rafmacalaba/datause-encoder-data huggingface dataset:
7

**exchange rate** "overvaluation"<sup>or "misalignment"? A systematic</sup> **analysis will** now **be presented**<sup>to deal with</sup><sup>**thesa**questions</sup>

# 1. Th? Basic Model: Switching<sup>and Expenditure Adiustment</sup>

Figure I is the "Swan diagram" from<sup>Swan (1963). While</sup> familiar, it calls for careful<sup>interpretation here. It will be</sup> assumed that the country **is** small<sup>in world markets, so that any</sup> **nhanges** in **the terms** of trade<sup>are exogenous. The vertical axis</sup> shows the relati-e price of traded<sup>to non-traded goods in</sup> **domestic** currency terms, allowing<sup>also for</sup><sup>**the effocts**of</sup> tariffs, quantitative restrictions,<sup>and so on, that affect this</sup> relative price ratio. This is the<sup>S ratio, S standing either for</sup> "Salter" or for "switching".<sup>3</sup> It is sometimes called the "real<sup>4</sup> exchange rate", a movement upwards<sup>being a real depreciation.</sup> 3 T'he reference is to Salter (1959),<sup>where the first</sup> systematic, diagrammatic model with<sup>traded and nontraded goods</sup> was presented.

> 4 The assumption is made here that imports and domesticallyproduced "importables" are perfect<sup>substitutes an assumption</sup> which, in a world of product differentiation,<sup>is clearly</sup> unrealistic. Hence it should be<sup>regarded as no more than a</sup> simplifying assumption. It should<sup>be noted here that the real</sup> exchange rate cou. _ be defined<sup>as the relative price of</sup> domestically produced tradables<sup>to nontradables, as here, or as</sup> an index of "competitiveness".<sup>The latter definition is favored</sup> in Balassa (1987) and is the relative<sup>price of traded goods in</sup> foreign countries, adjusted for the<sup>nominal exchange rate, and</sup> their prices in the domestic economy.<sup>This definition hinges on</sup> the raalistic assumption that foreign<sup>and domestic tradables are</sup> imperfect substitutes so that their<sup>prices adjusted for the</sup> exchange rate can indeed differ.<sup>The main arguments in this paper</sup> - especially the comparison between<sup>the real targets and the</sup> - nominal anchor approaches apply fully when imports and domestically-produced import-competing<sup>goods are imperfect</sup> substitutes, the latter, in terms<sup>of the (Salter) model of this</sup> paper, being, in effect, nontradables.
|Intercropping<br>6076|551|9.07%|$2,339.56|0.00%|0.39%|Intercropping<br>5348|1438|26.89%|$24,872.49|0.00%|0.11%|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Strip Tillage<br>6076|2058|33.87%|$1,169.78|0.03%|2.90%|Strip Tillage<br>5348|375|7.01%|$24,872.49|0.00%|0.03%|
|Mulching<br>6076|1399|23.03%|$1,169.78|0.02%|1.97%|Mulching<br>5348|722|13.50%|$24,872.49|0.00%|0.05%|
||rrect Response|ave Correct Response||centage of population that have correct response|ercentage of population that have correct response|||e Correct||centage of measures that are correct|ercentage of measures that are correct|
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26
such results. On the other hand, if there is one country in which we would expect this reaction, it is Norway. Asatryan and Peichl, 2017 underscored that in developing countries, where formal institutions might be weaker, the effect of audits could be different from that in developed economies. This is precisely the context we explore in our analysis of Indonesian data.

The second class of mechanisms behind the increase in tax compliance is often referred to as "tax morale". It represents a more recent body of literature that has investigated the role of nonpecuniary motives more broadly Kirchler, 2007, Luttmer and Singhal, 2014, Besley et al., 2023. In that sense, the visitation program would increase the intrinsic value of being compliant.

The last class of information explains compliance behavior through "information frictions" Bhargava and Manoli, 2015, H. J. Kleven and Kopczuk, 2011, Cox et al., 2020. Here, evasion could originate from taxpayers who are not aware of their obligation or who do not know how to be compliant. The visits would increase compliance to the extent that tax authorities inform the taxpayers how to fulfill their obligations Kirchler, 2007 and Slemrod et al., 2001. Recently, De Neve et al., 2021 explored this hypothesis in an experiment in Belgium using quantitative methods similar to those used in this paper.

