rafmacalaba/datause-encoder-data huggingface dataset:
_4_ The baseline data collection was conducted in October and November 2014 in six Ethiopian cities: Addis Ababa, Adama, Bahir Dar, Dire Dawa, Hawassa, and Mekelle.

Baseline survey data were collected from a sample of 2,369 female entrepreneurs who had registered for WEDP at the time of the survey, and an additional survey module was administered to a subset of 800 female entrepreneurs that included more questions about sector choice. The sampling for the additional survey module was stratified according to sector of business reported in the WEDP registration database, and we oversampled entrepreneurs who we thought might operate in a male-dominated sector to ensure that our sample had a representation of women-owned businesses in male-dominated sectors. We obtained full baseline and additional sector data on 790 female entrepreneurs who form the sample for this paper.

The WEDP baseline questionnaire contained a set of questions on household demographic characteristics, socioeconomic status, business sales, profits, costs, employees, entrepreneurial profile (e.g., age, place of birth, education level), and questions designed to elicit an entrepreneur’s business knowledge and level of financial literacy. The additional survey module asked detailed questions about family background, entrepreneurial capability (digit span forward recall, Raven’s Test, entrepreneurial psychology), role models, and access to social networks and retrospective questions about motivations for becoming an entrepreneur. The survey module additionally included some open-ended questions about how the women became exposed to their sector of operation, including, “Please elaborate on the story that led you to have a business in this sector.” In addition to the quantitative surveys, 90 qualitative interviews were conducted with WEDP-registered women. The qualitative research was designed to capture narratives from WEDP entrepreneurs about their attempts to obtain loans from formal financial institutions. For contextual information, all qualitative interviews started with questions about the history of the woman’s business. The qualitative sample included 17 women who were operating businesses in male-dominated sectors, and we use their business histories in this paper to provide illustrations of the ways in which husbands may facilitate women entering male-dominated sectors.

# **III. Which Sectors Are Male Dominated?**

To proceed with the crossovers analysis, we first need to categorize which sectors are male dominated. In the literature, male-dominated sectors are often defined as those in which men own more than 50 percent of the firms. In the Uganda study, Campos and colleagues (2011) defined male-dominated sectors as those in which men owned more than 75 percent of enterprises.<sup>5</sup> In this study, we took paid employment.

> 5Although male-dominated sectors are often defined as those in which more than 50 percent of the firms are owned by men (overrepresentation), in this study, we focused on enterprises in which male
# **TABLES** 5A-5C: **ABATEMENT ACTIVITY BY MEASURES OF FORMAL REGULATION**

**TABLE 5A VARIATION BY NATIONAL REGULATION**

|National Regulation|Mean Score|
|---|---|
|1|5.75|
|3|16.0|
|TOTAL|14.5|

**TABLE 5B VARIATION DUE TO PROVINCIAL REGULATION**

||**VARIATI**|**ON DUE TO PRO**|**VINCIAL REG**|**ULATION**<br>||
|---|---|---|---|---|---|
|l_____________<br>||<br>_____________|C oun|try|||
|Provincial<br>Regulation|_<br>Indonesia|_<br>Thailand|India|1<br>Bangladesh|TOTAL|
|**-1**|||7.3||**7.3**|
|0|12.8|26.3|18.3|5.7|**14.6**|
|**1**|17.2||17.3||17.2|
|TOTAL|14.6|26.3|14.3|5.7|14.5|

**TABLE 5C VARIATION BY FORMAL REGULATION: NATIONAL AND PROVINCIAL**

|Formal<br>Regulation|Mean Score|
|---|---|
|1|5.7|
|2|7.3|
|3|17.5|
|4|17.2|
|TOTAL|14.5|

