rafmacalaba/data-use-mentions-extended huggingface dataset:
expand an existing one. In this way, countries are able to benefit from inward investment to achieve a higher growth rate.

Aside from financial market regulations, the impact of broader government regulations on the interaction between FDI and growth has not been analyzed so far. Our paper intends to fill this gap, as we explore the linkages between government regulations, FDI and economic growth in a comprehensive manner. It can be argued that countries may only benefit from foreign investment inflows if they have appropriate local government regulations and institutions in place. Excessive regulations are likely to restrict growth through FDI if human and capital resources are prevented from reallocation. For example, if starting and closing down a business are hindered by extensive and costly government regulations, involving many bureaucratic procedures demanding entrepreneurs’ time and resources, capital flows are prevented from being reallocated to the most productive sectors.

Likewise, if restrictive employment laws for hiring and firing of employees cause a lower labor market turnover, technology spillovers to domestic firms are less likely to occur. A similar argument can be made for other forms of government regulations, such as protecting (foreign and domestic) investors by ensuring creditor rights and enforcement of contracts. Both are difficult tasks involving high uncertainty, considerable time and very large expenses. Hence, multinationals would reduce forward and backward linkages with the local economy, thereby affecting the likelihood of horizontal or vertical spillovers taking place. In summary, restrictive government regulations may prevent productivity increases related to the exploitation of technology spillovers from foreign direct investment inflows.

Against this backdrop, we will use a broad range of government regulations, employing the extensive Doing Business database on government regulations provided by the World Bank (2004), and test our hypothesis that countries with restrictive regulations cannot exploit FDI inflows efficiently. The World Bank database has the main advantage of focusing on relatively consistent and objective data in measuring regulations across countries. More explicitly, the effects of starting and closing down a business, labor market regulations, enforcing contracts, creditor rights and obtaining credit are examined
words” in Spanish, Italian, and French as some of the project descriptions are in the donor’s language.

Our method of categorizing aid flows through “key words” also has some drawbacks, starting from omitted “key words” to projects that include more than one key word from different categories. Nonetheless, the gains outweigh the drawbacks; using detailed project information is a crucial element of our analysis as we set out to evaluate the effectiveness of aid on its main objective.

After employing our “key word” search process we are left with close to 41,000 aid flow observations. Most of the observations from the CRS dataset are dropped as they do not match any of the seven categories of interest. Most aid flows are either for disaster relief, humanitarian purposes (including health, water, and food), and/or for primary and secondary educational projects.<sup>6</sup> The distribution of all aid flows and the distributions of all firm perceptions amongst the different obstacles are presented in Figure 1.<sup>7</sup> The size of the bubbles describes the distribution of aid flows. We can clearly see that the _trade_ area has received more money than the other areas as 32.17% of all disbursements in the sample target this area. This is mainly driven by transportation related disbursements such as projects designed to build/improve roads and ports which require sizeable investments and are included under the trade category. The other two areas which aid targets more intensely are _rule of law_ and economic and political _stability_ .

> 6 We only include projects that target the education of the labor force such as vocational and professional training under our Labor category. Aid targeted for primary or secondary education is not included in our sample.

> 7 The distribution of firm perceptions and aid flows will change in each country, region, and year. Figure 1 is intended to display the general data and is not meant to be used to reach any conclusion about the relation
future policies and so on) induces people to save a larger fraction of their income for precautionary motives.”? While one might be tempted to conclude that inflation stabilization could have an adverse effect on saving, it is important to keep in mind that stabilization also affects saving through other indirect channels that are likely to more than compensate for any negative direct effect of inflation. In this regard, there is systematic evidence that lower inflation raises growth (see Fischer 1993, Andrés and Hernando 1997, among many other studies) and, as discussed below, the latter has a major positive effect on private saving. Further, the fiscal-adjustment component of macroeconomic stabilization also has an unambiguously positive effect on national saving, as noted above.

# 4.2 Alternative Estimators

Table 4 also presents results obtained with alternative estimation techniques. The first two columns present static OLS estimates, using respectively the cross-section data (i.e., country averages) and the pooled annual data. Both specifications are often encountered in saving studies. The third column adds the lagged dependent variable to the second. It is important to keep in mind, however, that in all three cases OLS is likely biased and inconsistent because it ignores unobserved country-specific effects and joint endogeneity of the explanatory variables. In the fourth column, the Within estimator is used to control for country-specific effects, but still ignoring joint endogeneity. An
The forcing variable age, ���, is defined in weeks and the cut-off �̅ is the last week of December 1998. Thus, ��� takes the value of 1 for the first week of January 1999, 2 for the second week, and so on. Analogously, ��� takes the value of -1 for the third week of December 1998, -2 for the second week, and so on. ���� is a vector of covariates that includes mother’s education in the main specification. We also estimate an alternative specification that includes the additional controls of father’s years of schooling, mother’s and father’s age, and household size; and ���� is the error term.