# **2.2. Use of Machine Learning to Investigate Heterogeneous Effects**

The advent of Machine Learning (ML) techniques signifies a pivotal advancement in the toolkit available to applied researchers across various fields. It encompasses a broad spectrum of statistical learning methods such as Random Forest, Neural Networks, and Penalized Regression, which have revolutionized prediction and pattern recognition, especially in highdimensional settings (Athey and Imbens, 2019; Hastie et al., 2009;Bishop, 2006).

Machine Learning (ML) methods excel in conducting research within observational studies, particularly in scenarios where significant sample imbalances exist between treated and nontreated groups. Traditional approaches in this domain have largely relied on propensity score 7
15 simply weighted averages of the concentration indices for each category of care where the weight is the share of subsidies for that type of care in total subsidies. The left‐hand panel shows the results when the grossed‐up survey data on out‐of‐pocket payments are used for total fees, and the right‐hand panel shows the results when the survey data on out‐of‐pocket payments are scaled down so they gross up to the health ministry’s official NHA figure.

The first set of results in Table 3 are when the traditional unit‐cost assumption is made. Unit costs are dramatically smaller when the NHA data are used in the case of hospital care reflecting the lower aggregate fee figure when the NHA data are used. This results in less (negative) weight being put on the concentration index for _Fk_ in eqn (2); as a result, subsidies appear to be more pro‐rich or less pro‐poor when the NHA data are used. An even bigger influence on the results under the constant‐cost assumption is whether or not negative subsidies are set to zero. Irrespective of whether the grossed‐up survey data or the NHA data are used, the effect of setting negative subsidies to zero is to change subsidies from being pro‐ poor to being pro‐rich. In other words, it is highly likely that had previous studies of the incidence of government health spending in Vietnam not adopted the practice of setting negative imputed subsidies to zero, the conclusion would have been that government health spending in Vietnam disproportionately benefits the poor! Note that in this case the concentration index for polyclinic visits is outside the ‘normal’ interval for a concentration index, i.e. the range when the values of the underlying variable are all nonnegative.

It is worrying that what is done with negative subsidies makes such a difference to the results. It casts some doubt on the constant cost assumption and the view implicit in it that fees in general—and especially very large ones causing negative subsidies—tell us nothing about the cost of the care being received. The constant cost assumption treats negative subsidies as a
|applied to Spoke<br>Post PECS|11,80|4,11|17,42|6,02|3,76|4,73|1,91|3,94|5,41|4,66|5,53|7,21|5,46|7,62|4,33||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Avg Tarif <br>Pre PECS|8,75|4,57|14,78|9,46|5,54|5,54|1,75|5,95|6,86|7,21|6,88|9,65|6,28|6,03|5,77||
|applied to RoW<br>Post PECS|10,49|4,73|19,49|9,62|4,55|5,62|1,91|5,38|6,45|7,21|6,65|10,24|6,54|8,15|5,39||
|Avg Tarif <br>Pre PECS|8,60|5,60|14,89|10,13|6,38|5,98|2,23|7,15|7,96|7,77|7,58|10,13|7,26|6,50|6,64||
|imports from Spoke<br>Post PECS|314,88|1 201,98|1 739,38|791,91|1 077,37|958,77|12 659,99|569,63|1 775,21|518,69|668,08|395,90|3 244,34|353,71|1 728,52||
|Avg Spoke <br>Pre PECS|180,00|698,03|683,80|339,72|394,25|529,70|3 674,93|207,30|858,21|201,82|337,48|164,20|1 234,72|345,75|846,92|e terms.|
|imports from RoW<br>Post PECS|224,44|183,74|255,67|129,61|342,53|141,12|6 073,35|94,55|129,95|103,78|62,25|72,11|532,02|129,08|109,40|e expressed in percentag|
|Avg Spoke <br>Pre PECS|152,79|114,13|200,35|59,27|90,37|69,54|3 511,16|46,50|73,46|63,48|30,65|38,18|185,43|137,82|66,75|$. All tarifs ar|
|In sample averages:|Animal Products|Chemicals|Foodstufs|Footwear/Headgear|Machinery/Electrical|Metals|Mineral Products|Miscellaneous|Plastic/Rubbers|Raw Hides,Skins,Leathers|Stone/Glass|Textiles|Transportation|Vegetables|Wood Products|_Notes_: Values are in thousands of US|

13
_DHS data on the treatment offever_ In a subset of the DHS questionnaires, mothers were asked to report what, if any, action was taken if they responded that their child had a fever in the past two weeks. Analyzing this data can be done for Burkina Faso 1992-93, Cameroon 1991, C6te d'Ivoire 1994, Ghana 1993, Niger 1992, Nigeria 1999, and Senegal 1992-93 in Western and Central Africa, and for Kenya 1998, Malawi 1996, Rwanda 1992, Madagascar 1992, Tanzania 1992, Zambia 1992 and Zimbabwe 1999 in Eastern and Southern Africa. Note that Niger, Madagascar, Tanzania, and Zambia refer to a survey from a different year from that used in the incidence analysis.

