rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6. Marginal effects of the likelihood of doing secondary activities and regression estimates of the relationship between the availability of childcare and time allocation on secondary childcare**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||With|individual cont|rols|With individ|ual + househo|ld controls|With individ|ual + househo<br>controls|ld + regional|
||Marginal<br>probit<br>estimates:|Equal<br>weight<br>secondary|Deflated<br>secondary<br>childcare<sup>a</sup>|Marginal<br>probit<br>estimates:|Equal<br>weight<br>secondary|Deflated<br>secondary<br>childcare<sup>a</sup>|Marginal<br>probit<br>estimates:|Equal<br>weight<br>secondary|Deflated<br>secondary<br>childcare<sup>a</sup>|
||secondary|childcare<sup>a</sup>||secondary|childcare<sup>a</sup>||secondary|childcare<sup>a</sup>||
||childcare|||childcare|||childcare|||
|_Ref: A. No child or youngest child is 6 years_||||||||||
|_and older_||||||||||
|B. Youngest child is 0–5 years and|0.606***|2.414***|1.207***|0.650***|2.401***|1.200***|0.653***|2.440***|1.220***|
|childcare available|(0.039)|(0.298)|(0.149)|(0.042)|(0.302)|(0.151)|(0.042)|(0.299)|(0.149)|
|C. Youngest child is 0–5 years and no|0.625***|2.518***|1.259***|0.674***|2.582***|1.291***|0.677***|2.674***|1.337***|
|childcare available|(0.041)|(0.277)|(0.139)|(0.039)|(0.282)|(0.141)|(0.039)|(0.276)|(0.138)|
|Age|0.079***|0.057|0.028|0.021|-0.036|-0.018|0.021|-0.047|-0.023|
||(0.022)|(0.087)|(0.044)|(0.025)|(0.088)|(0.044)|(0.025)|(0.087)|(0.043)|
|Age squared|-0.001***|-0.001|-0.001|-0.0004|0.0004|0.0001|-0.0004|0.0004|0.0002|
||(0.0003)|(0.001)|(0.001)|(0.0004)|(0.004)|(0.001)|(0.0004)|(0.001)|(0.001)|
|_Ref: Education: No education_||||||||||
|Class 1 to 5|0.030|0.129|0.065|0.013|0.110|0.055|0.008|0.116|0.058|
||(0.055)|(0.236)|(0.118)|(0.057)|(0.279)|(0.140)|(0.057)|(0.278)|(0.139)|
|Class 6 to 8|0.029|0.316|0.158|0.042|0.408|0.204|0.045|0.439|0.219|
||(0.073)|(0.314)|(0.157)|(0.077)|(0.353)|(0.177)|(0.077)|(0.341)|(0.170)|
|Class 9|0.046|-0.314|-0.157|0.064|-0.131|-0.066|0.058|-0.139|-0.069|
||(0.129)|(0.374)|(0.187)|(0.122)|(0.409)|(0.205)|(0.124)|(0.411)|(0.206)|
|SSC and above|0.148|0.004|0.002|0.162|0.171|0.086|0.148|0.163|0.081|
||(0.090)|(0.311)|(0.156)|(0.104)|(0.382)|(0.191)|(0.102)|(0.382)|(0.191)|
|Wear burkha to travel outside|0.019|-0.335*|-0.167*|0.003|-0.399**|-0.199**|0.004|-0.408**|-0.204**|
||(0.051)|(0.183)|(0.092)|(0.054)|(0.193)|(0.097)|(0.056)|(0.197)|(0.098)|
|Head’s age||||-0.004|-0.016|-0.008|-0.004|-0.016|-0.008|
|||||(0.003)|(0.012)|(0.006)|(0.003)|(0.012)|(0.006)|
|Head is female (=1)||||-0.105|0.030|0.015|-0.100|0.004|0.002|
|||||(0.088)|(0.348)|(0.174)|(0.087)|(0.346)|(0.173)|
|_Ref: Head’s education: No education_||||||||||
|Class 1 to 5||||0.032|0.207|0.104|0.025|0.193|0.096|
|||||(0.061)|(0.280)|(0.140)|(0.062)|(0.275)|(0.138)|
|Class 6 to 8||||-0.044|0.034|0.017|-0.058|0.014|0.007|
|||||(0.074)|(0.409)|(0.205)|(0.075)|(0.417)|(0.208)|
|Class 9||||-0.029|-0.553|-0.276|-0.045|-0.642|-0.321|

