rafmacalaba/datause-encoder-data huggingface dataset:
providing the decision maker with easy-to-use tools. Simplification is Rften dictated<sup>by the</sup> nature of the data. The crudeness and unreliability of some of the existing data makes oversophistication inappropriate.

The first case study is discussed in this paper and deals with Ghana.

Ghana is a typical example of tiie malaise that plagues SSA, and its transport<sup>sector</sup> is a microcosm of the country's economy as a whole. The extensive road system<sup>bulilt before</sup> and during the first years of Ghana's independence shared the fate of the rest of<sup>the</sup> economy', and fell into serious disrepair in the 1970s and 1980s. By the mid-1980s it<sup>was</sup> found (Kocks Consults, 1986, pp. 3-6) that only 15 percent of the roads could be deemed in good condition, only 40 percent are in fair condition, and over 45 percent are<sup>in poor</sup> condition - some of them are barely motorable. The condition of feeder roads is even worse: some have 'disappeared' altogether.

Glhana has embarked on an ambitious 7-year program of road rehabilitation: to clear the backlog of maintenance and establish a maintenance system that will stabilize<sup>road</sup> conditions thereafter. At this point it is only natural to ask whether the resources<sup>marshaled</sup> by government in the form of transport taxes or road user charges will prevent<sup>the</sup> reoccurrence of this scenario.

The questions have been asked before. David Walker conducted a survey<sup>of transport</sup> taxation in Ghana (TecnEcon, 1988b) with emphasis on the administrative nosts of raising these funds. A more comprehensive study was conducted by Kocks Consultants<sup>as part of</sup> the Fourth Highway Project (1986). The study tried to estimate tlht size of the<sup>road fund</sup> required to finance current and periodic maintenance once the maintenance backlog<sup>was</sup> cleared, and the most effective way of raising these funds. In the absence of data, Kocks<sup>did</sup> not try to estimate road user costs; instead, they focused on road maintenance costs,<sup>traffic</sup> forecasts (and forecasts of the vehicle stock and fuel consumption) to derive the tax rates that would generate the target revenue.

This study tries to pick up the trail where earlier studies left off. It tries to estimate road user costs, including not only the road damage component but also the cost<sup>of</sup> congestion (urban and inter-urban). Given these estimates, it evaluates the adequacy<sup>of the</sup> current transport tax system.

The study opens with background discussion of the Ghanaian road sector, followed<sup>by</sup> the estimation of road user costs. The next section examines the adequacy of<sup>the current tax</sup> system, and the paper clobs S ith concluding remarks.
**Table 2: Life satisfaction across different scales**

||**Satisfaction**<br>**with life (1-**<br>**10 scale)**|**Satisfaction**<br>**with life**<br>**(disagree –**<br>**agree scale)**|**Satisfaction**<br>**with**<br>**finances**<br>**(disagree -**<br>**agree scale)**|
|---|---|---|---|
|Ln(household expenditures)|0.118***<br>(0.013)|0.112***<br>(0.014)|0.170***<br>(0.015)|
|HH asset index|0.161***<br>(0.006)|0.132***<br>(0.006)|0.134***<br>(0.006)|
|Primary education or less|-0.136***<br>(0.022)|-0.039*<br>(0.022)|-0.008<br>(0.023)|
|Secondary (baseline)||||
|Post-secondary education|0.139***<br>(0.015)|0.112***<br>(0.015)|0.155***<br>(0.016)|
|Did not work during past 12 months|-0.083***<br>(0.015)|-0.065***<br>(0.015)|-0.135***<br>(0.016)|
|cut1|-1.237***|-0.828***|0.091|
||(0.095)|(0.096)|(0.101)|
|cut2|-0.754***|0.077|1.031***|
|cut3|(0.095)<br>-0.203**|(0.096)<br>0.820***|(0.101)<br>1.762***|
|cut4|(0.095)<br>0.254***|(0.097)<br>2.332***|(0.102)<br>3.129***|
||(0.095)|(0.099)|(0.105)|
|cut5|0.944***<br>(0.096)|||
|cut6|1.383***<br>(0.096)|||
|cut7|1.878***<br>(0.097)|||
|cut8<br>cut9|2.475***<br>(0.099)<br>2.860***<br>(0.102)|||
|Pseudo R2|0.063|0.069|0.068|
|Obs|37402|37402|37402|

Notes: Ordered probit regressions. Robust standard errors, clustered at PSU level in parentheses. Regressions also included as independent variable age, sex, religion, marital status, area of residence and country dummies (coefficients not reported). Significance: * 0.10 ** 0.05 *** 0.01.

