rafmacalaba/datause-encoder-data huggingface dataset:
With these tools in hand we can investigate the strength of the instruments<sup>in the</sup> specifications in Tables 2 and 3. Consider first the core<sup>DK specificationinregression</sup> 2.3. In the first-stage regressions, we see that the linguistic<sup>origin variables and the FR</sup> instrument are highly significant predictors of both endogenous<sup>variables.In our</sup> previous paper we noted that this implied a very high correlation<sup>betweenthe fitted</sup> values of institutions and trade in the second-stage regression,<sup>whichmade theIV</sup> estimates highly sensitive to a few influential observations.<sup>Table 2reports that this</sup> correlation of fitted values from the first-stage regression<sup>is 0.74,indicating strong</sup> collinearity in the second-stage regression. From this we concluded<sup>informallythat the</sup> cross-sectional variation in institutions, trade and incomes was<sup>not very informative</sup> about the partial effects of the former two on the latter.

The weak instrument diagnostics discussed in the previous subjection<sup>help us to</sup> make this point more precisely. Consider first the Shea<sup>(1997) partial R-squared</sup> statistics. We find in our specification that these statistics are<sup>very small at only 0.02</sup> and 0.04 respectively. In contrast, the R-squareds from the first-stage<sup>regressions in the</sup> first two univariate specifications in Table 2 were 0.14<sup>and0.29.Thisprovides a first</sup> indication of the idea that while linguistic origin and distance<sup>are reasonableinstruments</sup> when the effects of institutions and trade are estimated separately,<sup>they are quite poor</sup> instruments when the effects of the two are considered jointly.

The middle panel of Table 2 also reports the eigenvalues of<sup>the matrix of</sup> estimated coefficients in the first-stage regression, F, as<sup>wellas the eigenvalues of G.</sup> The first observation here is that the ratio of the largest to the smallest<sup>eigenvalues ofr,</sup> i.e. the condition number of r, is nearly 100<sup>and thesmallest eigenvalueis small at</sup> 0.025. This indicates that the matrix of estimated coefficients<sup>in</sup> the<sup>first-stage</sup> regression is very close to not being of full rank, contributing<sup>to weakidentification of the</sup> parameters of interest. The smallest eigenvalue of G<sup>is1.36.To interpret this value,</sup> suppose we were to define the set of weak instruments very<sup>narrowly as only those</sup> instruments that would lead to size distortions of 25% or more<sup>(i.e.the size of a nominal</sup> 5% test about a parameter of interest were actually<sup>25%),we wouldnot reject the null</sup> hypothesis that the instruments in this application are weak<sup>according to this stringent</sup> criterion, since the critical value provided by Stock<sup>andYogo is5.75.</sup> 9
being taken into account and prices are generally assessed at market levels rather than at their shadow level.

**Table 2.** Social rates of return on World Bank Projects Unweighted Average 1960-2000

|**Region**|**Energy/Mining**|**Telecoms &**<br>**Information**|**Transport**|**Urban**|**Water &**<br>**Sanitation**|
|---|---|---|---|---|---|
|**Africa**|14.1|20.6|25.5|21.3|7.5|
|**East Asia**|18.3|19.5|24.8|20.3|10.5|
|**Eastern**<br>**Europe**|30.9|31.1|25.8|15.7|9.8|
|**Latin America**|12.8|16.6|22.4|19.2|11.0|
|**Middle East**|12.3|26.9|25.1|16.5|7.8|
|**South Asia**|23.2|22.0|24.1|14.9|9.8|
|**Developing**<br>**World**|18.4|21.5|25.4|19.2|9.2|

Source: World Bank- Operations and Evaluation Department Database There are several other striking results – first, the returns to the “market enlarging” sectors – ICT and transport – are considerably higher than the more service/welfareoriented sectors, like water and sanitation, which is roughly consistent with the conclusions derived from macroeconometric assessments of sectoral impacts.<sup>iv</sup> Second, the regional distribution of returns suggests a more complex pattern. Within each sector, projects in Eastern Europe have tended to have the highest returns, while projects in Latin America have tended to have among the lowest (except in urban and water and sanitation for both regions). There is no other clear pattern emerging from the data. Finally, the relatively high rates of return observed support the value of independent screening and technical assessment provided by an organization such as the World Bank.

