rafmacalaba/datause-encoder-data huggingface dataset:
Figure 7: Different sources of revenue (Millions of Brazilian reais) by functions in Roraima.

<!-- Start of picture text --> Total Revenue Income Taxes<br>−20 −10 0 10 20 −20 −10 0 10 20<br>Social Contribution Indirect taxes<br>−20 −10 0 10 20 −20 −10 0 10 20<br>Transfers Fiscal Balance<br>−20 −10 0 10 20 −20 −10 0 10 20<br>200 2.5<br>150 21.5<br>100 1<br>.5<br>50 0<br>30 40<br>20<br>30<br>10<br>0 20<br>−10 10<br>150 200<br>100<br>100<br>0<br>50 −100<br>0<br>−200<br><!-- End of picture text --> Note: The dots show the bimonthly data on government revenue by functions or categories in Roraima from year 2015 to 2020 and the x-axis shows distance from the second bimonth of 2018 in units of 2 months with 0 representing April 2018 and y-axis is in millions of Brazilian reais and is adjusted for inflation using national IPCA. The solid line represents the predicted values of a local linear smoother estimated using raw data on each side of the threshold at zero. Outer dashed lines indicate 95 percent confidence intervals.

Figure 7 shows that the fiscal balance is improving since 2018 with a steep drop in the second bi-month of 2020 due the additional spending related to COVID pandemic, but the overall positive trend suggests that the Venezuelan influx did not have a drag on the fiscal balance and may have been contributing equal if not more revenue than the additional expenses on them incurred by the government of Roraima.

It should be noted that most spending in Brazil is directly tied to revenue by fiscal rules, making expenditures inflexible in the short-run and difficult to respond to the influx of displaced Venezuelans and their needs. In the longer term growing population can lead to higher transfers from the government and the larger tax bases will allow for an increase in spending. So, it is not surprising that the diagrams on the fiscal variables, which do not control for seasonality and unit roots, do not observe the sharp conspicuous jumps as most cross-sectional RD diagram exhibits and suggests that most fiscal variables did not respond to the population shock.

12
lice officers’ characteristics. First, applicants’ characteristics might be important determinants of renewal outcomes. For instance, if an applicant finds it hard to navigate the renewal process steps because he is illiterate, this would translate into differential renewal outcomes based on the level of education of the applicant. Second, police officers’ characteristics such as age, rank, and education may be important determinants of variation in efficiency within the administrative procedures required to complete the renewal process. For instance, if the skills required to complete the process are highly correlated with police officers’ characteristics, these might translate into different renewal outcomes based on police officers’ characteristics. Third, police officers’ tenure may affect the renewal process through a learning effect. For instance, police officers who have been working at the Identification Unit for a longer time might be more familiar with the process and, thus, be able to outperform compared to police officers recently brought into the process. To formally explore all of these hypotheses, we use the following three regression models: where _Ai_ is a vector of applicant characteristics including age, education, gender, rural status, attended private school (which serves as a proxy of social status), indigenous language spoken, and indigenous attire; and _Pj_ is a vector of police officer characteristics including tenure at the Identification Unit, tenure at the renewal table, rank, age, education, and gender.

# **5 Data**

The data for this paper come from multiple sources and combine different methods of inquiry. The primary data collected at the applicant level include the following: (i) starting and completion renewal times; (ii) renewal table number; and (iii) basic socioeconomic and demographic characteristics. The administrative data collected at the police officer level include the following: (i) renewal table number; (ii) basic socioeconomic and demographic characteristics; and (ii) technical failures at renewal tables. All of the data were collected between October 27 and December 5, 2009.

## **5.1 Primary data**

The primary data were collected using a very simple software specifically designed for this study. Figure 1 shows a screenshot of the software interface used for the data collection.<sup>6</sup> The software collected basic

> 6The software was installed in three computers located next to the randomization table, and two computers located at the exit door. All of the five computers were operated by police officers from the Identification Unit, which were

8
|Decision number|||(13.08)|||(13.08)|(19.31)|
|---|---|---|---|---|---|---|---|
|Treatment X||||198.0||197.2|197.2|
|Risk preference||||(138.1)||(138.6)|(138.6)|
|Treatment X|||||0.502|0.476|1.209|
|Social preference|||||(1.21)|(1.21)|(1.44)|
|Social preference X|||||||0.046|
|Decision number|||||||(0.08)|
|Treatment X|||||||-0.113|
|Social preference X Decision<br>number|||||||(0.12)|
|Constant|2959.0***|2268.8*|2364.2*|2223.8*|2202.5*|2256.5*|2296.4*|
||(95.43)|(1269.20)|(1269.90)|(1263.60)|(1284.00)|(1279.10)|(1281.00)|
|Within R2|0.357|0.357|0.360|0.357|0.357|0.360|0.360|
|Between R2|0.020|0.037|0.037|0.054|0.038|0.056|0.056|
|Overall R2|0.258|0.263|0.264|0.268|0.263|0.270|0.270|
|P|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. * 10%, ** 5%, *** 1% significance level.
before the start of crises, governments in most regions were already running budget deficits, a fact that should hamper their ability to deal with the cost of these episodes subsequently.

