rafmacalaba/datause-encoder-data huggingface dataset:
Table 8: Regression results from baseline specification for post‐crisis year, i.e. post‐2008

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐TFP Ac|kerberg_et al_|. using count|ry specific IO‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐T|FP Ackerberg|_et al_. using EU‐|wide IO‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐0.076||||||‐0.021||||||
||(0.469)||||||(0.894)||||||
|Entryt‐2||0.080|||0.051|0.057||0.173|||0.078|0.135|
|||(0.302)|||(0.584)|(0.540)||(0.238)|||(0.625)|(0.376)|
|Conductt‐2|||‐0.082*||‐0.069|‐0.070|||‐0.046||‐0.037|‐0.056|
||||(0.085)||(0.197)|(0.189)|||(0.565)||(0.654)|(0.495)|
|FDI Overallt‐2||||‐0.050||‐0.061||||‐0.196***||‐0.209***|
|||||(0.401)||(0.247)||||(0.003)||(0.000)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|4209830|4548544|4290363|4947880|4209830|4209542|4192387|4531101|4272920|4923729|4192387|4192099|
|R2A|0.984|0.983|0.984|0.987|0.984|0.984|0.984|0.983|0.983|0.987|0.984|0.984|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.124|0.129|0.124|0.130|0.124|0.124|0.124|0.129|0.123|0.130|0.124|0.124|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level.

36
Table R.SA4:0LS Estimates, Dependent variable: TCC of only VAT in hours

|T<br>E-file|o|t<br>Hours|a<br>Hoursp|l<br>erCycle_|
|---|---|---|---|---|
||-0.2718"<br>(0.128)|<br>0.184<br>(0.129)|||
|E-file Experience|||-0.039"*<br>(0.019)|0.041"<br>(0.020)|
|E-file Experience”2|||0.001"|0.001"|
||||(0.000)|(0.000)|
|Turnover|-O.014<br>||-0.043<br>||
||(0.043)||(0.072)||
|Employment|0.775"<br>(0.060)||0.146.<br>(0.109)||
|Productivity||0.043<br>||-0.065<br>|
|||(0.042)||(0.074)|
|Industry|YES|YES|YES|YES|
|Pravines|YES|YES|YES|YES|
|Legal Farm|YES|YES|YES|WES|
|Constant|2496"<br>|3.265"""<br>|0.906<br><br>|1.485<br>|
||(0.668)|(0.610) ||(1.071)|(1.042)|
|Observations|S56|596|236|236|
|R-squared|0.092|0.077|0.7155|0.150|

Samipling weights are used in all regressions. Aegianal, industry and legal farm dummies are controlled in all regressions but mat reported. Dependent variables, furnover employment and Productivity (turnoyerfemployment) are in logs. *** pedi, ** p<0.05, * pedi
21 Middle East and North Africa have the lowest levels of political and civil liberties, while **Eastern** Europe and Central Asia and Latin America have the highest levels of liberties.

**Figure 3 Measures of political and civil liberties** (1990)

<!-- Start of picture text --> 6<br>5 -<br>4l--<br>Emnean polgO<br>Fri II~~~~~~~~~~~~~~~ nmean civ9O<br>-l...<br>0<br>East East Asia Latin Mddle South West<br>Africa America East & Asia Africa<br>N.Africa<br><!-- End of picture text --> Note: Lower values for POL and CIV indicate higher levels of rights<sup>and liberties</sup> Source: Freedom House On the basis of the preceding discussion, we estimate three equations. Equation (15) is derived from (12), and considers trade distortions. The next two equations examine labor market distortions: (16) is derived directly from (14), and (17) is a variant using the alternative measure of labor market distortion, PWE: DOLLAR = aO + a, GDP per capita + a2 urbanization + a3 education + a4 tax rate + aC POL + U1 (15)
<!-- Start of picture text --> Figure 5a: Distribution of newly registered firms Figure 5b: Distribution of newly registered firms<br>around GEL 100,000 in 2008 around GEL 100,000 in 2009<br>50000 100000 150000 50000 100000 150000<br>Gross Revenue from Income Tax return Gross Revenue from Income Tax return<br>Figure 6a: Distribution of newly registered firms Figure 6b: Distribution of newly registered firms<br>around GEL 30,000 in 2008 around GEL 30,000 in 2009<br>10000 20000 30000 40000 50000 10000 20000 30000 40000 50000<br>Gross Revenue from Income Tax return Gross Revenue from Income Tax return<br>150<br>100<br>80<br>100<br>60<br>40<br>Number of new firms 50 Number of new firms<br>20<br>0 0<br>400 300<br>300<br>200<br>200<br>Number of new firms Number of new firms 100<br>100<br>0 0<br><!-- End of picture text -->

