rafmacalaba/datause-encoder-data huggingface dataset:
37 (7)

* (p* - p R )jt = α j + β 1hbljt + β 2hbjt+ δ 1ftjt + + δ 2ftcompjt + δ 3ownjt + δ 4multijt + δ 5nyearsjt + γ 11975/76j+...+ γ 251999/2000jt + ejt

The model of (7) uses harvest year dummies to control for variation over time. The omitted category is 1974/75, so that the interpretation of time dummy variable coefficients is relative to this base year.

The use of time dummies is found to be preferable to the ad-hoc inclusion of individual dummy variables for natural disasters, for weather, for policy changes, price spikes or for other time variant events. This is because the potential sources of time-related variations are numerous, interrelated and complex. For example, while the overall correlation between the mark-down measure (p* - p*R ) and world coffee prices in insignificant,<sup>40</sup> it is not unreasonable to think that dramatic price spikes and natural disasters can affect the mark-down measure. In Figure 8, there are three years in which the international market exhibited modest to major price spikes, largely due to adverse climatic conditions. The spike in 1976/77, for example, was caused by the (southern hemisphere) winter frost in Brazil, and those of 1985/86 and 1994/95 were due to a drought in Brazil in 1985/86 with minor frosts and low rainfall in Central America in both 1985/86 and 1994/95 (Gilbert 1995).

**Figure 8 The ICO ‘Other Milds’ World Coffee Price Series**

<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>0<br>-50<br>-100<br>ICO 'Other Milds' average annual p*-p<br>1974/751976/771978/791980/811982/831984/851986/871988/891990/911992/931994/951996/971998/99<br>US cents/lb<br><!-- End of picture text --> In addition to generalized price responses to adverse climatic conditions, variations observed in either or both series in Figure 8 can be due to changes in national or international policy. For example, the introduction of a binding coffee ICA in the 1982/83 harvest or its final suspension in 1989 might have an effect on mark-down behavior. So might the natural disaster history of Costa Rica, Central America and the

> 40 The Pearson correlation coefficient, rxy = 0.18 with a t-stat of 0.84.
<!-- Start of picture text --> 4<br>15<br>~c<br>Bo)<br>ar)<br>£<br>o<br>fe}<br>oO<br>.25<br>0<br>Exporter<br>[__] GBFEXT [4 GBFINT [__] SBFEXT [GG SBF INT<br><!-- End of picture text --> <!-- Start of picture text --> Business Admin.<br>Quality control Production Planning<br>Payment Sourcing<br>4<br>5<br>Sales Marketing<br><!-- End of picture text --> <!-- Start of picture text --> Business Admin.<br>Quality control Production Planning<br>Payment Sourcing<br>Sales Marketing<br><!-- End of picture text -->
treat 2011 as the pre-refugee year in our IV analysis. Since 2014 is the first year for which we have complete information on the number of refugees in each province, it is set as the treatment year. The summary statistics for the outcome variables used in the IV analysis are presented in table 1.

Table 1: IV estimation annual summary statistics: 2011 and 2014

|Province average:|2011|2014|
|---|---|---|
|New frms|659.37|712.47|
|New foreign frms|44.14|58.47|
|Share of foreign frms (x100)|2.56|3.48|
|Gross profts per capita (TL)|1969.59|2740.89|
|Net sales per capita (TL)|16561.75|23381.10|
|Population|922522|959209|
|N|81|81|

_Source:_ Summarized data from the sources described in text. _Note:_ Averages at the level of 81 provinces presented for each variable. ”New firms” and ”New foreign firms” are the number of firms. ”Share of foreign firms” is the average of the ratio of ”New firms” and ”New foreign firms”. ”Gross profits” and ”Net sales per capita” are the province totals divided by the province population and are provided in nominal Turkish Liras.

To estimate the DD model, we use data from the years 2009 to 2013. Only data on firm entry are available in 2009, the rest of the outcome data are available from 2010 onwards. Table 2 shows the descriptive statistics for the treatment and control group provinces before and after 2012 along with the averages for the rest of Turkey. While the rest of Turkey has generally higher outcome averages than the treatment region, the control region has smaller averages. The region definitions are shown in figure 3 and are constructed by following the definitions used by Ceritoglu et al. (2017). Including the treatment provinces, there are in total 29 provinces in this limited sample.

