rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>EXECUTIVE SUMMARY</u>**

Even though, in many studies, it has been shown that total public spending and some of its components are significant for economic growth, the empirical literature results are still mixed. We try to understand the importance of country sample selection and expenditure classification to explain these conflicting results. We conclude that public spending can be a significant determinant of growth for countries that are capable of using expenditures for productive purposes. As a follow up to a previous study<sup>2</sup> , this paper empirically investigates how the impact of public spending on growth varies when countries are classified according to their overall growth performances. The analysis compares fast-growing versus a mix of countries with different growth patterns. Seven countries are included in each group and the dataset covers the period of 1970-2005. In the regression specifications we include different components of public expenditure and fiscal revenues, always considering the overall government budget constraint. _A priori_ , while the size of the government does not appear to be much different (on average) in these two groups, the composition of public expenditures varies significantly. _A posteriori,_ the empirical analysis based on OLS and dynamic GMM techniques for panel data shows that the link between public spending, especially both its productive and “core” components, and growth, after controlling for other macroeconomic and private sector variables, and taking into account country initial conditions, is both economically and statistically strong only for the fast-growing group. When all countries are combined in the same regression analysis, the link between government spending and growth gets much weaker on average and even turns negative in some cases. However, when group effects in the combined set are controlled for, through interactive dummies, the stronger link between public spending and growth in the fast-growing group is clearly confirmed. A possible nonlinear relationship between growth and public spending is also investigated, but no statistical significance is found for it. Macroeconomic stability, openness, and private sector investment are confirmed to be significant in the fastgrowing country group, which point out to the existence of a policy environment more conducive to growth in that group of countries. A final implication of the analysis is that differences in empirical findings of the previous literature linking public spending and economic growth may be explained by the selection of countries included.

> 2 Moreno-Dodson (2008) shows that the volume of total public spending as well as its composition are relevant in explaining economic growth for a set of fast-growing developing countries during 1970-2004.

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distribution of schools across several characteristics. It appears that, among other things, the shares of secondary schools, single-sex schools, urban schools, and registered schools are larger in the phase-3 SLQAT sample than in the phase-4 SLQAT sample. These distributional differences across phases lose their statistical significance when we restrict our attention to the cutoff neighborhood samples (Columns 4–6). Some part of the explanation for the loss in significance is likely due to reduced statistical power resulting from the smaller sample sizes for the cutoff neighborhood samples relative to the SLQAT samples.

Table 9 presents means and standard deviations for selected outcomes measured at baseline, again separately by phase and for the SLQAT and cutoff neighborhood samples. In the phase-3 SLQAT sample (Column 1), we find that schools on average have 253 students; 10 teachers, classrooms, and blackboards; and 3 toilets. Mean student-teacher and studentclassrooms ratios are 25:1 and 28:1, respectively, which, to begin with, are below the stipulated maximums for program benefit maintenance. The differences across phases in the SQLAT samples (Columns 1–3) lose their statistical significance when we restrict our attention to the cutoff neighborhood samples (Columns 4–6). Again, some part of the explanation for the loss in significance is likely to be the lower statistical power of the cutoff neighborhood samples relative to the SLQAT samples. Notwithstanding, the sizes of the inter-phase differences in the cutoff neighborhood samples are generally substantially smaller than the corresponding differences in the SLQAT samples. In sum, the phase-3 and phase-4 cutoff neighborhood samples appear to be similarly composed with respect to mean outcomes measured at baseline.

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AVGEU15. In sum, the coefficient on retaliation or deterrents is not robust across the years and across the models estimated. This also holds true in Table 6 for the maximum US tariff.