# **4. Measurement error in subjective well-being data: Consistency across scales and within households**

# **_4.1. Framing and scaling_**

We exploit the existence of two separate life-satisfaction questions in the LiTS (with different scales and placement in the survey) to examine the role of framing in variations in subjective 12
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mortgage finance (EBRD, 2006), although further reforms are needed to fully establish clearly-defined property rights.

The rapid increase in household credit, however, is partly also demand-driven, by higher incomes as well as by higher expected future incomes. Reliance on foreign-currency loans, at significantly lower interest rates, can be explained by the rational expectation of appreciating local currencies following the Balassa-Samuelson hypothesis. The aggregate numbers presented in this section show the rapid increase in household indebtedness over the past years. They do not, however, give any insight into the distribution of access to credit and indebtedness within countries and the effect thereof on welfare and fragility. We turn to these questions now.

# **4. Who has access to mortgage finance?**

Mortgage debt holdings vary significantly across countries as well as within countries by selected household characteristics. Using EU-SILC survey data, this section utilizes graphs and multinomial probit regressions to explore household characteristics that can explain mortgage debt holding. In particular, we compare the significance and elasticity of these different characteristics across countries. We distinguish between renting, owning with and owning without mortgage and relate an array of household and individual characteristics to this tenure status.

Figure 4 shows the distribution of renters, outright owners and owners with mortgage across countries in our sample of SILC countries for 2007.<sup>11</sup> We note two countries in Western Europe without any outright owners in our database – Denmark and the Netherlands – and thus only renters and mortgage holders.<sup>12</sup> In 2007, the share of mortgage holders varied from 1.9% in Slovenia to 57.6% in Denmark, while the share of renters ranged from 10.7% in Lithuania to 48% in Austria.

> 11 We weight all household observations according to the weights given in the survey.

> 12 This is most likely due to the fact that mortgage interest payments are completely tax-deductible in these countries and both have very high marginal tax rates.

12
# **Mines, Migration and HIV/AIDS in Southern Africa**

**Lucia Corno**<sup>**1**</sup> **Damien de Walque**<sup>**2**</sup>

**University College London, CReAM                   The World Bank, Development Research Group**

_JEL Classification:_ I10, O12, O15 _Keywords: HIV/AIDS, Africa, miners_

> 1 Corno: Centre for Research and Analysis of Migration (CReAM), University College London, Department of Economics, Gower Street, WC1E6BT, London, UK. Phone: +44 02076795451, Fax: +44 0207 679 1068. Email: <u>l.corno@ucl.ac.uk.</u>

> 2 de Walque (corresponding author):  The World Bank. Development Research Group, 1818 H Street, NW Washington DC, 20433, USA. Phone: +12024732517, Fax: +12026140234 Email: ddewalque@worldbank.org. We thank Martina Bjorkman, Rachel Kline, Eliana La Ferrara, Peter Nicholas, Patrick Osewe and seminar participants at Bocconi University for very helpful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
# **VI. Concluding Remarks**

While freight forwarding firms are not important to most economies in terms of their employment or the value of their output, they play a critical role as facilitators of international goods trade. But the sector’s operation and its challenges are often not well understood. This study provides evidence on the operational behavior of international freight forwarders operating in Serbia, with a specific focus on the challenges involved with moving goods across international borders. These insights are derived from a survey of 153 freight forwarders in Serbia conducted in 2014.

The key lesson from this study concerns the relative importance that freight forwarders attach to time savings versus monetary cost savings. Responses to several hypothetical questions as well as to questions about actual operational decisions suggest that often monetary savings have a higher priority than time savings. First, Serbian freight forwarders indicate that transport costs are a substantially more important determinant of route choice than delays at borders or administrative costs. Second, the choice of Northern Adriatic ports over more distant European ports that would allow faster delivery also suggests a preference for monetary savings. Third, in three out of four specific hypothetical situations that impose a direct trade-off between monetary costs and time-savings, Serbian freight forwarders reported that they would give up time savings in order to have monetary savings. Freight forwarders indicated that they (or their clients) typically claimed preferential tariffs, even if doing so increased the risk of delay. They also risked delay by consolidating shipments, and by delaying shipments in order to get a full truck load. The only situation in which time savings concerns dominated monetary costs was the freight forwarders’ choice of border posts as they indicated that they tend to choose faster border posts even when doing so means higher monetary costs.

There are a host of operational decisions that lie behind the delivery of an international shipment. It can therefore be difficult to summarize the particular factors that determine these choices. This study provides some context through a comprehensive survey of the firms that make such choices in Serbia, an upper-middle-income country. One key lesson from the study is that lower operating costs often dominate speed in key operations decisions. An interesting policy question that arises from our study is whether, given that freight forwarders generally absorb the logistics costs of delay (which presumably leads them to charge higher average rates) 27
distribution of Ramo 33 for health, in terms of pesos per person in the open system. In health, unlike education, one sees lower funding in the poorer states, because those have a larger share of their population in the open system.