Yet, crises also represent opportunities for developing countries since they prompt governments to think differently and creatively about the problems they confront. In many developing countries, crises have encouraged governments to deregulate their banking sectors and to allow the entry of foreign banks. Table 4 shows average values of a measure of banking sector restrictions across countries that had crises between 1995 and 2002 and those that did not.<sup>8</sup> The index takes values from 1 to 5 with higher values representing greater restrictions. With the exception of Asia, countries that have experienced crises tended to be more open subsequently than countries that never experienced a crisis.

Though many studies have discussed the catalytic role that crises can play in promoting foreign bank participation in developing countries, few have offered systematic evidence to this effect. We begin by plotting the share of foreign bank assets in each region in a five year period following crises occurring between 1995 and 2002. The most reliable time series on foreign bank assets, which was constructed by Micco _et al._ (2006) from _Bankscope_ data, are only available for those years. Because crises did not occur in the same years in all countries, we date as “t” the first year of the crisis for each country and we take the average share of foreign bank participation at that point in time for each region. We compute regional averages for each year after the crisis within a five year window in the same manner.

We see from Figure 2 that crises in Africa, Latin America, and in particular, in Eastern Europe and Central Asia have been followed by an increase in foreign bank participation. On average, the share of assets held by foreign banks in Eastern Europe and Central Asia increased from 10 to 32 percent within the five year period following crises. In Africa, foreign bank participation increased from an average of 32 percent to almost 45 percent five years after crises. In Latin America, the increase was smaller, with foreign bank participation increasing from 28 to 32 percent.

> 8 Among the restrictions that the Heritage Foundation considers in constructing this index are: the ease with which foreign banks can open branches and subsidiaries, government interference in the allocation of credit including government ownership of banks, the ability of private banks to operate without government regulation such as deposit insurance, and the ability of banks to provide a wide range of financial services (including real estate and securities transactions, and insurance).

