rafmacalaba/datause-encoder-data huggingface dataset:
periods of economic expansion, bundling fuel prices volatility to economic volatility (Gupta, 2002).

- 3) Subsidies hinder competitiveness. In Indonesia, Pertamina is currently the sole company responsible for fulfilling Public Service Obligation (PSO) and thus the only channel for subsidies to flow to retail consumers. Other companies have recently been allowed to sell market-priced higher octane fuels and other products, but their penetration remains very small, mainly because their non-subsidized fuels can be at least 50 percent more expensive than Pertamina’s subsidized prices.<sup>6</sup>

- 4) Subsidies distort price signals to industry and households. As a consequence, they make inefficient and internationally uncompetitive choices, resources are used in ways that do not maximize their returns, and production processes are less efficient than they would be if producers faced the true cost of their activities (e.g., consumers buy fewer fuel efficient cars or live further from their workplaces than they would if they faced the true opportunity cost of their fuel consumption). In turn, inefficient production technologies entail higher costs and more energy-intensive production and, thus, higher subsidies. Additionally, these distorted price signals lead to wasteful or economically excessive consumption of petroleum products with the associated environmental effects, and ensuing health costs. Energy-wasteful and polluting production choices are likely to become far more expensive when carbon emissions are priced globally. The costs are immediate: fuel subsidies undermine the price inducement to innovate in terms of alternative fuels to more efficient urban design. They are also long term: today’s distorted choices are creating an uncompetitive capital stock, and the costs of adjusting away from that stock will grow as oil prices rise.

- 5) Subsidies reduce fiscal space. This means the government has fewer resources to promote growth through investments in infrastructure or human capital.

- 6) Lastly, subsidies generate opportunities for corruption and smuggling. Products bought domestically at below-market prices can be profitably smuggled to neighboring countries, or they can be used for unintended purposes, such as mixing the subsidized, household fuel with other fuel types and using the doctored fuel for industrial purposes. This in turn may undermine governance institutions and the rule of law. This also represents an inefficient use of scarce entrepreneurial talent.

> 6 See World Bank (2007b) for a detailed discussion of the policy reforms of the downstream industry required for a liberalization of the fuel market in Indonesia.

16
error. For example, there is a wide range of macro- and micro-nutrients contained in nearly all individual food items captured in a typical HCES and it is difficult to assign the caloric and nutritional content of each food item consumed (e.g., USDA 2019); it is also difficult to identify the nutritional content of many processed foods and food consumed outside the household that are becoming important increasingly to modern diets (e.g., Deaton and Subramanian 1996); and many of the dietary needs of individuals are unobservable and based on individual choices and activities (e.g., Institute of Medicine of the National Academies 2006). These issues compound other traditional sources of measurement error that affect the measurement of expenditure in HCES’s, such as recall biases, impacts of different questionnaire formats, and a wide variety of other concerns (e.g., Beegle et al. 2012; etc.). The variance on estimates of the quantity and quality of food consumption are therefore large and are potentially increasing over time as households consume more processed foods and meals outside the household (e.g., Tandon and Landes 2011; Tandon and Landes 2014).

Given these difficulties and the need to obtain estimates of food access in real time and in data-poor environments, practitioners and researchers have increasingly relied on metrics that are relatively easy to implement while also approximating the degree of food access in its many dimensions (e.g., Swindale and Billinsky 2006; WFP 2009; Maxwell and Caldwell 2008; etc.). Two common approaches include (1) measuring dietary diversity and the frequency with which individuals and households consume certain food groups, such as the Food Consumption Score and the Household Dietary Diversity Score (e.g., Swindale and Billinsky 2006; WFP 2009); and (2) measuring food coping strategies often associated with consuming too little or consuming a poor quality diet, such as the Coping Strategies Index and the Reduced Coping Strategies Index (e.g., Maxwell and Caldwell 2008).

