rafmacalaba/datause-encoder-data huggingface dataset:
between Chad and CAR and their neighboring states are $103.7 million for imports and $10 .1 million for exports. Trade flows with the CSAL states, only one country removed, are effectively nonexistent. By contrast, the CSAL states’ total trade flows with other African states are $3.3 billion for imports and $1.6 billion for exports.

**Table 4: Trade Flows Linking Chad and CAR with Neighboring and CSAL States ($US 2004)**

|Chad and CAR|Imports|Exports:|
|---|---|---|
|NeighboringAfrican States|103,692,188|10,079,443|
|CSAL States|356|0|
|CSAL States(Total African trade)|3,252,777,590|1,640,256,173|

# **4.2  Gravity Model Estimation: Results**

Table 5 provides estimation results for alternative specifications of the log-log regression model in equation (2).<sup>18</sup> Columns (1) and (2) present results for the basic gravity model, with two country-level measures of road quality. Road-quality results in both columns are based on average values for city pairs in each pair of trading countries. Column (1) presents results for average link quality between city pairs, while (2) tabulates results for minimum link quality. As we explained above, we believe that the latter is more realistic. In the econometric analysis, the city-level distinction between the two measures is blurred by averaging in our country-level dataset. In any case, both measures yield strong results, with elasticities around 2.0 (a 2% increase in trade for each 1% increase in road quality) and very high levels of significance. For both measures, the elasticities are somewhat higher than those obtained by Coulibaly and Fontagné. We

> 18 The model is estimated using trade flows between countries where each country is both an exporter and importer. The actual number of trading partners included is 36 since trade for the five countries in the South African Customs Union is reported jointly and no trade data are available for Somalia and Liberia. In addition, no trade is reported for 132 country pairs where it is not clear whether this is due to non-reporting or actual absence of trade. The final number of observations is thus 1128.

14
In the sections that follow, we will use the latest version of the WVS-EVS to address the two questions stated earlier: the definition and relation between bonding and bridging indicators; and the relation between these indicators and life satisfaction in the context of world regions characterized by different histories, cultures and levels of development.

# **3 Data, model and variables**

The data we use include all rounds of the WVS-EVS carried out between 1980 and 2008. The data set was created by combining the 1981-2004 World and European integrated data file (version 2006.04.23) and the last round of the World Value Survey following the instructions provided by the World Values Survey organization (www.worldvaluessurvey.org). The resulting dataset includes five rounds of the World Values Surveys and three rounds of the European Values Surveys for a total of 98 countries, 1639 regions, 24 recorded years and 355,298 observations. This is the largest dataset of its kind to date.

Our final objective is to assess the impact of bonding and bridging attitudes on life satisfaction worldwide. For this purpose we use what can be described as a standard model when working on happiness cross-country and longitudinally with the WVS-EVS. The model is as follows: where _H_ stands for happiness, _B_ is a vector of variables representing bonding and bridging attitudes, _X_ is a vector of control variables representing individual and household characteristics, _C_ is a vector of countries dummies, _Y_ is a vector of years dummies, _α_ , _β_ , _γ_ , _η_ , _δ_ , are the parameters to be estimated and _μ_ is the error term. The subscript _i_ stands for individuals and the subscripts _c_ and _t_ stand for countries and time (years) respectively. Therefore, in the most general model, we pool all available observations for all countries and years and estimate the happiness equations on individuals with countries and years fixed effects. The same equation is applied to the full set of countries available and to 16 separate groups of countries representing different geographical areas, recent histories, cultures and levels of economic development. All equations are estimated with robust standard errors (Huber-White estimator) and regional clusters 10
Our estimated equation is therefore as follows:

> When dyadic country-pair fixed effects ( δ _ij_ ) are included in the estimation, timeinvariant country-pair controls (e.g., distance, common language) are dropped, as are importing country and exporting country fixed effects. Equation (4) becomes

_SS_ (5) _xijt_ = exp( δ _t_ + δ _ij_ + α 1ln GDP _it_ + α 2 ln GDP _jt_ + **T** _ijt_ **γ** ) η _ijt_ . .