8
<!-- Start of picture text --> Imported Content of Exports<br>Manufacturing Exports (ISIC15-37)*<br>os<br>0.4<br>Cross-Country Average=0.32<br>03<br>0.2<br>Canada France Germany Japan Korea Mexico United Brazil China European United States<br>Kingdom Union<br>Source: OECD STAN. *Mid-2000s; Largest US trade partners (no data for HK). Intermediatesfrom all sectors. EU & USincluded for reference<br><!-- End of picture text -->
transition to cropland by 1.3 percent. Land suitability is a predictor of conversion to cropland, with a one standard increase in suitability leading to a pixel being 1.9 hundredths of a percent more likely to transition to cropland. Consistent with the result from Table 1, distance to coast increases the probability of transition to cropland with a one standard deviation increase in distance coast leading to an increase in the likelihood of cropland transition by 0.8 hundredth percent. The only surprising result is that distance to a river is associated with a greater likelihood of transition to cropland. This surprising result could be explained by our inability to for groundwater irrigation potential, for which there is no globally available data. We perform a robustness check by redefining the left-hand side variable to measure transitions into cropland or cropland mosaic. Table A4.2 presents the results for this extended definition of the land-use transition. Results are very similar to those in Table 4, with coefficients having the same sign and order of magnitude except for the coefficient on distance to a city which is noticeably greater in absolute value.<sup>8</sup>

**<mark>Table 4.  Drivers of cropland gains (pixel level)</mark>**

||Conversion to<br>cropland|
|---|---|
|(Intercept)|0.05704<br>(0.03909)|
|Distance to city (in km)|-0.00001180<sup>***</sup><br>(0.0000009872)|
|Number of drouht months|-0.0004752<sup>***</sup>|
|g|(0.000002580)|
|Land suitability for agriculture|0.000009667<sup>***</sup><br>(0.00000003148)|
|Distance to Coast (in km)|0.00005991<sup>***</sup><br>(0.0000001792)|
|Distance to River (in km)|0.00004178<sup>***</sup><br>(0.0000006777)|
|Country fixed effect|Y|
|Observations|30,306,919|
|R<sup>2</sup>|0.04|

_Note: The results are for a regression that is restricted to the sample of pixels that were not cropland in 2003 but are located within 3 kilometers of existing 2003 cropland. The dependent variable is equal to 1 if the pixel became cropland in 2018 and 0 otherwise. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1_

> 8 The coefficient is 13 times larger in absolute value indicating that proximity to a city is a greater driver when considering transitions towards cropland mosaic.

14
# **B.2: Coding the Investment Code**

To assess the relationship between firm performance, political connections and the regulation, we created a database of the Tunisian Investment Code, the _Code d'Incitation aux Investissements (CII)_ . The dataset contains annual information at the NAT96 5-digit sector level, the most disaggregated sector classification available in Tunisia, on which activities were covered by the Investment Code and whether these activities were subject to i) prior authorization as stipulated in Article 4 of Décret n°94492 and subsequent amendments ii) restrictions on foreign investment in the form of having to obtain permission from the _Commission Superieure d’Investissement_ as stipulated in stipulated in Article 5 of Décret n°94-492 and subsequent amendments. In addition, there are a number of sectors in which foreign firms are never allowed to have majority foreign ownership including agricultural activities and medical laboratories, which we also consider subject to FDI restrictions.

