rafmacalaba/datause-encoder-data huggingface dataset:
are operating out of the dwelling of the owner.<sup>8</sup> The point estimate of the treatment effect is still 0.78 for this sub-sample, although with the smaller sample size, it is not statistically significant.

A second potential threat to the exclusion restriction is that despite controlling for the distance to the city center, distance to the tax office may be correlated with other interactions with the government, which could also affect profitability. To examine the robustness of our results to this possibility, in column 3 we add the distance to the municipal office when municipal licenses are issued as a further control. The mean distance from the municipal office to the tax office is only 1.4 kilometers. We see this variable has no effect on firm profitability, and leads to no significant change in the estimated effect of a NIT. This adds further support to our claim that the distance to the tax office is picking up the specific information and time costs of obtaining a NIT, and not something else to do with other interactions with the government.

Finally, while we have controlled for the general tendency of firms in a particular location to receive visits from a tax inspector, in column 4 of Table 5 we also include whether a firm itself has received a visit from a tax inspector. This is likely to be endogenous to profits, but conditioning on this provides a further check of the robustness of our results to controls for enforcement. Again there is no significant change in the coefficient after adding this control, and if anything, the point estimates show even larger impact of a tax identification number. Thus our results appear robust to several potential threats to our identifying assumption.

# **5.2. Comparison with OLS**

Columns 5 through 8 of Table 4 provide OLS estimates of the impact of having a NIT on log profits, for comparison with the treatment effect estimates provided by MLE and 2SLS. We begin by including the same control variables as in columns 1 through 4. Columns 5 and 6 show that OLS provides much lower estimates of the impact of a NIT.

> 8 Firm owners were surveyed at their business locations, and therefore we do not have the GPS coordinates of the home locations which would allow us to say precisely how many firms are operating within a kilometer or two of their homes.

- 15 -
16 consecutive years 2008-11 to 15 percent of the population (46.2 million people). In Europe, a growing number of people are believed to have fallen below the poverty threshold, where poverty is measured in relation to the median living standards in each country. An estimated 16.4 percent of the population (or 80 million people) were living below the poverty threshold in 2010, with the highest rates observed in Bulgaria, Romania, Greece, and Spain (around 20 percent). Across the EU, the poverty rates are higher among women (17.1 percent on average against 15.7 percent for men), children (20.2 percent), and the young (21.6 percent) who are either unemployed or hold precarious jobs (part time or fixed term). In terms of households, the poverty rate has been the highest among single-parent families (36.9 percent) with no possibility of risk sharing within the family.<sup>45</sup> While social welfare programs shielded some of the poorest Europeans (especially children and youth) from the most severe effects of the financial crisis through 2010,<sup>46</sup> the cuts in these programs as part of the austerity measures adopted by governments are expected to further worsen the situation going forward.

Opinion survey results support these warnings. According to the Flash Barometer survey 289 that monitors public views about the social impact of the global crisis in EU member states, many EU citizens considered that poverty was rather widespread in their country (at least 20 percent of their country’s citizens), more so for respondents in East and Southeast Europe than Northern and Central European member states.<sup>47</sup> Respondents, notably in Greece, Romania, Portugal and Spain, thought that poverty had increased strongly in their countries. Respondents also noted difficulties in paying ordinary bills, keeping up with credit commitments, bearing the cost of general health care, childcare and social care services for themselves and their relatives, and expressed pessimism about their financial prospects and the ability to maintain their current jobs in the future.

**Figure 8. Poverty headcount ratio at $1.25 a day (PPP) (% of population)**

<!-- Start of picture text --> 0 20 40 60 80<br>post-crisis poverty<br>East Asia and Pacific Central-Eastern Europe Latin America/Caribbean<br>Middle East and North Africa South Asia Africa<br>80<br>60<br>40<br>pre-crisis poverty<br>20<br>0<br><!-- End of picture text --> _Source_ : Authors’ computations based on 57 countries that have data for both precrisis and postcrisis periods. _Note_ : Countries below the 45 degree line had higher average poverty rate in the post crisis period than precrisis.

> 45 US Census Bureau 2011. 46 OECD 2012.