However, others have argued that additional psychological aspects related to food access should be captured in standard metrics (e.g., Webb et al. 2006; etc.). An additional set of experiential measures of food access, such as the Latin America and Caribbean Food Security Scale and the Household Food Insecurity Access Scale, extend food access 7
|Lag<br>of<br>°F!|(7a)<br>Pf{Level 4}<br>PrLevel <4}|(7b)<br>P{Level 5}<br>PfLevel4}|(7c)<br>P{Level 6}<br>PfLevelS}|(Ba)<br>PfLevel 4)<br>| P{Level <4}|(ab)<br>PfLevel 5}<br>PfLevel 4}|(8c)<br>PfLevel 6}<br>PfLevel 5}|
|---|---|---|---|---|---|---|
||O13|0.23|O24|Oa|0.18|O.2|
|SameTrackingforMathClasses|(0.19)|(0.16)|(0.22)|(0.18)|(0.21)|(0.26)|
|<br>|[1.14]<br>|[0.73]<br>|[0.73]<br>|[1.31]<br>|[0.84]<br>|[1.22]<br>|
|Female|-0.86"*<br>(0.13)|0.4**2**"<br>(0.4|-**0**.07<br>(.31)|O%**2**"<br>(0.3)|0**.**42<br>(031)|0.**3**5<br>(0.6)|
||[0.43]|<br>[0.66]|<br>[0.93]|<br>[0.49]|<br>[0.66]|<br>[0.70]|
||o.7a"*|O27|0.05|Oo.65°|0.33|0.16|
|Female x Some Tracking|(0.19)|(0.21)|(0.34)|(0.25)|(0.32)|(0.37)|
||[2.05]|[1.31]|[0.92]|[1.91]|[1.33]|[1.17]|
|Other Controls||No|||Yes||
||0.12|0.15|0.14|-0.07|-0.08|0.19|
|Female+SomeTrackingxFernale|(0.11)|(0.12)|(0.12)|(0.15)|(0.17)|(0.16)|
||[0.89]|[0.86]|[0.87]|[0.93]|[0.45]|[0.83]|
||a.er*|0.05|0.52|og26*|O.16|0.36|
|Sore Tracking + Some Tracking x Fernale|(0.2)|(0.19)|(0.38)|(0.21)|(0.23)|(0.36)|
|Note: Cells indicate the association between the ro<br>these are calculated by using a linear combination <br>are reported in square brackets below. Standard er<br>denoted **, **, and *, respectively. Estirnates were <br>Repreated Replication with Fays(0.5) adjustment w<br>Source data: PISA 2003 Korea, General School Str|[2.39]<br>w's denoted va<br> of the standard<br>rors are reporte<br> calculatetd us<br>ere used to es<br>ata, Grade 10|[1.05]<br>riable and the <br> coefficients es<br>d in parenthes<br>ing 5 plausible <br>timate the stan<br> Students|[0.73]<br>change in the <br>timated in the <br>es and signifi<br> values for est<br>dard errors.|[2.51]<br>log of the colurn<br> rnultinornial lo<br>cance at the 1%<br>imates of achie|[1.17]<br>n's denoted <br>git models. <br>, 5%, 10% l<br>vernent and|[1.43]<br> odds ratio;<br>Odds ratios<br>evels is<br>Balanced|
# 1 Introduction

Uncertainty in project planning and appraisal is still topical (it won't go awayl) in the World Bank and other lending and development agencies (see, e.g., Sarris and Adel ian 1982), although it is certainly not a new issue, given the pioneering ruethodological studies that emerged from the 1960s (Reutlinger 1970, Pouliquen 1970). It is appropriate to reconsider the issue now because more than a decade of active research or. risk analysis has transpired without, however, the seeming emergence of agreed procedures and practice. In particular, the implications for what information price forecasters should provide for risky project appraisers have yet to be clarified. A comprehensive review of decision making under risk and uncertainty is well beyond the scope of this modest study. Fortunately, several apposite reviews are available (Anderson, Dillon and Hardaker 1977, Hey 1979, Jean 1970, Keeney and Raiffa 1977), and these provide entrees to the ever-expanding horizons of literature on risk aversion, uncertainty, consumption and saving, capital budgeting (Weingartner 1963, 1966, Bromwich 1970, Van Horne 1971), information (Bradford and Kelejian 1977, Green 1981, Hilton 1981, Hess 1982) 1982) and stabilization (Newbery and Stiglitz 1981), to mention some of the key related fields. Rather, the purpose here is to explore the most cogent matters with a view to discovering methods that are simple and low-cost enough to implement in the operational environment of project appraisal.

First, in Section 2, theoretical arguments about the proper role of uncertainty in appraisal are reviewed, and this section is closed by a discussion of the various 'practical' methods that have been proposed, in and outside the World Bank. Further procedures for quantifying uncertainty in both
24 _Fiscal Crisis in Central Europe_ enterprises accounted for about 17 percent of revenues, but less than 12 percent of turnover tax payments and less than 8 percent of profit tax payments.

Table 4.3. Poland: Medium and Large Private Enterprises. Turnover Tax and Income Tax Payments.