To construct this dataset we code the original Investment Code enacted in December 1993 and all subsequent decrees resulting in substantive amendments to investment laws up until 2010. In total we record 25 decrees which result in revisions in the coverage of the Investment Code and/or changes in which activities are subject to authorization requirements and/or restrictions on foreign investment. We do not record decrees resulting in changes in registration requirements, customs regulations, fiscal advantages, or other regulations that are not analyzed in this paper. We also exclude artisanal activities from our analysis as they only account for a very small fraction of economic output.

Arguably the most important challenge in coding these regulations is that the list of activities stipulated in the investment code and the Tunisian NAT 96 classification do not overlap perfectly. Sometimes the activities listed in the Investment Code are more general than the NAT96 classifications (for example, the activity “Hébergement” in the Investment Code corresponds to a number of NAT96 categories, notably “Hôtels avec restaurant”, “Hôtels de tourisme sans restaurant”, “Hôtels non classes”, “Auberges de jeunesse et refuges”, “Exploitation de terrains de camping”, “Autre hébergement touristique”, “Hébergement collectif non touristique”). In other instances, they are more specific (for example the activities “Pêche côtière”, Pêche au feu, Pêche au chalut” are encompassed by the NAT96 sector “Pêche”) whereas other activities are very specific and hard to map to specific sectors (for example “Sélection de couleurs pour les imprimeries”). Occasionally it is hard to create a satisfactory correspondence (for instance the activities “autres industries diverses” 42
significantly higher levels of fluctuation in monthly eamings. The figures in Table 10 demonstrate another interesting pattern: variability in income does not appear to be correlated with the level of earnings. Java and Eastern Indonesia have similarly low income levels but have very different drops from peak to low quarters, with Java earnings reflecting far more stability than do earnings in Eastern Indonesia; Sumatra and Sulawesi have similarly high levels of earnings but experience very different fluctuations in earnings across seasons.

**Table 10. Seasonal Fluctuations in Earnings Among Farm Workers By Region**

|**All Farm Workers**|**Ist**|**2nd**|**3rd**|**4th**|**Total**|
|---|---|---|---|---|---|
|Sumatra|94,916|93,191|97,686|101,291|96,590|
|Java|52,525|52,256|60,014|57,140|55,156|
|Kalimantan|105,988|129,873|97,290|136,866|116,624|
|Sulawesi|69,378|82,930|134,374|71,525|90,596|
|Eastern Indonesia|43,956|49,008|56,433|42,840|46,576|
|Bali|96,850|76,504|87,078|78,231|83,266|

Note: Jakarta data have been omitted, as there are very few observations, and these observations are very different from the rest of Java. The figures for Bali are distinguished from both Java and Eastern Indonesia (its two most common regional placements), as Bali's labor market appears very different from both. Eastern Indonesia includes West and East Nusa Tenggara and East Timor. Both Maluku and Irian Jaya were omitted because of the small number of observations.

Recalling that income insecurity comes from a combination of low income levels and fluctuations in income levels, the data in Table 10 suggest that Eastern Indonesia may be characterized by the greatest extent of insecurity. The three provinces have the lowest income level of any region, but are still characterized by considerable fluctuation in earnings across quarters. Earning levels on Java also appear to be very low, but exhibit less fluctuation in earnings. Nonetheless, where these low earnings are combined with variability, a combination that almost certainly occurs in some parts of Java, income insecurity may be a real problem.<sup>1</sup> " Unfortunately, the earnings data provided by SAKERNAS do not allow this analysis to be disaggregated to a level that would help identify such regions.

" See A. Mason (1996) for a description of rural poverty on Java. Although Mason finds that poverty is not really localized in pockets if poverty, his framework for studying the distribution of poverty across zones defined by their physical and economic characteristics may be useful in the context of identifying regions that have many who suffer from income insecurity (i.e., places that are both poor and cut off from neighboring markets).