20
Table 3: Policy Selectivity (1985–2014)

|Dependent Variable:|Log of To|tal Aid|||||
|---|---|---|---|---|---|---|
|||OLS|||FE||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged CPIA|0.12<sup>_∗∗∗_</sup>|0.35<sup>_∗∗_</sup>|0.36<sup>_∗∗_</sup>|0.20<sup>_∗∗∗_</sup>|0.49<sup>_∗∗∗_</sup>|0.50<sup>_∗∗∗_</sup>|
||(0.04)|(0.15)|(0.15)|(0.07)|(0.19)|(0.19)|
|Lagged CPIA, squared||-0.12|-0.13<sup>_∗_</sup>||-0.18<sup>_∗_</sup>|-0.18<sup>_∗_</sup>|
|||(0.07)|(0.07)||(0.10)|(0.10)|
|Lagged Total Aid|0.88<sup>_∗∗∗_</sup>|0.88<sup>_∗∗∗_</sup>|0.88<sup>_∗∗∗_</sup>|0.63<sup>_∗∗∗_</sup>|0.63<sup>_∗∗∗_</sup>|0.62<sup>_∗∗∗_</sup>|
||(0.02)|(0.02)|(0.02)|(0.03)|(0.03)|(0.03)|
|Lagged GDP per capita|-0.06<sup>_∗∗∗_</sup>|-0.05<sup>_∗∗∗_</sup>|-0.05<sup>_∗∗∗_</sup>|-0.04|-0.04|-0.04|
||(0.01)|(0.01)|(0.01)|(0.05)|(0.05)|(0.05)|
|Lagged Population|0.06<sup>_∗∗∗_</sup>|0.06<sup>_∗∗∗_</sup>|0.06<sup>_∗∗∗_</sup>|0.36<sup>_∗∗∗_</sup>|0.31<sup>_∗∗_</sup>|0.29<sup>_∗∗_</sup>|
||(0.01)|(0.01)|(0.01)|(0.14)|(0.13)|(0.13)|
|Lagged Democracy (ANRR)|||0.01<br>(0.01)|||0.07<sup>_∗∗_</sup><br>(0.03)|
|Constant|0.08|-0.05|-0.06|-3.51|-2.78|-2.49|
||(0.10)|(0.13)|(0.13)|(2.36)|(2.25)|(2.25)|
|N|2905|2905|2905|2905|2905|2905|
|R-squared|0.93|0.93|0.93|0.48|0.48|0.48|
|F statistic|2053.37|2034.66|2177.25|62.67|64.26|61.91|

Dependent variable is the log of total aid disbursements measured in constant USD excluding emergency aid, food aid, and debt relief (OECD Table 2a). All covariates are in logs, except for democracy. Democracy measure taken from Acemoglu, Naidu, Restrepo, and Robinson (forthcoming) and updated in accordance with their methodology. All regressions use recipient-year observations and include a constant term and period dummy variables, which are not reported. Recipient level cluster-robust standard errors reported in parenthesis. Significance levels : _∗_ : 10 percent _∗∗_ : 5 percent _∗∗∗_ : 1 percent for more recipients to compete for the aid. The larger the pool of competitors, the higher the policy levels we observe in equilibrium. From an empirical perspective, the global aid budget that is policyselective is exogenous to a given recipient’s policy measure and other recipient-specific factors, which allows us to identify the incentive effect of the policy-selectivity of aid. This is certainly the case for our second measure of policy-selective aid that removes influential observations for the aid selectivity regressions as discussed earlier. Note that in our empirical analysis we use the share of policy-selective aid in contrast to the total aid budget as it was used in our model. Our model describes the one-donor case but in practice the total aid budget is provided for by many donors. For that reason, the share provides in our view a better measure of the scarcity of policy selective aid.

16
check whether labor productivity and backward GVC participation are associated at country‐ sector level, in general for the average country in the sample, and in the specific case of the developing East Asia. Third, we subject this association to more rigorous scrutiny, to see whether it is robust to controlling for other obvious drivers of labor productivity.

Labor productivity growth rates in developing East Asia are among the highest in the world but some of the countries have seen declines in the past two decades. In level terms, labor productivity in the developing East Asian countries is yet to catch up with developed countries. The 2015 rankings range from 48/123 for Malaysia to 113/123 for Cambodia, with China being ranked 83/123 and Vietnam 102/123. However, the picture is completely different as relates to labor productivity growth: the 2015 rankings range from 2/123 for Vietnam to 28/123 for Thailand. Labor productivity growth in the countries of focus has been consistently above the averages of the mature economies, and, with few exceptions, also above the world and the emerging market averages. There has been plenty of discussion about the average world productivity being sluggish in recent years, particularly since the Great Recession (World Bank, 2018). Slower productivity growth in the 2010s relative to the 2000s seems to also affect some of the developing East Asian countries, most notably China, Malaysia and Myanmar (Table 4).

