rafmacalaba/datause-encoder-data huggingface dataset:
Table 26

|Dependent variable: Nee|d of fiscal a|djustment|based on E|scolano et|al.(2014)||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|0.010***|0.004|0.017***|0.010***|0.014***|0.003|0.010**|0.021***|
|structural as % GDP|(0.003)|(0.003)|(0.003)|(0.002)|(0.003)|(0.007)|(0.005)|(0.007)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-1.701***||-1.117***|-1.078***|-1.144***|-1.117***|-1.058***|
|||(0.381)||(0.272)|(0.257)|(0.272)|(0.272)|(0.274)|
|Inflation (t-1)||-0.296||-0.433***|-0.465***|-0.456***|-0.433**|-0.399**|
|||(0.220)||(0.165)|(0.139)|(0.169)|(0.173)|(0.159)|
|U.S. Interest rate||-0.021**||-0.011|-0.010|-0.010|-0.011|-0.010|
|||(0.010)||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|**Political factors**|||||||||
|Gini|||0.004**|0.003***|0.002**|0.001|0.003***|0.003***|
||||(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Rule of law|||-0.024***<br>(0.009)|-0.020***<br>(0.007)|-0.008<br>(0.008)|-0.019***<br>(0.007)|-0.020<br>(0.013)|-0.019***<br>(0.007)|
|Margin of Majority|||0.192**<br>(0.084)|0.163*** <br>(0.055)|0.136*** <br>(0.045)|0.158*** <br>(0.055)|0.163*** <br>(0.055)|0.310***<br>(0.093)|
|Election's year|||0.034**<br>(0.014)|0.035*** <br>(0.012)|0.034*** <br>(0.012)|0.034*** <br>(0.012)|0.035*** <br>(0.012)|0.035***<br>(0.012)|
|**Interactions**|||||||||
|EMEs|||||0.112*<br>(0.063)||||
|EMEs * Deviation|||||-0.004<br>(0.004)||||
|Gini * Deviation||||||0.000<br>(0.000)|||
|Rule of law * Deviation|||||||-0.000<br>(0.001)||
|Majority * Deviation||||||||-0.017<br>(0.011)|
|Observations|1,467|1,451|1,318|1,304|1,304|1,304|1,304|1,304|
|Country FE|No|No|No|No|No|No|No|No|
|r2_p<br>Robust standard errors cl<br>*** p<0.01, ** p<0.05, *|0.0374<br>ustered by<br>p<0.1|0.125<br>country in|0.114<br>parenthese|0.211<br>s|0.227|0.213|0.211|0.216|

- 55 -
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persistently lower in RS (38%, but rising) than in FBiH (46.2% on average). Our estimates of involuntary unemployment are about 19% for both entities.

This labor status classification allows us to directly observe transitions to selfemployment from year to year. We are able to identify 229 entries into self-employment status during the course of the panel (Table 2). If we look at the subsample in 2001 of household heads, we identify 119 new entries (not shown)<sup>4</sup> . Of further interest are the outcomes of the new entries, as business start-ups bear the higher risk of failure during their first years of operation. Indeed, almost half of our observed transitions to selfemployment do not make it through their first year in business. This is a pattern observed in both samples of individuals and household heads. Thus, it is of further interest to examine the determinants of entrepreneurial survival as well as entrepreneurial entry. The emphasis is placed on the role of labor market experience and issues of access to finance and financing constraints.

The next section provides the background to the empirical analysis of the determinants of self-employment transition in BiH, discusses the empirical strategy and presents the results. Then, Section 5 presents the results and discussion for the analysis of firm-survival in the sample.

# **4. Entry into Self-Employment**

Entrepreneurship has been linked to both economic and social attributes. The literature has distinguished between the role of institutions, socioeconomic factors, individual characteristics, and psychological factors, in identifying the determinants of the decision to start an entrepreneurial activity (Djankov et al., 2005, 2006a, 2006b). Other studies emphasize the importance of labor market characteristics (Earle and Sakova, 2000; Dutz et al., 2001), as well as financial wealth and constraints (Paulson and Townsend, 2004; 2005; Paulson et al., 2006). In the first stage of our analysis, we examine the microeconomic determinants of entry into self-employment in BiH, comparing differences in human, social and financial capital between groups of new entrepreneurs and individuals who did not become self-employed. In a second stage, we

> 4 This latter sub-sample is of interest, despite reducing the number of observations, because it is likely to capture new business openings and exclude participation in family enterprises.

6
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here); (ii) relevant personal characteristics (e.g. age, education, employment); (iii) current status on the socio-economic ladder; (iv) past mobility experience; (v) past shocks; (vi) the degree of personal risk aversion; and (vii) luck. Below we motivate the importance of accounting for these confounding factors.

