultural and livestock systems, and for that reason are often incorporated in the design of index insurance products (Maccini and Yang, 2009; Jensen et al., 2019).

> 24 Figure A1 shows the coefficient of the regression in Table A1 for various definitions of the shock variable. Specifically, we vary the percentile of the historical distribution used to define the shock variable. For each percentile, we show the regression coefficient for per capita consumption, food consumption score, self-declared shocks and agricultural production.

15
<!-- Start of picture text --> a of Om or . GNP Short-S rm Intorest Rte, t,V Excha. RatO Aftgr a TOur PerCent<br>Sustained Incrs se in Money ase Srset of the U.S.. Goeran. Jaan and Other Industrializd Countries<br>O O 5<br>! 25<br>07.2<br>a 06 \<br>05 19C S<br>04-<br>03 1<br>02. -02 .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0 .<br>0- 0 5<br>-00<br>-02 .<br>I96l 1;99 1990 1991 1992 1993 1958 1989 1990 1991 '992 ,993<br>CII, PCI,np<br>-0 04 -ro<br>-006- G -36<br>-006 /<br>-01 3 7<br>-0 32 A<br>-3<br>-0.<br>-016 -42<br>-026<br>-0 28 4<br>-0 3<br>-0 32<br>1989 1969 199 1991 1992 1993 '988 1989I;. 1990 99 '992<br><!-- End of picture text -->

***The** figures for CU, PMII, and **exchange rate** are Percentage deviation from the baselina. ***The figUres for short-toer intezeat rate are change** In the percentage **level.**
Restuccia, D., and R. Rogerson. 2008. “Policy Distortions and Aggregate Productivity with Heterogeneous Establishments.” _Review of Economic Dynamics_ 11: 707–20.

Ross, Michael L. 2015. “What Have We Learned About the Resource Curse?” _Annual Review of Political Science_ 18. Annual Reviews: 239–59.

Ross, M.L. 2012. _The Oil Curse: How Petroleum Wealth Shapes the Development of Nations._ Princeton University Press.

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https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3048585.

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Stiglitz, Joseph. 1999. “On Liberty, Their Right to Know, and Public Discourse: The Role of Transparency in Public Life.” Oxford Amnesty Lecture.

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United Nations University World Institute for Development. 2017. “World income inequality database.” https://www.wider.unu.edu/project/wiid-world-income-inequalitydatabase.

Van der Ploeg, Frederick. 2011. “Natural Resources: Curse or Blessing?” _Journal of Economic Literature_ 49 (2): 366–420.

Van Ruijven, B.J., M.A. Levy, A. Agrawal, F. Biermann, J. Birkmann, T.R. Carter, K.L. Ebi, M. Garschagen, B. Jones, and R. Jones. 2014. “Enhancing the Relevance of Shared Socioeconomic Pathways for Climate Change Impacts, Adaptation and Vulnerability Research.” _Climate Change_ 122 (3): 481–94.

Varangis, Panos, Akiyama Takamasa, and Donald Mitchell. 1996. _Managing Commodity Booms and Busts._ Venables, A. J. 2016. “Using Natural Resources for Development: Why Has It Proven so Difficult?” _Journal of Economic Perspectives_ 30 (1): 161–84.

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World Bank. 2019a. “Commodity Prices.” https://www.worldbank.org/en/research/commodity-markets.

———. 2019b. “PovcalNet.” http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx.

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did not have paved roads, although in practice many projects under the scheme upgraded pre-existing roads. As the objective was to connect the greatest number of locations to the external road network at the lowest possible price, routes terminating in villages were prioritized over routes passing through villages and on to larger roads.