Typically, the surveys will ask "did you seek advice or treatment for the fever" for a child who is reported to have had a fever in the past two weeks. In some cases, the question is asked whether the child is reported to have had a fever _or_ a cough with rapid breathing in the past two weeks. In those cases where the child is reported to have had both a fever and a cough it is impossible to know whether the advice or treatment was sought for the fever and not for the cough. In this analysis we ignore the problem and include the advice/treatment seeking behavior as long as the child is reported to have had at least a fever.

The types of modern sector facilities/persons that the mother can report having visited are grouped into: "higher-level public" (i.e., government hospital); "lower-level public" (e.g., government health center, government health post, mobile clinic, community health worker); "private medical" (e.g., private hospital/clinic, private doctor, private mobile clinic); "private commercial" (e.g., pharmacy, shop); and traditional healers.20 In addition to these generic options, country specific options (for example a nurse's practice, public health post, and a pharmaceutical depot) have been mapped to the basic classification. Among the responses included in the "no modern sector" category are "no treatment or advice" and "advice from friends or family." Table 10 shows the basic results in on treatment seeking behavior in the study countries. There is about a ten-percentage point difference between the two regions in the overall level of modern sector use. In Western and Central Africa about 56 percent of cases of fever resulted in a visit to the modern health sector, whereas in Eastern and Southern Africa about 65 percent of cases did.

While the focus here is on regional averages, the data do show wide variation across countries within each region. For example, 43 percent of cases of fever among children in Western and Central Africa did not result in any medical advice sought, but this ranges from 15 percent in Cote d'Ivoire to about 75 percent in Burkina Faso and

> 20.Private medical facilities may be "commercial" in nature. This terminology is used purely to distinguish the two types of private services here.

_20_
Table 8: Regression results for CDM projects cooperation level (Dependent variable is project CO2 abatement level)