Table 4. Labor productivity in the developing East Asian countries

|||Lab<br>|orpro<br>|ductiv<br>|ity grow<br>|th<br>||
|---|---|---|---|---|---|---|---|
|Country/region|1980s|199|0s|200|0s|201|0s|
|World|n.a.|n.a.|1.5||2.5||2.3|
|Mature Economies|n.a.|n.a.|2.0||1.3||1.1|
|Emerging Markets and Developing Economies|n.a.|n.a.|0.9||4.0||3.4|
|China|3.2||4.9||8.4||6.4|
|Indonesia|2.6||2.8||3.5||3.8|
|Cambodia|1.3||2.6||4.3||4.9|
|Myanmar|0.8||4.2||9.3||5.3|
|Malaysia|3.6||3.9||2.6||1.9|
|Philippines|0.5||0.4||2.1||3.8|
|Thailand|3.4||4.4||2.7||3.8|
|Vietnam|1.4||4.8||4.5||4.5|

Source: Conference Board, The Total Economy Dataset. Note: Arrows denote change from previous decade. Decade labelled 2010s refers to period 2010‐2016.

Prima facie evidence indicates a positive correlation between backward GVC participation and productivity growth for developing East Asian countries. For the average country in the world, sectors with larger growth in backward GVC participation tend to also have larger growth in labor productivity. In Figure 17, we explore the time variation in the TiVA data (yearly from 1995‐2011) and find that, for the average developing East Asian country‐sector combination, the level of 36
14 Table _5._ Average OECD Tariffs Applied to Major Export Products of the Former Soviet Union

|Product Group (SITC)|1991<br>Total OECD<br>Imports (S miD.)|<br>EEC(12?|Average applied t<br>Finland|ariff rate(%)<br>Jaan|Sd|||
|---|---|---|---|---|---|---|---|
|Al Food Products (0 + I + 22 + 4)|<br>940|<br>|<br>|p<br>|ween|Switzland|U.S.A|
|<br>Fresh and frozen fish (03)|<br>710|14.6<br>_155_|14.6<br>|15.3<br>|2.7<br>|6.7<br>|8.9|
|Fresh and preserved fruit (051 to 053)|<br>56|_._<br>153|4.0<br>|7.0<br>|1.2<br>|0.0|_5.3_|
|<br>Beverages (11)|<br>59|.<br>144|15.7<br>|23.3<br>|0.0<br>|6.3|18.1|
|Agricultural Materials (2 - 22 - 27 - 28)|<br>2115|.<br>16|40.0<br>|35.5<br>|1.2<br>|10.3|14.2|
|<br>Wood and lumber (24)|,<br>1380|.<br>13|0.6<br>|2.4<br>|0.6<br>|0.4|1.6|
|<br>Pulp and paper (25)|,<br>125|.<br>|0.0<br>|2.3<br>|0.0|2.0|0.0|
|<br>Texfile fibers (26)|<br>435|0.0<br>2|0.0<br>|2.2<br>|0.0|0.2|0.0|
|<br>Ores, Minerals and Metals (27 + 28 + 68)|<br>4022|.9<br>20|0.0<br>03|2.2<br>||0.4<br>|2.3|
|<br>Metal ore and scrap (28)|,<br>493|.<br>02|.<br>|2.2<br>|G.3<br>|0.6|0.6|
|Nonferrous metals (68)|<br>3434|.<br>38|0.0<br>08|0.0<br>|0.0<br>|0.1<br>|0.6|
|All Manufactured goods (5 to 8 - 68)|,<br>5048|.<br>67|.<br>|4.3<br>|0.5<br>|1.0|0.7|
|<br>Chemical elements (51)|.<br>1085|.<br>81|9.0<br>|4.6<br>|5.2<br>|1.7|4.7|
|<br>Manufactured fertiizer (56)|,<br>293|.<br>65|2.1<br>|4.9<br>|1.9|0.7|2.2|
|<br>Leather and goods (61)|<br>34|.<br>49|0.0<br>|0.0<br>||||
|<br>Wood manufactures (63)|<br>132|.<br>|10.3<br>|_14.5_<br>|3.1<br>|0.3|2.6|
|<br>Textileyarn and fabric (65)|<br>112|7.1<br>99|2.9<br>241|2.5<br>|2.6<br>|4.4<br>|5.4|
|Ferrous metals (67)|<br>925|.<br>_55_|.<br>51|8.3<br>|11.6<br>|5.4<br>|7.8|
|Metal Manufactures (69)|<br>30|_._<br>_51_|.<br>63|4.9<br>|2.3<br>|2.1<br>|2.4|
|Nonelectric machinery (71)|<br>254|_._<br>46|.<br>50|4.9<br>|4.1<br>|1.2<br>|3.4|
|Electrical machinery (72)|<br>126|.<br>64|.<br>|2.4<br>|3.6<br>|0.7<br>|3.2|
|Transport equipment (73)|<br>895|.<br>67|8.9<br>|2.1<br>|3.7<br>|1.2|3.5|
|<br>Furnitu (82)|<br>71|.<br>57|4.8<br>|4.4<br>|5.3<br>|2.3|1.7|
|Clothing (84)|<br>45|.<br>121|5.8<br>|4.8<br>|3.8<br>|9.9|3.7|
|Footwear (85)|<br>27|.<br>|30.7<br>|16.3<br>|11.7<br>|12.3|11.4|
|<br>Scientific instruments (86)|<br>50|9.8<br>60|12.8<br>|23.5<br>|11.1<br>||9.3|
|Misc. Manufactures (89)|<br>167|.<br>_58_|4.3<br>64|4.0<br>|2.8<br>|1.1<br>|4.4|
|All||_._|.|3.6|3.4|_0.5_|4.5|
|non energy goods (0 to 9 -3)<br>All Goods (0 to 9)|13,654<br>29443|6.8<br>|8.7<br>|5.3<br>|4.5|2.2|5.2|
||,|6.6|8.5|5.2|4.4|2.1|5.0|