> 47 European Commission 2010.
**Table 3**

Colombia: Industrial Policy in the Large

|**Program to Promote World Class**<br>**Sectors**|**PND New Sectors based on**<br>**Innovation**|
|---|---|
|_Outsourcing of Business Services_|ITC|
|_Software_|Health|
|_Cosmetics_|Biotech|
|Fashion and design|Electronics|
|Electric energyand transmission|Logistics|
|Autoparts and vehicles|Design|
|Chocolate Confection|Energyand Natural Resources|
|_Health Tourism_|Creative industries|

Italics= new major export sector under the program of World Class Sectors Source: Ministry of Planning, National Development Plan 2010-2014 Latin America’s most ambitious industrial policy is in Brazil, which began in continuum with the Industrial, Technological and Trade Policy, or PITCE (2003-2007), continued with the Productive Development Policy, or PDP (2008 -2010), and now is expressed in its closely related follow-up Plano Brazil Maior. While one of the objectives of the industrial policy in the PDP period has been the diversification of the productive structure, enhanced productivity and exports with special emphasis on innovation, in fact much of the effort appeared to have focused on consolidating competitiveness as well as the international investment expansion of existing national champions in resource-based industries (Programs to Consolidate Leadership in Figure 1)<sup>15</sup> . Promoting national champions for market positioning, learning and upgrading, linkages to the domestic and international economies, etc., is a legitimate goal of industrial policies. Whether the promotion of Brazilian national champions is serving as an eventual platform for new upgraded comparative advantages, and whether the already large firms needed BNDES financing to conquer the world market are issues that merit more investigation (Almeida and Ross Schneider, 2012)<sup>16</sup> .

> 15 The focus on competitiveness of areas of existing comparative advantage gained a higher profile in the PDP after public criticism of the PITCE, which aimed principally at four high technology sectors: biotechnology, ITC, semiconductors and pharmaceuticals.

> 16 Zebral (2011) argues that the PDP was aimed at bolstering the traditional “clientalist” network of big Brazilian firms.

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# Figure 10: The 2016 amnesty raised the wealth tax revenue

## (a) Wealth tax revenue

<!-- Start of picture text --> Billions of<br>2015 pesos<br>2009 2013-15 2016<br>40 2.25<br>Amnesty Amnesty Amnesty<br>30<br>Top wealth tax rate (right)<br>1.25<br>20<br>0.75<br>Wealth tax<br>10<br>revenue (left) 0.50<br>0.25<br>Counterfactual revenue<br>0 (using 2015 tax base) 0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text -->

## (b) Forgone wealth tax revenue

<!-- Start of picture text --> Billions of<br>2015 pesos<br>2009 2013-15 2016<br>40 2.25<br>Amnesty Amnesty Amnesty<br>30 Forgone revenue<br>Top wealth<br>(absent evasion)<br>tax rate (right)<br>1.25<br>20<br>0.75<br>Wealth tax<br>10<br>revenue 0.50<br>0.25<br>0 0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> _Notes:_ this figure plots the wealth tax revenue and two counterfactuals against the top wealth tax rate (right axis). Panel (a) presents the counterfactual revenue absent the 2016 enforcement initiatives, while Panel (b) plots the forgone wealth tax revenue absent evasion, as detected through the 2016 enforcement initiatives. Argentina’s policy package more than doubled the wealth tax revenue by 165–180% from 2016 to 2018. Moreover, the wealth tax revenue increased more than sevenfold from Arg$4.9 billion in 2018 to Arg$35.4 billion in 2019, after the 2019 reform increased the wealth tax rates. Relative to the counterfactual revenue in 2019, Argentina raised tax revenue more than threefold thanks to the prominent disclosures of offshore wealth it induced.

_Source:_ authors’ calculations using data from AFIP statistical yearbooks.

33
26 hyperinflation, one might ask whether these factors can account for some of the unexplained increase in inequality, which shows up in our analysis as increases in “pure dispersion” within the various population subgroups.

High and rising inflation is a particularly plausible culprit. The inflation tax tends to be a regressive wealth tax, since the ability to protect wealth through portfolio adjustments is generally held to be increasing in income, at least over an initial range. In addition, there is some evidence that indexation is not perfect, and that real wages are lower during high-inflation periods (Cardoso, 1992). There is, in fact, considerable support in the literature for the proposition that inflation has distributional consequences, and can lead to higher poverty and inequality. Looking at a reasonably large sample of countries over the 1970-2000 period, Easterly and Fischer (2001) find that inflation is robustly associated with a lower income share for the bottom quintile of the population.<sup>34</sup> For slightly different cross-country samples, they also find a negative correlation between inflation and minimum wages; and a positive correlation between inflation and poverty incidence. Similar results are obtained by Romer and Romer (1999). A number of single-country studies also find evidence that higher inflation is associated with lower income shares for the poor, including Blejer and Guerrero (1990) for the Philippines; Datt and Ravallion (1998) for India; and Ferreira and Litchfield (2001) for Brazil.