15
|________________<br>**Table III.4 Exp**|_____________<br>**orter producti**|_________<br>**vity prem**|_______________<br>**ia II: Share of e**|_______________<br>**xports in total sa**|______________<br>**les and its squar**|_______________<br>**ed value**|______________|___________________|
|---|---|---|---|---|---|---|---|---|
|Country|Model||All<br>firms|10 - 19<br>employees|20 – 49<br>employees|50 – 249<br>employees|250 – 499<br>employees|500 and more<br>employees|
|_________________<br>Belgium|_______________<br>Pooled|__________<br>ß1|_________________<br>0.069|_________________<br>0.170|________________<br>0.399|_______________<br>0.096|________________<br>-0.069|______________________<br>0.410|
|1996-2005||p|[0.006]|[0.000]|[0.000]|[0.001]|[0.000]|[0.000]|
|||ß2|0.000|-0.001|-0.002|-0.001|0.000|-0.002|
|||p|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
||Fixed|ß1|-0.040|-0.144|0.136|0.015|0.511|0.209|
||effects|p|[0.003]|[0.000]|[0.003]|[0.405]|[0.000]|[0.044]|
|||ß2|0.000|0.003|-0.076|-0.001|-0.181|-0.051|
|||p|[0.296]|[0.639]|[0.000]|[0.021]|[0.000]|[0.049]|
||N||6,661|2,270|2,235|1,730|241|185|
||NxT||39,292|9,013|13,453|13,281|1,924|1,621|
|Chile|Pooled|ß1|1.422|0.743|1.071|1.011|0.150|-0.653|
|1990 - 1999||p|[0.000]|[0.112]|[0.000]|[0.000]|[0.504]|[0.043]|
|||ß2|-1.129|0.376|-0.787|-1.198|-0.255|0.209|
|||p|[0.000]|[0.546]|[0.011]|[0.000]|[0.360]|[0.598]|
||Fixed|ß1|0.237|0.036|0.438|0.315|0.155|-0.309|
||effects|p|[0.007]|[0.919]|[0.018]|[0.007]|[0.518]|[0.461]|
|||<br>ß2|-0.145|0.578|-0.269|-0.241|-0.060|-0.004|
|||p|[0.184]|[0.170]|[0.331]|[0.078]|[0.821]|[0.991]|
||N||7,745|2,643|2,710|1,993|280|119|
||NxT||47,820|13,810|17,109|13,828|2,084|989|
|China|Pooled|ß1|0.848|0.211|0.517|0.725|1.034|1.136|
|1998-2005||p|[0.000]|[0.125]|[0.000]|[0.000]|[0.000]|[0.000]|
|||<br>ß2|-0.865|-0.015|-0.440|-0.731|-1.080|-1.196|
|||p|[0.000]|[0.920]|[0.000]|[0.000]|[0.000]|[0.000]|
||Fixed|ß1|0.411|0.745|0.404|0.378|0.467|0.414|
||effects|p|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|||<br>ß2|<br>-0.417|<br>-0.693|<br>-0.395|<br>-0.381|<br>-0.464|<br>-0.465|
|||p|[0.000]|[0.001]|[0.000]|[0.000]|[0.000]|[0.000]|
||N||408,710|17,594|86,454|228,716|45,176|30,770|
||NxT||1,347,038|36,321|214,456|760,454|189,662|146,145|

74
Table 10. Summary of expected (based on earlier trends) and estimated (based on fitted model) measures of mortality

||Year|Expected|Estimated|Relative<br>Difference|Absolute<br>Difference|
|---|---|---|---|---|---|
|INFANT MORTALI1Y||||||
||1985|59.68|_51.50_|13.70|8.18|
||1986|56.79|48.61|14.40||
||1987|53.91|45.73|15.10||
||1988|51.03|42.85|16.00||
|DIARRHEA||||||
|ASSOCIATED INFANT<br>MORTALITY|1985|26.11|18.76|28.15|7.35|
||1986|24.05|16.68|30.57||
||1987|21.96|14.61|33.46||
||1988|19.89|12.54|36.95||
|POST-NEONATAL||||||
|MORTALITY|1985|47.58|38.37|19.37|9.21|
||1986|44.99|35.78|20.48||
||1987|42.40|33.18|21.73||
||1988|39.81|30.59|23.15||

