rafmacalaba/datause-encoder-data huggingface dataset:
11 recently, Bujumbura was also the political capital of the country. Excluding Bujumbura from the analysis is common in studies in Burundi given the different dynamics in the city (Fransen et al, 2017; Verwimp and Van Bavel, 2014).

The survey is a follow-up to a survey conducted with the same households in 2011. However, most of the variables related to social cohesion were only collected in 2015, hence the analysis in this paper focuses on that round.

# **4.2 Measuring social cohesion**

Social cohesion is a multi-dimensional concept. Therefore, we use a series of different variables to measure it, including: (A) measures of support across households, (B) feelings towards conflict and reconciliation, (C) trust in others and on specific groups, and (D) participation in different community organizations.

Table 2 reports means for all the dependent variables and the Appendix provides a description of all the variables included in the analysis (Table A1). We present the results for all respondents and then separately for those in more/less ethnically diverse communities, those in communities with more/less pre-war land availability and those in communities with better/worse attitudes towards return migration. In order to construct an index to proxy migration-related divisions at the community level we add the four responses to the return question in Table 1, Panel C (i.e. help the country, fit in, bring new ideas, and receive preferential treatment), in a way in which higher values indicate worse attitudes, and create a dummy to indicate that the community is above or below the mean for this variable.

Table 2 – Means of independent variables

||All|Ethnic di<br>|versity<br>|Land av<br>(pre-19<br>|ailability<br>93 war)<br>|Negativ<br>toward<br>|e attitude<br>s return<br>|
|---|---|---|---|---|---|---|---|
|||Less|More|Less|More|Less|More|
||||Panel<br>|A: Support<br>|<br>|||
|Help each other|0.4343|0.4435|0.4235|0.4635|0.3982|0.4306|0.4348|
|Borrow money|0.5851|0.5612|0.6039|0.6180|0.5459|0.5694|0.5968|
|||Pan<br>|el B: Violen<br>|ce and reco<br>|nciliation<br>|||
|Peaceful community|0.9601|0.9568|0.9608|0.9583|0.9597|0.9689|0.9526|
|Unlikely reoccurrence of conflict|0.1330|0.1259|0.1350|0.1346|0.1315|0.1510|0.1186|
|Reconciled with war|0.7630|0.7663|0.7598|0.7678|0.7590|0.7957|0.7366|
|Justice has been done|0.3730|0.4209|0.3347|0.4026|0.3349|0.3788|0.3705|
||||Panel<br>|C: Trusts in<br>|<br>|||
|People in the community|0.8898|0.8868|0.8922|0.8985|0.8724|0.8935|0.8793|
|Returnees|0.8862|0.8857|0.8845|0.8994|0.8792|0.9017|0.8792|
|Other ethnic groups|0.9223|0.9205|0.9235|0.9120|0.9329|0.9187|0.9266|
|Community leaders|0.8013|0.8101|0.7918|0.8113|0.7830|0.8201|0.7861|
|Ex-combatants|0.7652|0.8010|0.7366|0.7851|0.7448|0.7700|0.7675|
|||Panel D:|Household|membersp|articipating|in||
|Agricultural cooperative|0.1229|0.1247|0.1216|0.1357|0.1096|0.1340|0.1146|
|Credit/savings association|0.1972|0.2014|0.1961|0.1733|0.2260|0.1794|0.2134|
|Religious group|0.2274|0.2302|0.2275|0.2255|0.2304|0.2225|0.2332|
|Politicalparty/group|0.3513|0.3741|0.3314|0.3528|0.3468|0.3540|0.3439|
on the importers’ side (-$69.6 billions), rendering a net loss of about $14 billions. This latter result is still a puzzle for us and deserves further attention.<sup>15</sup> Tables 5 and 6 summarize respectively the change in exports and imports flow for Mexico. The first two columns in Table 5 shows that Mexico’s unilateral improvements in trade facilitation measures are expected to increase manufacturing exports by $31.8 billion, which are equivalent to 22.4% of the average export level for years 2000-2003<sup>16</sup> . In turn, improvements in trade facilitation in Mexican partners would increase Mexican exports by $2 billion (1.4% of Mexican exports). Combining both impacts gives a total expected increase in Mexican exports of $33.8 billion, equivalent to 23.8% of Mexican average exports level for years 2000-2003.