**Table 6: Determinants of agricultural tariffs (incl. Specific tariffs) on products with export subsidies**

||De<br>|pend|ent Varia<br>|ble:Maximum<br> <br>|Tariffbe<br>|tween 1<br>**%PAC**<br>|995 and1<br>**RANKP**<br>|998<br>|<sup>2</sup>|<sup>2</sup>|
|---|---|---|---|---|---|---|---|---|---|---|
|**year**|**Dep. Var.**||**constant**|**M/Y**<br>**X/Y**|**AvgEU15**|**EXP**|**ACEXP**|**_N_**|_R_|Adj._R_|
|1995|MaxRateUS|est|0.022|−0.010 0.101|−0.002|1.358||24|0.537|0.439|
|||_t_|0.505|0.960 0.934|0.005|**4.256**|||||
|1995|MaxRateUS|est|−0.036|−0.008 0.077|−0.536||0.302|24|0.382|0.252|
|||_t_|0.550|0.629 0.611|1.426||**2.968**||||
|1996|MaxRateUS|est|0.077|−0.006 0.084|−0.507|1.785||26|0.464|0.362|
|||_t_|**2.220**|0.878 0.796|**3.186**|**4.089**|||||
|1996|MaxRateUS|est|−0.083|−0.006 0.098|−0.239||0.370|26|0.428|0.319|
|||_t_|1.384|0.887 0.891|**1.837**||**3.782**||||
|1997|MaxRateUS|est|0.068|−0.007 0.112|−0.442|1.832||28|0.339|0.224|
|||_t_|**1.792**|0.934 0.872|**2.551**|**3.272**|||||
|1997|MaxRateUS|est|−0.072|−0.010 0.157|−0.218||0.332|28|0.329|0.212|
|||_t_|1.096|1.195 1.192|1.550||**3.190**||||
|1998|MaxRateUS|est|0.044|−0.017 0.315|−0.468|1.860||30|0.399|0.303|
|||_t_|1.213|**1.775 1.731**|**2.946**|**3.527**|||||
|1998|MaxRateUS|est|−0.077|−0.016 0.307|−0.224||0.302|30|0.374|0.274|
|||_t_|1.345|1.648 **1.652**|**1.717**||**3.304**||||

Notes: Data Source: Hoekman, Ng, and Olarreaga (2004) In sum, the econometric estimates point to two fairly strong associations from US data from the 1990s across agricultural products, and a fairly surprising non-association. The first strong association is that between lobbying spending by agricultural PACs and measures of nontariff barriers. This association is seen to be robust across aggregate NTMs as well NTMs disaggregated by type. While there is no evidence of such an association with ad valorem tariffs, probably because they have been multilaterally lowered, lobbying is still associated with specific tariffs. The second strong association is that between the average US tariff (taken over the 1995-98 period) on those goods that also enjoy an export subsidy in the US and lobbying spending. The surprising non-association is between the agricultural protection, however it is defined, and trade measures such as import penetration and export-to-output ratio. If anything, the surprise is that it is not these variables, often the key issue variables in 53
i) the brain drain raises the expected return on education; ii) this induces additional investment in education (a brain gain); iii) this may result in a ‘beneficial brain drain’ or _net_ brain gain, i.e., in a brain gain that is larger than the brain drain; and iv) a _net_ brain gain raises welfare and growth.

These results are said to hold independently of other potential effects of the brain drain on the level of education, whether through remittances or through the skills return migrants might have acquired in the destination countries.

Seminal papers in the new brain drain literature include Mountford (1997) **,** Stark et al. (1997, 1998), Stark and Wang (2002), Stark (2004), Stark et al. (2004), Beine et al. (2001, 2003), Docquier and Rapoport (2004), and Vidal (1998).<sup>4</sup> Their work has led to a reconsideration of the impact of the brain drain on the number of skilled individuals and on economic welfare and growth in the source country.

Most studies in that literature are theoretical, though empirical results are slowly emerging thanks to the work of Carrington and Detragiache (1998, 1999), Adams (2003) Docquier and Marfouk (Chapter 6 in this volume; 2004), and Dumont and Lemaitre (2005).<sup>5</sup> These studies have estimated the stock of skilled migrants from developing countries who are living in OECD countries.<sup>6</sup>

> 4 Commander, Kangasniemi and Winters (2004) provide a survey of the brain drain literature.

> 5 An analysis of regional differences in the brain drain is provided by Docquier et al. (2005).

> 6 Carrington and Detragiache used the 1990 US census data to estimate the brain drain for a number of developing countries in 1990. Docquier and Marfouk (Chapter 6 in this volume) improved the measurement of the brain drain by expanding data sources to all OECD countries, estimating the brain drain for a larger number of developing countries, and doing so for the year 2000 as well as for 1990. They also provide estimates of the brain drain between developed countries.