21
# **Weak administrative data systems**

Previous literature has indicated that weak data systems may give rise to inaccurate administrative statistics in the case of routine immunization data in LMICs (Cutts et al. 2016; Sandefur and Glassman 2015). This may also be the case for reported COVID-19 vaccination rates. We investigate whether there is a correlation between the size of the gap between survey and administrative data (measured as their absolute percent difference) and countries’ statistical capacity (measured by the World Bank’s Statistical Performance Indicators (SPI)).

|**Table 9: Statistical per**|**formance and survey-a**<br>(1)|**dmin gap in vaccinat**<br>(2)|**ion rates**<br>(3)|
|---|---|---|---|
|VARIABLES|Gap Survey vs. Admin|Gap Survey vs. Admin|Gap Survey vs. Admin|
|SPI: Total score|-1.608**|-1.187|-0.990|
||(0.743)|(0.770)|(0.776)|
|SPI: Dimension 4.2 score (Admin data)|-55.37***|-46.28***|1.160|
||(15.36)|(15.75)|(9.335)|
|GDP per capita (PPP 100K)||-0.180|0.0321|
|||(0.108)|(0.0955)|
|Region Code = 2, ECA|||4.716<br>(13.76)|
|Region Code = 3, LCN|||4.613<br>(7.408)|
|Region Code = 4, MEA|||41.25|
||||(28.46)|
|Region Code = 7, SSA|||103.1***|
||||(18.60)|
|Constant|182.0***|167.0***|78.99*|
||(47.69)|(46.97)|(44.66)|
|Observations|48|48|48|
|R-squared|0.345|0.364|0.603|

Note: Results from OLS regressions of the absolute gap between survey estimates and administrative figures of vaccination coverage on indicators of statistical performance. To avoid extreme leverage at low values of coverage, the sample is constrained to those survey waves where administrative data coverage was at least 5% of the adult population. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 We find lower statistical performance scores to be significantly associated with higher gaps between the survey and administrative data in our sample of LMICs (Table 9). In a bivariate regression, the coefficient estimate suggests that a 1 percentage point increase in the total SPI score is associated with a 1.6 percentage point reduction in the percent gap between both data sources. Further, the availability of Civil Registration and Vital Statistics (CRVS), the SPI’s metric for administrative data quality specifically, is associated with a 55.4 percentage point smaller gap. When additionally controlling for GDP per capita, we find that only the SPI indicator of administrative data quality is significantly and negatively associated with the percent gap between 25
-114-

# Note on the Adjustment for Unit Value Basis for Passenger Cars

The 1977 Census of Manufactures for the USA gives only a single<sup>entry</sup> for passenger cars, while Brazil and Mexico both<sup>provide simple, but</sup> different, breakdowr-z; the former according to horsepower and the latter according to engine type.

With limited census information (see table A.1) we would<sup>have to value</sup> passenger cars produced in Brazil and Mexico by the single average<sup>price for</sup> all passenger cars produced in the United States, i.e. US$ 5,200. But the USA produces more large (and expensive) cars than the other two countries, so the average US price is too high for revaluing car output in Brazil and Mexico.