Besides updating the estimating approach, the main contribution of this paper is the analysis of impacts by sector. Following Table 5, it can be seen that the simulation brings a higher impact of Mexico’s unilateral improvements in trade facilitation measures on Machinery exports (59%), and lower, although still important, percentage impacts in Textiles (41.2%), Vehicles (28.4%) and in Food exports (11.5%). As our simulation involves improvements in trade facilitation in other countries that are below the mean, Mexican exports get an additional boost (although only of relative importance in Food exports) from this source. In the remaining sectors, more that 95% of the increased exports are coming from Mexico’s own reforms.

The impact on Mexican imports is shown in Table 6. The overall expected impact on imports is about $24.2 billion, equivalent to 16% of Mexican average imports level for years 2000-2003. The simulation brings a higher percentage impact on Food, Beverage and Tobacco imports (19.7%) Textile imports (13.9%), and lower impacts in Machinery (12.2%) and in Vehicles (3.9%). At the aggregate level, about 70% of the changes in imports are due to Mexican reforms in trade facilitation, and in particular from improvements in Port Efficiency ($19 gains from Mexico’s reform divided by total gains of $24.4 billion).

Which trade facilitation reform measure is more important for Mexico? Table 7 shows a summary of results for the expected impact of own reform on exports and imports, as a share of the overall effect of the simulations carried out in the paper. On the exports side, Mexico’s own reforms in Port Efficiency (a “border measure”) are as important as improvements in the Control

> 15 Although the specification of the gravity model and the years covered by the data are different from the ones used here, it is worth to compare this figure with that of WMO(2004), where total export gains were estimated in $377 billion, about 9.7% of total world trade in manufacture. In any event, expected gains are important. 16 In WMO (2004), total export gains for Mexico were estimated in $17.3 billion.

16
12 subsectors. The survey includes weights that allow the survey data on utilization and fee payments to be grossed up to the population level.

Table 1 shows the means and grossed‐up population figures for the utilization data for each subsector, and the out‐of‐pocket spending data. The survey also has comprehensive data on household consumption of food and nonfood items, whether purchased or home‐produced. This allows us to estimate the concentration indices, ���� and ���� . These are also shown in Table 1. Outpatient utilization rates at the CHC level are higher among the poor, while the opposite is true of outpatient visits to other facilities and inpatient admissions. Out‐of‐pocket spending at CHCs is less concentrated among the poor than CHC utilization is, and out‐of‐pocket spending at other facilities is more concentrated among the better off than utilization is.

The exercise also requires data on total government spending (i.e. subsidies) for each subsector, as well as total out‐of‐pocket payments to the subsectors as recorded in the government statistics. These are obtained from Vietnam’s more detailed NHA. Table 2 presents the figures, and the note to the table explains how the figures are arrived at. Inevitably, assumptions have to be made in deriving the figures, as the NHA headings do not correspond exactly with the utilization breakdowns permitted by the household survey. Under ‘personal health services’ (item HD 1.1) the NHA records spending on inpatient and outpatient treatment by households and government (at the central, provincial and district levels), as well as spending by households on medicines for self‐medication. Under ‘health prevention and public health’ (item HD 1.2) the NHA records spending by households and government (at the commune level) on ‘primary health care and school health care’. The assumption made in Table 2 is that the spending on inpatient and outpatient care recorded in HD 1.1 refers to spending at the hospital level (district and above), while the spending on ‘primary health care and school health care’
number of antidumping import restrictions. Such breaks remind us that these barriers are temporary as they reveal a sudden foreign market access liberalization facing the exporter – at least with respect to it confronting less TTB policy coverage of its _potential_ exports. Nevertheless, none of the indicators presented thus far address whether exports previously confronted by a TTB _actually_ resume. The next section turns to this question.

# **4 The Removal of Antidumping Import Restrictions: Do Exports Resume?**

The indicators of the previous two sections document at least two pieces of important evidence. First, major emerging markets have increased the scope of their imports covered by TTBs. Second, major emerging countries have a sizeable share of their total exports and exports sent to other emerging economies impacted by foreign-imposed TTBs. An important question yet to be addressed is what happens to exports when these _temporary_ trade barriers are finally removed? To what extent do exports resume?