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disappearance of the trend inflexion (apparent in Table 3 which included fixed effects by products but not by importing country because the unit of observation was allOECD imports) suggests that the inflexion resulted from a composition effect between importers as described above.

To sum up, the observed re-concentration of Table 2 is robust to the decomposition of OECD imports by importing country. However, as Tables 3 and 4 show, it does not occur at the extensive margin, all of the action being at the intensive margin.

Table 4 Regression results, OECD import concentration on time trend

# Herfindahl

||_(1)_|_(2)_|_(3)_|_(4)_|
|---|---|---|---|---|
|Time|-0.010|-0.009|-0.009|-0.007|
||(115.74)***|(86.33)***|(140.45)***|(92.51)***|
|Time, squared|0.000|0.000|0.000|0.000|
||(51.13)***|(27.11)***|(41.94)***|(3.42)***|
|Post 2000||0.005||0.008|
|||(14.76)***||(39.35)***|
|Constant|0.807|0.802|0.798|0.789|
||(932.13)***|(859.28)***|(1320.17)**|(1216.18)***|
|Observations<br>|1'154'420|1'154'420|1'154'420|1'154'420|
|R-squared<br>|0.07|0.07|0.13|0.13|
|Fixed effects a/|no|no|yes|yes|
|||Theil|||
||_(1)_|_(2)_|_(3)_|_(4)_|
|Time|-0.012|-0.009|-0.019|-0.015|
||(63.78)***|(41.27)***|(155.47)***|(100.10)***|
|Time, squared|0.000|-0.000|0.000|-0.000|
||(7.94)***|(7.32)***|(35.37)***|(7.05)***|
|Post 2000||0.012||0.020|
|||(19.11)***||(47.64)***|
|Constant|4.236|4.222|4.358|4.336|
||(2336.35)**|(2160.58)**|(3626.10)**|(3363.56)***|
|Observations|1'154'420|1'154'420|1'154'420|1'154'420|
|R-squared|0.05|0.05|0.18|0.18|
|Fixed effects a/|no|no|yes|yes|

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percent in 1993 to 91 percent in 1998. At lower secondary level, net enrolment rates more than doubled in five years, from 30 percent in 1993 to 62 percent in 1998. Increases in net enrolments rates at higher levels were even more impressive; they quadrupled at the upper secondary level and tripled at the post-secondary level (Table 1).

**Table 1: Net and gross school enrolment rates by quintile and education level in Vietnam, 1993-98** (%)

||_Pri_|_mary_|_Lowers _|_econdary._|_Upperse_|_condary_|_Postsec_|_ondary_|
|---|---|---|---|---|---|---|---|---|
||1993|1998|1993|1998|1993|1998|1993|1998|
|_Netenrolmentrates_|||||||||
|_Vietnam_|87|91|30|62|7|29|3|9|
|Poorestquintile|72|82|12|34|1|5|0|0.4|
|Richest quintile|96|96|_55_|91|21|64|9|29|
|_Grossenrolmentrates_|||||||||
|_Vietnam_|120|115|42|78|9|36|4|12|
|Poorestquintile|100|112|15|47|1|8|0|0.4|
|Richestquintile|130|104|77|105|24|75|13|37|

_Source:_ Author's estimates based on 1992-93 and 1997-98 VLSS<sup>2</sup> .

Table 1 shows that for the poorest quintile, enrolments expanded at all education levels during the five-year period from 1993 to 1998. Enrolment of poor children increased in primary schools from 72 percent in 1993 to 82 percent in 1998, and in lower secondary schools from 12 percent in 1993 to 34 percent in 1998. At higher levels, although enrolment increased, very few children from the poorest households went to upper and post secondary schools.

**Figure 2: Net enrolment rates by quintile and level, 1993 and 1998** (%)

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**2** Motof data used in this paper estimated from VLSS93 and VLSS98 by author, otherwise will be cited.

