rafmacalaba/datause-encoder-data huggingface dataset:
- Rosenthal, Stuart, and William Strange, “Evidence on the Nature and Sources of Agglomeration Economies”, in Vernon Henderson and Jacques François Thisse (eds.) Handbook of Regional and Urban Economics, Volume 4(Amsterdam: North-Holland, 2004), 2119-2171.

- Rosenthal, Stuart, and William Strange, “Female Entrepreneurship, Agglomeration, and a New Spatial Mismatch”, Review of Economics and Statistics, forthcoming (2011).

- Rosenthal, Stuart, and William Strange, “Small Establishments/Big Effects: Agglomeration, Industrial Organization and Entrepreneurship”, in Glaeser, Edward (ed.) Agglomeration Economics (Chicago, IL: University of Chicago Press, 2010).

- Rosenthal, Stuart, and William Strange, “The Determinants of Agglomeration”, Journal of Urban Economics 50 (2001), 191-229.

- Rotemberg, Julio, and Garth Saloner, “Competition and Human Capital Accumulation: A Theory of Interregional Specialization and Trade”, Regional Science and Urban Economics 30 (2000), 373404.

- Schoar, Antoinette, “The Divide Between Subsistence and Transformational Entrepreneurship”, in Lerner, Josh, and Scott Stern (eds.) Innovation Policy and the Economy, Number 10 (Chicago, IL: University of Chicago Press, 2009), 57-71.

- Verheul, Ingrid, André van Stel, and Roy Thurik, “Explaining Female and Male Entrepreneurship at the Country Level”, Entrepreneurship and Regional Development 18 (2006), 151-183.

World Bank, Gender Equality and Development, World Development Report (2012).

World Bank, Poverty and Social Exclusion in India (Oxford: Oxford University Press, 2011).

World Bank, Reducing Gender Based Differences in Formality and Productivity (2008).

- Zucker, Lynne, Michael Darby, and Marilynn Brewer, “Intellectual Human Capital and the Birth of U.S. Biotechnology Enterprises”, American Economic Review 88 (1998), 290-306.

27
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 21 Ahrend, R., Farchy, E., Kaplanis, I., & Lembcke, A. C. (2014). What makes cities more productive? Evidence on the role of urban governance from five OECD countries. OECD Regional Development Working Papers, 2014(5), 0_1.

Barlow, I. M. (1991). Metropolitan Government. London; New York: Routledge.

- Blair, J. P., Staley, S. R., & Zhang, Z. (1996). The central city elasticity hypothesis: A critical appraisal of Rusk's theory of urban development. Journal of the American Planning Association, 62(3), 345-353.

- Bollens, S. A. (1986). A political-ecological analysis of income inequality in the metropolitan area. Urban Affairs Quarterly, 22(2), 221-241.

- Branson, J., Campbell-Sutton, A., Hornby, G. M., Hornby, D. D., & Hill, C. (2016). A geospatial database for Latin America and the Caribbean (draft version 1). Geodata, University of Southampton.

- Cadena, A., Remes, J., Manyika, J., Dobbs, R., Roxburgh, C., Elstrodt, H. P., ... & Restrepo, A. (2011). Building globally competitive cities: The key to Latin American growth. McKinsey Global Institute.

- Cao, Z., Wu, Z., Kuang, Y., Huang, N., & Wang, M. (2016). Coupling an intercalibration of radiancecalibrated nighttime light images and land use/cover data for modeling and analyzing the distribution of GDP in Guangdong, China. Sustainability, 8(2), 108.

Carroll, M. L., Townshend, J. R., DiMiceli, C. M., Noojipady, P., & Sohlberg, R. A. (2009a). A new global raster water mask at 250 m resolution. International Journal of Digital Earth, 2(4), 291308.
20 percent is attributable to s(j,t) + m(t). The individual coefficients reported in table 8 enable us to gain further insight over the role of this variable.

**Table 8. Size coefficients and significance levels in private and**

||**cooperative enterprises**||
|---|---|---|
|Size|Beta<br>t|P>t|
|21-50|.016<br>.299|.765|
|51-300|.152<br>2.794|.006|
|>300|.089<br>1.814|.071|

Source: Authors' calculations Given the regression implementation technique, these coefficients represent, other things being equal, the differential contribution of each category to productivity growth compared with that of the group size 6-20. Only the last two coefficients are statistically significant. In medium enterprises (51-300 employees), productivity growth has been clearly higher than in small ones (6-20). This is true for large enterprises (over 300 employees) as well. We interpret this result as suggestive of increasing returns. However, the coefficients also reveal a relatively worse performance of large enterprises compared to medium ones. We can therefore argue that the private Polish industrial system still suffers from the presence of inefficiently oversized large firns.