Importantly for this paper, national guidelines prioritized larger villages according to arbitrary thresholds based on the 2001 Population Census. The guidelines originally aimed to connect all villages with populations greater than 1,000 by 2003, all villages with population greater than 500 by 2007, and villages with population over 250 after that.<sup>5</sup> The thresholds were lower in desert and tribal areas, as well as hilly states and districts affected by left-wing extremism. These rules were to be applied on a state-by-state basis, meaning that states that had connected all larger villages could proceed to smaller localities. However, program guidelines also laid out other rules that states could use to determine allocation. Smaller villages could be connected if they lay in the least-cost path of connecting a prioritized village. Groups of villages within 500 m of each other could combine their populations. Members of Parliament and state legislative assemblies were also allowed to make suggestions that would be taken into consideration when approving construction projects. Finally, measures of local economic importance such as the presence of a weekly market could also influence allocation. Different states used different thresholds; for instance, states with few unconnected villages with over 1,000 people used the 500-person threshold immediately. Some states did not comply with the threshold guidelines at all. We identified complying states based on meetings with officials at the National Rural Roads Development Agency, which was the federal body overseeing the program.

Although funded and overseen by the federal Ministry of Rural Development, responsi-

> 5The unit of targeting in the PMGSY is the habitation, defined as a cluster of population whose location does not change over time. Revenue villages, which are used by the Economic and Population Censuses, are comprised of one or more habitations (National Rural Roads Development Agency, 2005). In this paper, we aggregate all data to the level of the revenue village.

8
total investment by origin in the SAM. Besides, investment by destination is used to calibrate the sectoral capital stock in base run.

Total labor supply is an endogenous variable, although it is assumed to simply increase at the exogenous population growth rate. Note that the minimal level of consumption in the LES function also increases (as do other nominal variables, like transfers) at the same rate. The exogenous dynamic updating of the model includes nominal variables (that are indexed), government savings and the current account balance. The equilibrium between total savings and total investment is reached by means of an adjustment variable introduced in the investment demand function. Moreover, the government budget equilibrium is met by a neutral tax adjustment.

The model is formulated as a static model that is solved sequentially over a 20 period time horizon.<sup>3</sup> The model is homogenous in prices and calibrated in a way to generate "steady state" paths. In the baseline all the variables are increasing, in level, at the same rate and the prices remain constant. The homogeneity test, e.g. a shock on the numéraire, the nominal exchange rate, with the “steady state” characteristics, generates the same shock on prices, and unchanged real values, along the counterfactual path. This method is used to facilitate welfare and poverty analysis since all prices remain constant along the business as usual (BaU) path.

# **2.2 The Bangladesh Social Accounting Matrix for 1999/2000**

In our study we calibrate our model numerically to a 1999/2000 Social Accounting Matrix (SAM) of Bangladesh. The main sources of information for the SAM are: (a) 1999/2000 Inputoutput table prepared by Sustainable Human Development Project, Planning Commission, Government of Bangladesh; (b) Household Income and Expenditure Survey 2000 by Bangladesh 8
# **3.  A Framework and Methodology for the Model Estimates**

Based on the econometric estimates in the former section, our study adopts a computable general equilibrium model and database from GTAP (Global Trade Analysis Project)<sup>10</sup> to estimate the effects on welfare and other variables, under the baseline scenario, namely to improve ITI in the APEC region such that no economy is below the current regional average. The model simulation provides a rough idea of the magnitude of welfare gains and other effects, together with the theoretical mechanism of the creation of such effects. The aggregation of the region and industrial sectors (commodity) is 21 regions of the world times 4 sectors (see Appendix B for the list of aggregation). All the member economies in APEC except for Papua New Guinea and Brunei have their own entry in the aggregation. The four sectors in the industrial aggregation correspond to the classification used in the empirical analysis by Helble, Shepherd and Wilson (2007). The database with its base year at 2004 is updated to reflect the changes in the relative scale of the economies to the year 2006, by extrapolating the growth of labor force, capital stock and technical progress. In addition, the abolishment of the multi-fiber agreement is applied to the database, as a major revision. Our simulation adopts the basic model option that assumes constant returns to scale with perfect competition. The simulation with the basic option identifies only the long-run economic effects through efficiency improvement, providing results that are easier to trace. The section below outlines the methodological issues regarding the selection of the shock variables, and quantification of such policy shocks.