For the short-term effects, we use the surveys of 1999 and 2001. We also use surveys from 1998 to 2006 to check robustness as well as the persistence of the effect of the ban. In the short-term analysis, we used the following outcomes: work incidence in paid and unpaid activities, school attendance, and proportion of children ‘only working (in paid activities and unpaid activities)’, ‘only studying (attending school)’, ‘working (in paid and unpaid activities) and studying’, and ‘neither working nor studying’. For binary outcomes, we estimate linear probability models.

For the long-term effects, we pooled the surveys from 2007 to 2014 to look at education attainment and labor-market outcomes when the individuals of the affected cohort were aged 22 to 29. We selected the following outcomes: the likelihood of having completed at least high school, the likelihood of holding a college degree, the likelihood of being employed, the likelihood of being employed in the formal sector, and the log of monthly earnings.

We estimate the effects for each year individually and then pool all years to improve efficiency. When doing that, we added survey-year dummies to Eq. (1) to control for both cohort composition and time effects.
**Figure 3: Post-treatment CDFs of Capital Stock for Males by Treatment Group**

<!-- Start of picture text --> 0 1000 2000 3000 4000 5000<br>Capital Stock in Waves 5 and 6 (Cedi)<br>Control Cash<br>Equipment<br>1<br>.8<br>.6<br>.4<br>Cumulative Distribution<br>.2<br>0<br><!-- End of picture text -->

**Figure 4: Post-treatment CDFs of Capital Stock for Females by Treatment Group**

<!-- Start of picture text --> 0 1000 2000 3000 4000 5000<br>Capital Stock in Waves 5 and 6<br>Control Cash<br>Equipment<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text -->
049|0.161|0.228|0.023|0.514|
|Observations|5236|5236|5236|5236|5236|5236|5236|
|Number of months|11|11|11|11|11|11|11|
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The resolution of the apparently contradictory findings may lie in the question of whether or not a given budget deficit is sustainable. Deficit spending to finance productive infrastructure expenditures may be part of a strategy to speed up the rate of GDP growth. Governments may utilize this strategy so long as inflation remains in check. However, once prudent financing strategies are exhausted and inflation begins to rise, deficit increasing infrastructure expenditures may be scaled back in an effort to stabilize the economy.

# _8. External Balance_

The regression runs using the trade balance relative to GDP lagged one year (TBWTGDPL) provide no evidence that the external balance influences consolidated budget infrastructure expenditures. While the estimated sign of the coefficient is generally negative, it is never significant even at the 15 percent level, as can be seen from Tables 9 and 10 and Appendix C.

One's conclusion concerning the importance of the external balance for consolidated infrastructure expenditures is sensitive to how the external balance is measured, as can be seen from Table C5 in Appendix C. When the external balance is assessed on the basis of the black market foreign exchange premium, BLACK, a robust negative and highly significant relationship is found; The greater is the black market foreign exchange premium, the lower are consolidated budget per capita infrastructure expenditures. The value of the coefficient is about -.04, implying an elasticity of -0.07, so the impact is not very pronounced. This conclusion is further substantiated by the results of the regressions using DOLLAR4 as an index of external balance. Recall, that DOLLAR4 is actually an index of trade orientation, with low values (below 100) corresponding with inward orientation and high values (above 100) corresponding with outward orientation. One expects the external balance will be worse the lower is the value of DOLLAR4. If a poor external balance reduces infrastructure expenditures, then the estimated coefficient for DOLLAR4 should be positive. This is exactly what is found. The estimated coefficient for DOLLAR4 is positive and significant at the .05 level
are unlikely to be quantitatively important in the case of Ecuador. The reason is that new evidence from a new survey on the Venezuelan population in Ecuador indicates that the average education level of Venezuelan workers is substantially higher than for their Ecuadoran counterparts.<sup>24</sup> At any rate, here we address this concern by removing all foreign-born individuals from the ENEMDU micro data prior to creating our canton-level labor market indicators. Table 5 presents our difference-in-difference estimates based on the only-natives sample. As it turns out, the estimates that we obtain here are practically identical to the main

> 24EPEC is a survey conducted in the summer of 2019 that will soon become available. According to a preliminary version of these data, 35% of Venezuelans in Ecuador have tertiary education, compared to 15% among Ecuadoran nationals.