Note: Blank spaces indicate no trade ocourred for the product group. Source: OEC tade statistics from COMTRADE records. Tariff records from the SMART Data base.

continuation, as well as the specific exclusion of imporant Republic exports, we report EcMFN ta on tariff levels, i.e., it reduces the average MFN duty on manufactures (excluding chemicals ) from 8.1 to 6.4 percent.The EC announced it wil extend GSP treatment to the Republics in 1993 as 'an ex c onal and tglray below Laird and Yeats (1987, p. measure." Due to the uncertainty associated with the plan's longer-term<sup>_95_</sup> _show_ the EC GSP has only a modest
**<u>Table 1: Rice consumption in Mali for different household groups, 2006</u>**

||Percentag|e of households co<br>Auto|nsuming|Average c|onsumption for all<br>Auto|households|Average con|sumption for househ<br>consumption<br>Auto|olds with positive|
|---|---|---|---|---|---|---|---|---|---|
||Purchase|consumption|Total|Purchase|consumption|Total|Purchase|consumption|Total|
|**All**|82.20%|38.50%|95.10%|106,264.57|39,639.63|145,904.20|129,290.57|102,881.81|153,440.03|
|**Residence area**||||||||||
|Urban|93.30%|19.60%|96.00%|171,547.58|14,430.79|185,978.38|183,803.39|73,606.23|193,772.14|
|Rural|75.50%|49.80%|94.60%|67,299.98|54,685.70|121,985.67|89,091.18|109,757.41|129,006.10|
|**Region**||||||||||
|Kayes|88.90%|24.50%|95.40%|109,893.48|9,957.25|119,850.73|123,573.18|40,570.47|125,605.61|
|Koulikoro|79.90%|31.10%|93.70%|90,337.99|17,629.43|107,967.42|113,085.18|56,680.58|115,179.77|
|Sikasso|77.10%|32.00%|89.40%|60,452.56|17,606.31|78,058.87|78,458.20|54,998.14|87,316.51|
|Ségou|72.40%|52.60%|96.70%|76,444.23|48,060.66|124,504.90|105,593.58|91,372.99|128,719.45|
|Mopti|87.70%|47.50%|96.70%|111,418.89|69,796.45|181,215.34|127,099.69|147,071.86|187,359.78|
|Tombouctou|78.10%|70.80%|97.80%|88,144.03|122,662.75|210,806.78|112,911.94|173,185.98|215,465.30|
|Gao|81.70%|51.70%|97.90%|159,453.73|85,852.87|245,306.61|195,236.78|166,030.19|250,564.86|
|Kidal|99.30%|10.40%|99.30%|201,732.66|4,498.24|206,230.91|203,185.17|43,310.52|207,715.80|
|Bamako|94.30%|15.10%|95.90%|201,619.48|9,575.45|211,194.94|213,827.44|63,301.80|220,238.63|
|**Quintile**||||||||||
|Q1|65.90%|41.00%|87.90%|22,811.22|21,468.60|44,279.82|34,640.33|52,307.88|50,385.83|
|Q2|77.70%|43.20%|94.90%|53,176.76|40,056.87|93,233.63|68,471.18|92,808.03|98,267.83|
|Q3|81.10%|48.10%|95.70%|86,490.69|52,960.09|139,450.78|106,639.09|110,215.71|145,694.09|
|Q4|83.20%|41.00%|96.60%|117,981.55|48,836.13|166,817.67|141,728.09|119,088.93|172,773.52|
|Q5|93.40%|25.70%|97.30%|186,913.28|31,995.06|218,908.34|200,193.16|124,531.98|224,969.84|