## **_Model Specification for Reducing Trade Costs: The Iceberg Approach_**

The model simulation chosen requires identifying and quantifying the initial shocks in our exogenous variables. In the case of trade facilitation as an initial shock to a CGE model, modelers have used a so-called iceberg specification<sup>11</sup> as a standard approach. The latest GTAP model, version 6.2, provides a ready-made variable ( _ams)_ to enable

> 10 The GTAP released the latest version (version 7, pre-release 6) of the database in 2008. The base year of the database is the year 2004.

> 11 This illustrates the situation that a fraction of goods shipped is lost (melt) in transport, which is a pure loss in the process of trade.

11
Where _d_ ( _xi_ ; _xj_ ) is the geographical separation between two points;  , , are parameters of the covariance function defining, respectively, its amplitude, degree of differentiability, and scale;<sup>_K_</sup> is the modified Bessel function of the second kind of order , and is the gamma function.

<mark>Bayesian inference was implemented using the INLA algorithm to generate approximations of the</mark> marginal posterior distributions of the stunting prevalence _Y_  _xi_ at each location on a regular 1×1 km spatial grid across Tanzania <mark>and of the unobserved parameters of the mean, covariance function and Gaussian random noise component. At each location, the posterior distribution was summarized using the posterior mean as a point estimate and the width of the posterior 95% credible intervals (CIs) as a metric of uncertainty, and maps were generated of each of these metrics in ArcGIS 10.4.</mark> While 1×1km resolution maps provide the most fine‐grained picture of variation in stunting across the country, it is also useful to summarize these patterns at higher levels of aggregation corresponding to administrative unit levels at which program planning, implementation and decision making are carried out. Aggregated stunting prevalence and the count of stunted children was therefore calculated at regional (1<sup>st</sup> sub‐national administrative level) and district (2<sup>nd</sup> ) levels, using an underlying gridded population surface to provide pixel‐level population denominator values.<sup>40</sup> The predictive performance of the model was assessed via out‐of‐sample validation. A four‐fold hold‐ out procedure was implemented whereby 25% of the data points were randomly withdrawn from the data set, the model run in full using the remaining 75% of data, and the predicted values at the locations of the hold‐out data compared to their observed values. This was repeated four times without replacement such that every data point was held out once across the four validation runs. Standard validation statistics were computed as measures of model precision (mean absolute error) and bias (mean error).

# **Results**

# _Model validation_

Figure 1 shows a scatterplot plotting values predicted in the out‐of‐sample validation exercise ( _x_ ‐axis) against the corresponding observed values that were held out ( _y_ ‐axis). Points close to the 1:1 line were 5
Venezuela, or a decrease in homicide rates accompanied by an increase in income inequality (Costa Rica and Mexico).<sup>71</sup> Larger income inequality as measured by the Gini coefficient is also correlated with an increased incidence of robberies, but not to the same extent as in the case of homicides.<sup>72</sup> As with the homicide regressions, level of per capita income is not robustly correlated with the robbery rate.<sup>73</sup> Correlations between crime and violence and unemployment offer less conclusive results, and the exact relationship between the variables remains to be clarified in further research. Some analysts have concluded that there is no correlation between unemployment and crime and violence in Latin America. But as Ayres (1998) points out, the fact that there is no direct causality does not signify that the two issues are unrelated. Data clearly shows that violence is counter-cyclical: homicide rates rise in periods of low economic activity, suggesting that unemployment has some effect on crime.<sup>74</sup> A considerable body of evidence supports the notion that young men in particular respond to the economic returns of crime, and these returns will be perceived as larger if legitimate employment is scarce or non-existent. Thus there is an argument that unemployment is a factor motivating crime and violence in urban areas in Latin America.<sup>75</sup> Regarding the effect of education, studies find that average years of schooling do not have a conclusive impact on crime and violence rates.<sup>76</sup> However, when the average years of schooling of the adult population is used as a proxy for the country’s educational attainment, education appears to have a significant crime reducing impact. The contrast of results obtained using secondary enrollment rates and average years of schooling may indicate that the efforts to educate the young may not reduce crime and violence levels immediately, but eventually lead to a significant reduction of crime, especially of the violent sort.<sup>77</sup> The level of educational attainment of the adult population also has a robbery-reducing impact.<sup>78</sup> As mentioned above, the lack of education is one of the main risk factors for domestic violence: each year of extra schooling reduces the probability of domestic violence by more than 1%.<sup>79</sup> As mentioned in Section II, crime and violence in Latin America is a predominantly urban phenomenon. The significance of the correlation between urbanization and crime and violence is somewhat disputed in the literature. Buvinic and Morrison (2000) argue that crime rates in Latin America are strongly correlated with city size.<sup>80</sup> They suggest that crowding intensifies anti-social behavior and that transient populations cannot foster the level of social cohesion that tends to keep violence at bay. Furthermore, evidence