26
**6.7. Mechanism Analysis:** We conducted exploratory analysis to examine the mechanisms behind the effect of the bundled treatment that systematized inquiry on risk, reward and progress. We examined whether the differential effect in score by treatment and gender was driven by two mechanisms: the consistency of inquiry – by likelihood of asking a prevention-focused question to a startup (H2); or the investors’ assessment of dynamic improvement when evaluating startups (H3).

We reran the analysis from Stage 1 on the panel dataset and found that during the VilCap program (when VilCap provided due diligence questions for trainee investors to use), female founders were equally likely to receive prevention-focused questions as males, so the mechanism could not work as predicted (see Appendix F). Prompting investors to inquire about risk and reward had similar results to Stage 1, where males were asked more prevention-focused questions, and this effect was lower for females, but this did not affect the score. These results did not provide evidence in support for H2 in this setting, where investors and founders interacted repeatedly over a long period of time. Combining these results with results from Stage 1, we suggest two explanations. First, prompting systematic inquiry around risk and reward may be most valuable to retain startups presented by female founders at early stages in the evaluation pipeline, to keep them in consideration for the further selection process. Second, any prompt to systematically inquire (either from Vilcap during its program, or from the intervention we designed) may have a substitution effect. Both prompts increased scrutiny for startups led by all founders, but particularly for startups with all-male teams.

We next assessed whether investors’ assessment of dynamic improvement differed by treatment and control groups (H3). As shown in Appendix G, treated investors weighted the criterion of “improvement” as a higher part of their evaluation criteria (20.8%) than control investors (18.5%). This difference was close but not statistically significant at p = 0.108, on a sample of 65 investors, providing some directional evidence in support of H3. However, it is difficult to interpret the import 28
# <u>3. Was Africa always more unequal?</u>

But if Africa is more unequal now, was it always the case? If we go back to the first available income statistics for Africa, in the 1960’s, we indeed find a similar regularity.<sup>11</sup> As Figure 2 shows, the mean Gini for African countries (calculated from the WIDER data set) in the decade of the 1960’s was almost 10 Gini points higher than the mean Gini for the rest of the world.<sup>12</sup> The difference diminished in 1980’s as inequality in Africa declined (and inequality in the rest of the world increased), and then in the 1990’s the gap opened up again. Compared to Latin America and the Caribbean, inequality in Africa was always lower but there was a clear “catch-up” in the 1990’s. Now, the coverage of the rest of the world and of African countries in particular is uneven (as shown in Table 2).<sup>13</sup> The sample size and the country composition in Africa change a lot; income and expenditure concepts are mixed. All of that adds tremendous noise to the data displayed in Figure 2.

11 Kuznets (1965, [1954)] thought that income distribution in under-developed countries was getting worse in the 1950’s and that the only exception to that tendency was the highly inegalitarian countries where “privileged minorities from metropolitan countries” (p. 169) lost their economic power and left the country at the time of independence.

12 As for the years before 1960, the WIDER data give only two (person-based) Gini coefficients for Africa. They are for Nigeria and Zambia, both for the year 1959, and are respectively 51 and 48. Kuznets (1965 [1954], p. 155) quotes a UN document that gives income distribution estimates for the early 1950’s, for Zimbabwe (Southern Rhodesia) where only 5 percent of the population received 57 percent of total income, Kenya, where 2.9 percent of total population controlled 51 percent of total income, and Zambia (Northern Rhodesia) where the proportions were 1.4 and 45 percent. The same document ( _National income and its distribution in underdeveloped countries_ ) gives the following ratios for the per capita incomes of nonAfrican and African population: 35 to 1 in Kenya, 58 to 1 in Zambia, and 34 to 1 in Zimbabwe (quoted from Mandel, 1962, vol. 3, p. 144).

13 Strangely enough, there are very few person-based Ginis in the 1970’s. This is the case for the rest of the world too. A greater proportion than usual of the 1970’s Ginis included in the WIDER and DeiningerSquire data bases are household-based (distribution of household income across households).