Table 2: DD estimation annual summary statistics: 2009 to 2013

|||pre-2012|||post-2012||
|---|---|---|---|---|---|---|
|Province average by region:|Control|Southeast|Rest|Control|Southeast|Rest|
|New frms|202.00|465.00|796.38|147.05|396.50|715.36|
|New foreign frms|3.87|7.85|55.29|6.53|19.20|66.79|
|Share of foreign frms (x100)|1.33|1.74|2.51|2.05|5.05|4.01|
|Net sales per capita (TL)|6795.29|12336.84|18365.23|10115.69|18680.56|25549.34|
|Gross profts per capita (TL)|700.79|1122.56|2323.96|985.28|1501.67|2932.98|
|Population|585092|1074304|1027701|517817|1106135|1062457|
|N|10|19|52|10|19|52|

_Source:_ Summarized data from the sources described in text. _Note:_ Averages at the level of the province groups shown in Figure 3 presented for each variable. ”New firms” and ”New foreign firms” are the number of firms. ”Share of foreign firms” is the average of the ratio of ”New firms” and ”New foreign firms”. ”Gross profits” and ”Net sales per capita” are the province totals divided by the province population and are provided in nominal Turkish Liras.

13
In 1997, through amendments made to the Basic Education Law (no. 4306, dated 16 August 1997) the government of Turkey increased compulsory education from five to eight years by merging the first two levels under the umbrella of basic education. The amendment was short and simple, requiring all children—without making any exceptions for any group of children—to complete eight years of schooling. Parents who do not comply with the compulsory schooling laws face monetary penalties and in extreme cases incarceration.<sup>5</sup> Tracking of children is the teachers’ responsibility, along with the local administrative body. However, noncompliance is quite common both before and after the policy.<sup>6</sup> Although the extension of compulsory schooling was not a new issue, its enactment in the summer of 1997 was politically motivated. The secular government at the time seized the opportunity to curb religious education by extending compulsory schooling.<sup>7</sup> The Turkish Ministry of National Education (MONE) reacted to the challenge of accommodating new students by expanding the number of classes in existing schools, by bussing rural children to 5 Parents who do not comply with the compulsory schooling laws face only monetary penalties in the first two violations. In the third violation, there are additional monetary penalties, as well as a risk of incarceration. After four or more violations, parents would face incarceration up to 6 months.

6 Among the youngest birth cohorts that are not affected by the policy, about ten percent of girls and two to three percent of boys did not complete primary schooling (according to the 2003 and 2008 Turkish Demographic and Health Surveys).

> 7 This fulfilled their goal in two ways. First, it eliminated lower secondary religious schools ( _İmam-Hatip_ ), where both religious and secular courses were given, by making this level a part of compulsory secular schooling. While İmam-Hatip schools were originally established to train government-employed imams, the students in these schools could go on to secular education and major in any field at university. In the 1996–97 school year, 11.5 percent of male and 13.1 percent of female secondary school students were enrolled in this type of schools. Almost all of these schools were in urban areas; less than 1 percent of rural children were enrolled in these schools. Second, the new policy delayed enrollment in Quranic Studies, which involved only religious education but could be done only after the completion of compulsory schooling.

7
# **MEXICO: HUMAN CAPITAL EFFECTS ON WAGES AND PRODUCTIVITY**

## **I. INTRODUCTION**

Human capital is a stock of skills produced by education, experience and training (Welch, 1970; Mincer, 1989). The empirical evidence on the links between human capital, on the one hand, and productivity and/or wage growth on the other, is strong.

Human capital is viewed not only as an investment, but also as a factor of production. Numerous studies using worker and firm level data have shown that more educated and/or trained individuals are more productive. In a work environment that is rapidly changing due to technological advances, the cognitive abilities for workers to process new information become increasingly important and thus command higher wages, and in turn higher incomes (Welch, 1970; Mincer, 1989; Tan and López-Acevedo, 2002; Hellerstein and Neumark, 2004). Moreover, highly educated workers have a comparative advantage with respect to the adjustment and implementation of new technologies, by being able to adopt them more easily or faster than non-educated workers. For this reason, the productivity of highly educated workers, relative to lesseducated workers, is greater (Bartel and Lichtenberg, 1987, Tan and López-Acevedo, 2002; Hellerstein and Neumark, 2004).

Similarly, there is limited, but growing, empirical literature on the link between human capital and a firms’ performance (Koning, 1994; Ravenga, 1995; Batra and Tan, 1995; Barrett and O’Connell, 1998; World Bank, 1998a, 1998b, 1999, 2000, 2001a, 2001b; Dearden, Reed, and Van Reenen, 2000). Using panel data, several studies have demonstrated the positive impact of education and training on productivity (Nielsen and Rosholm, 2002; Batra and Tan, 1995; Dearden, Reed, and Van Reenen, 2000).