|Equation|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Estimationprocedure|Tobit|Tobit|Tobit|Tobit|Tobit|Tobit|
|Dependet variable|_Total CO2_<br>_Abatement_|_Total CO2_<br>_Abatement_|Ln(_Total_<br>_CO2_<br>_Abatement)_|Ln(_Annual_<br>_CO2_<br>_Abatement)_|_Total CO2_<br>_Abatement_|Ln(_Total_<br>_CO2_<br>_Abatement)_|
|_hst_Average Annual_|114.15***|114.90***|0.021***|0.018***|64.93***|0.022**|
|_Energy Use_|(5.48)|(5.53)|(2.67)|(2.64)|(2.92)|(2.38)|
|_inv_Average Annual_|218.51***|215.16***|0.090***|0.077***|175.52***|0.091***|
|_Energy Use_|(7.95)|(7.66)|(8.33)|(8.46)|(6.48)|(8.26)|
|_host_inv_Average_|||||0.554***|-1.03E-06|
|_Annual Energy Use_|||||(4.49)|(-0.21)|
|_hst_Ease of Doing_|-195.70***|-206.87***|-0.095***|-0.077***|-228.42**|-0.113***|
|_Business_|(-2.45)|(-2.60)|(-3.23)|(-3.09)|(-2.21)|(-2.84)|
|_inv_Ease of Doing_|59.17|148.23|0.085|0.055|-151.26|-0.029|
|_Business_|(0.27)|(0.68)|(1.10)|(0.84)|(-0.39)|(-0.21)|
|_host_inv_Ease of_|||||1.77|0.001|
|_Doing Business_|||||(0.49)|(0.84)|
|_hst_Governance_|1435.95**|1488.29**|0.799***|0.657***|1946.27|0.996|
||(1.86)|(1.93)|(2.82)|(2.72)|(0.71)|(1.00)|
|_inv_Governance_|8680.39***|9433.50***|3.91***|3.17***|8067.71***|3.69***|
||(5.13)|(5.51)|(6.07)|(5.83)|(5.00)|(5.73)|
|_host_inv_Governance_|||||-50.55|-0.020|
||||||(-0.20)|(-0.23)|
|_hst_Renewable_|24.98***|24.67***|0.011***|0.010***|29.69***|0.011***|
|_Energy_|(2.91)|(2.88)|(3.65)|(3.67)|(3.63)|(3.43)|
|_inv_Renewable_|-27.68***|-26.01***|-0.010***|-9.601E-3***|-18.30*|-0.011***|
|_Energy_|(-2.69)|(-2.53)|(-2.89)|(-3.00)|(-1.62)|(-2.76)|
|_host_inv_Renewable_|||||-0.063***|2.44E-6|
|_Energy_|||||(-2.89)|(0.32)|
|_hst_Impact_|784.79***|751.08***|0.361***|0.314***|874.22*|0.470***|
|_Vulnerability_|(4.44)|(4.28)|(5.46)|(5.55)|(1.91)|(2.59)|
|_inv_Impact_|975.92***|1016.79***|0.430***|0.355***|1210.89*|0.547**|
|_Vulnerability_|(4.52)|(4.74)|(5.36)|(5.16)|(1.74)|(2.08)|
|_host_inv_Impact_|||||-5.47|-3.10E-03|
|_Vulnerability_|||||(-0.42)|(-0.61)|
|_Trade1_|6.91E06***|||1651.83***|4.12E06***|1879.84**|
||(3.82)|||(2.50)|(1.96)|(2.29)|
|_Trade2_||1.85E6***<br>(3.46)|441.40**<br>(2.04)||||
|_Constant_|-2.53E5***|-2.60E5***|-110.46***|-93.66***|-2.35E5***|-111.55***|
||(-8.58)|(-8.69)|(-9.37)|(-9.43)|(-6.42)|(-7.41)|
|Observations|2771|2771|2771|2771|2771|2771|
|Uncensored obs.|182|182|182|182|182|182|
|LogLikelihood|-2477.73|-2477.86|-1113.53|-1083.83|-2467.98|-1112.32|
|LR|431.28***|431.04***|422.78***|426.03***|450.79***|425.21***|
|Pseudo R<sup>2</sup>|0.080|0.080|0.159|0.164|0.083|0.160|

In parentheses are t-values; *** (=0.01); ** (=0.05); * (=0.10).