The detailed mechanisms through which higher inflation can lead to increases in inequality (and poverty) are well-discussed by Neri (1995), who lists five channels of impact. In each case, he presents substantial supportive empirical evidence from Brazil. The five channels are: (i) economies of scale in financial transactions: while shoe-leather costs may not vary with the amount involved in a financial transaction aimed at protecting assets from inflation, the benefits do. This would remain the case even if there were no barriers to entry into certain asset markets. (ii) But these barriers to entry are widespread, and restrict access to some assets that are particularly effective in avoiding the inflation tax, to larger depositors. Neri presents revealing evidence about the incidence of ownership of overnight deposits and credit cards across the distribution of income. (iii) Tighter

> the rise in inequality.

> 34 Easterly and Fischer’s results suggest that an increase in inflation from zero “to hyperinflation” would decrease the income share of the poorest quintile by 1.7 percentage points (from an average of 6.2%). They also study opinion poll responses from some 32.000 households across thirty-eight countries, and find that “inflation aversion” declines with self-reported socio-economic status, even after controlling for education.
will increase by transfers of money from extreme poor to less poor (who become nonpoor), and from poor to non-poor. Furthermore, transfers among the poor have no effect on the poverty gap measure.

The P2 measure of poverty is sensitive to the distribution among the poor as more weight is given to the poorest below the poverty line. P2 corresponds to the squared distance of income of the poor to the poverty line. Hence, moving from P0 towards P2 gives more weight to the poorest in the population.

This paper sets its poverty bar very low. To define “extreme poverty” or indigence it uses a US$1 a day poverty line, which is annually 2,681 gourdes.<sup>10</sup> Those that earn a per-capita income above US$1 are above the indigence line and therefore not extremely poor. The poverty lines used for rural, urban, and metropolitan areas are identical, as consumer price index data do not exist for different regions or locations in Haiti. This may overestimate poverty in rural areas slightly.

# **Quantile Regressions**

# _Model_

The underlying economic model used in the analysis will simply follow Mincer’s (1974) human capital earnings function extended to control for a number of other variables that relate to location. In particular, we apply a semi-logarithmic framework that has the form: where ln yi is the log of earnings or wages for an individual, i; xi is a measure of a number of personal characteristics including human capital variables, etc.; and zi represents location specific variables. The functional form is left unspecified in equation (1). The empirical work makes extensive use of dummy variables in order to catch nonlinearities in returns to years of schooling, tenure, and other quantitative variables. The last component, ui, is a random disturbance term that captures unobserved characteristics.

# _Quantile regressions_

Labor market studies usually make use of conditional mean regression estimators, such as OLS. This technique is subject to criticism because of several, usually, heroic assumptions underlying the approach. One is the assumption of homoskedasticity in the distribution of error terms. If the sample is not completely homogenous, this approach, by forcing the parameters to be the same across the entire distribution of individuals may be too restrictive and may hide important information.

> 10 The conversion is based on the 2000 PPP. The questionnaire asks for information about income in the last 12 months and self-consumption in the last week (which is multiplied by 52 to obtain the annual selfconsumption).

15
# **Table 6: Marginal Contributions of Taxes and Transfers to Inequality and Poverty** **<u>Reduction</u>**