# **4.1 Regression approach and data sample**

We use the _Temporary Trade Barriers Database_ to construct a baseline sample of TTBs that one of the fourteen member economies of the G20 (listed in Table 1) imposed and removed between 1992 and 2008. Let _t_ be the year that the TTB was first imposed and let _T_ be the year that the TTB was ultimately removed. Given the need for two years of data both prior to _t_ and subsequent to _T_ , we use the 19922008 period because of constraints imposed by the availability of the HS-06 import data. We also focus on antidumping alone (and not all TTB policies) as it is most frequently used TTB policy and one for which the decision to impose and remove barriers is undertaken on a trading-partner specific basis.<sup>9</sup> The approach is to investigate the trade impact associated with a sample of 746 antidumping cases pooled across fourteen policy-imposing G20 member economies. We take information from the _Temporary Trade Barriers Database_ regarding when the import restriction was imposed, when it was removed, and the product codes associated with the TTB, and we match this to annual HS-06 trade data available from UN Comtrade. We require sufficient time coverage in the trade data so as to trace out ten

> 9 We also clean the data set of instances in which a policy-imposing country may have removed an antidumping duty over a particular product _h_ but left in place another TTB over the same product against the same trading partner.

11
12 OMA is the elasticity of transformation -- i.e. the extent to which production mAy be shifted betweon outputs destined for different markets.

Using quarterly data over the period 1975 I to 1986 IV, we estimate the elasticity of transformation between supplies of leather footwear destined for three markets -- the USA, wunconstrained-EC' and the rest of the world. The USA imposed the OMA on Korean exports of non-rubber footwear between the third quarter of 1977 and the second quarter of 1981, inclusive. As explained below, the observations corresponding to the OMA -- period are not included in the estimation period. The second group unconstrained-EC -- comprises France, West Germany, Italy and the Netherlands -- which, according to Hamilton (1989), imposed no quantitative restrictions on Korean footwear exports over our sample period. The rest of the world comprises all other countries, some of which did have import restrictions on footwear, but which may be reasonably treated as unconstrained overall. Although the OMA operated formally betbreen 1977 and 1981, the evidence suggests that the restrictions on Korea ceased to bind by mid-1980 (Aw and Roberts, 1986).

As in the analysis in section 2, the export allocation model presumes that individual Korean exporters are price-takers and that they seek to maximize profits subject to a CET transformation function relating the quantities of each type of footwear export to an overall index of output (input). That is

> max E Pi Xi subject to EaiX7 | -X Xi [
household characteristics) and find that many variables affecting CONTRIBUTION1 are also important for BELIEF1.

After analyzing the determinants of CONTRIBUTION1, we test is whether those who contribute more to the public good in the experiment (i.e. cooperative individuals, conditional or otherwise) are more likely to participate or more extensively engage in costly collective action activities that benefit community forests. We analyze the six behaviors and associated variables described below, all of which are costly to respondents, either in terms of time, land, or money.

The first three variables are binomial, variables 4 and 5 are count data and variable six is an interval variable.

- (ANYTREE_CF) Whether the subject planted any trees in the community forest during the previous month;<sup>7</sup>

- (MEETING_DUM) Whether the subject attended a community forest user group meeting during the previous month;

- (BIOGAS_DUM) Whether the subject had adopted biogas;

- (MONITORING) Number of hours the subject spent monitoring and guarding the community forest during the previous month;

- (NUMTREES_FARM) Number of trees planted by the subject and his or her family on their private land during the last five years;

- (NUMTREES_CF) Number of trees planted by the subject in the community forest during the previous month by category (0, 1-3, 4-6, 7-10, >10);

All these behaviors have important common benefits, but we anticipate that respondents incur the costs associated with the behaviors at least partly because they are in their own interests in light of their personal characteristics and the institutional environments in which they live. For example, attending user group governance meetings, while costly, also offers private benefits, such as the opportunity to see friends and take a break from agricultural work. Biogas adoption and planting trees on household farms have particularly strong private aspects, but also

> 7 This variable relies on the same information as NUMTREES_CF, but is included because only 38 respondents had planted any trees during the previous month.

13
To deal with these concerns, we use an equilibrium concept from the international capital asset pricing model (ICAPM) to input expected returns in the construction of the efficient frontier. Furthermore, we account for potential frictions –those that might limit domestic pension fund managers from reaching the unconstrained level of international diversification– by estimating a benchmark level of foreign holdings according to each country’s financial integration.