(private sector shares of total)

|||1993 2/|||1992|||1991||
|---|---|---|---|---|---|---|---|---|---|
|% of total 1/|revenues|tax<br>I tax|pta<br>tax||turnover<br>tax<br>||proit<br>tax|reve ue<br>s|turnover<br>tax|profit<br>tax|
|Total|30.5|14.9|13.9|28.1|13.1|13.3|27.4|10.2|13.3|
|Industry|17.5|11.6|7.7|14.9|9.5|7.1|10.6|5.8|7.1|
|Construction|57.6|71.9|52.0|49.3|59.0|46.2|26.4|38.3|46.2|
|Agriculture|17.3|19.5|19.2|43.2|53.8|14.9|54.3|15.1|14.9|
|Transportation|14.7|42.9|15.0|14.8|42.2|11.3|8.0|30.0|11.3|
|Communic.|2.2|6.7|0.3|1.9|7.1|0.4|1.1|28.6|0.4|
|Trade|53.6|63.1|34.7|48.3|61.9|29.0|56.8|45.0|29.0|

Source: GUS (1992, 1993a and 1993b). See references.

Medium and large enterprises are those with more than 50 employees in industry and construction, and more than 20 employees in other sectors.

1/ The total refers to all medium and large enterprises.

2/ First and second quarter only.

Hlowever, at least with regard to turnover tax, the tax compliance of private enterprises - in all sectors - appears to be improving. In fact, their share in turnover tax payments increased by about 50 percent from 1991 to 1993, while the share of revenues increased by only about 10 percent. On the contrary, the profit tax compliance has worsened. For example, in the industry sector - which accounted for about 60 percent of total profit tax in 1993 - private enterprises' share of revenues has increased since 1991 by about 70 percent, while its share of profit tax payments by only about 10 percent.<sup>22</sup> As it does not seem logical to assume that private enterprises would be

> 22 The explanation for such performance is suggested by some other data available from the same source. In fact, the data on costs and profits reported there would imply a markedly higher profitability in the public sector than in the private one. The profits reported by medium ard large private enterprises are only 16 percent of total profits within the category, while their sales are 28 percent of the total. Also, in 1992, more private businesses reported losses, in both absolute and relative terms, than public ones. Even allowing for a good performance of a few public managers,
<!-- Start of picture text --> 3000<br>m 2012: Min. wage=0.33 Average wage<br>2500 m 2016: Min. wage=0.44 Average wage<br>Wn<br>®<br>> 2000<br>2<br>Q<br>=<br>2 1500<br>fo)<br>—<br>oO<br>2<br>€ 1000<br>=)<br>2<br>500<br>Oo<br><0.33 0.33-0.45 0.45-1.00 1.00-2.00 2.00-3.00 >3.00<br>Ratio of employee's gross wage to average gross wage<br><!-- End of picture text --> <!-- Start of picture text --> “ 2012 “ 2016<br>35 35<br>30 30<br>25 25<br>20 lm Private sector 20 lm Private sector<br>15 m Public sector 45 m Public sector<br>:5<br>0 °<br>= oo lo) Oo fo} Oo Oo fo} fo}<br>fc Fe as 4 AH FH GBR GB B eT eT tts & A<br>= = = S5RBSSSESESESESSR SB<br>e288 28888<br>3 8 8 6 gS2RRSSRER2 SBS<br>© 2 a aA NN MH FT H ORK = » Ss<br>= = : =£<br><!-- End of picture text -->
**Table S4.8.** ITT effects on different knowledge outcomes, FREG- and non-FREG samples separately; using kernel matching weights in the outcome regressions.