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Table 2: Model selection criteria

|Criteria|Al|l countrie|s||
|---|---|---|---|---|
||Num|ber of fac|tors||
|0|1|2|3|4|
|IC2<br>0.000|**-0.054**|-0.018|0.029|0.076|
|BIC<br>**1.003**|1.491|2.450|3.469|4.489|
|HQ<br>0.503|**0.299**|0.566|0.892|1.221|
||Adva|nced coun|tries||
|IC2<br>0.000|-0.449|**-0.452**|-0.439|-0.408|
|BIC<br>0.144|**-0.029**|0.219|0.483|0.764|
|HQ<br>0.808|**-2.903**|-2.399|-1.749|-0.921|

We turn to the main estimation results. Table 3 reports model estimates for the full sample (left panel) and the advanced-country-sample (right panel). In each case, the two columns in the table correspond to the two alternative spatial weight matrix-bilateral trade and bilateral inverse distance.

Consider first the full-sample results on the first two columns of the table. The coefficient estimate of lagged output growth is positive and statistically significant across all specifications indicating a significant degree of inertia in output growth.

Table 3: Estimation results

|||All co|untries|Advanced|countries|
|---|---|---|---|---|---|
||Weight matrix|Trade|Distance|Trade|Distance|
|_gt−_1||0.328|0.326|0.373|0.376|
|||(24.748)|(24.447)|(12.020)|(12.122)|
|_Wgt_||0.286|0.110|0.422|0.195|
|||(9.328)|(5.120)|(8.233)|(3.941)|
|_Wgt−_1||-0.104|0.035|-0.283|-0.262|
|||(-2.069)|(1.307)|(-3.077)|(-3.136)|
|Pesaran CD statistic||-0.732|-0.825|0.161|-1.601|
|(p-value)||(0.232)|(0.205)|(0.436)|(0.055)|
|Exponent of CSD||0.402|0.511|0.766|0.362|
|_R_<sup>2</sup><br>||0.224|0.225|0.526|0.516|
| ̄_R_<sup>2</sup>||0.198|0.199|0.489|0.478|

Notes: GDP growth is the first difference of the log of real GDP. ’Exponent of CSD’ is the exponent of cross-sectional dependence of Bailey, Kapetanios and Pesaran (2015). The sample period covers 1970-2016.

Turning to the spatial effects, the coefficient estimate of the contemporaneous spatial lag is positive and statistically significant. Its magnitude is much bigger under the trade 13
Income distribution data, particularly when extrapolated from quintiles or from the Gini coefficients, are even noisier. Furthermore, due to the absence of income distribution data for many countries, some authors (e.g., Bhalla and Sala-i-Martin) resort to very dubious assumptions, taking for example that income distributions either do not change in time or change in a certain (linear) fashion or even that everyone in a country has the same income. This myriad of assumptions and measurement errors in most cases tend not to bias the results in one direction only, but probably to offset each other, producing relatively similar levels of inequality. But when one re-estimates global inequality for another year, while the level hardly changes, the result is (on the account of the measurement error if nothing else) likely to be slightly different. It is that slight difference that is then interpreted as the evidence of a change, or in some cases even of a trend.

How big is a Gini of around 65? It is larger than inequality found in any single country including South Africa and Brazil, two among most unequal countries in the world, whose Ginis are in the upper fifties or low sixties. The Gini coefficient however does not give an intuitive feeling of how large global inequalities are. A better way to look at it is to consider how the overall pie is distributed across different fractiles of the distribution, Thus, for example, the top 5 percent of individuals in the world receive about 1/3 of total world (PPP-valued) income, and the top 10 percent one-half. If we take the bottom 5 and 10 percent, they receive respectively 0.2 and 0.7 percent of world total income. This means that the ratio between the average income received by the richest 5 percent and the poorest 5 percent of people in the world is 165 to 1.<sup>28</sup> The richest people earn in about 48 hours as much as the poorest people earn in a year.

Another important question is to ask how much of global inequality is due to differences in mean incomes of countries and how much to income differences within countries. Some 70 percent of global inequality is “explained” by differences in

> 28 This is based on Milanovic (2005).