9
our study on East Asia where the dominant proportion of the trade in volume is made between sea ports.

Another implication of Limao and Venables (2001) concerns the level of infrastructure development in both trading partners and transit countries and that these can have a significantly negative effect on transport cost factors regression. If a country can improve its infrastructure level from the median to the top 25<sup>th</sup> percentile, the transport cost factor of the country would fall from 1.28 to 1.11. This is equivalent to becoming 2,358 km closer to all the trading partners, with the estimated elasticity of distance about 0.38. Moreover, the pseudo-R<sup>2</sup> shows that the distance alone explains only 10 percent of the variation of transport costs. The other geographical factors and infrastructure explain the larger part of the variation in transport costs, showing their significance.

Clark, Dollar, and Micco (2004) specifically examine the relationship between port efficiency and maritime transport costs. Instead of using the CIF/FOB matching method, they directly use the “import charges” from the United States trade statistics. The U.S. official statistics record every year the HS 6-digit commodity based, via liners, port-to-port import values, weights and “import charges”, the latter roughly reflecting the transport costs between the ports<sup>8</sup> . They run regression analysis for cross-section data in 1998: the dependent variable is port-to-port via-liner import charge per weight at HS 6-digit commodity level; the independent variables are bilateral (port-to-port) distance, port-to-port via-liner trade value per weight at HS 6-digit level, total import volume from the exporting country, directional imbalance in total trade between the U.S. and the exporting country, containerization ratio of the HS 6-digit based import from the exporting country, and various policy variables, as well as the efficiency indicators of sea ports of exporting countries to the ports of the U.S.<sup>9</sup>

> 8  According to the official source, the import charge represents the aggregate cost of all freight, insurance, and other charges (excluding U.S. import duties) incurred.

> 9 The amounts of the trade and weight in their regression cover those transported by liners only, not include those by tankers nor tramps. They use an Instrumental Variable technique to control the endogeneity of the variable of total volume, with the instrumental variable of exporting country’s GDP.

12
When societal consensus for judicial reform is lacking, Messick (1999) recommends that the development community forgo judicial reform altogether. Instead, governments can focus on building a consensus for reform by opening a dialogue with bar associations, business groups, and other nongovernmental organizations to campaign publicly on the benefits of reform (Botero et al 2003). Hammergren (2014) takes issue with this prescriptive view. She observes that building consensus may create such strong opposition that reform will be stalled. Hammergren also cautions that nongovernmental organizations have their own interests that may be at odds with the broader judicial reform agenda. Business groups may, for example, be interested solely in the creation of commercial courts or steps that reduce legal fees.

In line with the arguments of Messick and Botero et al, Heise (2000) analyzes the determinants of judicial effectiveness for cases that reach a jury trial. The study uses one year of civil jury case outcomes from 45 of the United States’ 75 most populous counties and identifies supporting institutions such as bailiffs as one important variable, along with political characteristics (which party won the last elections). The findings from this study call into question the efficacy of ADR reform efforts and increases in judges’ pay. Instead, variables that exert systematic influence on the judicial system prevail. Partial reform, absent political will, worsens court results rather than improve them even in an advanced economy like the United States.

Armytage (2010) finds that reform of the Papua New Guinea judiciary between 2003 and 2007 yielded little improvement in court effectiveness, despite the significant foreign aid that went into increasing judicial budgets, improving court infrastructure, and addressing judges’ incentives. Instead, reform needs to be based on domestic political resolve, an ingredient that was missing during the period under study. Dias and Gomes (2018) reach the same conclusion in the case of Portugal, where court reforms during 2013-2016 did not reduce disposition time and costs despite significant investment in physical infrastructure and increased judges’ pay.

Changes to legal rules may assist in creating the preconditions for future reform while in the meantime improving judicial effectiveness (Hay, Shleifer and Vishny 1996). This rules-first strategy attempts to raise effectiveness within the confines of the existing inefficient judiciary, by 7
that can be used to conduct policy analysis under a Social Accounting Matrix (SAM) framework. It was developed by Parra and Wodon (2008), and it is distributed free of charge, together with the necessary MATLAB components. The accompanying user’s manual describes how to use it and the theory behind the computations. The application can be used to perform various types of analysis and decompositions, and to obtain detailed and graphical results for experiments.