The regression results of equation (3) for public enterprises, reported in Table 9, are quite different.19 The interaction of industry and time effects s(i,t) is still highly significant, and the overall fit of the model is quite similar to that obtained with private sector data. However, size has no significant explanatory power (also time effects are not significant, but this is less interesting for our purposes). 20

**'9** Results are heteroskedasticity consistent, thus we report 32 statistics. Other test results: (1) Ramsey RESET test g2(3)=.42 p-value=.90, the model has no omitted variable bias; (2) Durbin-Watson statistic= 2.28. 20 The ANOVA analysis shows that its contribution to the explained variance of the model is 12.5 percent.
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<!-- Start of picture text --> Participation change vs exposure<br>14<br>IRL<br>12<br>10<br>8 CYP<br>SVN<br>LTU BGR<br>6<br>4<br>DEU<br>ESP<br>FRA<br>RUS<br>AUS<br>2 BRA<br>CHNUSAJPN<br>0<br>-0.5 0 0.5 1 1.5 2<br> AAE exposure to shock<br> Participation change, in %<br><!-- End of picture text -->

**Figure 6:** Scatter plot of the percentage change in home production participation between 2019 and 2022 against the AAE exposure measure across countries. For country abbreviation codes see appendix B.1, for the definition of AAE exposure see the text.

higher labor shares in more open sectors are more exposed to a change in trade costs.

Figure 6 gives a scatter plot between the AAE exposure measure on the _x_ -axis and the change in participation in home production between 2019 and 2022 on the _y_ -axis. The plot reveals a tight relationship between the exposure measure and the change in participation. Although the AAE exposure measure is a first-order approximation and it does not allow for differences in labor shares or input-output coefficients, it provides a reasonably good fit. A regression of participation change on the AAE exposure plus a constant has an R- squared of 72%, and the exposure measure has a p-value substantially below 1%.

The reduction in welfare due to the increase in trade costs is almost perfectly correlated 20
.. - 20 -

# dhviour of Fun

to enoyment chages but tdere if Oer aynfzty with rgd to the si of shock to oupu. For the outmos obsevato when outtt losses ranged betwen 35-50%, employmnt contraction avead no more tha 15%.

Tabe 7 ad Figures 11-14 pat togedher the diection of output, gro profit and employnt chqns for the survey fire. It is clar that the gap btween ouW ad emuployment chan was quite large. The scauers aga classifv m tums of mrku power and ae mainly remadrable fo; showing no pdiable relationip beten ouptp or profits and eloyment cbges for ither types of fim. The clear cowcusion thu can be drwn. bearig in mind the limitatons of the ow quare recall period , is that employment adjus_new have been suggh, _uneven_ ad resticd gien the sine of c_ages to ouqau At fist approxination, we may assume that labour prodctivity has decined. Ibe survey rults renforce the concsion gathered from more aggreate data sours regarding the caoning high aties of ovaer, very low lvels of involhny separtions, significant hiin **and** a gen_aly low levd of net job loes in Russia though 1992. Hove, sevra fctors repqay more atenin Fst, the high level of quits ad hir for workas - i both cases dte prq nom we Inict above the shre of worrm in the fin' labour force. Second, thee is the d_mn of prodtonworkers, **_rather_** n **_unskl_** workers, in thse quits. It se likely at this process has been prmotd by emgig competitio for workers and by the pesite of sperp shorages for skldled or producom worken. Th recet _liberalizion_ of the wage settig and wag stuctu - while _qu videnly_ highly ncomple (see Seai _5)_ - pes liely to have promotd local job tuwver as production woes chase relative wage adju us. Tis obviously begs the quesin of why labor demnd for such workers remains so rlatvely buoyan Te nswer seems aily to found in fixed fators or techology. What we know about work orgaimut i Soviet industrial plants aso enma the strong and somewhat mechanical asociaton of plant to labour.

Involuntary labour sheddig ha consequently been concenrtd on nowprodction workers and, in parcular, women. We know frm the unemployment daa that women comprse over 705 of the unemployed in the seond half of _1992._ We also bnow that tbis share has not been vasdy shiftd by dte growing weight of layoffs in total inflows to unemployment. While inflows to unew oy _t hve_ Iap y dominated by releases frm the admisative sector, it also seems to be the case that pducte sector fim have shed a ive, unskilled and fe bour first. The clear impict is that producion workers have remained largely untouched by unemloyment and by the process of 'These _lim_ ons inlude possble seasonal disturn and out-of-period dnges.
# **Table 4. Juhn, Murphy and Pierce (1993) decomposition**

|||**T**<br>|**Q**<br>|**P**<br>|**U**|
|---|---|---|---|---|---|
|**All**|mean|0.1013|-0.0156|0.1129|0.0040|
|**Teenagers**|mean|0.0559|-0.4840|0.5368|0.0031|
|**Young adults**|mean|0.1125|-0.1174|0.2243|0.0056|
|**Young old**|mean|0.1059|0.0188|0.0838|0.0033|