|||**Redistribu**<br>**Effect**<br>|**tion**<br> <br>|**Poverty R**<br>**Effe**<br>|**eduction**<br>**ct**<br>|
|---|---|---|---|---|---|
|||**Men**|**Women**|**Men**|**Women**|
|Direct t|axes|0.5241|0.4314|-0.5791|-0.5911|
|Direct t|axes (including informal tax)|0.4645|0.3758|-0.7886|-0.8641|
|•|Personal income tax|0.4793|0.4083|-0.2579|-0.2366|
|•|Non-farm enterprise income tax|0.1118|0.0839|-0.0166|-0.0125|
|•|Land use fee and agricultural income<br>tax|-0.0761|-0.0825|-0.2804|-0.2876|
|•|Urban housing tax|0.0003|0.0067|-0.0063|-0.0031|
|•|Other income tax|0.0085|0.0129|0|-0.0174|
|•|Informal tax|-0.0587|-0.0546|-0.3095|-0.3549|
|Direct t|ransfers (all)|0.3257|0.4251|0.7959|1.0425|
|•|PSNP|0.3075|0.3724|0.6444|0.8914|
|•|Other transfers|-0.0289|0.0574|0.1094|0.1065|
|Indirec|t tax (all)|-0.1345|-0.0735|-1.7757|-2.3759|
|•|VAT|-0.0887|-0.0098|-1.4454|-1.8335|
|•|Excise|-0.0555|-0.0734|-0.6108|-0.5086|
|Indirec|t subsidies (all)|-0.0185|-0.0371|0.0215|0.0807|
|•|Kerosene subsidy|0.0004|0.0005|0|0|
|•|Wheat subsidy|-0.0189|-0.0376|0.0215|0.0807|
|In-kind|transfers|1.2335|1.1798|7.2199|9.2815|
|_Educat_|_ion (all)_|-0.252|-0.4331|3.7104|5.2462|
|•|Primary education|1.7633|1.624|3.5244|4.898|
|•|Secondary education|-0.2567|-0.3547|0.1519|0.3175|
|•|Tertiary education|-1.6217|-1.5366|0.0341|0.0307|
|_Health_||1.3802|1.5309|2.7075|3.6565|

_Source: Authors’ calculations; ESS2018/19 data._ _Note:_ The marginal contributions—redistributive and poverty reduction effects—are in percentage points. A positive marginal contribution means it reduces inequality and poverty; a negative means that it increases inequality and poverty.

PSNP reduces income inequality by 0.37 pp for women and 0.31 pp for men. It also reduces the poverty headcount by 0.9 pp for women and 0.6 pp for men. The effectiveness of PSNP transfers in improving welfare—reducing inequality and poverty—is in line with the findings of PSNP impact evaluation studies (Gilligan, Hoddinott, and Taffesse 2009; Hill et al. 2017). The positive distribution impacts of PSNP might also be attributable to the program’s targeting (Coll-Black et al. 2013).

22
government was considered. In all the analyses, benefiting from the literature (Alcan, Can, and Pektaş 2015; Tansel and Kan 2012), we added the control variables that would be correlated with both the social assistance status and the labor market status of the individuals. The control variables included income, gender, age, marital status, level of education, number of children, home ownership, and family status.

**Table 2. Multinomial Logit Estimation with 2013 SILC (Marginal Effects at Average): Disaggregated Social Assistance**

||**FE**|**IE**|**U**|**OLF**|**Avg. X**|
|---|---|---|---|---|---|
|Avg. Probability.|0.9292|0.0265|0.0195|0.0245||
|HH Income (log)|0.1573***|-0.0188***|-0.0209***|-0.1175***|10.1361|
|Child Social Assistance|-0.1295***|0.0759***|0.0252***|0.0283**|0.0762|
|Housing Assistance|-0.0812|0.0474|0.0044|0.0293|0.0010|
|Other Social Assistance|-0.0487***|0.0495***|0.0132***|-0.0140|0.1084|
|Number of Obs.|47828|Design Df|47827|Population Size|50604002|
|F-Stat.|255.32|P>F|0.0000|||

_Notes_ : FE = formal employed, IE = informal employed, U = unemployed, and OLF = out of the labor force. Control variables: age, gender, marital status, education, number of children, home ownership, family status (extended family in household), and spatial difference (living in urban or rural area). Coefficients are significant at 1 percent (***), 5 percent (**), 10 percent (*). Avg. X: the average values of explanatory variables. Due to the nonlinear nature of the multinomial logit model, the effects of explanatory variables on dependent variables vary along the explanatory variable vector X. Therefore, we estimated the effect coefficients around the sample average of X.

The initial static analysis indicated that social assistance beneficiaries are less likely to be formally employed and more likely to be informally employed or unemployed. There was no evidence that they are more likely to be out of the labor force altogether, whether or not being in the labor force is correlated with beneficiary status (aggregate) (see table 1). However, disaggregation with respect to benefit types revealed that child assistance beneficiaries are more likely to be out of the labor force; they also have a lower probability of working as formal employed and a higher probability of working informally (see table 2).