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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,
**Figure 5. Skill content of jobs (O*NET-based) by level of GDP per capita, all countries, latest year.**

<!-- Start of picture text --> Non-Routine Analytical, US Non-Routine Interpersonal, US Routine Cognitive, US<br>LB<br>SB LB<br>ETKMZWGMBFUGSLNPTZKHBDCMCIZMGHMMNIMDVNPKGYINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNALBATNLKECMVPEDOCNZACORSTHCRMEIQBGBRPAVEMXMUUYARLVTRCLRULTPLHUSCEESKGRPTSICZESCY PR ITGBFRCAISFIBEDEIESEDKATNLUSNOLU ETKMZWGMBFUGSBSLNPTZKHBDCMCIZMGHMMNIMDVNPKGYINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNALBATNLKECMVPEDOCNZACORSTHCRMEIQBGBRPAVEMXMUUYARLVTRCLRULTPLHUSCEESKGRPTSICZESCYPRITGBFRCAISFIBE DE IESEDKATNLNOUS LU ETKMZWGMBFUGSLNPTZKHBDCMCIGHZMMMNIMDYEVNGYPKINCVSZAOALBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNBATNLKECPEMVDOCNZACORSCRMEIQBGBRPAVE MX MUUYARLVTRCLRUHUPLLTSCEESKGRPTCZSIESCYPRITGBFRCAISFIBEDEIESEDKATNLUSNOLU<br>LB<br>SB<br>YE<br>YE<br>700 800 900 1000 1100 700 800 900 1000 1100 700 800 900 1000 1100<br>GDP level, log points GDP level, log points GDP level, log points<br>Routine Manual, US Non-Routine Manual, US<br>YE<br>PA<br>ETKMZWGMBFUGSLNPTZKHBDCMCIZMGHMMNIMDVNGYPKINCVSZAOBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNALBATNLKECMVPEDOZACNCORSTHCRIQBGBRLBVEMXMUUYARLVTRCLRUHUPLLTSCEESKGRPTCZSIESCYPRITGBFRCAFIBEDEIESEDKATUSNOLU ETKMZWGMBFUGSLNPTZKHBDCMCIZMGHMMNIMDYEVNPKGYINCVSZAOALBOBZGTPHSVBTPYFJIDAMEGJMGEJOMNBATNLKECMVPEDOZACNCORSTHCRIQBGBRPALBVEMXMUUYALVTRCL R HUPLLTSCEESKGRPTUCZSIESCYPRITGBFRCAISFIBEDEIESEDKATUSNOLU<br>ME ISNL SB ME NL<br>SB<br>700 800 900 1000 1100 700 800 900 1000 1100<br>GDP level, log points GDP level, log points<br>.5 .4<br>.4<br>.2 .2<br>0<br>0<br>0<br>Skill index Skill index -.2 Skill index<br>-.5<br>-.2<br>-.4<br>-1 -.6 -.4<br>1<br>.6<br>.4<br>.5<br>.2<br>0<br>Skill index Skill index 0<br>-.2<br>-.4 -.5<br><!-- End of picture text --> Note: Each point shows the skill content of jobs using O*NET indexes per country and year. The horizontal axis measures GDP per capita in PPP from WDI.

When looking at changes over time, both measures show the same patterns with regards to the non-routine cognitive, non-routine interpersonal and routine manual
cohort are those who completed age 14 in 1998. We use the data from the PNAD 1998 to perform the estimates.

The second placebo test uses data from 2001 and compares boys who turned 14 before and after December 2000, two years after the change in the law. The third placebo inspects the existence of discontinuities in the outcomes of interest at the cutoff of first week of July 1999. The two cohorts examined are the males that completed 14 years in the first half of 1999 and the ones that completed 14 years in the second half of 1999. The treatment dummy is equal to 1 for those that completed 14 in the second half of 1999. We use the data from the PNAD 1999. This analysis compares two cohorts affected by the ban.

Table 4 shows the results of the three placebo tests with piecewise linear specification. Except for one outcome in 2001, the results suggest no discontinuities in the outcomes variables.

# **6.2. Persistence of Short-Term Effects**

This sub-section explores the persistence of the ban effect along the school ages. Specifically, we look at the ban effect comparing the outcomes of ‘treated’ and ‘control’ cohorts between 1998 and 2006.<sup>27</sup> We then follow the ‘treated’ cohort between ages 13 and 21. We start with 1998 so that we have additional robustness checks since we look at the affected and unaffected cohorts one year prior to the change in the law.