# **4.2 Data**

The trade data come from the BACI database developed by the CEPII.<sup>4</sup> Our dependent variable is the _total_ bilateral imports of country _j_ from country _i_ in year _t_ ( _xijt_ ). Note that in BACI, flows are reconciled, and the value is equal to exports from _i_ to _j_ in _t_ . In BACI, the values are FOB. Our analysis covers the period 1990–2006 (except for some newly independent countries in Central Asia and Africa).

Countries’ GDPs are from the World Bank Development Indicators. Transport costs are measured using the bilateral distance between both partners ( _dij_ ). These distances are extracted from the CEPII database.<sup>5</sup> In addition, a dummy variable for contiguity (cbord _ij_ ) that

> 4 <u>http://www.cepii.fr/anglaisgraph/bdd/baci.htm. This database uses original procedures to harmonize the United</u> Nations COMTRADE data (evaluation of the quality of country declarations to average mirror flows, evaluation of cost, insurance, and freight rates to reconcile import and export declarations).

> 5 <u>http://www.cepii.fr/anglaisgraph/bdd/distances.htm. These distances are calculated as the sum of the distances</u> between the largest cities of both countries, weighted by the share of the population living in each city (Mayer and Zignago, 2011).

19
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# **Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis**

_Antonio Scognamillo_<sup>_1_</sup> _; Gianluca Mele_<sup>_2_</sup> _; Luca Sensini_<sup>_3_</sup> Keywords: Natural Capital, Economic Growth, Income Inequality JEL codes: C01, C13, C3, E01, D31, D63, O11, O43, Q32

> 1 Food and Agriculture Organization of the United Nations (FAO), antonio.scognamillo@fao.org

> 2 The World Bank; <u>gmele@worldbank.org</u>

> 3 University of Salerno; lsensini@unisa.it
9 OVERVIEW

# Housing SuRply

A survey of the number of houses in Kumasi in 1988 supports impressions formed in 1986 that building had continued only slowly since 1981. Less than 800 houses appear to have been added between 1982 and 1985 and only 900 between 1986 and 1988, with the index for houses falling further behind that of population. However, the increase in rooms per house between the 1980 and 1986 samples (supported by no increase in occupancy rates) gives some grounds to believe that extensions to existing houses have allowed the growth in rooms to keep pace with population growth since 1980.

Many of the problems following from decontrol could be avoided if the supply of new houses is increased, especially at the lower end of the market. Analysis of a number of developing countries by Ferchiou (1982) has shown that some downward filtering of housing takes place but the shortage of accommodation for middle income households tends to prevent its reaching the lowest income groups. Furthermore, the availability of low cost, appropriate local materials and technologies based on mud construction allow the lowest cost housing to be built new at a price most households can afford. If relatively low income households can be encouraged to build in local materials with local technology, rooms will be added much more cheaply than is possible with imported cement-based materials.

# Rent Control and Profitability

Rent control directly reduces profitability because it reduces the rents a unit can command. But reduced rents also affect maintenance (and depreciation), taxes and capital gains. These "indirect" effects can be large and should be taken into account. We build such a model in Chapter 6, and study a series of hypothetical representative investments (using 1986 as the base year of investment).

At a real discount rate of 10 percent, the present value of the controlled unit is about -1.3 million cedis. If the unit was uncontrolled, the present - value would still be negative, but the "loss" would be smaller about -260,000 cedis. We can interpret these numbers as follows. If investors could receive a real return of 10 percent on an asset with similar risks, they would prefer such an investment over rental housing in either case. But clearly they would lose less in the absence of controls.