_Figure 4. Distribution of Ramo 33 for Health (2002)_ <!-- Start of picture text --> 2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>MxP per uninsured person<br>Chiapas Guerrero Oaxaca Veracruz-llave Hidalgo San Luis Potosí Puebla Campeche Tabasco Michoacán de Ocampo Yucatán Zacatecas Guanajuato Nayarit Sinaloa Querétaro de Arteaga Durango Tlaxcala Morelos Quintana Roo México Colima Tamaulipas Sonora Jalisco Chihuahua Baja California Sur Aguascalientes Coahuila de Zaragoza Baja California Nuevo León Distrito Federal Weighted Average<br><!-- End of picture text --> _Source_ : SHCP and Bank staff calculations.

The states all want more effective control over resources received with _aportaciones._ This means particularly control of education and health personnel, although not necessarily the wage rate. This would allow the states to allocate personnel according to where the services are needed—on the basis of the number of students, school-aged children, patients in the open population, etc.—and to hold the personnel, schools, clinics, and hospitals accountable for certain levels of service delivery and client satisfaction. Thus far the national unions (along with local unions and allies in some parts of the national and state governments) have opposed and usually blocked such changes.

The political dimensions of federalism are largely beyond the scope of this paper, but one aspect deserves mention for its special importance to fiscal management issues—that is, the (non)re-election of congressional delegates at both the state and federal levels. Not only are the three-year terms of deputies and mayors too short to plan ahead, but the no re-election rule means that once in office they have little incentive for loyalty to their state and locality, since their next job in the legislative or executive branch depends on connections within the party rather than the satisfaction of the constituents who elected them. The widely recognized benefits of change do not, however, guarantee that it will overcome resistance from traditional party interests that gain from the status quo.

**_Municipal governments_** in Mexico now seem to have the best of all worlds – good tax bases if they will use them (predial, water fees), relatively high transfers per capita (at least for small municipalities), and not an excessive burden of responsibilities.  The federal and state transfers to municipalities are only loosely earmarked— for social infrastructure—and more importantly did not come with a powerfully unionized work force, as was the case for the states.

8
When the gap is caused by the firm not being surveyed that year we replace both firm level and trade variables with their last observed values. The interpretation of the coefficients is that a positive sign on a given covariate implies an increase in the probability of death, meaning a higher hazard of terminating the trade transaction conditional on the duration of the transaction. We present full results of the estimations in the appendix.

# **4.2 Results**

The baseline regression results are shown in the first column of Table 5. Before discussing the results in detail we discuss the role of spells’ duration. The regression analysis confirms that there is negative duration dependence. The longer the spell is in place the lower is the probability that it will end. The key feature of the discrete survival model is the ability to control for the duration of the trade transaction including tenure dummies. These dummies define a baseline hazard rate in function of the duration of the relationship. Figure 8 shows how the baseline hazard varies with the duration of the trade relationship. There is a big drop in the hazard of ending the relationship after the first year. Spells that survive the first year tend to have a higher probability of surviving in the future.

Figure 8: Export Mortality Rates Across Length of Export Spell <!-- Start of picture text --> 0 2 4 6 8<br>Duration years<br>Source: Authors calculation based on GeoStat firm level data<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> The value of the export transaction reduces the hazard of terminating the transaction meaning export spells of higher value tend to last longer. The initial value of the export flow is positively associated with its survival. Taking the average initial value as a benchmark (of about 158 thousand dollars), firms that start exporting with an initial value of double that average, are 0.45 percentage points more likely to survive than firms starting with an average size export.