In addition, extensive literature has been compiled in the closely related field of human capital and its effects on workers’ wages. A near-consensus suggests that earnings increase with more training (Middleton _et al._ , 1993; Dar _et al._ , 2000; Nielsen and Rosholm, 2001).

1
the studies had high methodological quality; 60% had intermediate quality; and 20% had limited quality (Figure 11).

**Figure 11. Methodological quality of the studies in MSM populations, by country. Andean Region; 2009**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>Limited<br>50%<br>Intermediate<br>40%<br>High<br>30%<br>20%<br>10%<br>0%<br>Peru n= 15 Ecuador n= 9 Bolivia n= 5<br><!-- End of picture text -->

**Source:** Authors’ calculation using information from _Database and References for an Evidence-based Strategy_ in Peru, Ecuador and Bolivia.

**Findings:** In Peru and Ecuador, the proportion of studies in MSM populations with sound methodological content was similar to the proportion observed in the general analysis for all studies (80% vs. 82% in Peru; and 56% vs. 52% in Ecuador).  In Bolivia, the percentage of studies in MSM populations with sound methodological quality was far lower than the proportion for all studies (20% vs. 68%). This finding reveals that in Bolivia, there have been proportionally fewer studies in key populations and their methodological quality is lower than the general median.

# **Female sex workers**

In the Peru’s database, there were 11 studies done on female sex workers. Seventy-three percent of them had high methodological quality; 18% had intermediate quality; and 9% had limited quality. In Ecuador, there were eleven studies done on female sex workers. Forty-five percent of them had high methodological quality; 45.5% had intermediate quality; and 9% had limited quality. In Bolivia, there were three studies done on female sex workers. One had high methodological quality (33.3%); and two had intermediate quality (66.6%) (Figure 12).