But in Ghana's disrupted economy there are a limited range of investments which yield 10 percent in real terms. Returns to financial savings are, in fact, negative. With how high a rate of return could housing compete? The internal rate of return for a controlled unit is estimated at roughly zero. In other words, landlords could at least preserve capital with housing if other investments were yielding negative returns. Without controls, housing could compete for capital with investments yielding up to 8 percent. Controls reduce the rate of return by about 8 or 9 percentage points. Such current investment as exists is motivated more by capital gain (or more accurately avoiding capital loss) and by nonpecuniary income (status conferred) than by current income from the unit.
Female teachers increase learning achievement. Interestingly, training (measured by the number of courses taken by the teacher) has not impacted student achievement. Moreover, each one of these courses separately failed to have an impact on learning achievement. Thus, investment in primary school teachers seems most effective when targeted towards increasing practical experience and developing content-specific knowledge.

Teacher’s years of residence in the community increases student’s achievement, possibly because of the teacher’s involvement with the community. Type of post (permanent or short-term) negatively impacts learning achievement. A possible explanation is that in public schools, a temporary post has almost the same benefits as a regular or permanent post. It is extremely rare to find a case where an individual had to leave his job because his “short-term post” was not renewed or because it was not changed to a long-term post. For practical purposes, “short-term post” does not mean that the teacher has to go through a probation period. This system of posting and assignments generates a conflict within learning.

Teacher’s years of schooling failed to demonstrate significant effects on student learning, which is expected since there is little variance in the level of schooling. A teacher's income has no significant effect on learning achievement, but many studies have found that teacher's salary is a poor predictor of a student's achievement.

Frontline educators feel that problems relating to school infrastructure and facilities negatively affect teaching effectiveness and student learning achievement. Their foremost recommendation for raising school quality is to address this inadequacy. To what extent this recommendation will actually lead to student learning achievement is questionable. Some studies in other countries show that improvement in school infrastructure can have a significant positive impact on student learning. Mexican data do not appear to support this hypothesis.

Teacher’s pedagogical efforts show a positive and significant marginal effect on learning achievement. Pedagogical effort and teacher answers to student questions are highly correlated with greater learning achievement. Other work or secondary activity does not effect a student’s test scores, possibly because only a small proportion of 5<sup>th</sup> grade teachers have a secondary occupation. A large number of public school teachers, however, have two or more posts. As part of ANMEB, teachers have at least two posts, one at the primary school level and another at the lower secondary school level. Didactic materials available to the teacher and school facilities failed to demonstrate a significant effect on learning achievement.

19
Uganda: Buy-Back of Commercial Debt _5_

# Restructuring Uganda's Debt

Despite the favorable developments discussed above, macro-economic stability in Uganda has continued to remain fragile, largely because of the inability of the economy to generate adequate resources, i.e. tax and non-tax revenues on the domestic front and export revenues on the external front. As a result, the economy has experienced severe cash flow problems during the past few years, which have been aggravated by the severe external debt burden.

# Uganda's External Debt Situation

Using the ratio of present value of total debt service to GNP and the ratio of present value to total exports, Uganda is classified as a Severely Indebted Low Income Country (SILIC) by the World Bank's Debtor Reporting System<sup>2</sup> .

As summarized in Table 1, Uganda's stock of debt outstanding and disbursed (DOD) was estimated at US$2.65 billion as of June 30, 1992, of which approximately US$583 million was in arrears. The stock of debt had grown significantly from about US$1.2 billion in 1985, and largely reflected the vast reconstruction needs of the war-torn Ugandan economy. Approximately 66 percent of this debt was owed to multilateral organizations, with the World Bank and the IMF accounting for about 76 percent of multilateral debt. Bilateral debt accounted for about 25 percent of DOD, with OECD bilaterals accounting for about 11 percent and nonOECD bilaterals 14 percent. The remaining 9 percent of DOD was owed to commercial 2 For furtherdetail **s** ee _World Debt Tabks, 1993-94,_ Appendix 1, The World Bank, Washington, D.C.
|Arellano Bond test - Order 2|2.22***|
|---|---|
|Arellano Bond test - Order 3|-1.26|
|Hansen-J|chi2(13) = 8.95|
|Dif-Hansen|chi2(9)= 4.29|