Source: Authors’ estimation using ELIM 2006.
AMERICA

# 1. Estimates and Projections

|TYa|P_Atfat|S".<br>ratfo|Perfid|Stoinh<br>rate|C|cm|TFR|b<br>kim|**Fmim**|**In**|axl00|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1965<br>1.J0|666055<br>723006|63.0<br>61.0|1965-89<br>1990-94|1.58<br>1.46|23.4<br>21.5|7.9<br>7.3|2.84<br>2.63|67.1<br>68.3|73.4<br>74.8|44<br>38|55<br>48|
|1995<br>2000<br>**2005**<br>2010<br>2015|r77581<br>829079<br> <br>6r779<br>925092<br>971776|58.9<br>55.1<br>51.4<br>49.1<br>49.3|1995-99<br>2000-04<br>2005-09<br>2010-14<br>2014-19|1.28<br>1.14<br>1.05<br>0.96<br>0.90|19.4<br>17.8<br>16.9<br>16.4<br>15.7|6.8<br>6.6<br>6.6<br>6.7<br>6.8|2.43<br>2.28<br>2.18<br>2.15<br>2.12|69.5<br>70.7<br>71.6<br>72.6<br>73.5|76.2<br>77.6<br>78.4<br>79.4<br>80.3|33<br>27<br>23<br>19<br>15|40<br>33<br>Zs<br>23<br>18|
|2020<br>**205**|101669<br>1057762|51.1<br> <br>53.5|2020-24<br>2025-49|o.79<br>0.48|14.8<br>13.4|<br>7.0<br> <br>8.6|2.10<br>2.07|<br>74.6<br> <br>76.9|<br>81.3<br> <br>83.6|11<br>5|<br>14<br> <br>7|
|2050<br>2075<br>2100<br>2125<br>2150|1193246<br>1248391<br>1273526<br>1269142<br>1296853|<br>62.7<br> <br>68.8<br> <br>71.6<br> <br>n.3<br> <br>74.1|2050-74<br>2075-99<br>2100-24<br>2125-49|0.18<br>0.0<br>0.05<br>0.02|12.4<br>12.0<br>11.8<br>11.7|<br>10.6<br> <br>11.2<br> <br>11.3<br> <br>11.5|<br>2.06<br>2.06<br> <br>2.06<br> <br>2.06|<br>79.5<br> <br>80.9<br> <br>81.9<br> <br>82.3|<br>66.1<br> <br>87.6<br> <br>88.8<br> <br>69.3|3<br>3<br>2<br>2|<br>4<br> <br>4<br> <br>3<br> <br>3|

# 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 754.<br>70-74<br>05.69<br>60-64<br>55.59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5.9<br>- ~~~~0-4<br>.... .. _ ... ~~~~~~~~......... . . .... 0 .. ,./.<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 810<br><!-- End of picture text -->

# 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> A~~~~OICA C1AFIC3C2 %<br>6l c 14 so<br><!-- End of picture text --> <!-- Start of picture text --> ASIA -(57 M)<br><!-- End of picture text --> 9
emissions per dollar of GDP. We exclude from our sample any countries that belong to the World Bank’s Small States Forum<sup>2</sup> as small countries, many of which are island countries, can exhibit significant year-to-year fluctuations in their CO2 emissions and the financial deepening that might confound our results. We also exclude any countries that are offshore financial centers due to their overly large and complex financial sectors that are largely disconnected from direct financing of the domestic real economy. We end our sample in 2019 to avoid potential confounding effects in subsequent years from (i) the COVID-19 pandemic which led to lower economic activity, and (ii) the Russian Federation’s invasion of Ukraine in February 2022 which affected energy markets and might have abruptly affected CO2 emissions per dollar of GDP in some economies (World Bank, 2022).