14
<!-- Start of picture text --> subsection. The closed-form formulas provide a first guess of sample size to start the simu-<br>lation algorithm.<br>domization rate should also take MHT into account. Otherwise, significance levels will be<br>higher and/or test power will be lower than planned. Note that the number of observations<br>might dramatically increase with the number of tests. Splitting samples might help keep the<br>sample, we estimate the resulting model and test the significance of each variable, limiting<br>Simulation is an alternative strategy to determine the required sample size. Simulation for<br>DIA presents no particular challenge, so we only sketch a brief overview here. See<br>Similar to the previous subsection, the selection criteria are the significance and power<br>of a hypothesis test against the ZNH. Instead of computing these error probabilities with as-<br>ymptotic methods, however, we use simulation. To be precise, we simulate pseudo-samples<br>from the model of interest under the null and the alternative hypothesis. For each pseudo-<br>tions until empirical rejection rates satisfy the desired level of accuracy.<br>Simulation offers a number of advantages over asymptotic theory. Firstly, it accommo-<br>dates estimators with unknown or excessively complex asymptotic distribution (e.g., the IPW<br>estimator of QTTs), as well as difficult inference problems (e.g., uniform inference or multi-<br>ple hypotheses). Secondly, it reflects the finite-sample behavior of estimators which might<br>converge slowly to their limiting distribution. Thirdly, it is straightforward to incorporate<br>To conduct a simulation exercise, it is necessary to choose the number of pseudo-<br>samples. Unless the computational burden is high, it should be large: five thousand or ten<br>thousand are good figures. One also needs to specify the data generating process. Choosing<br>distributions of covariates and the error term is often challenging: they should reproduce<br>the conditions of the RCT, including dependence patterns in the error term (such as clusters<br>or serial correlation in panel data). Previous studies might provide some guidance. If an ex-<br>isting dataset contains the covariates of interest (or some of them), it is possible to draw<br>pseudo-samples from it to minimize distributional assumptions. Sensitivity analyses are al-<br>Applications<br>In this part, we present applications of the methods discussed above using data from two<br>RCTs. Section 7.1 revisits the study of financial education by Bruhn et al. (2016). In Section<br><!-- End of picture text -->
_LCOE_ (in _kWh_ ), or international commodity price (tons of oil equivalent, _toe_ ) assumptions plus any carbon taxes or subsidies. For _j_ the price can be expressed as:<sup>6</sup> where _FXt_ is the bilateral exchange rate to the dollar. The price can be modified by including a subsidy, _τt_<sup>_j_whichwillberepresentedintermsof</sup><sup>_toe_units.Arenewablesubsidy</sup> would lower the price of renewables relative to fossil fuel prices and prompt a substitution towards renewable use.

# 3.1.3 Consumption

Demand for electricity is mainly derived from household consumption choices and from electricity used as an intermediate input (which may be large). Deriving energy (electricity) demand from final demand components is a standard feature in many CGE and integrated assessment model (IAMS), see, for example, Loulou and Labriet (2008) for the TIMES model and Böhringer and Rutherford (2013), He et al. (2011), and Holmøy (2005) for CGE approaches.

Aggregate consumption in MFMod remains unchanged from previous versions. It is a function of disposable income (nominal wage bill ( _WtNt_ ) adjusted for taxes ( _τt_<sup>_N_)and</sup> deflated by the consumption deflator ( _Pt_<sup>_C_),transfers(</sup><sup>_GTRN_</sup> _t_ ), remittances ( _REMTt_ ) and the real interest rate ( _it − πt − rt_<sup>_n_).ItisstilldeterminedbythestandardMFModequation:</sup> The consumption bundle for MFMod contains electricity consumption ( _Ct_<sup>_E_)andother</sup> consumption ( _Ct_<sup>_O_).They are represented as substitutes.The first-order condition yields the</sup> demand for electricity as a function of the relative price of consumption to electricity prices: where _σ_ is the constant elasticity of substitution, _γ_ 1 is the share of electricity in the consumption basket, and _Pt_<sup>_C,E_</sup> is the (market price based) energy price index.<sup>7</sup> The expression

> 6In some cases, one may prefer to use the index of the cost of production because the cost is likely to be closer to the infrastructure you already have, not the infrastructure you could have (based on the _LCOE_ ). 7 See Equation (28) for the definition of _Pt_<sup>_C,E_</sup> .

10
proxy for bank efficiency. Unfortunately the latter association is not sufficient to infer the effects of judicial efficiency on the cost of credit as the ratio may be affected by several assets composition effects. For example, a decrease of NII may derive from an increase of fixed assets or government bonds at the expenses of the loan portfolio. Another potentially important source of bias is given by the inclusion among total assets of noninterest accruing non-performing loans (NPLs). Other analysis of the determinants of bank interest rate margins such as Ho and Saunders (1981) and Saunders and Schumacher (2000) have tried to control for these factors through a two-stage regression procedure, but none of these studies focuses explicitly on the effects of judicial efficiency.

In this paper we shall focus explicitly on the cost of bank credit, but we believe that our results could be taken as indicative of the wider impact of judicial inefficiency on the cost of external finance. The policy relevance of an empirical evaluation of the relationship between the cost of credit and judicial reforms is manifold.

The quantification of the average impact that better judicial protection of property rights has on the level of lending rate may help to visualize the gains that a cooperative behavior may bring to the often opposed constituencies of bank debtors and of bank creditors. The potential reduction of the cost of credit would in fact show the extent to which a better protection of the creditors could help the debtors to access cheaper sources of finance.

It may also show which one of the competing forces that in principle affect lending rates is stronger and matters from the standpoint of countries at a different level of development. For instance, the cross country evidence provided by Demirguc-Kunt 3