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the deworming spillover effects, as discussed further in Section 5. Panel C simply shows the distribution of indicators for age at the time of community deworming, explained in Figure 1.

In Panels D and E, I further restrict the sample to those for whom a cognitive survey was carried out. Because the cognitive survey takes roughly ten times as long as anthropometric measurement, the cognitive outcomes were gathered only for a random subsample of respondents. Panel E shows that the characteristics of the respondents sampled for cognitive surveys do not substantially differ from the characteristics of all respondents.

The cognitive module included two measures of “verbal fluency,” in which children name as many items in a category as they can in one minute. The first category is foods; the second is animals. The Peabody Picture Vocabulary Test (PPVT-4, Form B) measures “receptive vocabulary,” in which children point to one of four pictures that best matches a word that has been read aloud to them. There are eighteen levels of the test, each with twelve words; respondents proceed up through levels of increasing difficulty until they make nine mistakes in a single level, and are likely to be simply guessing. For reasoning, I use the 12-question Set B of J. C. Raven’s Progressive Matrices, a series of puzzles commonly used to measure nonverbal reasoning and general intelligence.<sup>14</sup> For short-term memory, I use “digit-spans” of increasing length, in which respondents attemp to repeat a string of numbers back to the interviewer, either forwards or backwards. I provide raw means and standard deviations in Table 1, but for all regressions, I consider standardized versions of these cognitive measures, each re-scaled to have mean zero and standard deviation one.

Though it is not tabulated, I also condense these six measures using their first principal component in some parts of the analysis. Interpretation of coefficients on cognitive tests is clarified in Appendix Tables A2 through A5. The first column of Table A2 shows the weights on each outcome that yield the first principal component used in the analysis. Weights are almost equal across the different cognitive outcomes.<sup>15</sup> Because almost equal weight is given to each measure, I also construct a simple normalized sum of cognitive measures as

> 14See discussion in Cattell (1971) and Raven (1989) of the matrices and what they measure.

> 15The lowest weight is for “Verbal Fluency: Foods,” perhaps the noisiest measure because it was the first exercise in the cognitive module. Low _R_<sup>2</sup> for regressions with this outcome also speak to its relative noisiness.

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15 which is applied to all sales;<sup>3</sup> the sales tax (TUT), **tQ,** which is applied to all sales on the<sup>domestic</sup> market; and the value-added tax, **tVA,** which is applied to imports and to domestic value-added.<sup>The</sup> commodity tax structure is completed by tariffs on imports (equation 14) and by taxes on exports (equation 5).

The next set of equations (16, 17, 18, 19) describes the conditions for equilibrium in the<sup>goods</sup> and factor markets. The model includes a foreign trade constraint (equation 20) so that the equilibrium real exchange rate is determined endogenously. To facilitate the welfare interpretation of tax reforms, all government revenue, YO (equation 22) is returned to the representative consumer in lump-sum fashion (equation 21). Because of the linear homogeneity of all demand and supply functions, only relative prices can be determined. Hence the need to select a numdraire (equation 23).'

# 4. An Evaluation of **Tax** Revenue Less

In this section we use the model to estimate the potential government revenue loss through exemptions and various forms of tax evasion (smuggling, bribery, etc.). Table 4 describes the ten-sector sectoral aggregation and tax revenues by tax instrument. The disaggregation into ten sectors was deemed the minimum one to capture the incidence of the main tax instruments used in Madagascar. The structure of the economy in table 4 and the values of the endogenous variables correspond to a 'base" solution of the model. This calibrated base simulation replicates the actual disaggregated flows in the Malagasy economy in 1988.5 Three quarters of exports originate in agriculture which is also, by far, the largest

# 3 In the numerical application, this tax is only applied to sales of agricultural products.

4 By Walras law one of the equations in the model is redundant. However, for expositional purposes all equations describing the model are included in table 3.

5 How we updated the 1984 input-outpu **t** able and reconciled it with national accounts, foreign trade figures and budgetary revenue figures is discussed in an appendix available upon request. It would have been desirable to disaggregate agriculture into export cash crops and other (mainly for domestic use) agriculture. However, the input-outpu **t** able did not make this distinction so further disaggregation was not possible.