27
_TABLE_ 5

# ESThAlBS OP NET AND GROSS EXPORTS SUBSIDIES PER DOllAR OF EXPORT FOR KOREA, 1958-1983 (ANNUAL AVERAGES)

|J<br>|~~~~~~~o<br>|friciai||Vaious e|xpot subsidie|s calcuied per|U.S. dollar of|export (won)||Ratio to ex<br>(per|change mte<br>cent)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Year|exchange<br>fae_(wonS)_<br>|Direct<br>cash<br>subsidies<br>|Expt<br>dollar<br>pemium<br>|Direct<br>tax<br>reductions<br>for exporter|Intest<br>rae<br>preference<br>s for expes|Net<br>expt<br>subsidiesa|Indirect<br>tax<br>exempios<br>for expos|Taiff<br>Rebates<br>for<br>exprs|Grss<br>expert<br>sbsidies|Net<br>expet<br>subsidies|Goss<br>export<br>_subsidies_|
||(1)|(2)|(3)|(4)|(5)|(6=2+3+4+5)|<br>(7)|(8)|(9=6+7+8)|(10=6/1)|(11=9/1)|
|1958<br>|<br>50.0<br> <br>|0.0<br>|64.0<br>|-|**1.2**<br>|65.2|-|-|652|130.4|130A|
|1959<br>|<br>50.0<br>|0.0<br>|84.7<br>|-|1.3<br>|86.0<br>|-|-|86.0|172.0|172.0|
|1960<br>|62.5<br>|0.0<br>|83.9<br>|-|1.2<br>|85.1|-|-|85.1|136.2|136.2|
|1961<br>|127.5<br>|7.5<br>|14.6|-|1.0|23.1|-|-|23.1|18.1|18.1|
|1962<br>|130.0<br>|10.3<br>|-<br>|0.6<br>|0.9<br>|11.8|_5.1_|4.7|21.6|9.1|16.6|
|1963<br>|130.0<br>|4.1<br>|39.8<br>|0.8<br>|2.9<br>|47.6|5.3|6.6|59.5|36.6|48.8|
|1964<br>|214.3<br>|2.9|39.7|0.7<br>|6.0|49.3|7.6|10.1|67.0|23.0|31.3|
|1965<br>|265.4|-|-|2.3|7.6|9.9|13.9|15.4|39.2|3.7|14.8|
|1966<br>|271.3<br>|-|-|2.3|10.3|12.5|17.8|21.3|51.6|4.6|* 19.0|
|1967<br>|270.7<br>|-|-|5.2<br>|14.7<br>|20.0|17.8|24.6|62.4|7A|23.1|
|1e68<br>|276.6<br>|-|-|3.0<br>|15.2|18.2|19.9|39.6|77.7|6.6|28.1<br>s|
|1969<br>|288.2<br>|-|-|3.7|14.7|18.4|27.4|34.3|80.1|6.4|27.8|
|1970<br>|310.7<br>|-|-|3.5<br>|17.3<br>|20.8<br>|27.0|40.4|38.1|6.7|28.4|
|1971<br>|347.7<br>|-|-|4.8<br>|18.1<br>|22.8<br>|32.2|48.0|103.0|6.6|29.6|
|1972<br>|391.8<br>|-|-|1.9<br>|10.5|12.5|26.4|66.3|105.2|3.2|26.9|
|1973<br>|398.3<br>|-|-|1.4|7.4|8.7|21.0|64.4|94.2|2.2|23.7|
|1974<br>|401.0<br>|-|-|-|8.6|8.6|22.5|55.1|86.3|2.1|2' 2|
|1975<br>|484.0<br>|-|-|-|12.9<br>|12.9<br>|33.8|34.3|81.0|2.7|16.7|
|1976<br>|484.0<br>|-|-|-|12.3|12.3|33.6|35.9|81.8|2.5|16.9|
|1977<br>|484.0|-|-|-|9A|9.4|53.1|30.6|93.1|1.9|19.2|
|1978<br>|484.0|-|-|-|11.0|11.0|53.6|30.0|94.6|2.3|19.5|
|1979<br>|484.0<br>|-|-||11.0<br>|11.0|56.6|30.3|97.9|2.3|20.2|
|1980<br>|618.5<br>|-|-|-|20.6<br>|20.6<br>|74.6|36.4|131.6|3.3|21.3|
|1981<br>|686.0<br>|-|-|-|15.0<br>|15.0<br>|n.a.|n.a.|na.|2.2|na.|
|1982<br>|737.7<br>|-|-|-|3.0<br>|3.0|na.|n.a|n.a.|0.4|na.|
|1983|781.2|-|-|-|0.0|0.0|n.a|n.a|n.a|0.0|na.|

n.a.: not available alotals may not add up due to rounding enws. Sourct: Kim (1988), Table 3.1
- 37 -

Table 10. Cost elasticity with respect to energy prices

||SUR|
|---|---|
|Pooled model||
|All 7 countries|0.158 ***|
||(0.007)|
|Separate models||
|Albania|0.111 ***|
||(0.027)|
|Bosnia & Herzegovina|0.123 ***|
||(0.016)|
|Bulgaria|0.148 ***|
||(0.018)|
|Croatia|0.126 ***|
||(0.014)|
|Macedonia|0.110 ***|
||(0.021)|
|Romania|0.118 ***|
||(0.011)|
|Serbia|0.084 ***|
||(0.012)|

*, ** and *** represent the 10%, 5% and 1% level significance, respectively.

Figure 17. Cost elasticity with respect to energy prices by SUR regressions <!-- Start of picture text --> 0.18<br>0.158<br>0.16 0.148<br>0.14 0.123 0.126 0.118<br>0.12 0.111 0.110<br>0.10<br>0.084<br>0.08<br>0.06<br>0.04<br>0.02<br>0.00<br>Cost elasticity with respect to energy price<br>All Albania Bulgaria Croatia Romania Serbia<br>Bosnia & Herzegovina Macedonia<br><!-- End of picture text -->

# **_Other characteristics of estimated cost functions_**

Besides price elasticities, there are at least two important characteristics of the estimated cost structure of SEE firms. From the industrial organization point of view, first, one interesting question is whether the cost function exhibits economies of scale in production.<sup>38</sup> When evaluated at the sample means, the output elasticities of costs in the pooled models shown in Table 4 are estimated at 0.956 with a standard error of 0.008 and 0.968 with a standard error 38 See, for example, Kolstad and Turnovsky (1998), Kleit and Terrell (2001), and Filippini and Wild (2001).
# Reference:

Abe, K., Study on Japanese Urban System, Tokyo : Chijin Shobo, 1991.

Abe, S., "Rationale of Tohoku Intelligent Cosmos Plan" Technology and Economy, 279, 1990: 27-35.