# **3. Data and Empirical results**

# **_3.1. Structure of the Ghana SAM_**

The 2005 SAM for Ghana used here was provided by IFPRI (2007). The SAM has 56 activities and 59 commodities, self-employed labor, skilled and unskilled labor; agricultural capital and other capital, land, rural and urban households, 5 accounts for government (government, direct taxes, sales taxes, import tariffs, and export taxes), 1 account for investment, and one for the rest of the world.

The technical coefficients of the macro SAM in table 1 give us an overall picture of the macroeconomic profile of the Ghanaian economy. Some 48.9 percent of the costs of production for activities are accounted for by intermediate inputs, 35.2 percent by labor payments, 12.2 percent by payments to capital, and 3.9 percent to land payments; 0.2 percent of total production is received as subsidies from government. The supply of commodities is satisfied at 68.2 percent by the marketed domestic output, 2.2 percent by the marketing margins of imported products, 6.0 percent by indirect taxes, and 23.6 percent by imports. Labor and land resources are used solely to pay households for their own use, and 19.4 percent of capital resources is used to pay direct taxes. Households spend 92.9 percent in final consumption, 3.5 percent paying income taxes, and 3.7 percent as savings. The government spends 30.9 percent of its income purchasing goods and 9
government’s commitment to such policies. [ _Source: Governance Indicators of Kaufmann, Kraay, and Mastruzzi (2007_ )].

11) Regulatory quality: normally distributed index with mean 0 and standard-deviation of 1 (across a total of 207 countries) whose higher values imply a better institutional framework. The index measures the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. [ _Source: Governance Indicators of Kaufmann, Kraay, and Mastruzzi (2007_ )].

General note: for all the country characteristics, values for West Germany were used for East Germany also.

67
idence from field experiments on referrals.’ _Journal of Political Economy_ 124(6), 1793– 1828

- Pellizzari, Michele (2010) ‘Do friends and relatives really help in getting a good job?’ _Industrial and Labor Relations Review_ 63(3), 494–510

- Schmutte, Ian M (2015) ‘Job referral networks and the determination of earnings in local labor markets.’ _Journal of Labor Economics_ 33(1), 1–32

- Simon, Curtis J., and John T. Warner (1992) ‘Matchmaker, matchmaker: The effect of old boy networks on job match quality, earnings, and tenure.’ _Journal of Labor Economics_ 10(3), 306–330

- Topa, Giorgio (2011) ‘Labor markets and referrals.’ _Handbook of Social Economics_ 1, 1193–1221

- Wang, Shing-Yi (2013) ‘Marriage networks, nepotism, and labor market outcomes in China.’ _American Economic Journal: Applied Economics_ 5(3), 91–112

22
# Trade-offs in the Design of Simplified Tax Regimes: Evidence from Sub-Saharan Africa<sup>*</sup>

Christopher Hoy<sup>1</sup> , Thiago Scot<sup>1</sup> , Alex Oguso<sup>2</sup> , Anna Custers<sup>3</sup> , Daniel Zalo<sup>2</sup> , Ruggero Doino<sup>1</sup> , Jonathan Karver<sup>1</sup> and Nicolas Orgeira Pillai<sup>4</sup>

## **JEL Classification:** D04, D80, D90, H20, H30, H50

**Keywords:** Taxation, Public Finance, Small Businesses, Randomized Experiment

> *We would like to thank the staff and management of the Kenya Revenue Authority (KRA) for supporting this study. This work was financed by the World Bank Global Tax Program. Particularly, we appreciate the invaluable support from the following KRA staff members that ensured the successful completion of the study: Alex Mwangi, Joseph Sirengo, Daniel Munge, Sheila Mugusia, Beatrice Mundia, Stella Kirigo, James Githinji, Jacob Aliet, Francis Njukia, Paravian Kwamboka, Gladys Cheruiyot, Gladys Kitony, Ombasa Kiame, among others We are incredibly grateful for the support, input, and guidance provided by many colleagues, including Anne Brockmeyer, Emilia Skrok, Zeina Afif, Benu Bidani, Stephen Davenport, Matthew Collin, Chiara Bronchi and Cezen Ozer. We also greatly appreciate comments provided on an earlier version of this paper by Anne Brockmeyer, Pierre Bachas, Mahvish Shaukat, Matthew Collin, Daniel Prinz, and Pablo Garriga.<sup>1</sup> World Bank,<sup>2</sup> Kenya Revenue Authority,<sup>3</sup> Amsterdam University of Applied Sciences,<sup>4</sup> International Centre for Tax and Development. Christopher Hoy is the corresponding author (choy@worldbank.org). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The authors have no financial or material interests in the results in the paper.
**Table 3. Trade Partners Predicted to See the Largest Decreases in Exports to Russia**