# _3.4 Reform take-up_

According to the database, by the end of 2012, a total of 16,721 firms had registered for the small firm regime and 13,266 firms had registered for the micro firm regime. We have data on 2012 revenues for 15,543 of the firms in the small firm regime and for 9,701 of the firms in the micro firm regime. Based on this date, Table 4 shows the percentage of firms in each revenue group that have signed up for either the micro or small firm regime. Note that some firms with revenues below GEL 30,000 signed up for the small taxpayer regime instead of the micro regime, probably because they had employees, which disqualifies them from micro taxpayer status. Very few businesses signed up in revenue groups that are not technically eligible for the specific regime (shaded in grey). We believe that these observations are due to recording errors.

11
a business visa or to allow for smaller visa durations. Moreover, even the visa processing times and the duration of visas for CSSs, ISSs and ICTs are negatively correlated (see Figure 6), which suggests that even if it may take longer for a country to issue a business visa, that may not result in longer stays in that country for service professionals.

# **Table 6: Statistical correlation between average MNP restrictiveness and attributes of business visa applications**

||**Average Mode 4**<br>**restrictiveness**<br>**index **|**Cost to obtain**<br>**a business**<br>**visa (USD)**|**Duration of visa**<br>**for crew (no. of**<br>**months allowed)**|**No. of documents**<br>**needed to obtain a**<br>**business visa**|**Range of visa**<br>**processing**<br>**time (days)**|
|---|---|---|---|---|---|
|**Average Mode 4**<br>**restrictiveness**||||||
|**index**|1|||||
|**Cost to obtain a**||||||
|**business visa**||||||
|**(USD)**|-0.0249|1||||
|**Duration of visa**||||||
|**for crew (no. of**<br>**months allowed)**|0.2084|-0.013|1|||
|**No. of documents**<br>**needed to obtain**||||||
|**a business visa**|-0.2923|-0.1922|-0.1751|1||
|**Range of visa**||||||
|**processing time**<br>**(days)**|0.0512|0.0356|0.0221|-0.0323|1|

Source: OECD STRI; own calculations

# **5. Conclusion**

The growing importance of services trade for countries across the world is well-documented in the services and trade literatures. At the same time, regulatory and administrative barriers on the movement of service suppliers to deliver services internationally have resulted in very low shares of Mode 4 trade in total services trade. Against this background, this paper attempts to conceptualize an agenda for TFiS as it would apply to MNP, providing descriptive statistical evidence from the OECD’s STRI database.

In doing so, it takes the prevailing domestic regulation on MNP across sectors as given and suggests, as components of a Mode 4 TFiS agenda, that (a) administrative and financial costs of complying with necessary policies be brought down and aligned with prevailing regulation in other countries, also to reduce the costs imposed by regulatory heterogeneity; and (b) predictability, certainty and meaningful transparency be brought to the whole process governing the design and implementation of rules and procedures regulating the temporary movement of service suppliers to deliver services across borders. The paper also suggests that some excessively restrictive and burdensome policies be sufficiently toned down if not eliminated altogether to liberalize Mode 4 trade.