Abe, S., "Regional policies and the re-interpretation of local-regional culture and history" in N. Watanabe

- ed., The Recovery of Histories in Tohoku, Tokyo : Kawade Shobo, Shinsha, 1997.(in Japanese)

- Abe, S., "Regional Innovation Systems in Japan: the Case of Tohoku" Braczyk et al. eds., Regional Innovation Systems, London : UCR Press, 1998: 286-318.

Abe, S., "Tohoku Intelligent Cosmos Plan in the Middle Milepost" Intelligent Cosmos, 35, Spring, 2002.

Abe, S., "Regional Innovation Systems in the Less-favoured Region of Japan: the Case of Tohoku" P. Cooke et al. eds., Regional Innovation Systems, 2nd ed., London : Routledge, 2004: 261-290.

Acs, Z. J., Innovation and the Growth of Cities, Cheltenham, UK : E. Elgar, 2002.

Amano, I., Study on Higher Education in Modern Japan, Tokyo : Tamagawa University Press, 1989. Amano, I., Education and Modernization, Tokyo : Tamagawa University Press, 1997.

Ariizumi, S., "The Local and the Center in Political History of Modern Japan" Japanese Society for Historical Studies, Study of Japanese History, Vol.271, 1985: 139-158.

Arnold, E., K. Guy, Parallel Convergence, London : F. Pinter, 1986.

Asheim, B. T., M. S. Gertler, "The geography of innovation: regional innovation systems" in I. Fagerberg et al. eds., The Oxford Handbook of Innovation, Oxford : Oxford University Press, 2005.

Bartholonew, J. R., The Formation of Science in Japan: building a research tradition, New Haven : Yale University Press, 1989.

Bok, D., Universities in the Marketplace : the commercialization of higher education, Princeton : Princeton University Press, 2003.

Branscomb, L. M. et al. eds., Industrializing Knowledge : UILs in Japan and the United States, Cambridge, Mass. : MIT Press, 1999.

Bush, V., Science: the Endless Frontier, reprinted ed., New York : Aron Press, 1980.

Castells, M., P. Hall, Technopoles of the World, London : Routledge, 1994.

CCMH (Compilation Committee of Miyagi's History), History of Miyagi Prefecture: Vol.9 Industry, Miyagi Prefecture Press, 1968.

- CHCCS (City History Compilation Committee of Sendai) ed., History of Sendai: Vol. 1, Main Edition 1, Sendai City Office, 1954a.

- -- History of Sendai: Vol. 4: Separate Edition 2, Sendai City Office, 1954b.

- -- History of Sendai, Sequel 1: Administration and Construction, Sendai City Office, 1969.

- -- History of Sendai, Sequel 2: Economy and Culture, Sendai City Office, 1969.

- -- History of Sendai, Kobun-do, 1970.

Compton, K. T. "Mission to Tokyo", The Technology Review, Vol. 48, No. 2, Dec. 1945.

55
for work in person.<sup>11</sup> Unless everyone is sent home, everybody goes to work. The lack of a stronger coordination mechanism through official measures is a plausible explanation for both for the absence of much of an impact of formal measures, as well as limited self-motivated reductions in mobility.

# **Figures 2a-f: Workplace Mobility vs. Cases and Closure of Non-Essential Businesses**

<!-- Start of picture text --> Brazil Italy<br>-4 -2 0 2 4 6 -2 0 2 4 6 8<br>Log(case per 1 million people) Log(case per 1 million people)<br>No shutdown Shutdown No shutdown Shutdown<br>Japan South Africa<br>-2 0Log(case per 1 million people)2 4 6 -2 Log(case per 1 million people)0 2 4<br>No shutdown Shutdown No shutdown Shutdown<br>Sweden UK<br>-2 0 Log(case per 1 million people)2 4 6 8 -6 Log(case per 1 million people)-4 -2 0<br>No shutdown Shutdown No shutdown Shutdown<br>20 0<br>0 -20<br>-20<br>-40<br>-40<br>-60<br>-60<br>Workplace mobility (% deviation from baseline) -80 Workplace mobility (% deviation from baseline) -80<br>20 20<br>0<br>0<br>-20<br>-20<br>-40<br>-40<br>-60<br>-60 -80<br>Workplace mobility (% deviation from baseline) Workplace mobility (% deviation from baseline)<br>20 20<br>0 0<br>-20 -20<br>-40 -40<br>-60 -60<br>-80<br>-80<br>Workplace mobility (% deviation from baseline) Workplace mobility (% deviation from baseline)<br><!-- End of picture text --> Notes: Workplace mobility is Google measure of work-related mobility index. See Annex II for country-specific sources.