32
|LSMS-ISA Burkina Faso Panel Surveys|Marco Tiberti|
|---|---|
|Afghanistan Technical Assistance Facility|Yousif Mubarak Elmahdi|
|Afghanistan Technical Assistance Facility|Yousif Mubarak Elmahdi|
|Afghanistan Technical Assistance Facility|Yousif Mubarak Elmahdi|
|Employment Opportunities for Vulnerable Youth Project|Friederike Uta Rother|
|Employment Opportunities for Vulnerable Youth Project|Friederike Uta Rother|
|Employment Opportunities for Vulnerable Youth Project|Friederike Uta Rother|
|Employment Opportunities for Vulnerable Youth Project|Friederike Uta Rother|
|Employment Opportunities for Vulnerable Youth Project|Friederike Uta Rother|
|Employment Opportunities for Vulnerable Youth Project|Friederike Uta Rother|
|Employment Opportunities for Vulnerable Youth Project|Friederike Uta Rother|
|SafetyNets and Basic Services Project|Gbetoho Joachim Boko|
|SafetyNets and Basic Services Project|Gbetoho Joachim Boko|
|SafetyNets and Basic Services Project|Gbetoho Joachim Boko|
|SafetyNets and Basic Services Project|Gbetoho Joachim Boko|
|SafetyNets and Basic Services Project|Gbetoho Joachim Boko|
|SafetyNets and Basic Services Project|Gbetoho Joachim Boko|
|SafetyNets and Basic Services Project|Gbetoho Joachim Boko|
|Modernization of Water Supplyand Sanitation Services|Martin Benedikt Albrecht|
|Modernization of Water Supplyand Sanitation Services|Martin Benedikt Albrecht|
|Modernization of Water Supplyand Sanitation Services|Martin Benedikt Albrecht|
|Modernization of Water Supplyand Sanitation Services|Martin Benedikt Albrecht|
|Modernization of Water Supplyand Sanitation Services|Martin Benedikt Albrecht|
|Madhya Pradesh Rural Connectivity project|Tesfamichael NahusenayMitiku|
|Madhya Pradesh Rural Connectivity project|Tesfamichael NahusenayMitiku|
|Madhya Pradesh Rural Connectivity project|Tesfamichael NahusenayMitiku|
|Madhya Pradesh Rural Connectivity project|Tesfamichael NahusenayMitiku|
|Madhya Pradesh Rural Connectivity project|Tesfamichael NahusenayMitiku|
|Cote d'Ivoire - ElectricityTransmission and Access Project|Thierno Bah|
|Cote d'Ivoire - ElectricityTransmission and Access Project|Thierno Bah|
|Cote d'Ivoire - ElectricityTransmission and Access Project|Thierno Bah|
|Cote d'Ivoire - ElectricityTransmission and Access Project|Thierno Bah|
|Cote d'Ivoire - ElectricityTransmission and Access Project|Thierno Bah|
|Cote d'Ivoire - ElectricityTransmission and Access Project|Thierno Bah|
|Heat SupplyImprovement Project|Yun Wu|
|Heat SupplyImprovement Project|Yun Wu|
|Heat SupplyImprovement Project|Yun Wu|
|Heat SupplyImprovement Project|Yun Wu|
|Heat SupplyImprovement Project|Yun Wu|
|STP Power Sector RecoveryProject|Nicolas Jean Marie Sans|
|STP Power Sector RecoveryProject|Nicolas Jean Marie Sans|
|STP Power Sector RecoveryProject|Nicolas Jean Marie Sans|
|STP Power Sector RecoveryProject|Nicolas Jean Marie Sans|
|STP Power Sector RecoveryProject|Nicolas Jean Marie Sans|
|STP Power Sector RecoveryProject|Nicolas Jean Marie Sans|
|STP Power Sector RecoveryProject|Nicolas Jean Marie Sans|
|STP Power Sector RecoveryProject|Nicolas Jean Marie Sans|
not. Another plausible explanation is that only governors who can be re-elected can fully internalize the benefits of privatization. If the costs of privatization (i.e. closing branches and realizing past losses) are immediate, whereas the benefits come over time, then politicians with short time horizons might be unwilling to privatize. Other than the coefficient on the share of loans made to the public sector, the other coefficients remain statistically significant at at least a 10% level.

As a final exercise, Figure 2 shows the estimates of the base hazard rate for each period when at least one bank was privatized.<sup>3 7</sup> Essentially this is the rate at which the surviving public provincial banks will be privatized after controlling for all independentiX iX Te tebs variables.<sup>38</sup> Thelargerthebase hazard rate, the more likely privatization is for any surviving public provincial bank in that period. Although it is hard to reach any strong conclusions based upon <!-- Start of picture text --> Base Hazard Rate for Privatization privatized. 3 7 Essentially this is the<br>I - rate at which the surviving public<br>0.9<br>0. 7 provincial banks will be privatized<br>0.6 0.5 5 after controlling for all independentiX iX<br>0.4 Te tebs<br>0.3 variables. 38 Thelargerthebase<br>0.2<br>0.1 hazard rate, the more likely<br>0<br>Apr- Oct- May- Nov- Jun- Jan- Jul- Feb- Aug. privatization is for any surviving<br>92 92 93 93 94 95 95 96 96 public provincial bank in that period.<br>Month<br><!-- End of picture text --> Figure 2: Base Hazard Rate.

Figure 2, the figure suggests that after controlling for bank quality, provincial fiscal deficit and political affiliation of the governor and legislature, privatizations appear more likely after mid 1995.39 One possible explanation for this is the Tequila crisis of 37 Estimates are only for those months when at least one bank was privatized.

38 Technically, it is the rate at which surviving public provincial banks would be privatized if all independent variables were set to zero.

39 Two additional pieces of evidence suggest that the base hazard rate is not constant. First, when we estimate the model with aWeibull base hazard rate, we reject the null hypotheses that the base hazard rate is flat or monotonically decreasing. The fully parametric Weibull model, a special form of the proportional hazard model with base hazard rate 2,_(t) _=_ p _tP-',_ has a monotonically increasing base hazard rate if p is greater than one, monotonically decreasing base hazard rate if p is less than one, and a flat base hazard rate if p is equal to one. The point estimate of p from our model is greater than one, and the null hypothesis that it is equal to one can be rejected at conventional significance levels. Second, as Kalbfleisch and Prentice(1980, p.32) note the exponential hazard model is also a special case of the proportional hazards 19
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