|||Overall k<br>|nowledge<br>|||Principles<br>|knowledge<br>|||How-to k<br>|nowledge<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Non-FRE|G sample|FREG|sample|Non-FR|G sample|FREG|sample|Non-FRE|G sample|FREG|sample|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|T1 (extension)|0.009|0.009|0.122***|0.116***|-0.007|-0.004|0.078***|0.072***|0.022*|0.023*|0.174***|0.170***|
||(0.014)|(0.013)|(0.023)|(0.018)|(0.017)|(0.015)|(0.026)|(0.023)|(0.013)|(0.012)|(0.026)|(0.021)|
|p-value|0.532|0.471|0.000|0.000|0.678|0.787|0.003|0.002|0.094|0.055|0.000|0.000|
|T2 (extension + video)|0.044***|0.045***|0.147***|0.149***|0.032**|0.037***|0.130***|0.127***|0.048***|0.047***|0.152***|0.154***|
||(0.013)|(0.012)|(0.018)|(0.016)|(0.015)|(0.013)|(0.024)|(0.021)|(0.014)|(0.013)|(0.018)|(0.015)|
|p-value|0.001|0.000|0.000|0.000|0.031|0.005|0.000|0.000|0.001|0.000|0.000|0.000|
|Test T1 >= T2 (p-value)|0.006|0.005|0.145|0.060|0.015|0.006|0.046|0.026|0.051|0.047|0.792|0.760|
|Endline control mean|0.4|68|0.5|13|0.|541|0.5|81|0.4|01|0.|446|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-squared|0.011|0.134|0.144|0.260|0.005|0.103|0.069|0.166|0.014|0.128|0.195|0.305|
|Observations|1,915|1,915|1,324|1,324|1,915|1,915|1,324|1,324|1,915|1,915|1,324|1,324|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ OLS regressions of household heads' knowledge scores, ranging from 0 to 1, and calculated based on the number of correct answers relative to the total number of questions in a respective domain. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws, and control observations weighed with Kernel matching weights according to their similarity with FREGs and non-FREGs, respectively. Additional control variables identical to those listed in notes of Table 5. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

16
CILSS. Field supervision generally improved over time (Daho, 1992). Moreover, the fact that the decline in household size arises from recorded declines in nuclear family members does not support the hypothesis that there was an increasing tendency to under-enumerate when completing the household roster. If enumerators were tempted to under-record when completing the household roster they would most probably have left out more distant relatives and marginal household members (Coulonibe and Demery, 1992).

The case for sampling bias as the main responsible factor is based - the observation that although household size declined throughout the period for all regions, a discor.tinuity is observed in the data. In particular, the drop in recorded household size is most severe between 1986-87, especially when non-panel households are considered. This discontinuity coincides with a change in the sampling procedures used in the CILSS. For the years 1985 and 1986, the sample was drawn from a sampling frame derived from demographic projections based on the 1975 Population Census. In 1987, the sampling frame was changed, and was derived from an electoral census. In 1988, the frame was again updated and derived from preliminary results of the 1988 Population Census.

Moreover, the procedures used to draw the sample were also not constant over time. In mid-1984, a pre-survey was carried out to list 64 households in each of 100 primary sampling units (PSU) which had been selected with probabilities proportional to their population, stratified by region. For 1985, a sample of 16 households was randomly drawn from the 64-household listing in each PSU, so that a sample of about 1600 households was achieved for 100 clusters. In 1986 half the sample (from 50 clusters) was surveyed a second time to form a panel of households, and 800 new households were selected from the other 50 clusters. In 1987, changes were introduced to this selection procedure. A complete listing was made of _all_ households in each cluster, and a random selection of 16 households was taken from this complete list. This change in procedure (and the revised sampling frame mentioned earlier) only affected half the sample of 1987, since the other half of the sample (forming the 1986-87 panel) had been selected in 1986 under the previous sampling regime. Thus in 1987, households in panel set 8 were selected on the original sampling frame and 7
The typical concern with a regression of this form is that if individuals who stand to gain more income from migrating self-select into migration, then the estimate of β will be biased upwards. This may be less of a concern in our application given that the entire sample consists of very high ability individuals.

As an alternative approach, we can measure the gain in income from migrating by asking the top students directly. We asked current migrants what job they would do if working in their home country and how much they thought they would earn from it, and return migrants what job they thought they would have if working abroad and how much they thought they would earn from it. Both groups were assumed to have reasonable knowledge of employment prospects in the other country, and almost all could provide an answer to this question. We did not ask non-migrants how much they thought they could earn abroad, so restrict this to those who have ever migrated only. The income gain from migration is then the self-assessed difference in incomes. For comparison purposes, we also re-estimate equation (4) just for the current and return migrants. These selfassessments will not suffer from the same bias as the regression. However, they may be biased upwards if people self-validate by asserting they would earn less in the other location than they would in the current location, or be biased downwards if both current migrants and return migrants believe the “grass is greener” in the other location. In practice the magnitudes of the counterfactual incomes people report seem to a first order accurate for both groups, so such self-reporting biases do not appear large.