More specifically, the paper makes the following recommendations to facilitate Mode 4 trade:

- (i) Lower the average cost of obtaining a business visa; (ii) Reduce the number of documents required to obtain a business visa;

- (iii) Require a common set of documents to obtain a business visa;

14
# **The Trade-Growth Nexus: Evidence of Causality from Innovative Instruments for Trade**

Ibrahim Nana<sup>1</sup> ; Sephooko Ignatius Motelle<sup>2</sup> ; Susan K. Starnes<sup>3</sup>

## **JEL Classification Codes :** F15, F43, O47

**Keywords:** International trade, GDP, growth, instrumental variable

**Acknowledgments:** We would like to thank **Daniel Lederman,** Deputy World Bank Chief Economist for the Middle East and North Africa Region, **Michael Jansson** Professor of Econometrics at University of California, Berkeley and **Filipe Lage de Sousa** Program Coordinator and Senior Lecturer - M.S. in Applied Economics Program at The Johns Hopkins University for their comments and guidance.

The paper has also benefitted from thoughtful guidance and encouragement from **Pablo Fajnzylber** , Director of the Development Impact Measurement department (IFC/CDI), **Issa Faye** Senior Economic Adviser at the CGIEU, **Dan Goldblum** Manager of the CDIFI Unit and **Arun Prakash** Strategy Officer at the CDIFI Unit.

> 1 ET Consultant, Sector Economics and Development Impact Department: inana@ifc.org 2 Senior Economist, Sector Economics and Development Impact Department: smotelle@ifc.org

> 3 Lead Global Trade and Commodity Finance Strategist, Sector Economics and Development: sstarnes@ifc.org
Table 5: panel regression, GMM

|equation no.|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|variation|GMM|PSG _>_0|closing year|excl. ARG|controls|
|cumul. cond.|.006|.007|.006|.005|.006|
||(.003)<sup>_∗∗_</sup>|(.003)<sup>_∗∗_</sup>|(.003)<sup>_∗_</sup>|(.004)|(.003)<sup>_∗∗_</sup>|
|cumul. cond, sq.|-.00004|-.00004|-.00004|-.00003|-.00003|
||(.00001)<sup>_∗∗∗_</sup>|(.00001)<sup>_∗∗∗_</sup>|(.00002)<sup>_∗∗_</sup>|(.00003)|(.00001)<sup>_∗∗_</sup>|
|log GDP per capita|.243|.223|.233|.240|.394|
||(.059)<sup>_∗∗∗_</sup>|(.065)<sup>_∗∗∗_</sup>|(.064)<sup>_∗∗∗_</sup>|(.060)<sup>_∗∗∗_</sup>|(.069)<sup>_∗∗∗_</sup>|
|aid over GDP|-.260|-.312|-.312|-.287|1.024|
||(.552)|(.691)|(.590)|(.556)|(.764)|
|Political Rights|-.135|-.081|-.139|-.136|-.139|
||(.028)<sup>_∗∗∗_</sup>|(.026)<sup>_∗∗∗_</sup>|(.031)<sup>_∗∗∗_</sup>|(.028)<sup>_∗∗∗_</sup>|(.029)<sup>_∗∗∗_</sup>|
|log of gross IDA|.|.|.|.|.088<br>(.021)<sup>_∗∗∗_</sup>|
|debt crisis t-1|.|.|.|.|-.574<br>(.140)<sup>_∗∗∗_</sup>|
|growth t-2|.|.|.|.|.001<br>(.006)|
|trade openness|.|.|.|.|.353<br>(.250)|
|prog. party|.|.|.|.|.030<br>(.083)|
|trans. to democ.|.|.|.|.|.020<br>(.117)|
|age of democracy|.|.|.|.|.004<br>(.002)<sup>_∗_</sup>|
|_country fxed efects_|_yes_|_yes_|_yes_|_yes_|_yes_|
|_year fxed efects_|_yes_|_yes_|_yes_|_yes_|_yes_|
|Observations|1690|753|1456|1677|1526|
|Number of countries|139|61|137|138|127|
|Number of instruments|114|114|114|114|121|
|Wald statistic|197.46|139.46|164.03|198.21|292.06|
|_p-value_|0.0001|0.0001|0.0001|0.0001|0.0001|
|Hansen J-test|99.84|51.42|92.34|99.78|92.94|
|_p-value_|0.374|0.999|0.587|0.376|0.570|
|Dif-in-Hansen test|23.14|2.67|25.43|27.48|25.42|
|_p-value_|0.625|0.999|0.495|0.385|0.495|

