rafmacalaba/datause-encoder-data huggingface dataset:
17 <!-- Start of picture text --> x2 V-4(3)<br><!-- End of picture text -->

# where **_4=0wP_GLs'_** _and ow_ OG,S stand for within and GLS estimators, respectively,

> _V=cov(4)=cov(OO,)-cov(G,,,)_ and k, the number of parameters of interest, is the degree of

freedom. The acceptance of Ho means that a, is random. Before reporting the results, we point out that in many cases the models passed the F-test of whether "to pool or not to pool. " Although the panel data approach is designed to reduce the problem of multicollinearity, we also use principal components of explanatory variables to further attenuate this problem (multicollinearity is manifested in the form of large standard errors or wrong coefficient signs as seen in several tables in Annex 2).14 We regroup variables which are both highly correlatedi and which are believed to exert a similar qualitative influence on flows. A case in point is the first principal component of a country's credit rating and its secondary market price. We interpret this first principal component as an index of country-specific credit standing (PCS).<sup>15</sup> A first principal component of five US interest rates and the US industrial production variable is also constructed and is viewed as an index of global factors (PH).

Results. Our findings support the importance of both country-specific and global factors in explaining capital flows to emerging countries in Latin America and Asia. There is some variation in the results by region and by type of flow, however. In addition, there is also some variation in results across models arising from the use of nominal versus real interest rates, particularly for Latin America. The adjusted R1 in the different models are high for Latin America and low for Asia. The high level of the statistic in Latin America is mainly explained by the dummies which capture the privatization (Telmex) phenomenon for equity flows and the Brady Plan for bonl flows."<sup>6</sup> In fact, a lower **_R2_** is generally expected for these types of models. Table _5_ and 6 present the panel data estimates for bond and equity flows to all Latin American (9) and Asian (9) countries in our sample. Tables 7 and 8 present these same results for a slightly smaller groups (6 and 7 countrie, respectively) for which data on price-earnings ratios and rates of return on domestic stock markets were available .

**14** To detect multicollinearity, one should primarily look at the variance of residuals of the model, the R2, and the different **_R2,_ (12** from the regression of one explanatoLy variable on the other explanatory variables (Maddala 1988, p. 223-249)).

lS Although the two variables are not the only country-specific factors, in these models they seem to be the most important ones. Note that their index is not derived for Asia for lack of availability of secondary market prices data.

> 6 In fact, if we adjust for these dummies, the slope coefficients of the explanatory variables are not affected, but a much lower R<sup>2</sup> results (i.e., for bonds, it drops from .98 to 0.16).
WPS4249 With these elements in mind, a change in poverty due to a change in remittances (which we will denote by _R_ ) can be expressed as: or According to equation (3), the impact of a change in remittances, _R_ , on poverty will depend on: (i) the impact that _R_ has on growth; (ii) how growth is translated into poverty reduction; (iii) the simultaneous impact that _R_ has on inequality (as measured by the Gini coefficient), and finally (iv) how inequality changes are translated into poverty reduction.. Clearly, the usefulness of expression (3) for empirical work still requires knowledge about how remittances translate into income growth and changes in income inequality. We discuss this issue next.

# _(b) The impact of remittances on growth and inequality_

In order to estimate the links between remittances and growth in the data, our empirical strategy is based on the addition of a measure of remittances to an otherwise standard empirical growth regression: where _y_ is the log of per capita income, _r_ is a measure of remittances, _x_ represents a set of control variables other than lagged income, which we shall discuss shortly, ν _i_ is a countryspecific effect, and υ _it_ is an _i.i.d._ error term.

According to (4), growth depends on initial income, remittances and current and/or lagged values of the control variables. Our primary focus is to assess the nature and magnitude of the estimate of β in equation (4). If remittances have a positive impact on growth we should find β _>_ 0, whereas if they have no impact on growth we might find β _=_ 0.