_Note: T = Total difference (All_extm-All_extw); Q = Contribution of differences in observable quantities; P = Contribution of differences in observable prices; U = Contribution of differences in unobservable quantities and prices._ _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._

**Table 5. Quantity effects on the gender pay** **<u>gap based on JMP (1993)</u>**

|**Group of variables**|**All**|**Teenagers**|**Young**<br>**adults**|**Young**<br>**old**|
|---|---|---|---|---|
||mean|Mean|Mean|mean|
|**Working hours**|0.003177|-0.00072|0.002501|0.010033|
|**Education**|-0.00081|0.004885|0.000876|-0.0031|
|**Work experience**|0.046255|-0.21316|-0.00104|0.036798|
|**Work and study**|-0.00083|-0.01337|0.000129|-0.00142|
|**Self-employed**|0.038368|0.09482|0.025849|0.016266|
|**Migration**|-0.00797|0.000867|-0.01404|-0.01261|
|**Search**|-0.01934|-0.02834|-0.03817|-0.01025|
|**Contract**|-0.03571|0.011321|-0.03382|-0.04297|
|**Tax**|-0.01459|-0.05151|-0.00211|0.003799|
|**Moonlight**|0.008394|0.007887|0.012853|0.005201|
|**Job dissatisfaction**|-0.00881|-0.01152|-0.00864|-0.00846|
|**Training**|0.002281|-0.06683|-0.01398|-0.00084|
|**Union**|0.001759|-0.00858|0.001654|0.005738|
|**Firm’s size**|-0.03722|-0.14764|-0.0644|-0.01389|
|**Goal**|0.006756|-0.05809|0.015847|0.028457|
|**Rural area**|0.002582|-2.8E-05|-0.00083|0.006074|

_Note: decomposition of the Q effects in Table 3. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 29
|Firm has own website|0.324|0.201|0.025|
|---|---|---|---|
|Y:1 N:0|(0.564)|(0.551)|(0.543)|
|Fertility rate||0.024|33.785***|
|||(0.960)|(12.352)|
|Gender parity in<br>||0.067***<br>|0.195<br>|
|workplace laws<br>||(0.022)<br>|(0.190)<br>|
|Gender parity in<br>||10.586<br>|-115.999<br>|
|primary education||(15.893)|(191.349)|
|enrollment<br>||||
|Freedom from<br>||0.424<br>|0.494<br>|
|business regulations||(0.543)|(0.548)|
|Urbanization rate (%)||-0.006|-0.011|
|||(0.040)|(0.040)|
|Women in Parliament (%)||0.103|0.092|
|||(0.064)|(0.063)|
|Paternity leave (logs)||-0.098|0.278|
|||(0.472)|(0.469)|
|Square of Maternity leave<br>|||-0.069|
|(logs)||||
||||(0.588)|
|Maternity leave|||-6.928***|
|(logs)*GDP per capita||||
|(logs)||||
||||(2.606)|
|Maternity leave|||0.661**|
|(logs)*Number of<br>workers (logs)||||
||||(0.288)|
|Maternity leave|||0.156|
|(logs)*Female owner<br>present Y:1 N:0||||
||||(0.589)|
|Maternity leave|||-7.322***|
|(logs)*Fertility rate||||
||||(2.659)|
|Maternity leave|||-0.021|
|(logs)*Gender parity in<br>workplace laws||||
||||(0.043)|
|Maternity leave|||29.146|
|(logs)*Gender parity in||||
|primary education<br>||||
|enrollment||||
||||(41.262)|