Note: Dependent variable: CPIA cluster D average. Cluster-robust standard errors are reported. Coefficients estimated with forward orthogonal deviations and level equations for IV style instruments. * significance at 10%; ** significance at 5%; *** significance at 1%.

37
Figure 8 Kenya 91-Day T-bill rate (1991-2006 annualized yield, %) <!-- Start of picture text --> 90<br>91-Day Tbill (%)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1991M7 1992M4 1993M1 1993M1 1994M7 1995M4 1996M1 1996M1 1997M7 1998M4 1999M1 1999M1 2000M7 2001M4 2002M1 2002M1 2003M7 2004M4 2005M1 2005M1 2006M7 2007M4<br><!-- End of picture text --> Table 2 presents data organized around the preceding equation. It shows that Kenyan tbill rates have fallen along with US t-bill rates, suggesting that the favorable global interest rate climate has filtered through to Kenya; this is particularly evident in 2003 and 2004. In addition, changes in monetary policy may have contributed to the very low interest rates in 2003 and 2004: the cash reserve ratio was lowered from 10 percent to 8 percent and then 6 percent. This coincided with the maturing of a large tranche of repo operations, boosting short-run liquidity and lowering domestic interest rates.<sup>30</sup> Of course, since Kenya has an open capital account, this money could leave and be invested in US or UK T-bills; this would put upward pressure on Kenyan T-bill interest rates. Alternatively, banks and other investors may be hampered from investing overseas because of prudential restrictions; however, there is little evidence that such restrictions are binding.<sup>31</sup> Besides, investors have the option of investing in capital market or real estate if returns on T-bills are considered too low. One additional factor may be contributing to

> 30 Source: Meeting with CBK officials.  Interviews with current and former senior officials indicate a deliberate strategy to stimulate the economy after 2003 by lowering interest rates and targeting moderate inflation rather than inflation in the 0-5 percent.

> 31 For example, the Retirement Benefits Authority requires pension funds to invest no more than 15 percent of their funds overseas; but the actual volume is 5-10 percent.

23
**Table 10.** There is little <u>evidence of exclusion of poor households</u>

|||**Gini Coefficients**<br>||
|---|---|---|---|
||**2002**|**2003**|**2004**|
|Azerbaijan|36.6|24.8|26.8|
|Baku|36.3|27.9|27.5|
|Non-Baku urban|38.0|25.5|28.4|
|Rural|35.8|22.2|24.9|

Source: Azerbaijan HIES data.

# **_Hypothesis 5_**

Under the assumptions for hypothesis 5, the Gini coefficient for Azerbaijan increase more appreciably than under any other hypotheses. Here we made the assumption that the extremely low inequality in Azerbaijan HIES data arose from lack of and/or inadequate participation of rich households and significant amount of transfers among Azeri households. Table 11 presents the Gini coefficients after removing transfers and replacing the consumption per capita for missing households following the Pareto distribution assumption under hypotheses 2 and 3. After accounting for upper truncation and transfers, the Gini coefficient more than double in most cases (see Table 12). After removing all transfer incomes and accounting for refusal of rich households, the Gini coefficient in 2002 increases to over 200 percent for Azerbaijan as the whole. Inequalities in 2003 and 2004 similarly climb by about 145 and 194 percent respectively.

The overall findings here suggest that the main culprit for extremely low inequality in the Azerbaijan HIES data is not only refusal of rich households to participate in the survey, but also significant infusion of transfers to the Azeri household consumption. Transfers from both government and private sources are important sources of income. Furthermore the transfers disproportionately benefited those households in the lower wealth category than those in the upper income groups.