One limitation of the previous specification is that it imposes the same slope β for all the countries in the sample. To both allow for some heterogeneity and test whether the basic results would hold for a Latin American sub-sample the empirical section considers the following variation of (4): 6
Table 2: Variable names, **means** and standard deviations Philippine Grade 5 science, 1983 for totalA/ data set and analytic sample

||_N_-|4129/k.|_N_|<br>372|
|---|---|---|---|---|
|Name|Mean|S.D.|Mean|S.D.|
|Family Background (class|room average)||||
|MAGE|142.17|4.38|142.05|4.12|
|MFAMSIZE*|.44|.14|.44|.14|
|WEDUCAO*|.05|.09|.04|.07|
|WEDUCA1*|.47|.19|.47|.19|
|WEDUCA2*|.48|.20|.49|.20|
|FOCCR1*|.43|.26|-|-|
|FOCCR2*|.29|.18|-|-|
|FOCCR3*|.24|.16|.24|.16|
|FOCCR4*|.05|.06|.05|.07|
|MHOMEBOO|2.13|.48|2.13|.49|
|MHOME1*|.67|.40|-|-|
|MHOMEP*|.32|.40|.31|.40|
|MHOMEE*|.01|.04|.01|.04|
|School|||||
|URSUB1*|.08|.27|.08|.27|
|STUTOT10|1152.91|883.66|1181.30|911.22|
|RATIOST|33.63|14.96|33.65|15.50|
|CLSSTP|.08|.28|.08|.27|
|Teacher and classroom|||||
|TCHEXP1|16.91|8.44|16.94|8.52|
|TPOSTS34*|.15|.36|.16|.37|
|NTOT1M|36.15|7.27|36.13|7.20|
|Teacher practices|||||
|PRACWRK2|.57|.49|.57|.49|
|TCHLAB2|.42|.49|.42|.49|
|DTXT*|.32|.47|.31|.46|
|DTCHTST*|.29|.46|.30|.46|
|DGRPS*|.10|.30|.10|.30|
|Student achievement|||||
|TOTIMM|9.52|3.46|9.49|3.47|
|TOT1QM.|10.21|2.61|10.17|2.57|

*These variables are coded 0 or 1. Their mean can be interpreted as a mean % for that variable. For example, for Mfamsize, the mean of .44 can be interpreted as as meaning that 44% of students from each class come from families with 5 children or more.

A/ All non-private, complete primary (Grades 1-6) a,.d complete secondary (Grades 21-10).

k/ Sample size for each variable ranged from 396 to 419.
To conduct the analysis, we study corporate bond and syndicated loan markets, which have become key sources of new external finance for corporations in both developing and developed countries. These markets have rapidly expanded over the last decades, achieving a hefty size.<sup>2</sup> For instance, the annual amount of debt financing raised in these markets increased more than 6-fold from 1991 to 2014, when it reached $5.1 trillion or about 7 percent of world gross domestic product (GDP). We work with transaction-level data on publicly and privately placed corporate bonds and syndicated loans issued in both domestic and international markets during 1991-2014. Issuers of bonds and syndicated loans encompass both listed and unlisted firms domiciled in developed and developing countries. The data set includes 266,539 individual debt issuances conducted by 57,513 firms from 82 countries (39 developed and 43 developing).

Our analysis shows that, on aggregate, firms from developing countries raise capital through corporate bonds and syndicated loans at maturities slightly longer than those issued by firms from developed countries. In particular, the (value) weighted average maturity of the debt issued by firms from developing and developed countries is 7.3 and 6.2 years, respectively. In fact, the average maturity of debt at issuance is, if anything, shorter in countries with higher GDP per capita and higher private credit-to-GDP ratios. The differences in debt maturity at issuance between developed and developing countries become statistically insignificant once we control for industry-year effects. Developing countries with larger debt markets do not drive these patterns. In sum, contrary to what one might expect, firms from developing countries do not borrow shorter term in these markets.

The pattern in aggregate debt maturity across firms from developing and developed countries masks substantial differences in bond and syndicated loan markets, domestic and foreign markets, and

> 2 The development of regulated secondary loan markets and independently rated loan issuances have made syndicated loan and corporate bond markets converge as two different, but complementary sources of financing for firms (Altunbas et al., 2010). Some studies estimate that syndicated loans account for roughly one-third of the total outstanding loans, and their relative importance has increased over time. Moreover, syndicated loans also tend to be larger than other types of loans (Godlewski and Weill, 2008; Ivashina and Scharfstein, 2010; Huang, 2010; Cerutti et al., 2015).