**Table 7** <u>SYS-GMM parameter estimates</u>

|Variable|Coefficient|Std error|
|---|---|---|
|_lag(privinvt)_|.6655032***|.1001062|
|_growth_|.2784228**|.1496112|
|_regquality_|100.8325*|74.44351|
|_indepreg_|354.6202***|142.9526|
|Obs.|248||
|Fisher test|F(4, 33) = 20.17***||
|Arellano Bond test - Order 1|-2.22***||
|Arellano Bond test - Order 2|2.25***||
|Arellano Bond test - Order 3|-1.13||
|Hansen-J|chi2(13) = 11.08||
|Dif-Hansen|chi2(9)= 5.95||

# **6. The effect of the financial sector development level on private investment in the power sector**

Let us now examine the financial sector effect. Tables 8-10 below present the results of an analysis that follows the same approach as for the case of the overall economic development level discussed above. We first focus on the level of financial sector development and subsequently add risk and institutional quality variables. We then start from the estimation of a "naive" dynamic model in which we assume that the level of development of the financial sector, _findev_ , is the only factor that affects private investment (Table 8). We then introduce control variables to adjust for the effect of financial risk (Table 9) and both the effects of financial risk and institutional environment (Table 10). Here again, the results supports the proposition that the state of development of the financial sector matters for private investors.

As can be seen from Table 8, financial sector development significantly influences private investment in developing countries’ power projects as _findev_ is found to be significantly and positively related to private investment. We next examine the importance of the institutional environment and risk factors for private investors when considering the level of development of developing countries’ financial sector. Table 9 confirms that financial sector development positively and significantly affects private investment, and also emphasizes that among all institutional and risk measures considered in this study, exchange rate risk is key determinant to private investment in the 16
that we maximize the space explored in the sampling, Urquhart et al. (2020) provide an LHS routine that optimizes the choice of these points by minimizing where _L_<sup>2</sup> _pq_<sup>is the</sup><sup>_L_2</sup><sup>_−_norm between two sample points. To build the surrogate model, we use</sup> Gaussian radial basis functions. In general, radial basis functions depend on a set of center points, which are the sampled points, and take as argument the Euclidean distance between the interpolated points and the sampled points. Urquhart et al. (2020) provide a method in which the width factor that determines the size of the radial basis function changes across points. Finally, the optimization is done with a differential evolution algorithm.

These methods do not have stopping criteria. Typically, the user sets a maximum number of iterations or a maximum amount of time. So, while within the _computational budget_ , when new points are found through the optimization routine, we sample new points and repeat the whole process. We allow for 150 iterations in total and limit the space of the function to (0, 0.3] for the six spillover parameters.

After the surrogate optimization routine is done, we re-optimize using an adaptive differential evolution algorithm in the full function to improve upon the surrogate optimization solution. We limit the function space to be within _±_ 25% of the previous solution found. We simulate 400 economies of 100 regions each and use 45 points for the LHS sampling.

## **F.1 Assessing Identification of Moments**

One potential problem is that the number of simulations _S_ with _N_ locations might not be sufficient to evaluate the moments properly. To partially assess this, we simulate _M_ batches of _S × N_ simulations with different shocks and compute the moments and confidence intervals. This will tell us the variation across the _M_ batches of simulations of each moment conditional on a set of parameter values exogenously posed.

Table F.11 shows the mean and the 95% confidence interval for each moment across the _M_ simulations. It shows that the confidence intervals are small for all moments, which suggests all moments are computed with relatively high accuracy or that the number of simulations is enough to identify them. For this experiment, we set _M_ = 150 with _S_ = 400 and _N_ = 100 as in the estimation.

# **G Algorithm to Find the Equilibrium**

We solve for the decentralized equilibrium using the following algorithm.