**Table 11.** The most likely reasons for low inequality are lack of participation of the rich and <u>significant transfers from government and family networks</u>

|||**2002**<br>||**Gi**<br>|**2003**<br>**ni Coefficie**<br>|**nts**<br>||**2004**<br>||
|---|---|---|---|---|---|---|---|---|---|
||without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|
|Azerbaijan|55.8|46.0|49.4|44.5|33.3|37.4|47.9|38.0|40.3|
|Baku|57.5|47.7|51.4|51.3|39.8|44.9|50.7|42.9|44.0|
|Non-Baku urban|58.0|48.0|51.8|45.7|32.3|37.8|49.7|39.0|41.7|
|Rural|52.8|43.2|46.2|37.4|27.8|30.7|42.6|32.2|35.1|

Source: Azerbaijan HIES data.

15
|||||win large contracts,<br>and none on the<br>direct expansion of<br>procurement.|
|---|---|---|---|---|
||Increasing access<br>to digital<br>platforms and e-<br>commerce|There is a need<br>for more causal<br>estimates|Alhorr 2023 (RCT),<br>Jin and Sun 2020<br>(RCT), Bai et al.<br>2021 (RCT)|Mixed evidence<br>exists, one RCT<br>shows promising<br>results for women<br>with mobility<br>constraints, while<br>another finds low<br>effects due to<br>demand-side<br>constraints.|
||Changing gender-<br>biased laws|A lack of causal<br>estimates is noted|Elefante et al. 2023<br>(review), Adnane<br>2021 (non causal)<br>(review), Hyland et<br>al. 2020 (non-<br>causal), Islam et al.<br>2017(non-causal)|More evidence is<br>needed on specific<br>laws; there is a<br>consensus that<br>change in laws is<br>important but not<br>sufficient.|
|Contextual|Gender Norms|A lack of causal<br>estimates is noted|Muñoz Boudet et al.<br>2023 (review),<br>Halim et al. 2023<br>(review),<br>Jayachandran<br>2021b(review)|More evidence is<br>needed linking<br>change in norms to<br>business<br>performance.|
||Child and elder<br>care|There is a need<br>for more causal<br>estimates|Bjorvatn et al. 2022<br>(RCT), Delecourt<br>and Fitzpatrick 2021<br>(non-causal),<br>Ahmed et al. 2023<br>(review), Cali et al.<br>2022 (quasi -<br>experimental)|There is a growing<br>literature, but only<br>one RCT links access<br>to childcare with<br>business outcomes.|
||Gender-based<br>Violence|There is a need<br>for more causal<br>estimates|World Bank et al.<br>(2023 review),<br>Ashraf et al. 2023<br>(RCT), Lopez Pena<br>2022 (RCT), Saraf et<br>al. 2019 (RCT)|More evidence is<br>needed to establish<br>the link between<br>GBV and business<br>outcomes, existing<br>RCTs focus on<br>mental health with<br>mixed effects on<br>business outcomes.|

34
Figure 4.1 <!-- Start of picture text --> ASEAN Import Shares<br>30<br>-- s--EU<br>F A~~~~~~~~~~~~~~~~~~~~.' U.S.<br>2155hia<br>-e- Korea<br>10 - _ I Intra-SEANI<br>5<br>1980 1990 2000<br>ASEAN Export Shares<br>35 -<br>30<br>25 - _ Japan<br>B'20 -- EU<br>-. *-- U.S.<br>-- China<br>Co __ e Korea<br>10 Intra-ASEAN<br>1980 1990 2000<br><!-- End of picture text -->

> Source: Authors' calculations using IMF, Direction of Trade Statistics

> Japan was ASEAN's primary trading partner in both imports and exports in the

> 1980s. However, this relationship had declined significantly by 2000 with the share of

> imports from Japan dropping from 23 to 16 percent between 1990 and 2000 and export

> share declining from 30 to 13 percent between 1980 and 2000. ASEAN's imnports were, of course, negatively impacted by the East Asian crisis; however, the post-crisis decline