We then used all the transitions from the SILC panels over the period 2006–12 (as described in section 4) in order to assess the transition behavior of individuals and how this relates to their social assistance status. In this analysis, we aggregated all social assistance programs delivered by the government. Our main finding from the transition analysis is that individuals are more likely to transition into informal employment if they are recipients of social assistance, no matter what their initial status is.

14
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.2  Road condition along major transit corridors in ECOWAS**

||Perce|ntageincon|dition|Percentage|Per|centage in traffi|c band|
|---|---|---|---|---|---|---|---|
|**Corridors**|Good|Fair|Poor|Paved|<300|300-1000|>1000|
|**_GATEWAYS TO THESEA_**||||||||
|**_Tema-Ouagadougou-Bamako_**|**_67.1_**|**_30.7_**|**_2.2_**|**_100.0_**|**_3.8_**|**_25.7_**|**_25.4_**|
|BurkinaFaso|52.4|47.6|0.0|100.0|8.3|31.0|60.7|
|Ghana|62.8|32.5|4.8|100.0|0.0|0.0|0.0|
|Mali|100.0|0.0|0.0|100.0|4.7|72.0|23.2|
|**_Dakar-Bamako_**|**_48.0_**|**_19.8_**|**_32.1_**|**_100.0_**|**_24.9_**|**_55.6_**|**_19.5_**|
|Mali|75.6|24.4|0.0|100.0|46.7|47.2|6.1|
|Senegal|16.6|14.6|68.8|100.0|0.0|65.2|34.8|
|**_Abidjan-Ouagadougou_**|**_33.1_**|**_23.0_**|**_43.9_**|**_100.0_**|**_3.5_**|**_23.3_**|**_73.1_**|
|BurkinaFaso|68.6|27.3|4.1|100.0|4.1|27.6|68.3|
|Côte d'Ivoire|3.1|19.5|77.5|100.0|3.1|19.7|77.2|
|**_Lomé-Niamey_**|**_50.2_**|**_30.1_**|**_19.8_**|**_100.0_**|**0.0**|**_82.6_**|**_17.4_**|
|BurkinaFaso|35.4|38.9|25.8|100.0|0.0|77.3|22.7|
|Niger|99.0|1.0|0.0|100.0|0.0|100.0|0.0|
|Togo|51.7|0.0|48.3|100.0|0.0|37.2|62.8|
|**_Cotonou-Niamey_**|**_49.5_**|**_7.9_**|**_42.6_**|**_98.5_**|**_1.5_**|**_26.4_**|**_70.0_**|
|Benin|38.1|2.2|59.7|97.8|2.2|15.8|81.5|
|Niger|77.7|22.3|0.0|100.0|0.0|52.8|41.4|
|**_INTRA-REGIONALCORRIDORS_**||||||||
|**_Abidjan-Lagos_**|**_50.7_**|**_28.0_**|**_20.6_**|**_98.8_**|**_0.0_**|**_0.4_**|**_43.6_**|
|Benin|26.9|0.0|68.0|92.4|0.0|2.5|90.3|
|Côte d'Ivoire|0.0|100.0|0.0|100.0|0.0|0.0|100.0|
|Ghana|75.0|14.5|10.5|100.0|-|-|-|
|Nigeria|50.0|50.0|0.0|100.0|0.0|0.0|100.0|
|Togo|0.0|0.0|100.0|100.0|0.0|0.0|100.0|
|**_Nouakchott-Ndjamena_**|**_63.4_**|**_21.1_**|**_10.2_**|**_97.3_**|**_9.9_**|**_46.2_**|**_43.4_**|
|BurkinaFaso|70.7|29.3|0.0|100.0|6.2|37.6|56.3|
|Cameroon|84.8|15.2|0.0|100.0|31.0|56.5|12.5|
|Mali|62.9|31.3|0.0|94.2|5.8|75.8|16.4|
|Mauritania|50.6|23.8|25.6|100.0|0.0|21.5|78.5|
|Niger|66.4|4.4|29.1|99.1|3.8|46.8|48.5|
|Nigeria|-|-|-|100.0|0.0|0.0|100.0|
|Senegal|10.9|26.5|62.6|28.6|0.0|100.0|0.0|

**Source:** AICD calculations.

21