2
# FIGURE 3. CUMULATIVE MORTALITY RATES BY MEASLES VACCINATION STATUS **AND BY** SEX: MATLAB INTERVENTION AREA, 1982-85

<!-- Start of picture text --> 100- A-A Female unvaccinated<br>A--A Male unvaccinated<br>90- a-* Femaie vaccinated A<br>80- o--o Male vaccinated A- o<br>o Av<br>°O 70- ^<br>0<br>'a 60<br>50Q A/ i<br>-<br>40 a-<br>2 ~~AZ<br>.. 30-<br>o AK . .- O-O-O-..<br>20 A-"'<br>10 <A- 0.-<br>0 3 6 9 12 15 IC 21 24 27 30 33 36 39 42<br>Months after vaccination<br><!-- End of picture text -->
needs time to deliver social and economic outcomes. Second, bidders are not always rationale risk takers, and often engage in aggressive bidding strategies which finally deter the economic and financial viability of the projects bid out. The combination of all these factors weakens the political or social support to bidding outcomes. What should governments do to minimize these perceptions and anticipation gaps? Tenders are simple market based mechanisms which can be used to implement development programs in infrastructure sectors. As mechanisms to allocate resources, tenders should reflect social and political concerns in order to minimize these gaps. As discussed in Box 5, there is little that could have been done by the bidding process to address the social unrest generated by a reform program designed without sound consultations with all the stakeholders. Bearing this in mind, governments should not only emphasize transparency in the bidding process, but should also emphasize transparency and participatory process in designing and implementing infrastructure reforms<sup>8</sup> . An effective consultation process would have improved the social dimensions of the concession agreement and would have preserved the Cochambaba water concession from collapsing.

**Box 5: Social unrest and the Cochabamba water concession**

<mark>In 1999, the government of Colombia awarded a 40-year water concession to a consortium led by International Water from UK which was unfortunately the only bidder for the concession. To grant this concession to the private sector, a new law (law 2029 – the drinking water and sanitation law) was enacted. The law not only facilitated private sector participation, but it also ended subsidies to the water sector. Subsidies were in practice eliminated in January 2000 leading to a substantial rate increase of water. Opposition to the concession was formalized in January 2000 when a group of Cochabamba citizens formed a coalition and organized massive strikes. The coalition published a declaration calling for the reduction of water prices, the repeal of the water act, a ban on privatization and the repeal of the Cochabamba concession. The opposition to the reform grew stronger and stronger and ultimately led to the repeal of the</mark> water act in April 2000, as well as the withdrawal of International Water from the concession. _<mark>Source: World Bank (2003)</mark>_

# _4. Effective regulation ex-post_

The main purpose of regulation after the project has been awarded is to ensure and preserve the “ _sanctity of the bids_ ”. Regulation is an instrument or a mechanism used to hold _contracting parties accountable for their respective commitments_ . For instance, the project developer should be held accountable of its bid, whereas the government should

> 8 Governments can also consider stakeholders’ participation in the bidding and decision making in order to mitigate these social risks. Of course, this entails the implementation of safeguard measures needed to preserve the confidentiality and integrality of the bidding process.

11
# **5. Methodology**

## **5.1 Geospatial Analysis**

In this analysis, our land-use / land cover (LULC) classification problem was to identify six landuse/ land cover (LULC) types: forests, settlement/developed, cultivated/agricultural areas, cultured fisheries ponds, bare ground, and surface water for all selected years, 2010, 2014, 2017, and 2020. The geospatial analysis was carried out using the ArcGIS Pro software (Version 2.4.0, 2019) for image classification and map visualization. We used a pixel-based supervised classification method for image processing.<sup>10</sup> To execute the classification, three different algorithms, namely random forest (RF), maximum likelihood estimation (MLE), and support vector machine (SVM), were tested and compared to consider their relative accuracy. In this analysis, the random forest (RF)<sup>11</sup> produces a relatively better result than MLE and SVM. Using the image values of land cover maps created using the RF algorithm, we calculated the aggregate change for various LULC types between the periods 2010, 2014, 2017, and 2020 (Table **_2_** ).