Certain personal characteristics, such as the person’s education and employment status have been shown to have a strong effect on social mobility (Bozeat et al. 2010; Narayan, Van der Weide et al., 2018). The respondent’s age needs to be included in the model to account for the fact that there may be greater scope for younger individuals to move up the ladder in the future, compared to those who are nearing retirement. Similarly, the respondent’s sex and religious affiliation can be included to account for possible differences in ambition between men and women, or across religious denominations, which may lead to different aspirations for the future.

It is the case the respondents who are currently at the top of the current welfare ladder have less scope of moving up the ladder, and similarly those at the bottom are less likely to move lower still in the near future. Moreover, the current position on the welfare ladder can be correlated with the person’s assessment of the importance of having connections to succeed in the labor market or in terms of education.

In addition, the individual’s past mobility experience may influence future expectations. Here past mobility (upward, stable or downward) is derived from the comparison of the current welfare ladder position with the past welfare ladder position. It is difficult to say a priori how past experience may translate into future expectations. It may be, for instance, that past upward (downward) mobility may indicate further upward (downward) mobility in the future. It is also possible, however, that upward (downward) mobility in the past leaves less scope for further upward (downward) mobility in the future. The direction of the effect is likely to depend on other characteristics of the individual and is investigated empirically by including dummies indicating either upward or downward past mobility (no past mobility being the reference category).

Given that the data were collected in 2010, expectations of future mobility may be influenced by the recent financial crisis experience. Those who were affected by 13
consumption per capita, by around +300 USD at 1993 exchange rates and prices. This national account figure is consistent with the survey means comparison for cash expenditures (+221 USD difference), although with some caveats already mentioned. When focusing on administrative districts along the border, the advantage of the Cˆote d’Ivoire side is halved but remains significant at approximately +110 USD. Even if cotton output per capita was fairly comparable at the national level, the Malian border districts produced very little cotton in 1993, providing a +43 USD per capita advantage to Ivorian districts.

Particularly because of its bauxite resources, Guinea is wealthier than Mali, and the difference from Cˆote d’Ivoire only reaches +200 USD in consumption per capita (not shown). Furthermore, although mines are not located in the border areas, survey mean comparisons among border districts show that the Guinean side is no poorer than the Ivorian side (not shown).

At the Mali border, discontinuities in log cash expenditures per capita are even higher than those found at the border with Ghana, ranging from +0.84 to +2.02 depending on the estimates, the bandwidth used, and the population considered (table 4 top panel). Recall, however, that the narrower 75 km bandwidth can be deemed more reliable for identification (see above). With this bandwidth, the upper bound is decreased to +1.52. The restriction to household heads who are from the Mande-Voltaic group again slightly reduces the estimates by dropping a few Ivorian southerners, such as civil servants sent to the North.<sup>13</sup> When we ad-

> 13The Mande include, in particular, the Bambara, Bobo, Diula, Malinke, and Soussou, whereas the Voltaic or Gur include the Lobi, Mossi, and Senoufo people. The Mande and Voltaic groups are close together in linguistic terms and display some mixing on the map. Ethnic codifications are not homogeneous; in particular, the Malian survey records the language of interview rather than the “ethnic group”. However, district of birth is only recorded in the Cˆote d’Ivoire survey, so we have no alternative.