16
Table 4.7. Estimated OLS and Quantile Regression Coefficients. Dependent variable: change in log PCE.

||OLS||8=010|0=0.25||0=050|0=075|9=090|
|---|---|---|---|---|---|---|---|---|
|Othertowns|-02509|**|-03768|**<br>-0.3461|**|-01888|**<br>-02566|**<br>-0.2197|
|Rural|-0.1783|**|-0.2558|*<br>-0.2363|**|-0.1631|**<br>-0.1780|*<br>-01370|
|Age|00109||0.0203|0.0208||0.0086|0.0055|00064|
|Agesq|-00001||-0.0002|-0.0002|*|-0.0001|00000|00000|
|Headfemale|00221||0.1323|0.0935||-0.0067|0.0018|-00599|
|Single|-02184|**|-03736|**<br>-0.3309|**|-0.2376|-01055|-00323|
|Separate|-01686||-07410|**<br>-0.3306||-0.1299|0.0341|-00239|
|<br>Widowed|-00387||-00696|-00067||00501|0.0232|-0 0025|
|Divorced|-0.0743||-0.1006|-00518||-0.0487|-0.0486|00562|
|Illiterate|00222||0.1218|0.0496||0.0728|0.0226|-00565|
|Primaryeducation|-00179||-0.0603|-00347||-00088|-0.0653|00159|
|Highereducation|01820|**|02504|0.1681|*|0.2073|**<br>01307|*<br>01314|
|Farmers|-00716||00087|-0.0696||-0.0560|0.0044|-00840|
|Hiredinagnculture|00352||0.1704|*<br>0.0269||-0.0150|0.0384|00406|
|Selfemployed|0.0434||00098|0.1072||00256|0.0834|-01049|
|Pensioners|-0.0740||-0.0283|-0.0924||-0.0782|-00508|-00677|
|Other|00512||0.2425|*<br>-0.0263||0.0763|00791|-0.1844|
|Householdsize(log)|-01320|**|-0.1125|-0.0992||-0.1320|-01621|**<br>-00238|
|Number under6|-00413||-0.0323|-0.0616||-0.0335|-00567|-0.0235|
|Numberaged6-14|-00572|**|-0.1344|**<br>-00599||-0.0292|-0.0045|-00783|
|Numberaged15-17|-0.0184||-00590|-0.1105|**|-0.0218|0.0349|0.0308|
|Numberaged18-25|00227||00071|-0.0199||-0.0012|0.0640|*<br>-00331|
|Numberover 64|-00288||0.0472|0.0148||-00146|-00170|-01168|
|Numberearners|-00271||-00352|-0.0274||-00199|-00149|-00078|
|Plotsize|1.0362|**|0.9010|0.6552||11043|**<br>04900|1.0263|
|Fractionagnculture|-0.0404||0.1696|0.0872||0 0365|-0.0516|-0.3690|
|Housearea|0.0009|*|00011|0.0008||00010|*<br>00013|*<br>00011|
|Fractionlivingarea|02993|**|04856|**<br>0.2614||02573|01996|0.4116|
|Housingownership|01195|*|0.2550|02111|*|00676|0.0027|01386|
|YearquarterI|-01498|**|-0.0990|-0.1262|**|-0.1150|**<br>-01778|*<br>-0.2344|
|<br>Yearquarter2|-01342|**|-0.0372|-0.0342||-0.1008|**<br>-02037|**<br>-0.2164|
|Consumption(log)|-05276|**|-05412|**<br>-0.5018|**|-05261|**<br>-0.5276|**<br>-05339|
|Agricultural society|-00390||-0.0472|-00224||00215|-00447|-01194|
|Socialbenefits|00210||-00428|-00280||0.0237|0.0522|0.0864|
|Pnvatetransfers|-00447||-0.0688|-0.0844|**|-0.0569|*<br>-0.0586|00096|
|Constant|21441|**|0.9525|**<br>1.4491|**|21291|**<br>2.7037|**<br>28968|

Left-out variables are dummies for large cities, married, secondary education, hired in non-agriculture, number aged 26-64, year quarter 4. (*) Sigmnficant at the 10% level. (**) Significant at the 5% level. Std. Err. (not shown) from 50 bootstrap repetitions.

18
Table 3. Explaining Heterogeneity in Effect Sizes on Financial Behavior

||(1)<br>All|(2)<br>All|(3)<br>RCTs|(4) Low<br>inc. econ|(5) High /<br>middle inc.|(6) Low<br>income|
|---|---|---|---|---|---|---|
||||||econ|clients|
|RCT|-0.070**|-0.068**||-0.209**|-0.079**|-0.066**|
||(0.027)<br>|(0.028)<br>||(0.091)<br>|(0.036)<br>|(0.032)<br>|
|TOT|0.079***<br>|0.068**<br>|0.012<br>|-0.016<br>|0.076**<br>|0.031<br>|
||(0.027)|(0.027)|(0.040)|(0.066)|(0.035)|(0.032)|
|Delay|<br>0.000|<br>0.000|<br>-0.001**|<br>-0.001**|<br>0.000|<br>-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|1/SE|-0.000|-0.000|0.000|-0.003|-0.000|0.000|
||(0.000)|(0.000)|(0.001)|(0.002)|(0.000)|(0.000)|
|Intensity / week|0.004**|0.004***|0.007***|0.004**|0.003|0.004***|
||(0.002)|(0.001)|(0.001)|(0.002)|(0.003)|(0.001)|
|Duration|-0.000|-0.000|-0.001|-0.001|-0.000|0.000|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.000)|
|Age|-0.001||||||
||(0.001)||||||
|Percent female|-0.000<br>(0.001)||||||
|Low income clients|<br>-0.065***<br>(0.020)|-0.055***<br>(0.017)|-0.074***<br>(0.024)|-0.042**<br>(0.019)|-0.048**<br>(0.021)||
|Years of schooling|<br>-0.016***|<br>-0.019***|<br>-0.016**|<br>-0.026***|<br>-0.025***|-0.011*|
||(0.006)|(0.006)|(0.006)|(0.009)|(0.009)|(0.006)|
|FL in population<br>|-0.003<br>(0.002)||||||
|Country group|||||||
|<br>_a) Low/lower-mid. inc. econ._|-0.129*|-0.093**|-0.092**|||-0.059|
||(0.073)|(0.036)|(0.042)|||(0.042)|
|_b) Upper-mid. inc. econ._|<br>0.000||||||
||(0.060)||||||
|Channel|||||||
|_a) Classroom_|-0.003<br>||||||
||(0.028)||||||
|_b) Counseling_|<br>-0.018<br>(0.033)||||||
|_c) Online_|<br>-0.028||||||
||(0.028)||||||
|Setting|||||||
|_a) School_|0.022<br>(0.023)||||||
|_b) Workplace_|0.041<br>||||||
||(0.036)||||||
|Mandatory|<br>-0.074***|-0.051**|-0.078*|-0.015|-0.065**|-0.052|
||(0.024)|(0.023)|(0.044)|(0.042)|(0.025)|(0.033)|
|Incentivized|<br>-0.012||||||
||(0.029)||||||
|Teachable moment|<br>0.079***|0.064**|0.016|0.025|0.069**|0.072**|
||(0.021)|(0.026)|(0.035)|(0.026)|(0.029)|(0.032)|
|Constant|<br>0.477***|<br>0.332***|<br>0.338***|<br>0.514***|<br>0.406***|<br>0.188*|
||(0.157)|(0.079)|(0.095)|(0.110)|(0.114)|(0.095)|
|R<sup>2</sup>|0.210|0.183|0.149|0.170|0.204|0.109|
|n (Studies)|90|90|40|18|72|44|
|n(Effect sizes)|349|349|227|129|220|234|