We start with the unconstrained portfolio optimization. Consider a model of portfolio choice with N assets of a pension fund located in country _c_ . A number F of these are foreign assets and N-F are local. For example, the foreign assets might include fixed-income and equity securities in international markets. For each asset _i_ , we estimate its beta from a regression of realized excess returns on the world portfolio returns measured in domestic currency of country _c_ (i.e., from the point of view of an investor domiciled in country _c_ ), − r (R − r (2) ii,tt,cc tt,cc ii,cc ii,cc tt,cc tt,cc ii,tt,cc rr = αα + ββ RR ) + εε where r is the risk-free rate. We obtain the expected excess return on each asset by tt,cc multiplying the estimated beta of the asset by the world portfolio expected excess return; or in vector notation = β ]. Using the expected returns and the variance-covariance cc cc cc cc ππ EE[R RR − r matrix of the N assets Σ, the portfolio optimization problem is simply the selection of the vector of weights that maximizes the expected returns for a given level of risk. To be precise, assuming cc that the current asset allocation of a pension fund manager in country ww _c_ is 0, we want to find the portfolio that maximizes the expected returns, maintaining the variance of the current portfolio ww ( Σw0). More formally, 0 0 σσ<sup>2</sup> = ww<sup>′</sup>
that mostly took place in the late 1990s (Figure 1).<sup>34</sup> The number of pension funds in the market has been proportional to the number of PFAs. Thus, from July 1996 to December 2005, the number of pension funds increased from 15 (one per PFA) to 30 (five per PFA).

Assets under pension fund management increased substantially from 1996 to 2005 both in absolute and relative terms (Figure 2). In 2005, pension funds managed around 38.3 billion Chilean pesos, an amount that was almost 2.5 times the 1996 value in real terms. As a share of GDP, assets managed by pension funds increased 1.6 times, from 37.4 percent in 1996 to 57.5 percent in 2005 (Figure 3). Since the creation of the multifund scheme in August 2002, Fund C, which is the continuation of the old Fund 1, has been the fund with the largest relative share of assets in the system. However, the relative participation of the two riskiest funds (Funds A and B) has been steadily increasing (Figure 4).<sup>35</sup> The participation of Chilean pension funds in the markets for different instruments varies significantly. For fixed-income instruments, the average holding of bills and bonds by pension funds was around 65 percent of the total domestic debt during 2001-2005. For equities, it was 7.8 percent of the total domestic market capitalization, during 1996-2005, with a decreasing trend during this period (Figure 5). Moreover, relative to OECD countries and Colombia, the allocation was high in fixed-income instruments and low in equities (for a given level of pension fund assets as a share of GDP).<sup>36</sup> This might be explained by: (i) the high percentage of closely held shares, which in Chile averaged 64.7 percent during 2001-2005, and (ii) the relaxation of the

> 34 Of the three largest Chilean PFAs, two of them – _Cuprum_ and _Habitat_ – have never merged with or acquired competitors, while the current PFA _Provida_ results from three mergers between _Provida_ and _El Libertador_ in 1995, _Unión_ in 1998, and _Protección_ in 1999.

> 35 The increasing importance of Funds A and B is due to: (i) SP’s automatic allocation of contributors who do not choose a fund in which to deposit their monthly contributions to a specific fund, especially from November 2002 to November 2003, and (ii) the voluntary switching of accounts from more conservative to riskier funds as an attempt of younger affiliates to achieve higher returns (Figure 1). The automatic allocation works as follows: (i) contributors younger than 35 years old are assigned to Fund B, (ii) contributors older than 35 years old but younger than 55 (men)/50 (women) years old are assigned to Fund C, and (iii) contributors older than 55 (men)/50 (women) years old are assigned to Fund D. As of April 2007, about 68.6 percent of the system’s 8.63 million affiliates had been automatically assigned by SP, of which 42 percent, 46 percent, and 12 percent were assigned to Funds B, C, and D, respectively.

> 36 Source: OECD Global Pension Statistics and World Bank Financial Development Indicators (WDI).