26
|**Table 4. US A**|**gricultur**|**al Imports**|**: GSP C**<br>**Av. Tariff**|**overage**<sup>**1**</sup>**, Ut**<br>|**ilization**<sup>**2**</sup>**a**|**nd Avera**|**ge Tariff**<br>**Av. Tarif**|**Preference**<sup>**3**</sup>**, 2003**<br>**f**|||**Av. Tariff**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**GSP **|**Coverage**<br>**(%)**|**Utilization**<br>**(%)**|<br>**Preferenc**<br>**(%)**|<br>**e**|**Coverage**<br>**(%)**|**Utilization**<br>**(%)**|<br>**Preferenc**<br>**(%)**|<br>**e**|**Coverage**<br>**(%)**|**Utilization** **(%)**|<br>**Preference**<br>**(%)**|
|**_Albania_**|100|34|1.9|**_Kyrgystan_**||||**_Tonga_**|98|100|7.0|
|**_Anguilla_**|100|100|6.0|**_Latvia_**|6|98|4.2|**_Tunisia_**|94|98|4.2|
|**_Argentina_**|36|87|4.5|**_Lebanon_**|89|97|4.6|**_Turkey_**|43|97|4.3|
|**_Armenia_**|57|98|5.5|**_Lithuania_**|2|83|5.4|**_Turks and Caicos Is._**||||
|**_Bahrain_**||||**_Macedonia_**|76|97|6.6|**_Uruguay_**|11|94|5.3|
|**_Bosnia-Hercegov._**|90|85|5.8|**_Moldova_**|30|71|6.6|**_Uzbekistan_**|100|100|3.8|
|**_Br Indian_**||||**_Mongolia_**|0|||**_Venezuela_**|95|99|3.7|
|**_Brazil_**|24|95|4.0|**_Morocco_**|49|41|6.8|**_West Bank_**|100|100|1.3|
|**_Bulgaria_**|68|97|4.3|**_Niue_**||||**_Western Sahara_**||||
|**_Chile_**|14|83|3.8|**_Norfolk_**|100|57|2.4|**_Zimbabwe_**||||
|**_Christmas_**||||**_Oman_**|20|92|1.4|**_GSP-LDC_**||||
|**_Cocos Is._**||||**_Pakistan_**|49|99|4.2|**_Afghanistan_**|1|0||
|**_Cook_**|0|||**_Papua_**|100|100|3.4|**_Angola_**||||
|**_Croatia_**|87|99|4.6|**_Paraguay_**|68|100|3.7|**_Bangladesh_**|100|68|5.9|
|**_Czech_**|88|81|6.2|**_Philippines_**|41|86|4.9|**_Bhutan_**|100|100|3.0|
|**_Egypt_**|28|98|4.2|**_Pitcairn_**|100|100|5.6|**_Burkina_**|100|62|3.7|
|**_Estonia_**|99|97|3.7|**_Poland_**|88|69|5.3|**_Burundi_**||||
|**_Fiji_**|98|100|3.9|**_Romania_**|14|63|4.3|**_Cambodia_**|100|88|10.6|
|**_Georgia_**|23|82|4.6|**_Russia_**|84|95|3.5|**_Eq Guinea_**||||
|**_Gibraltar_**||||**_Slovak Rep._**|24|84|4.5|**_Kiribati_**||||
|_Heard/McDonald_|100|100|7.0|**_Solomon Is._**||||**_Nepal_**|100|92|5.0|
|**_Hungary_**|71|68|4.2|**_Sri Lanka_**|93|94|3.8|**_Samoa_**|100|23|4.0|
|**_India_**|40|95|4.2|**_St Helena_**||||**_Somalia_**|100|0||
|**_Indonesia_**|37|97|4.4|**_Suriname_**|100|100|1.8|**_Togo_**|69|100|8.3|
|**_Jordan_**|84|76|5.3|**_Thailand_**|39|96|4.9|**_Vanuatu_**|100|0||
|**_Kazakhstan_**|0|||**_Tokelau_**|100|0||**_Yemen_**||||

> 1 Ratio of eligible imports to total dutiable imports.   2 Ratio of imports entering under preference to total eligible imports. 3 Difference between nominal _ad valorem_ tariff equivalent and nominal preferential tariff.  Covers all HS 8-digit lines with eligible US imports in 2003. (.) indicates less than one percent.

34
marketplaces for these services with directories and quality rating systems may be a useful first step. However, it remains an open question for future research to test whether such assistance by itself will be enough to get SMEs to adopt these services, or whether firms also need help to overcome financial constraints and uncertainty.

# **References**

Anderson, Michael (2008) “Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects”, _Journal of the American Statistical Association_ , 103(484), 1481-1495.

Anderson, Stephen, Rajesh Chandy and Bilal Zia (2018) “Pathways to Profits: Identifying Separate Channels of Firm Growth Through Business Training”, _Management Science_ , 64(12): 5559–5583 Arráiz, Irani, Syon Bhanot and Carla Calero (2019) “Less is More: Experimental Evidence on Heuristic-Based Business Training in Ecuador”, IDB Invest Working Paper TN No.18.

Barney, Jay (1999) “How a Firm’s Capabilities Affect Boundary Decisions”, _Sloan Management Review_ Spring, 137-45.

Bellemare, Marc, and Casey Wichman (2020) “Elasticities and the Inverse Hyperbolic Sine Transformation,” _Oxford Bulletin of Economics and Statistics_ 82(1): 50-61.

Belloni, Alexandre, Victor Chernozhukov, and Christian Hansen (2014) “High-Dimensional Methods and Inference on Structural and Treatment Effects.” _Journal of Economic Perspectives_ 28 (2): 29-50.

Bloom, Nicholas, and John Van Reenen (2007) “Measuring and Explaining Management Practices Across Firms and Countries”, _Quarterly Journal of Economics_ 122(4): 1351-1408.