Both approaches requiring converting incomes earned in different currencies into a common unit of analysis. We consider two methods of doing this. The first is to convert all income figures into March 2008 United States Dollars at the Interbank Exchange Rate. The second is to convert into PPP U.S. Dollars using the Purchasing Power Parity exchange rate from the Penn World Tables. However, there are several reasons to believe that the PPP rates are not the right benchmark here. The first is that neither PNG nor Tonga were benchmark countries for the International Comparison Program (ICP). Their PPP exchange rates are therefore estimated solely on the basis of GNI per capita and the secondary school enrolment rate. Fiji appears to be the only Pacific country covered under the ICP, and it is a large outlier when such a model is used, with more expensive

- 23 -
In such settings where information is scarce, diffusion processes are likely to be influential. As petty corruption is often found to be dependent on the societal belief that it is widespread (Corbacho et al. <u>2016, Dong et al. 2012), migration might broaden the horizon and thus decrease</u> its likelihood by showing migrants that school systems can work without informal payments. In particular, payment schemes that depend on public-good-style contributions may dissolve if a few individuals cease contributing (Fehr and Gächter <u>2000).</u>

# II.  MOLDOVA AND CORRUPTION IN EDUCATION

Moldova is the poorest country in Europe with an estimated GDP per capita (PPP-adjusted) of $4,521 (World Bank <u>2014).</u><sup>5</sup> The potential effects of migration and societal spillovers are therefore particularly visible in a country like Moldova because it is the country with the third highest remittance to GDP ratio (24.9%), only surpassed by the Kyrgyz Republic and Nepal (World Bank <u>2014). In comparison, other commonly studied economies like Mexico</u> (remittances to GDP ratio of 2%) or the Philippines (9.8%) are considerably less dependent on remittances. Another advantage is that migration has been a relatively recent phenomenon. After the dissolution of the Soviet Union in 1991, some Moldovans continued working in what is now Ukraine and Russia and were thus suddenly called international migrants. Mass migration, however, only started when the Russian financial crisis of 1998 hit and increased unemployment and poverty considerably in Moldova. In 2011, emigrants made up 17% of the total population (MPC <u>2013), which means that 30-40% of children, depending on the sample, are affected by</u> emigration of at least one parent.<sup>6</sup> As a former member of the Soviet Union, Moldova’s public educational system has good coverage (even in rural areas) with enrollment rates of nearly 100% for primary and lower secondary schooling and 87% for upper secondary schooling (table S.1 in the supplemental

> 5. In 2013, countries with a comparable per capita GDP (in 2011 $-PPP) were, for example, Pakistan ($4,454), Nicaragua ($4,493), and Laos ($4,667).

> 6. The most common emigration destination for circular migrants is Russia. While migration to Russia is usually characterized by short-term stays and manual labor, emigration to the West is more permanent, servicesector oriented, and feminized (60% women). Italy and Romania are particularly important destination countries due to linguistic proximity.

5
Oblique rotation loadings in Figure A3. As Figure A2 indicates the difference in the loadings is almost negligible. In other words, in Principal Components Analysis the orthogonal rotation considerably simplifies the structure of loadings, and the oblique rotation reproduces it but does not simplify it further. However, in the True Factor analysis below (using ML) an oblique rotation produces significant improvement over the orthogonal rotation. We therefore adopt the oblique rotation results for computing factor scores.

Outside of economics and political science, in psychometrics for example, researchers conducting exploratory factor analysis have generally assumed orthogonal factors.<sup>12</sup> In economics and political science, however, there is every reason for believing that factors should be correlated. Multiple regression is prevalent in economics and political science precisely because non-experimental data are correlated. In order to satisfy the ceteris paribus assumption, considerable care is taken to include appropriate control variables. We should embrace the idea that political-economic data are correlated when such data are determined in general equilibrium. It is therefore almost impossible for the data to be orthogonal. Within a data class there may be strong interdependencies while across data classes these interdependencies may be weak. In that case, the assumption of partial equilibrium for each data class may be justified. In Chen and Dahlman (2004) this assumption leads the authors to think of their data classes as “pillars”. Here, we let the data decide how to form into groups. There are two related messages here. The first is that the main objective of the factor analysis is to be able to identify the underlying dimensions that the observed data purport to measure. Second, and related to this objective, there is absolutely no theoretical reason why the underlying factors should be uncorrelated. The underlying dimensions are

> 12 Traditionally, a clear distinction has been made between confirmatory factor analysis (CFA) and exploratory factor analysis (EFA). In CFA, if theory suggests two factors are correlated, then an oblique rotation is justified. In EFA, there is neither is there a theoretical basis for knowing how many factors there are nor whether they are correlated. In economics and political science, we argue, CFA will generally indicate correlated factors due to interdependencies of general equilibrium under which the data are generated.

22