> in the level of imports from Japan (37 percent between 1996 and 1998) was greater than

> the decline in ASEAN's total imports (26 percent for the same period). The dollar value of exports to Japan increased between 1980 and 2000 (to above pre-crisis levels), but this

6
**Table S5** . Association of Attrition with Baseline Sociodemographics

|**Outcome**|**df**|**Beta**|**SE**|**P value**|
|---|---|---|---|---|
|Personal Initiative Condition|2623|0.16|0.76|0.446|
|Interpersonal Initiative Condition|2623|-0.52|0.25|0.038*|
|PMT|2623|0.55|0.35|0.121|
|Age|2623|0.01|0.01|0.025*|
|Is head of household|2623|0.50|0.23|0.030*|
|Is nomad|2623|0.05|0.29|0.865|
|Lives in a hamlet|2623|-0.27|0.23|0.243|

Results are from a logistic regression predicting attrition. Regressions control for randomization strata and standard errors are robust.<sup>†</sup> p<.10, *p<.05, **p<.01, ***p<.001.

**Table S6.** Impacts of Personal Initiative and Interpersonal Initiative Interventions on Economic <u>Outcomes at Endline in Mechanism Experiment</u>

|||**Control**|**Personal Initiative**|**Interpersonal Initiative**|
|---|---|---|---|---|
|**Outcome**|df|Mean<br>(SD)|**Coefficient**<br>(SE)<br>Robust_p_-value<br>Cluster robust_p_-value|**Coefficient**<br>(SE)<br>Robust_p_-value<br>Cluster robust_p_-value|
|**Economic Composite**<br>**Index**|**2473**|**0.00**<br>**(1.00)**|**0.07**<br>**(0.05)**<br>**0.168**<br>**0.263**|**0.12**<br>**(0.05)**<br>**0.012**<br>**0.021**|
|**Food Security Index**|**2473**|**0.00**<br>**(1.00)**|**0.07**<br>**(0.05)**<br>**0.152**<br>**0.262**|**0.11**<br>**(0.05)**<br>**0.029***<br>**0.059†**|
|Food security|2473|6.83<br>(1.52)|0.15<br>(0.07)<br>0.034*|0.16<br>(0.07)<br>0.032*|
||||0.074†|0.054†|
||||0.07|0.59|
|Dietary diversity|2473|9.12<br>(8.75)|(0.42)<br>0.87<br>0.898|(0.43)<br>0.169<br>0.214|
||||**0.04**|**0.09**|
|**Business Omnibus**|**2474**|**0.00**|**(0.05)**|**(0.05)**|
|**Index**||**(1.00)**|**0.388**|**0.069†**|
||||**0.469**|**0.107**|

47
Figure 5. Croatia. Poverty Headcount Rate, 2014 <u>(US$10 PPP-a-day)</u> <!-- Start of picture text --> A. Income and Consumption-based B. By household type<br>measures (measured using income)<br>100 Market income plus pensions<br>60%<br>50.0% Disposable income<br>80 Consumable income<br>50%<br>40% 36% 60<br>31.3%<br>28.1% 40<br>30%<br>20%<br>20<br>20%<br>0<br>10%<br>0%<br>Market Disposable Consumable<br>Income + Income Income<br>Contributory<br>Pensions<br>Based on income Based on consumption<br>Source: World Bank estimates based on PIS and HBS (2014).<br>Poverty (share of population)<br><!-- End of picture text -->

# **V. Progressivity, marginal contributions, and pro-poorness of taxes and transfers**

How did each of the fiscal interventions contribute to the observed changes in poverty and inequality? Figure 6 presents the distributional impact of different components of the tax and benefit system as a share of market income (including pensions) in 2014. Most components of the system are progressive, with the bottom 10 percent of the distribution being net receivers of social benefits. However, in cash terms, households beginning in the second decile were net payers to the treasury in 2014, as the share of taxes paid exceeded the cash benefits received for all but the poorest 10 percent of the population.

17