Bloom, Nicholas, Benn Eifert, Aprajit Mahajan, David McKenzie, and John Roberts (2013) “Does management matter? Evidence from India”, _Quarterly Journal of Economics_ , 128(1):1-51.

Bruhn, Miriam, Dean Karlan, and Antoinette Schoar (2018) “The Impact of Consulting Services on Small and Medium Enterprises: Evidence from a Randomized Trial in Mexico”, _Journal of Political Economy_ 126(2): 635-687 Campos, Francisco, Michael Frese, Markus Goldstein, Leonardo Iacovone, Hillary C. Johnson, David McKenzie and Mona Mensmann (2017) “Teaching personal initiative beats traditional training in boosting small business in West Africa”, _Science_ 357(6357): 1287-90.

Coase, Ronald (1937) “The Nature of the Firm”, _Economica_ , 4(6): 386-405.

Dalton, Patricio, Julius Rüschenpöhler, Burak Uras and Bilal Zia (2020) “Curating Local Knowledge: Experimental Evidence from Small Retailers in Indonesia”, Mimeo. Tilburg.

31
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war. While individuals in the first control group would have attended schools that were still under the French system, those in the second control group would have attended schools that completely operated under the DRV government after the war in 1954. We combine both control groups to increase the sample size for our analysis, but also provide estimation results that compare the treatment group against each of these control groups. We also provide robustness checks when we further disaggregate these control groups. In this approach, we leave out of the estimation sample the cohorts that were born during 1936-1939 or 1946-1949, who were partially exposed to increased school access (i.e., the “contaminated” treatment cohorts) if they were living in DRVcontrolled areas. But we also provide robustness checks when we include these cohorts; we return to more discussion in the robustness checks section.

For the second measure, we explicitly take into account the differential length of exposure to the ME program that school-age children residing in DRV-controlled areas might have benefited from increased school access, which can vary depending on their age. Table 1.1 in Appendix 1 provides an illustration of this exposure, where a child born in 1935 or later in DRV-controlled areas or 1944 or later in French-controlled areas would have had five years of exposure. The number of years of exposure is similarly adjusted for children born in the provinces in North Vietnam that DRV gained control from around the middle of the war. The reason we restrict the number of years of exposure to five is that, children would likely drop out of school if they miss the crucial years of schooling at the primary school level.<sup>14</sup> Our second measure thus includes all the “contaminated” treatment cohorts in the estimation sample. This would likely weaken the impacts of the ME program, although the decrease may be somewhat mitigated due to larger

> 14 Various education studies suggest that late school enrollment is observed to lead to more school dropouts in a number of countries around the world (Wils, 2004; UIS, 2005; Chen, 2015; No et al., 2016).

14
except for **the total** fertility rate: not only is this indicator of population growth insignificant but,<sup>in</sup> **contrast** to previous cross-national studies of economic growth, the coefficient is positive. This<sup>may</sup> be due to the pardcular sample of countries included or, more likely, to the historical period under examination.

Table 4 also reports that the effect of overall annual hours of instruction across all subject areas is positive but not significant (equadon B). When this measure is broken down by subject area (equation C), two of the eight subject areas have significant effects on economic growth: science education (a positive effect) and pre-vocational or pracdcal education (a negative effect). Interestingly, there is no evidence that instructional hours devoted to elementary mathematics<sup>have a</sup> significant impact on economic growth -- indeed, the direcdon of the regression coefficient associated with mathematics education is negative.

Does this pattern of curricular effects change when a smaller sample of less-developed nations is examined? For the most part, no. Table _5_ shows that overall hours of instruction continues to be positive but non-significant, that the effects of science education and pre-vocational education are in the same direction and still significant, and that mathematics education continues to have little or no impact on economic growth. The main difference for less-developed countries is, surprisingly, with respect to aesthetic education and physical education. The results reported in equation C of table 5 indicate that Third World countries which taught more hours of art and music education experienced, other things being equal, stronger growth rates; those that devoted more time to physical education experienced weaker growth rates.

The reasoning behind these unexpected relationships is not entirely clear. Greater emphasis on physical education in some countries may reflect a formn of paramilitary training and a welldeveloped military infrastructure which, in the long run, may drain potentially productive<sup>resources</sup> from the overall economy. Greater emphasis on art and music education may indicate the cultural impact of Western educational conceptions and philosophies which have historically<sup>placed a</sup> prenium on this form of child socialization at the elementary level. If such systems have been penetrated culturally by the West, then their economies may also be oriented to Western market arrangements and thus more likely to have been targets of international economic aid and loans.

_1.-_