13
the census is a function not only of the parameter estimates from the first stage consumption models estimated in the survey, but also of the precision of these estimates and of those parameters describing the disturbance terms in the consumption models, we do not produce just one predicted consumption level per household in the census. Rather, _r_ predicted expenditures are simulated for each household (we carry out 100 replications). For each respective _r_ , parameter estimates are drawn from a multivariate normal distribution that respects the variance-covariance matrices estimated in the survey-based consumption and heteroskedasticity regressions. In addition, disturbance terms at the cluster and household level are drawn from their respective (parametric or semi-parametric) distributions. These draws are then applied to the census-level regressors and per-capita consumption is predicted. For the next _r_ , a new set of parameters and disturbances are drawn and a new per-capita consumption measure is predicted. The resulting database of _r_ predicted expenditures for every single household in the population census is the key database underpinning “poverty maps” and the policy-simulation exercise explored here.<sup>9</sup> The data used in this study consist of the pairing of the 2002 household survey with the 2004 population census.

# **III. Transfer Schemes**

As described in Section I, our main objective in this paper is to see to what extent the availability of poverty estimates for different geographic locations can help to improve the poverty impact of distributing a given budget. We postulate that the government has a budget, _S_ , available for distribution and wishes to transfer this budget in such a way as to reduce poverty. We specify a baseline case in which the government is assumed to have no

> 9 The poverty map estimate of poverty in community, province or region _c_ is produced from this database in the following manner: for every replication _r_ , poverty is estimated over all households in _c_ (after weighting by household size).  The average of all poverty

7
|**Country**|**Bill collection**|**T&D losses**|**Over-**<br>**staffing**|**Underpricing**|**Total hidden**<br>**costs**|
|---|---|---|---|---|---|
|Seychelles|0.06|0.18|0.35|-0.85|-0.26|
|Sierra Leone|0.21|0.47|0.08|0.10|0.87|
|South Africa|0.15|0.00|0.50|2.78|3.429|
|Sudan|0.00|0.10|n/a|1.32|1.41|
|Swaziland|0.04|0.04|0.43|0.73|1.24|
|Tanzania|0.13|0.14|0.07|-0.06|0.28|
|Togo|0.55|1.15|0.30|-0.40|1.60|
|Uganda|0.01|0.16|0.08|-0.32|-0.07|
|Zambia|0.12|0.09|0.23|0.79|1.23|
|Zimbabwe|1.26|0.58|0.75|2.59|5.19|
|Maximum|1.94|2.77|1.12|3.04|6.08|
|Minimum|0.00|0.00|0.00|-0.85|-0.26|
|Median|0.15|0.31|0.22|0.42|1.04|

_Source_ : World Bank staff calculations based on utility financial statements, annual reports, and other documents. _Note_ : — = not available.

# 6.1 T&D losses

As outlined in Table 2, T&D losses consist of technical and nontechnical losses. International reference values for well-performing power systems suggest that technical losses can be kept to less than 10 percent and non-technical losses close to zero. For simplicity, a combined benchmark losses of 10 percent are used in this study. Only four countries—Botswana, Lesotho, Mauritius, and South Africa— were found to have T&D losses of 10 percent or smaller (Figure 10). In the remaining 35 countries, losses ranged from 11 percent in Swaziland to more than 40 percent in Comoros, São Tomé and Príncipe, the Republic of Congo, and the Central African Republic. Weighted average losses in SSA were 15 percent, and 23 percent if South Africa is excluded.

In several reference utilities, a portion of sales are made at high voltage levels which will tend to skew T&D losses downward because T&D losses are proportionally greater in the low voltage distribution network. Examples high voltage sales include industrial and mining customers, and sales to other utilities who sell to the end user. Examples of the latter include all export sales, municipality customers in South Africa who buy electricity from Eskom and selling to end-users, and Copperbelt Energy Corporation which buys electricity from Zambia Electricity Supply Company and sells to mining companies.

T&D losses represent a significant hidden cost for many utilities in SSA. Relative to GDP, these losses represent more than one percent of GDP in five countries (Cape Verde, Comoros, The Gambia, São Tomé and Príncipe, and Togo), and more than 50 percent of the QFD in 11 countries (Benin, Cameroon, Cape Verde, Central African Republic, Republic of Congo, Gabon, Ghana, Liberia, Sierra Leone, Tanzania and Togo).

44