## **5.2 Econometric Analysis**

Our econometric analysis uses a linear probability model with a difference-in-differences framework to capture changes in land cover trends in response to camp openings. We focus on pixels 0-5 km from the nearest camp because we expect these pixels to be the most exposed to refugee activity as well as any camp-related changes in economic activity. As discussed in the previous section, we assign pixels 10-15 km from the nearest camp to the control group. We estimate the following regression:

> 10 A schematic model of the step-by-step GIS-based analysis is shown in Appendix A1.3 (contact author). 11 The RF algorithm, a nonparametric machine learning classifier, has widespread application in remote sensing because of its high classification accuracy, insensitivity to noise, ability to handle overtraining effects (Breiman, 1999; Pal, 2005; Chan and Paelinckx, 2008; Ghimire, Rogan and Miller, 2010), capability to determine variable importance (Rodriguez-Galiano et al., 2012), and because it requires fewer user-defined parameters compared to SVM (Pal, 2005).

18
<!-- Start of picture text --> 50,000 Trials Frequency View<br>0.06. 3,000<br>2,700<br>005<br>| 2,400<br>00s0.04 2,100 n<br>3 5<br>a8 003; - 1,50028<br>1200<br>002<br>900<br>600<br>001<br>300<br>0.00 0<br>0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500<br>TB Flat incomeTax [i ProgessiveincomeTax] 8 8 8 ss |<br><!-- End of picture text -->
# **Figure 23: Labor Market Indicators**

<!-- Start of picture text --> A. Employment and Wage Groups (%), 1991 to 2004<br><!-- End of picture text --> B. Educational Attainment of the Labor Force by Age <u>(1=None; 7=University)</u> <!-- Start of picture text --> Employment and Wage Groups (%)<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>25 35 45 55 59<br>0 Years of Age<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br>Monthly Wage Earners as % of Wage Earners Monthly Wage Earners as % of Workers Feb 1991 Feb 1996<br>Wage Earners as % of Workers Employment as % of 15 to 59 Year-Old Feb 2001 Feb 2004<br>Unemployment as % of 15 to 59 Year-Old<br>C. Educational Attainment of the Labor Force (%) D. Education Attainment of Wage Workers (%),<br>1991 to 2004<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>None Less than Primary Primary Lower Secondary None Less than Primary Primary Lower Secondary<br>Upper Secondary Vocational University Upper Secondary Vocational University<br>E. Education Attainment of Monthly Wage Workers F. Educational Attainment of the Labor Force in<br>(%), 1991 to 2004 1991 to 2004 991 to 2004 1 to 2004 to 2004 2004 004 4 East Asia, End 1980s and End 1990s<br>F91A91F92A92F93A93F94A94F95A95F96A96F97A97F98A98F99A99F00A00F01A01F02A02F03A03F04A04<br>4.5<br>4<br>3.5<br>3<br>2.5<br>2<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>None Less than Primary Primary Lower Secondary<br>Upper Secondary Vocational University<br>E. Education Attainment of Monthly Wage Workers<br>(%), 1991 to 2004 1991 to 2004 991 to 2004 1 to 2004 to 2004 2004 004 4<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>None Less than Primary Primary Lower Secondary<br>Upper Secondary Vocational University<br>F91A91F92A92F93A93F94A94F95A95F96A96F97A97F98A98F99A99F00A00F01A01F02A02F03A03F04A04<br>F91 A91 F92 A92 F93 A93 F94 A94 F95 A95 F96 A96 F97 A97 F98 A98 F99 A99 F00 A00 F01 A01 F02 A02 F03 A03 F04 A04<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Indonesia1988 Indonesia1998 Malaysia1988 Malaysia1999 Korea1990 Korea1999 Thailand 1991 Thailand1999 Thailand2004<br>Primary or less Secondary Tertiary<br><!-- End of picture text --> 56
**Appendix C – Regressions on Paper Prices**