Notes: Non-standardized coefficients from OLS regressions. Dependent variable in columns (1) and (2) is effect size (Hedges’ g) on financial behavior in the full sample of studies reporting on financial behavior as an outcome. Column (3) shows results for RCTs only. Column (4) and (5) show results for financial behavior split by country groups. Column (6) limits the sample to classroom trainings only. Robust standard errors clustered at the study-level in parentheses. ***, ** and * denote significance at the one percent, five percent and ten percent level. Source: Authors’ analysis based on data sources discussed in the text.

17
from 14 countries classified as low and middle income countries by the World Bank in 2007 and estimate the models using pooled data from TIMSS 2003 and 2007 in most of the analysis.<sup>9</sup> Table 1 reports descriptive statistics of the variables used in this study. Since the focus of this study is on classroom age variance, it is crucial that there is a considerable amount of variation in the classroom standard deviation of student age and classroom age distributions are fairly symmetric on average. Indeed, the classroom standard deviation of age has a standard deviation of 0.21 years (or 2.5 months) and a range of 1.7 years. Furthermore, the average classroom age skewness is only 0.24, indicating that the extent of asymmetry in classroom age distributions is reasonably low. Hence, using classroom standard deviation of age as the measure of age heterogeneity appears sensible. Nevertheless, alternative measures of age dispersion are also considered to assess whether the estimates are sensitive.

Table 2 verifies the claim that the classroom variance of student age is orthogonal to other influences of achievement and that teachers are not systematically assigned to students depending on classroom age heterogeneity. It reports the regression estimates of a set of student background characteristics and teacher characteristics as the dependent variable against the classroom standard deviation of student age and classroom average of student age, after controlling for school fixed effects, own age, and own age squared. If classroom age variance is exogenously determined, it should not be correlated with student background characteristics and teacher characteristics. Except in one instance where parental nativity status is significant at the 10% level, all other predetermined student and teacher characteristics are not significantly

> 9 These countries are selected because of their development status and their samples of multiple classrooms per school. Four of these countries are in TIMSS 2003 and thirteen in TIMSS 2007. Three of them classified as low or middle income countries appear in both waves of TIMSS. I also estimate the models using TIMSS 2003 and TIMSS 2007 data separately. The results are presented in a robustness check section.

12
# CHAPTER 5

# THE DISTORTIONARY IMPACT OF AID)

# 5.1 Introduction

Aid has four main types: project aid, programmne aid, commodity aid (mainly food aid) and technical assistance. We argued in Part 3.2 that each different type of aid may be expected to have different - effects on growth. More generally, the macroeconomic effects of different types of aid will differ yet miost analysis, including that presented in Chapter 3, does not distinguish between types of aid. There are, however, considerable literatures on each type of aid, elements of which are of macroeconomic relevance. The previous chapter discussed the issues surrounding the use of programme aid; in this chapter debates over the other three types of aid are considered.

Aid is, by definition, a government directed activity so that 'he use of aid implies the possibility of reinforcing the role of the state and enabling state-created economic distortions. Peter Bauer (e.g. 1976) has persistenty argued that aid is detrimental to development for precisely these reasons - earlier Friedman (1 957) had argued that aid was counter-productive as a weapon in the Cold War since it was promoting the 'communization" of the developing world.