Figure 4 shows the time evolution of school attendance rates, and the work incidence in paid and unpaid work for the unaffected cohort of boys. School attendance declines steadily from about 94 percent at age 13 to around 27 percent at age 21. In contrast, paid work incidence increases steadily from 8.4 percent at age 13 to 71 percent at age 21. The unpaid work incidence decreases steadily as boys get older.

Figure 5 presents the evolution of the six different combinations between schooling and work activities for the unaffected cohort. About 82 percent of
.2 Governance Indicators**

# _The World Bank_ _<u>Governance Indicators : 1996-2004</u>_

This dataset contains composite indicators of six dimensions of governance (Voice and Accountability, Political Stability/Lack of Violence, Government Effectiveness, Regulatory Framework, Rule of Law, and Corruption Control). The indicators reflect the statistical compilation of perceptions of the quality of governance of a large number of enterprise, citizens and expert survey respondents in industrial and developing countries, as well as nongovernmental organizations, commercial risk rating agencies, and think-tanks. Data are available for over 160 countries and can be accessed online. Existing indicators of governance and institutional capacity, by source with a brief description of how each indicator differs from one another, as well as selected papers are available at the _<u>public sector website</u>_ <u>.</u>

# _<u>Transparency International:</u> Corruption Perceptions Index_

The Corruption Perceptions Index ranks countries in terms of the degree to which corruption is perceived to exist among public officials and politicians. It is a composite index, drawing on 14 different polls and surveys from seven independent institutions carried out among business 9 ____________________________________________________________________________________________________________
), and those due to reallocation toward sectors with higher productivity growth (dynamic sectoral effect). Within-sector productivity growth may reflect the effects of improvements in

> 4The eight sectors distinguished in the dataset are agriculture, mining, manufacturing, utilities, construction, trade services, transport and financial services, and government and personal services. According to Economic Transformation Database, Business services include “Information and communication” whereas those in other databases are included in transport service.” Hence, we combined transport and financial services to construct a long time series database.

> 5McMillan et al. (2014) and Diao et al. (2017) employ 38 and 39 countries; Martins (2019) use 7 sectors and 169 countries, IMF (2018) use 10 sectors and 62 countries.

4
# **3.4 Tangible versus intangible investment in Brazil**

Figure 10 compares tangible and intangible investment in Brazil.<sup>21</sup> Compared to gross fixed capital formation (GFCF) in Brazil, which has ranged between 15-18 percent of GDP during 2000-2008, intangible investment is a small share of GDP. However, GFCF includes non-business tangible investment (such as government and household investment, and a part of infrastructural investment), so it is strictly speaking comparable to our estimate of intangible investment.<sup>22</sup> So we have also presented an estimate of business tangible investment, estimated from IBGE's surveys on Industry, Services, Commerce and Construction Industry using data on acquisitions and improvements to the firms‘ permanent assets. Our estimates indicate that the ratio of tangible to intangible investment in Brazil‘s firms is close to 1, which is lower than that reported for for the US and the UK, but comparable to that reported for some other advanced economies like France and Germany(CHS, 2004; Marrano and Haskel, 2006). It is, however, markedly higher than China‘s ratio, which is estimated to be 0.2 (Hulten and Hao, 2011).

There is some hint that in recent years, growth in intangibles has fallen behind that in tangibles. A caveat to this observation is that that the particularly sharp increase in business tangible investment after 2007 appears to be associated with a revision of IBGE's methodology for their surveys on Industry, Services, Commerce and Construction Industry. However, even the GFCF series – which should not have been affected by this methodological revision –shows a markedly higher post-2005 rate of growth than intangible investment.

> 21 Note that for reasons of comparability with tangible investment, the chart shows estimates of intangible _investment_ , and not expenditure. CHS and similar papers also present the same comparison of business tangible and intangible investment.

> 22 Source of GFCF Data: National Accounts

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.6|
|(Percentage points of GDP per year)|[2.5, 9.1]|[2.3, 4.4]|[2.7, 10.6]|[2.6, 6.8]|[2.2, 5.9]|[2.8, 7.6]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), based on 493 government debt booms, of which 103 booms are in advanced economies and 390 are in EMDEs. Amplitude is the trough-to-peak change in the government debt-to-GDP ratio during a government debt boom. Speed is the ratio of the amplitude to the duration. ** and *** denote that medians between advanced economies and EMDEs in all debt booms, and between completed and ongoing booms or between completed booms with and without crises in respective country groups, are statistically significantly different at the 5 percent and 1 percent levels, respectively.

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