||**Column 1**|**Column 2**|**Column 3**|**Column 4**|
|---|---|---|---|---|
||||**Printing**|**Household**|
||**Aggregate**<br>**Paper**|**Newsprint**|<br>**and**<br>**Writing**|**and**<br>**Sanitary**|
|Internet|||||
|Pentration (per<br>100 people)|-0.000535|0.00107|0.00104|-0.00119|
||(0.0022)|(0.0015)|(0.0013)|(0.0070)|
|GDP per capita (in<br>logarithm)|0.373|0.593***|0.17|0.695|
||(0.2630)|(0.1840)|(0.1550)|(0.5860)|
|Percentage Post|||||
|High School<br>Students (over<br>whole population)|-2.46|1.628|0.607|-12.47|
||(5.3270)|(3.7360)|(3.0840)|(14.2500)|
|Workers in|||||
|Primary Sector<br>(percentage)|-3.077***|1.117|0.759|-6.299|
||(0.9880)|(0.7040)|(0.6000)|(5.1130)|
|Workers in|||||
|Manufacturing<br>Sector|-4.095***|1.598|-0.133|0.634|
|(percentage)|||||
||(1.5400)|(1.0850)|(0.9200)|(6.4500)|
|Workers in the|||||
|Services<br>(percentage)|-3.602***|-0.532|0.00426|-5.006|
||(0.6160)|(0.3960)|(0.3370)|(4.9120)|
|Constant|6.153**|0.46|4.996***|4.205|
||(2.4510)|(1.7110)|(1.4410)|(8.6500)|
|Observations|383|395|383|89|
|R-squared|0.57|0.572|0.729|0.533|

30
- 19 -

**<u>Table 3. 3SLS Estimation Result</u>**

|Dep. Var.|ln_INV_|ln_SAVE_||ln_DUR_||ln_PAY_|
|---|---|---|---|---|---|---|
||Coef. Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef. Std.Err.|
|_dTAGT_|0.588 (0.236) **|0.237|(0.197)|0.058|(0.088)|0.495 (0.238) **|
|_dRISKMT_|0.917 (0.331) ***|-0.013|(0.276)|-0.169|(0.123)|1.743 (0.334) ***|
|_dRESV_|0.085 (0.258)|0.394|(0.216) *|-0.005|(0.096)|0.056 (0.261)|
|_dWALK_|1.187 (0.898)|1.928|(0.750) ***|-1.576|(0.334) ***|<br>0.988 (0.908)|
|_RISKTYPO_|0.002 (0.555)|0.327|(0.464)|-0.679|(0.207) ***|<br>-0.028 (0.561)|
|_RISKREG_|0.497 (0.215) **|0.786|(0.180) ***|-0.030|(0.080)|0.299 (0.217)|
|_RISKINF_|-0.004 (0.369)|-0.366|(0.308)|-0.420|(0.137) ***|<br>0.445 (0.373)|
|_wINV_|0.003 (0.025)|-0.093|(0.021) ***|-0.019|(0.009) **|0.021 (0.025)|
|_wSAVE_|0.045 (0.023) *|0.018|(0.019)|0.027|(0.009) ***|<br>0.053 (0.024) **|
|_wDUR_|0.041 (0.033)|-0.016|(0.027)|-0.033|(0.012) ***|<br>0.084 (0.033) **|
|_wPAY_|-0.101 (0.058) *|-0.156|(0.048) ***|0.076|(0.022) ***|<br>-0.240 (0.058) ***|
|ln_N_|0.322 (0.216)|0.569|(0.181) ***|0.266|(0.080) ***|<br>0.376 (0.219) *|
|_dSSC_|-0.236 (0.337)|-0.223|(0.281)|0.790|(0.125) ***|<br>0.242 (0.340)|
|_dPCKG_|-0.190 (0.305)|-0.517|(0.255) **|-0.088|(0.113)|-0.375 (0.308)|
|ln_KWH_|0.529 (0.179) ***|0.642|(0.150) ***|0.224|(0.067) ***|<br>0.222 (0.181)|
|ln_AGE_|1.287 (0.456) ***|-0.381|(0.381)|-0.190|(0.170)|1.749 (0.461) ***|
|ln_AREA_|0.120 (0.234)|-0.075|(0.195)|-0.354|(0.087) ***|<br>0.486 (0.236) **|
|_dOFFC_|-0.454 (0.289)|0.306|(0.242)|-0.011|(0.108)|-0.527 (0.292) *|
|_dHOSP_|-0.318 (0.321)|0.499|(0.268) *|-0.082|(0.119)|-0.159 (0.324)|
|_dSCHL_|0.261 (0.208)|0.605|(0.174) ***|-0.042|(0.077)|0.164 (0.210)|
|_dLIT_|0.932 (0.300) ***|0.516|(0.251) ***|-0.004|(0.112)|1.010 (0.304) ***|
|_dAC_|-0.816 (0.625)|-0.854|(0.522) *|-0.111|(0.232)|-1.318 (0.632) **|
|_dHOTW_|0.244 (0.192)|0.164|(0.160)|-0.027|(0.071)|0.166 (0.194)|
|_dCGS_|0.430 (0.187) **|0.693|(0.157) ***|0.055|(0.070)|0.529 (0.189) ***|
|ln_AWAD_|0.059 (0.037)|0.026|(0.031)|-0.075|(0.014) ***|<br>0.095 (0.038) **|
|_dLEAS_|0.067 (0.337)|0.462|(0.282) *|-0.186|(0.125)|-0.013 (0.341)|
|_dMANU_|0.189 (0.186)|-0.040|(0.156)|-0.185|(0.069) ***|<br>0.300 (0.188) *|
|_dENEG_|0.338 (0.294)|0.840|(0.246) ***|-0.098|(0.109)|0.322 (0.297)|
|constant|-5.485(2.750)**|6.689|(3.376)**|6.559|(1.502)***|<br>-2.978(4.084)|
|Obs|68||||||
|R-square|0.873|0.927||0.889||0.900|
|No. of dummy variables|||||||
|Bidder origin prefecture|<br>11|11||11||11|
|Contractyear|8|8||8||8|