It is the possibility that aid may impede market-oriented development and/or impose distortions to the pattern of such development that concerns us here. The best known example of an aid created distortion is the "disincentive efrect', whereby food aid may generate a number of disincentives affecting domestic agricultural production. In Part _5.2_ we trace the evolu-tion of thinking on the disincentive effect from concern over its potential strong impact in the 1960s to the current consensus that the effect can be, and mostly has been, avoided by careful u-se of food aid.

Far less extensively analyzed are the distortions that widespread use of technical assistance (TA) may impose on labour markets. Yet current writings on TA, which emphasize a general failure at institutional development, suggest that TA-created distortions of skilled labour markets in recipient countries may be quite significant. Part _5.3_ outlines the relevant issues, illustrated with such data as are available.

83
place (l), suggesting the notation px,l,t. Because we lack specific information about this vector, we use a triplet of dummies (x, l, t) to proxy the missing input prices.<sup>9</sup> Separate from differences in costs and expectations explained by start-dates of the projects, the duration of the projects may also matter. Booth (1991) points out that projects can exhibit ‘economies of time’, such that projects with longer duration are associated with lower cost per unit costs. On the other hand, CDM projects with longer duration are likely riskier than the projects of same size and type with shorter duration. In order to take account of such time relationships, project duration (in years) is also used as a continuous explanatory variable.

In the methodology outlined above, we use electricity prices to account for expected revenue from electricity sales when calculating abatement costs. Still, because the electricity pricing regimes are sometimes complex and subject to direct and indirect subsidies, using average reported prices to disentangle abatement and power revenue streams can introduce its own set of problems and errors. To account for this, we include estimates based on an alternative approach; we calculate the present value of the total project cost without adjustment and, to account for this omission, include the volume of the generated power as a state variable in the cost function. This also allows for the fact that there may be joint-production effects that the baseline methodologies ignore. In this case, Cin equations (7) and (8) represents the present value of the project costs and the vector of control variables _q_ includes electricity output.

# **3 Data Description**

Available information about CDM projects sent to the CDM Executive Board (EB) for consideration through December 2010 is obtained from the CDM/JI Pipeline Analysis and Database of the United Nations Environment Programme (UNEP) Risoe Center. The dataset includes information about individual CDM project, including the project name, type, registration or validation status, approved

> 9 This solves the missing price-information problem, but the associated parameter on the dummies likely pick up other attributes of time and place. This is a mixed blessing, since the net effect is to round up otherwise unobserved effects; however it does confound the interpretation of the estimated fixed effects.

9
As described below, previous work on foreign direct investment suggests a modest but significant correlation between risk and Foreign Direct Investment (FDI). By using the same panel data on risk ratings for both PPI and FDI, we are able to contrast the relationship of PPI to risk with the relationship of FDI to risk. Of course, FDI and PPI panels have some endogeneity— that portion of FDI which flows into infrastructure sectors overlaps with the portion of PPI which is derived from both private equity and foreign sponsors. However, the foreign investment component of PPI is central to the business of private investment in infrastructure so much so that isolating domestic-generated investments would render the exercise meaningless. That is, since an objective of the analysis is to see how investors respond to sovereign risk when partnering with a government in long-term investments, capturing foreign investors in the data set is central to the analysis.

Embedded within country risk are traits such as political instability so that civil and cross-border conflict affects risk ratings proportionate to their severity on economic activity. In order to isolate the unique risks associated with long-term investments, such as those in infrastructure, the authors look at PPI data for conflict-affected countries and zero out the years in which a set of conflict-affected countries exited their conflict periods. This reveals patterns related to how long it takes countries to see PPI begin to flow into their country after conflict ends, and which sectors are most likely to benefit from investment.

In order to address these questions this paper is structured as follows: Section 2 summarizes the existing literature regarding country risk and investments, both FDI and the more limited literature on private infrastructure investments. In Section 3, we describe the data used in this paper. Section 4 describes the methodology. In Section 5 the results of the models are shown and, finally, in Section 6 we offer possible explanations for the relationships and trends as well as their policy implications.

# **2. Existing Literature**

There is considerable economic and financial literature attempting to explain the determinants of investment and the relationship between investment and risk. Much of that literature is focused on foreign direct investment rather than infrastructure investments and most of the works utilize cross-country specifications. For example, Chakrabarti (2001) concludes that market size is a robust determinant of FDI and Nunnenkamp (2002) identifies exchange rate, openness, growth rate, and trade balance as determinants of overall investment levels.

4