TABLE A.1

|Industrial<br>Census Information<br>in the United<br>States<br>(1977).<br>B|on Passenger<br>Car O<br>razil<br>and Mexico (1|utput<br>975)|
|---|---|---|
|Quantity<br>(1000<br>units)|Value<br>(mill.<br>national<br>currencies)|Unit value<br>(innational<br>currencies)|
|United States (1977)<br>Complete passenger vehicles<br>9,192.2|<br>47,796.3|5,199.66|
|Brazil (1975)<br>Cars,assembled,less<br>than 75 hp.<br>132.2|<br>2,964.4|22,418.02|
|Cars n.e.s.<br>317.4|<br>6,322.1|19,915.86|
|Total<br>449.7|<br>9,286.6|20,651.65|
|Mexico (1975)|||
|<br>4cylinder cars<br>152.0|<br>6,038.0|39,712.58|
|<br>6cylinder cars<br>60.3|<br>3,183.1|52,813.89|
|8 cylinder cars<br>45.1|<br>3,002.2|66,626.81|
|Total<br>257.4|<br>12,223.3|47,492.67|

Source: Appendix Tables A17.2, A17.3 and A17.4.

Car output in the three countries was therefore divided into<sup>two</sup> groups: cars with 4 cylinders or less and those with "ore tha& 4 cylinders. Although, at first sight, this may seem a rather crude way of assessing passenger car quality, it appears to be appropriate for the purpose at hand. In the mid-1970s car output in the United States consisted largely of 6 and 8 cylinder models while in Brazil and Mexico it consisted mainly of 4 cylinder models.

The first step was to estimate quantities for all the three countries distinguishing between 4 cylinder cars and those with 6 or 8 cylinders. For Mexico we get this information directly from the census material (see table A.1). For the USA we used information from Automotive News, 1975 Almanac Issue, which is the most important trade journal of the automobile industry in the USA. It shows US production classified by model, together with technical specifications of each model. The figures show that<sup>4 cylinder</sup> cars accounted for only 9.7 per cent of total car output in the USA in 1975 (table A.2). For Brazil some indirect information on quantities<sup>is also</sup>
# 3.2 Overall impacts on inequality and poverty

Government taxation and spending reduce inequality. Measured by the Gini coefficient, inequality is reduced by 6.4 points when we shift from the concept of MIPP to consumable income (Figure 4). The reduction is a result of the imposition of direct taxes and direct transfer receipts. The effect of indirect taxes (from disposable to consumable income) appears to be small. This result does not seem to be inconsistent with the trend found by Silveira and Palomo (2023), which suggest that the marginal effect of indirect taxes on the Gini of final income fell from 0.022 in 2002-2003, to 0.016 in 2008-2009, and then 0.011 in 2017-2018. When we include the in-kind benefits such as education and health, the reduction turns out to be 15.6 Gini points. As we will see later, this is a consequence of education and health being progressive and relatively high in size for the bottom 60% of the population.

The inequality reduction from market income plus pensions to consumable is among the two highest in the CEQ Latin American sample of eighteen countries, similar to that of Argentina. 30F<sup>31</sup> It is also the second in terms of inequality reduction when final income is the end income, only trailing behind Argentina (16.9 Gini points).

**Figure 4.  Inequality. Gini Coefficient for CEQ Income Concepts**

<!-- Start of picture text --> 0.70<br>0.585<br>0.60<br>0.521<br>0.521<br>0.50<br>0.429<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>Market Income plus Disposable Income Consumable Income Final Income<br>pensions<br>Gini coefficient<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal.

Poverty is reduced only slightly when comparing the poverty headcount rate of pre-fiscal income with that of consumable income. However, the Brazilian fiscal system reduces extreme poverty. Extreme poverty based on consumable income is 5.1 percentage points lower than that of MIPP (Table 3). 31F<sup>32</sup> Direct

> 31 The sample includes Argentina, Brazil, Mexico, Costa Rica, Uruguay, Panama, the República Bolivariana de Venezuela, Colombia, Dominican Republic, Ecuador, Chile, Honduras, Peru, El Salvador, Bolivia, Nicaragua, Guatemala, and Paraguay.

> 32 The thresholds for determining whether a household is poor are BRL 499 for moderate poverty and BRL 249.5 for extreme poverty considering the per capita household monthly income.

21