70
PPR 'Aorking Paper Series

|Iiwa|Atnhor||Contact<br>forpaper|
|---|---|---|---|
|WPS228 Economic Performance of<br>Developing Countries in the 1980s|Robert Lynn<br>F. Desmond McCarthy|||
|WPS229 The Effect of Demographic Changes<br>for Life-Cycle Motives in Developing|on Saving<br>|||
|Countries|Steven B. Webb<br>Heidi Zia|July 1989|E. Khine<br>61765|
|WPS230 Unemployment, Migratior. and Wage<br>in Turkey, 1962-1985|s<br>Bent Hansen|July 1989|C. Bermudez<br>39248|
|WPS231 The World Bank Revised Minimum<br>Standard Model|Doug Addison|May 1989|J. Onwuemene-<br>Kocha<br>61750|
|WPS232 Women and Food Security in Kenya|Nadine R. Horenstein|June 1989|M. Villar<br>33752|
|WPS233 Public Enterprise Reform in Adjustm|ent|||
|Lending|John Nellis|August 1989|R. Malcolm<br>61708|
|WPS234 A Consistency Framework<br>Macroeconomic Analysis|William Easterly|June 1989|R. Luz<br>61760|
|WPS235 Borrowing, Resource Transfers<br>and Extemal Shocks to Developing<br>Countries: Historical and||||
|Counterfactual|Steven Webb<br>Heidi Zia|July 1989|E. Khine<br>61765|
|WPS236 Education and Earnings in Peru's<br>Informal Nonfarm Family Enterprises|Peter Moock<br>Philip Musgrove<br>Morton Stelcner|Ju!y 1989|M. Fisher<br>34819|
|WPS237 The Curricular Content of Primary<br>Education in Develcping Countries|Aaron Benavot<br>David Kamens|June 1989|C. Cristobal<br>33640|
|WPS238 The Distributional Consequences of a<br>Tax Reform on a VAT for Pakistan|<br>Ehtisham Ahmad<br>Stephen Ludlow|||
|WPS239 The Choice Between Unilateral and M<br>lateral Trade Liberalization Strategie|ulti-<br>s Julio Nogues|July 1989|S. Torrijos<br>33709|
# **6 Conclusion**

There is no definitive method to accurately measure the polarity effects that a country exerts on the other countries in the world economy. This paper has sought to provide a range of empirically-grounded measures that capture the spillover effects that emanate from growth poles, while accounting for the effect of the growth rate of the country in question relative to the global economy. While the identified poles and potential poles are consistent with the economies that have dominated the discussion in the literature (Buiter & Rahbari 2011; OECD 2010; O’Neill 2001; Wilson & Stupnytska 2007), the quantitatively-based selection methodology of the current approach also identifies some unexpected poles, while providing a sense of the magnitude of each pole.

Furthermore, the study of the proximate correlates and fundamental determinants of growth polarity point to the likely character of such poles: that they are dynamic economies that have a high-quality institutional framework and strong internal demand, and in possession of a relatively small nonworking population and efficient government. They are also, more likely than not, to have higher levels of income, although whether this characteristic will persist is in doubt, given the strong representation of developing countries in Table 3.

Future directions include applications of the polarity measure and multipolarity indices to outstanding questions in development and international political economy. As two examples, the growth polarity measure can be included as an explanatory variable in cross-country growth regressions to examine the extent of growth spillovers generated from global growth poles, and the multipolarity index can be included in regressions that examine the changing governance structures of international organizations.

# **References**

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AlAzzawi, Shireen (2011). “Multinational Corporations and Knowledge Flows: Evidence from Patent Citations”. _Economic Development and Cultural Change_ 59(3) (April): 649–680

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25
A more refined measure of rate of return accounts for home country earnings _Wj_<sup>_h_asan</sup> opportunity cost of migration. This excess earnings-to-cost ratio is given by the following expression: On average, the new ratio of earnings abroad to cost of migration is 5.8, and thus still clearly above 1. With this measure, the break-even point for migration duration is _N N_ <u>1</u> � _Cj/_ ( _Wj_<sup>_a−W h_</sup> _j_<sup>) = 2</sup><sup>_._3 years,with a median value of 1.4.Furthermore,75% of migrants</sup> _j_ =1 stay at the destination for long enough to break even. Panel (b) of Figure 5 shows the full distribution of (log) excess earnings-to-cost ratio.

While wages are considerably higher abroad, so are living expenses. This implies that not all excess earnings can actually be accumulated as savings. Using observed saving rates _s_<sup>_a_</sup> _j_<sup>byimmigrantworkersindifferentlocations,aswellassavingsrates</sup><sup>_sh_</sup> _j_<sup>inBangladesh,we</sup> can compute the excess saving-to-cost ratio as _N_ For excess savings, the break-even point of duration increases to _N_<sup><u>1</u></sup> � _Cj/_ ( _Wj_<sup>_asa_</sup> _j_<sup>_−W h_</sup> _j_<sup>_sh_</sup> _j_<sup>) =</sup> _j_ =1 4 _._ 2 years. In our data, 54% of the migrants still have a longer migration duration.

The returns to investment for temporary migration are not restricted to the migration episode. Indeed, when temporary migration enables workers to engage in self-employment and entrepreneurship after return, their lifetime incomes at home significantly increase compared to a counterfactual scenario where they could not have migrated. To calculate the long-term returns to migrating, we estimate a migrant’s income gains after return until retirement which can be attributed to the transition into self-employment, and compare it with the cost of migration. We approximate these long-term returns to migration as follows: first, we use the nationally representative HIES data to estimate the income trajectories for self-employed and dependently employed individuals. That is, we run an auxiliary regression of individual _j_ ’s income on a full set of age indicators **1** [ _agej_ = _a_ ] interacted with a dummy _SEj_ that takes value 1 if a respondent is self-employed, and 0 otherwise. Controls _Xj_ include 16