> 1. We start by guessing Ξ _≡{L_<sup>0</sup> _i_<sup>,</sup><sup>_w_</sup> _i_<sup>0,</sup><sup>_b_</sup> _i_<sup>0</sup><sup>_}i∈N_and pick tolerance level</sup><sup>_ε_.</sup>

52
The facilitator brought to each community five copies of the scorecard in English and five copies in the local language, a number line to graphically show the ranking of the community project, and sodas and soap as gifts to participants. Once the facilitators left, they did not return to the community.

The interventions we study here were based on a well-defined curriculum that was directly relevant for projects being implemented in communities. The training intensity was relatively long compared to other studies cited above. The scorecard information was also tailored to the projects and meant to encourage specific action by communities and presented direct comparisons to other communities in their area.

# **3 Experimental design**

Due to the large size of the NUSAF2 program, it was implemented in 12 rounds over five years. Working with the IG, we were given a list of all projects to be funded in the seventh and eighth rounds and randomized which communities would be given the social accountability and community monitoring training. The randomization was done in Stata. Due to the limited amount of administrative data from the government that had been digitized, we were only able to observe the location, budget, and rough classification of projects _ex ante_ (whether public works, livelihood investments, or infrastructure rehabilitation projects). The communities’ choice of project type was based on an endogenous process that we were not able to observe. We present some discussion in the next section about what predicts the type of project that is chosen. Note that every community in our sample received a NUSAF2 project. As the interventions we study here were randomly distributed across projects, and project types are well balanced for each treatment and control condition, the type of project chosen by a community does not bias our inference of impacts from the training and information treatments.

The timeline for this study is presented in Figure 3. An initial survey of local officials, discussed below, was conducted in early 2013. In November 2013, we received the list of NUSAF2 projects from the seventh and eighth funding rounds. We randomly assigned communities into social accountability training treatment or control in January 2014, with the NUSAF2 program and social accountability trainings beginning in June 2014. In December 2014, 80% of the funds were distributed, with the other 20% funded in the preceding six months.

11
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 25. Sierra Leone’s power and water utilities burden of inefficiency**

<!-- Start of picture text --> a. Uncollected bills and unaccounted losses in the power sector, as a<br>percentage of GDP<br>2.000<br>1.500<br>1.000<br>0.500<br>0.000<br>Sierra Leone LIC-fragile<br>Collection inefficiencies Unaccounted losses<br>Percenatage of GDP<br><!-- End of picture text --> b. Uncollected bills and unaccounted losses in the water sector, as a percentage of GDP <!-- Start of picture text --> 0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Sierra Leone LIC-fragile<br>Collection inefficiencies Unaccounted losses<br>Percenatage of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia and others (2009). LIC = low-income countries.

# Annual funding gap

Sierra Leone‘s infrastructure funding gap amounts to $278 million per year, or about 15 percent of GDP, once inefficiencies are captured. Every infrastructure sector is facing funding gap (table 15). By far the biggest funding gap, even after accounting for efficiency potential, is found in the water sector, followed by that in power, transport, and ICT.

**Table 15. Funding gaps by sector**

<u>US$ millions</u>

||ICT|Power|Transport|WSS|Total|
|---|---|---|---|---|---|
|Spending needs|(39)|(117)|(120)|(202)|(478)|
|Existing spending|23|36|60|16|134|
|Efficiency gains|0|36|18|11|66|
|Funding gap|(17)|(45)|(42)|(174)|(278)*|

_Source_ : Derived from Foster and Briceño-Garmendia (2009).

_Note_ : Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors.

— = data not available.

* Assuming complete fungibility across sectors

# What else can be done?

The remaining funding gap can be addressed only by raising additional finance or, alternatively, by adopting lower-cost technologies or less-ambitious targets for infrastructure development. Sierra Leone may have realistic prospects for increasing the flow of resources to infrastructure, and to power and water in particular, both from the public and private sectors.

Sierra Leone has attracted significant private finance into infrastructure in comparison with its African peers, but there is room for improvement. Over the early 2000s, Sierra Leone captured private 45