|**Partner**|**Change in the Value of Russian**<br>**Imports (US$, millions)**|**Decrease in Total Russian**<br>**Imports, 2012–20 (Percent)**|
|---|---|---|
|Ukraine|-322.9|-1.8|
|Belarus|-283.8|-2.2|
|Kazakhstan|-105.3|-1.1|
|Moldova|-21.4|-4.5|
|Uzbekistan|-20.9|-1.5|
|Azerbaijan|-15.2|-2.8|
|Armenia|-7.0|-2.3|
|Kyrgyz Republic|-6.1|-3.2|
|Turkmenistan|-5.4|-2.9|
|Georgia|-3.0|-3.1|
|Malawi|-0.8|-1.1|
|Tajikistan|-0.6|-0.9|
|Mozambique|-0.5|-0.8|
|Ethiopia|-0.5|-2.3|
|Tanzania|-0.4|-0.8|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 30
|P131228|2016|2020|6/22/2020|9 Active|Afghanistan|
|---|---|---|---|---|---|
|P131235|2014|2014|5/17/2014|1 Active|India|
|P131235|2014|2015|12/4/2014|2 Active|India|
|P131235|2014|2015|6/24/2015|3 Active|India|
|P131235|2014|2016|7/31/2015|4 Active|India|
|P131235|2014|2016|1/28/2016|5 Active|India|
|P131235|2014|2016|4/7/2016|6 Active|India|
|P131235|2014|2017|10/26/2016|7 Active|India|
|P131235|2014|2017|5/4/2017|8 Active|India|
|P131235|2014|2017|6/12/2017|9 Active|India|
|P131235|2014|2018|12/22/2017|10 Active|India|
|P131235|2014|2018|6/14/2018|11 Active|India|
|P131235|2014|2019|1/10/2019|12 Active|India|
|P131235|2014|2020|10/18/2019|13 Active|India|
|P131235|2014|2020|5/13/2020|14 Active|India|
|P131236|2013|2013||0 Closed|Yemen,Republic of|
|P131236|2013|2014|7/4/2013|1 Closed|Yemen,Republic of|
|P131236|2013|2014|1/18/2014|2 Closed|Yemen,Republic of|
|P131236|2013|2015|8/18/2014|3 Closed|Yemen,Republic of|
|P131236|2013|2015|6/5/2015|4 Closed|Yemen,Republic of|
|P131236|2013|2016|1/5/2016|5 Closed|Yemen,Republic of|
|P131236|2013|2021|7/6/2020|6 Closed|Yemen,Republic of|
|P131242|2013|2013||0 Closed|Bhutan|
|P131250|2012|2013|3/9/2013|1 Closed|Tonga|
|P131250|2012|2014|12/25/2013|2 Closed|Tonga|
|P131250|2012|2015|12/14/2014|3 Closed|Tonga|
|P131250|2012|2016|1/5/2016|4 Closed|Tonga|
|P131250|2012|2017|12/23/2016|5 Closed|Tonga|
|P131256|2015|2015|3/31/2015|1 Active|Morocco|
|P131256|2015|2016|11/16/2015|2 Active|Morocco|
|P131256|2015|2016|6/17/2016|3 Active|Morocco|
|P131256|2015|2017|11/3/2016|4 Active|Morocco|
|P131256|2015|2017|2/6/2017|5 Active|Morocco|
|P131256|2015|2018|9/18/2017|6 Active|Morocco|
|P131256|2015|2018|5/22/2018|7 Active|Morocco|
|P131256|2015|2019|1/28/2019|8 Active|Morocco|
|P131256|2015|2020|9/21/2019|9 Active|Morocco|
|P131256|2015|2020|4/9/2020|10 Active|Morocco|
|P131263|2013|2013|1/1/2013|1 Active|Bangladesh|
|P131263|2013|2013|6/20/2013|2 Active|Bangladesh|
|P131263|2013|2014|1/18/2014|25 Active|Bangladesh|
|P131263|2013|2014|6/28/2014|26 Active|Bangladesh|
|P131263|2013|2015|12/15/2014|27 Active|Bangladesh|
|P131263|2013|2015|6/10/2015|28 Active|Bangladesh|
|P131263|2013|2016|12/30/2015|29 Active|Bangladesh|
|P131263|2013|2016|6/9/2016|30 Active|Bangladesh|
|P131263|2013|2017|8/18/2016|31 Active|Bangladesh|
TABLE 9: TRAINER CHARACTERISTICS ON STUDENT PSYCHOLOGY (PERSONAL INITIATIVE)