> 71 Morrison, Buvinic, and Shifter (2003). 72 Fajnzylber, Lederman, and Loayza (1998). 73 Ibid.

> 74 Londoño (1996) quoted in Ayres (1998). 75 Ayres (1998). 76

> Fajnzylber, Lederman, and Loayza (1998). 77 Ibid. 78 Ibid. 79 Gaviria and Velez (2001). 80 Buvinic and Morrison (2000).

13
50 Rankings, with particularly poor scores for starting a business, getting electricity and registering property. Agriculture slowed in 2016 and is expected to do better in 2017. Lack of rain affects the outcome of the agricultural sector. CIA’s Factbook<sup>73</sup> reports that 46.5% of Sudan’s population is living below the poverty line. Only 35% of its population has access to electricity.

# Social Service

It is difficult for the staff of international humanitarian aid agencies to obtain travel permits in Sudan, severely hampering the relief effort.

# **<u>The Democratic Republic of the Congo (DRC)</u>**

# Security

The security situation in the Democratic Republic of Congo has been poor since 2012. An attempt to integrate a Tutsi rebel group into the Congolese military failed and prompted the defection and formation of the M23 armed group. The renewed conflict led to large population displacement and human rights abuses. Furthermore, the President of the DRC, Joseph Kabila is barred from running for a third term, but the DRC government has delayed national election originally slated for November 2016. The failure to hold election fueled sporadic street protests by Kabila’s opponents<sup>74</sup> . Although a deal signed by representatives of Kabila’s ruling party said the presidential election would be held before the end of 2017, the President himself has not endorsed the deal. Government officials repeatedly banned opposition demonstrations, fired teargas and live bullets at peaceful protesters, shut media outlets, and prevent opposition leaders from moving freely<sup>75</sup> . According to UNHCR, there are over 2 million IDPs in the DRC, and over 450,000 refugees. In year 2016, there were about 13,000 returned refugees and 619,000 returned IDPs. “Dozens of armed groups remained active in eastern Congo, many of their commanders have been implicated in war crimes, including ethnic massacres, killing of civilians, rape, forced recruitment of children and pillage.<sup>76</sup> ”

# Economic Opportunity

> 73 CIA the World Factbook, Sudan, <u>https://www.cia.gov/library/publications/the‐world‐ factbook/geos/su.html</u>

> 74 CIA, the World Factbook, <u>https://www.cia.gov/library/publications/the‐world‐ factbook/geos/cg.html</u>

> 75 ‐ ‐ ‐ Human Rights Watch, <u>https://www.hrw.org/world report/2017/country chapters/democratic</u> ‐ <u>republic congo</u>

> 76 Human Rights Watch, DRC, <u>https://www.hrw.org/world‐report/2017/country‐</u> ‐ ‐ <u>chapters/democratic republic congo</u>