Table A.2 provides detailed descriptions and sources for all variables used in the analysis. Our dependent variable is _CO2 emissions_ in kg per PPP$ of GDP. Our primary explanatory variable, _financial deepening_ , is the annual change in domestic credit to the private sector provided by the financial sector expressed as a percentage of GDP.<sup>3</sup> We use nominal GDP to scale both CO2 emissions and credit by the size of the economy because otherwise, these two variables might cotrend as markets and production grow. Expressing CO2 emissions per dollar of GDP enables us to interpret the dependent variable as a proxy for carbon intensity of the economy, so that we can study whether financial deepening contributes to the transition toward a less carbon-intensive economy. To build our financial deepening measure, we use credit-to-GDP, the most common proxy for financial development (see for instance Arcand et al. 2015 or Dabla-Norris and Srivisal 2013). The main benefit of credit-to-GDP lies in its simplicity, which both makes the indicator available for a large sample of countries and for a straightforward interpretation. The main drawback of this measure is that it does not reflect the developments of all components of the financial system (i.e., financial markets). Nevertheless, because most countries have a bank-based financial system,<sup>4</sup> not only are the developments in credit-to-GDP a relevant proxy for overall financing conditions, but also its environmental consequences are relevant from policy makers’

> 2 The World Bank’s Small States Forum includes countries that ‘share common challenges associated with the small size of their economies and vulnerability to exogenous shocks’.

> 3 The choice of these two variables is in the style of metrics used by financial sector firms to measure their carbon intensity as suggested by the Financial Stability Board’s Task Force on Climate-related Financial Disclosures (2021). 4 This is particularly the case for emerging economies.

6
in Appendix 2 shows both the Kakwani progressivity index for each tax and transfer in 2016 along with their marginal contribution to reducing inequality and poverty under the scenario where old-age pensions are treated as deferred income. Results for the alternative scenario where old age pensions are considered as transfers are presented in Appendix 2. We describe each of these in turn.

# **Taxes**

|Direct taxes and social|Figure 7. Progr|essivity and Redistributive Effect of|Direct|Taxes|
|---|---|---|---|---|
|contributions<br>are||and contributions<br>|||
|progressive and inequality-|(|rom market to disposable income)|||
|reducing, however they also|0.16<br>ve||0.014|ng|
|place a burden on the poor.|0.14<br>ssi||0.012|lizi|
|That they are progressive|0.12<br>nt<br>ogre||0.010|ect<br>qua|
|can be seen by the fact that|0.10<br>icie<br>Pr||0008|e eff<br>-> E|
|the Kakwani coefficient is|0.08<br>oeff<br>--->||.|utiv<br>-----|
|positive, the fact that they|0.06<br>ni c<br>-----||0.006|strib<br>g <-|
|are inequality-reducing can|0.04<br>wa<br><-||0.004|edi<br>lizin|
|be seen by a positive|0.02<br>Kak<br>sive||0.002|R<br>qua|
|marginal contribution to the|0.00<br> <br>gres||0.000|Une|
|redistributive effort (Table|tax<br>xes<br>Re|tion<br>nce<br>on -<br>nce<br>yer<br>nce<br>ss<br>yer<br>nce<br>er|||
|A2.1).<br>Personal<br>income|ome<br>ct ta|ribu<br>sura<br>n<br>buti<br>sura<br>plo<br>sura<br>yer<br>ckne<br>mplo<br>sura<br>ploy|||
|taxes are more progressive|inc<br>dire|cont<br>nt in<br>utio<br>ntri<br>r<br>nt in<br>- em<br>s in<br>mplo<br>/ si<br>- e<br>e in<br>em|||
|and<br>redistributive<br>than<br>social contributions, with<br>health<br>insurance|Personal<br>Other|h insurance<br>nemployme<br>contrib<br>insurance co<br>employe<br>nemployme<br>ontribution<br>t and disease<br>ribution - e<br>indemnities<br>contribution<br>ary guarante<br>ontribution -|||
|contributions being slightly||ealt<br>U<br>alth<br>U<br>c<br>den<br>cont<br>ical<br>nce<br>Sal<br>c|||
|more<br>progressive<br>and<br>redistributive<br>than<br>other<br>types<br>of<br>contributions|Source: Own estimates ba<br>Kakwani Coefficient|sed on the 2016 Romanian HBS.<br>H<br>He<br>Acci<br> <br>Med<br>insura<br>Marginal Contribution|||

(Figure 7). However, direct taxes are much less redistributive relative to other countries, such as Croatia, Poland, or Mexico (Figure 8A), either because they collect relatively lower amounts (for instance compared to Poland) or because they are less progressive (with a lower Kakwani when compared to Croatia or Mexico). Moreover, they are also poverty increasing, as shown by a negative marginal contribution to poverty reduction when poverty is measured using the 2011 PPP US$5.50 a-day line. These results point to potential improvements that could be achieved to improve the progressivity and redistributive impact of direct taxes in Romania.

> a Shapely decomposition to address this issue, which involves estimating marginal contributions in every possible path and then taking the average.

18