Note: A system of four equations is estimated by the three stage least squares. The number of bidders is treated as endogenous. Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively.
**Figure 6: Broad Regional Demographic Trends in Russia, 1996-2011**

<!-- Start of picture text --> 1996 1999 2002 2005 2008 2011 1996 1999 2002 2005 2008 2011<br>Year Year<br>Rich 1996 Poor 1996 Rich 1996 Poor 1996<br>100 106<br>98 104<br>96 102<br>94 100<br>Population (million), 1996=100<br>92 Working-Age Population (million), 1996=100 98<br><!-- End of picture text -->

**Figure 7: Dependency ratios fell faster in poorer regions compared to richer regions, 1996-2011**

**A. Total dependency ratio B. Old-age dependency ratio**

<!-- Start of picture text --> 1996 1999 2002 2005 2008 2011 1996 1999 2002 2005 2008 2011<br>Year Year<br>Rich 1996 Poor 1996 Rich 1996 Poor 1996<br>100 115<br>95 110<br>90 105<br>85 100<br>Dependency Ratio, 1996=100 80 Dependency Ratio, 1996=100 95<br>75 90<br><!-- End of picture text --> Looking at projected trends, the dependency ratio is expected to be larger in the poorer regions in the future. This bivariate analysis suggests that faster growth in poor regions in the past might be in part related to the more favorable demographic trends. The flipside of this is that the worse demographic trends in poor regions in the future could dampen economic convergence.

# **Section II: Econometric estimation**

# _Theoretical model_

Following Aiyar and Mody (2011), we use a conditional β-convergence equation to study the effect of demographic trends on regional GDP growth per worker<sup>8</sup> in logarithmic form:

> 8 Here, regional GDP per worker refers to regional GDP per capita of economically active population – employed and unemployed. We considered looking at GDP per employed worker only, but given that conventional definition is per capita of economically active population, we decided to use the former in our analysis. We also checked our results using GDP per employed worker and do not find much of a difference.

7