|||(1)<br>_y =_|(2)<br>_Personal Initia_|(3)<br>_tive score of st_|(4)<br>_udents after tra_|(5)<br>_ining_|
|---|---|---|---|---|---|---|
|||Personal<br>Initiative<br>(1-5)|Personal<br>Initiative<br>(1-5)|Personal<br>Initiative<br>(1-5)|Personal<br>Initiative<br>(1-5)|Personal<br>Initiative<br>(1-5)|
|**phics**|Age|-0.00495<br>(0.00326)|-0.00111<br>(0.00301)|0.00190<br>(0.00811)|-0.0102<br>(0.0103)|-0.0282**<br>(0.0124)|
|**ra**|Female|0.0918|0.0564|0.105|0.0360|0.498**|
|**mog**||(0.0833)|(0.0671)|(0.113)|(0.114)|(0.187)|
|**De**|Years at school|-0.0164|-0.00438|-0.0287|0.0000643|-0.0227|
|||(0.0112)|(0.0116)|(0.0192)|(0.0190)|(0.0207)|
|**preneur**<br>**Model**|Ever been a business||0.195***|0.151**|0.149**|0.339***|
|**Entre**<br>**Role**|owner||(0.0561)|(0.0562)|(0.0563)|(0.0818)|
|**e skills**|Digitspan score|||0.0213<br>(0.0257)|-0.0258<br>(0.0353)|-0.0375<br>(0.0411)|
|<br>**itiv**|Raven test score|||-0.0278|-0.00645|-0.0529*|
|stics<br>**ogn**||||(0.0192)|(0.0164)|(0.0307)|
|teri<br>**n-c**|Personal intitative|||-0.106|-0.270***|-0.120|
|rac<br>**no**||||(0.136)|(0.0798)|(0.189)|
|Cha<br>**and**|Error competence|||0.0868|0.505**|0.356*|
|iner<br>**ive**||||(0.140)|(0.204)|(0.186)|
|Tra<br>**Cognit**|Prosocial orientation|||0.151<br>(0.128)|-0.154<br>(0.157)|0.567<br>(0.398)|
|**tion**|How satisfied are you<br>with your job?||||0.123**<br>(0.0537)|0.00919<br>(0.0573)|
|**atisfac**|Given a choice, would<br>still be a teacher||||-0.0632**<br>(0.0291)|-0.0851**<br>(0.0313)|
|**b s**|Career satisfaction||||0.109***|0.0805**|
|**Jo**|||||(0.0299)|(0.0328)|
|**T**|Years as teacher|||||0.0182<br>|
|**VE**<br>**e**||||||(0.0222)|
|**at T**<br>**lleg**|Years at TVET Colleges|||||0.0379*|
|**ars**<br>**co**||||||(0.0220)|
|**Ye**|Perceived|||||-0.144|
||entrepreneurial skills|||||(0.0891)|
||Average score for|4.477|4.477|4.477|4.477|4.477|
||students at baseline|(0.0248)|(0.0248)|(0.0248)|(0.0248)|(0.0248)|
||Observations|565|565|565|565|565|

_Notes:_

* significant at 10% level ** significant at 5% level *** significant at 1% level

> (1) OLS regressions sequentially add explanatory variables from columns (1) to (5)