> 11 <u>https://www.nytimes.com/2020/04/19/world/asia/tokyo-japan-coronavirus.html?smid=em-share</u>

8
negative impact of the latter. The puzzle, however, is that the sign of the interaction runs counter to that suggested by theory.

Finally, but importantly, we find no spillover effects from the _KF_ - _METR_ in either the parent or non-host countries—an issue that, so far as we are aware, no previous study has addressed. And this is so on both the intensive and, perhaps more surprisingly, the extensive margin of FAI. Rankings of countries by _KF_ - _METR_ s have little real bearing on cross-border decisions on tangible foreign investments.

# **5.5 Comparing Tax Effects in FAI and FDI Data**

Before applying these results to the analysis of the global minimum tax, it remains to explain why the semi-elasticities emerging from the analysis here are noticeably higher than those in much of the previous literature. The natural suspicion is of course that this difference somehow arises from the use of data on FAI rather than, as in much of literature, on FDI. There are indeed hints from some previous work (albeit mainly on U.S. data) that effects are stronger when looking specifically at investment in plants, closer to the concept of real investment underlying FAI data, rather than at FDI.<sup>53</sup> As a first step towards establishing and explaining whether one should expect any systematic difference in apparent tax responsiveness emerging using FAI and FDI data, Table 11 reports results taking each in turn as dependent variables in an estimating equation of the form of equation (8). The sample period, dictated by the availability of bilateral FDI data, is 2003-2016, and so somewhat different from that of the baseline results of Table 2.<sup>54</sup> Columns (1) and (3) include the composite tax differential, columns (2) and (4) its distinct components.

For FAI, the semi-elasticity on the differential term in column (1) is much as in the baseline, and so too (though somewhat smaller) are the the coefficients on the host<sup>55</sup> and

> 53 Again, see De Mooij and Ederveen (2003) and Feld and Heckemeyer (2011), though note too that the evidence has not been overwhelming: the former finds (p.688) “...no strong evidence that studies using [Property, Plant and Equipment] data yield higher semi-elasticities than those using FDI.”

> 54In effect, we lose several years of observations for FAI parented in the United States (for which the data starts in 1997).

> 55The term ‘host’ is less apt in the FDI than in the FAI context, since there is no presumption that inward

34
**Table 6a: Consumption per capita growth with household credit**

|**Dep. Var:**|OLS|OLS|FE|FE|GMM|GMM|2SLS|2SLS|
|---|---|---|---|---|---|---|---|---|
|**growth_cons.pc**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|hhcredit|0.185**|0.1265|-0.0538|0.1483|0.192*|0.1317|0.1987|0.1400|
||(0.040)|(0.144)|(0.347)|(0.298)|(0.069)|(0.202)|(0.397)|(0.558)|
|hhcredit<sup>2</sup>|-0.002**|-0.0008||-0.0014|-0.002**|-0.0009|-0.0021|-0.0014|
||(0.026)|(0.206)||(0.152)|(0.029)|(0.289)|(0.348)|(0.544)|
|fdi|0.0835|0.1321**|0.0813|0.0963|0.0973|0.1997|0.1401|0.1189|
||(0.258)|(0.024)|(0.561)|(0.513)|(0.291)|(0.106)|(0.220)|(0.270)|
|enrolment|0.0156|0.0150|0.0404|0.0124|0.0125|0.0042|0.0426|0.0364|
||(0.652)|(0.651)|(0.394)|(0.803)|(0.719)|(0.927)|(0.302)|(0.372)|
|initialgdppc|-6.85***|-6.31***|-8.19**|-9.87***|-7.06***|-6.19***|-5.63***|-5.57***|
||(0.000)|(0.000)|(0.032)|(0.010)|(0.000)|(0.001)|(0.000)|(0.000)|
|financialcrisis||-9.7978***||||-12.2440***||-7.56**|
|||(0.000)||||(0.000)||(0.028)|
|Constant|25.31***|25.99***|33.62***|35.72***|26.71***|26.66***|21.09***|22.88***|
||(0.000)|(0.000)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|# of Obs.|174|174|174|174|174|174|127|127|
|R-squared|0.356|0.441|||||||
|Prob > F|0.000|0.000|0.001|0.001|0.000|0.000|0.000|0.000|
|# of countries|||38|38|38|38|||

*: in 2SLS, legal origin is used as instrument.

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

20