rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> – -<br>Figure 4: BICs and Korea distance of largest industries to the Korean Technological Frontier (1980 2005)<br>V.A. per Worker (Korea=100)- product 321 V.A. per Worker (Current '000 US$/worker) - product 321 V.A. per Worker (Korea=100)- product 311 V.A. per Worker (Current '000 US$/worker) - product 311<br>1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005<br>KOR BRA KOR BRA KOR BRA KOR BRA<br>IND CHN IND CHN IND CHN IND CHN<br>Product: Textiles Product: Food products<br>Source: UNIDO Industrial Statistics Database, Rev. 2 and 3 Source: UNIDO Industrial Statistics Database, Rev. 2 and 3<br>V.A. per Worker (Korea=100)- product 382 V.A. per Worker (Current '000 US$/worker) - product 382 V.A. per Worker (Korea=100)- product 383 V.A. per Worker (Current '000 US$/worker) - product 383<br>1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005<br>KOR BRA KOR BRA KOR BRA KOR BRA<br>IND CHN IND CHN IND CHN IND CHN<br>Product: Machinery, except electrical Product: Machinery, electric<br>Source: UNIDO Industrial Statistics Database, Rev. 2 and 3 Source: UNIDO Industrial Statistics Database, Rev. 2 and 3<br>Source: Authors’ calculations<br>100 40 100 60<br>80 80<br>30<br>40<br>60 60<br>20<br>40 40<br>20<br>10<br>20 20<br>0 0 0 0<br>100 60 100<br>100<br>80 80<br>80<br>40<br>60 60<br>60<br>40 40<br>40<br>20<br>20 20 20<br>0 0 0 0<br><!-- End of picture text --> 9
# Who Suffers the Most from the Cost-of-Living Crisis?

_Michael Lokshin, Zurab Sajaia, and Iván Torre_<sup>1</sup>

**Keywords:** Inflation, Cost of living, Poverty, Inequality, Europe and Central Asia

**JEL:** E31, D31, I32

> 1 The authors are with the World Bank. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. We thank Martin Ravallion, Dean Jolliffe, and participants of the seminar in the World Bank for their comments and suggestions. We are grateful to Aylén Rodriguez Ferrari for providing excellent assistance in preparation of this paper.
childcare services would expand the amount of time that parents have available for paid work. In developed countries women’s access to childcare (mainly through cost subsidies) has been found to increase female labor force participation (Bick, 2016; Givord and Marbot, 2015). For example, in the US, the military provides subsidized childcare for military families and the government provides public schooling but not pre-school or daycare. Evans, Jakiela and Knauer (2021) review 478 studies on early childhood development (ECD) interventions in low- and middle-income countries to find only 19 of them (4%) examined impacts on labor market outcomes for mothers. Scaling up public and private services for childcare (especially for preschool-age children) can be done through a range of policies.

In many developing countries, parents rely heavily on informal childcare arrangements in the absence of institutional childcare arrangements (Halim and Reynolds, 2021). In Kenya, a program that subsidized center-based childcare improved women’s ability to work. Women in Nairobi, Kenya, who were offered vouchers for subsidized early childcare were, on average, 8.5 percentage points more likely to be employed than those who were not given vouchers (Clark et al., 2019). In Mozambique, the provision of childcare through community-based pre-schools had a positive impact on mothers’ labor supply (Martinez, Naudeau and Pereira, 2017). Similary, in Burkina Faso the provision of childcare as part of a public works program increased women’s employment (Ajayi, Dao and Koussoub`e, 2022).

The failure to address the childcare needs of women who want to work undermines efforts to promote women’s economic empowerment. In the following we document the impact of a negative childcare shock and increased responsibilities within the home in a business context.

9
# **Figure 20. Investment growth slowdown in South Asia**

_Investment growth has been below the long-term average in more than half of SAR economies since 2012. Its composition has shifted away from private sector-driven investment growth during 2013-14 towards public sector-driven investment growth in 2015. While lower oil prices and easing political tensions supported investment, weak activity during 2010-12 and long-standing structural bottlenecks constrained investment._

# **A. Investment growth**

# **B. Share of SAR countries with weak investment growth**

<!-- Start of picture text --> Percent Percent<br>1990-2008 avg 2003-08 avg<br>14 100 Below long-term average Contracting<br>12<br>10 75<br>8<br>6<br>50<br>4<br>2<br>25<br>0<br>0<br>SAR SAR ex India EMDE 2010 2011 2012 2013 2014 2015<br>C. Contribution to investment growth D. GDP growth<br>Percentage points Public Private Percent 1991-2008 avg 2003-08 avg<br>10 12<br>8<br>6<br>8<br>4<br>2<br>4<br>0<br>-2<br>-4 0<br>SAR Excl. India India SAR SAR ex India EMDE<br>E. Terms of trade change F. Political stability<br>Percent ICRG index, 0-100 (100=best)<br>8<br>66<br>6<br>4 62<br>2<br>58<br>0<br>-2<br>54<br>-4<br>2010 2011 2012 2013 2014 2015<br>50<br>2010 2011 2012 2013 2014 2015<br>2010 2012 2014 2010 2012 2014 2010 2012 2014<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015 2010 2012 2014 2010 2012 2014 2010 2012 2014<br><!-- End of picture text --> Sources: Haver Analytics, PRS Group, Ministry of Finance of Sri Lanka, Reserve Bank of India, World Bank. A. Weighted averages. Includes annual data for Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka.

B. Share of SAR economies with investment growth below its long-term average or with negative investment growth. C. Weighted average for Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka.

D. GDP-weighted averages.

E. Investment-weighted averages. An increase denotes terms of trade improvements.

F. Investment-weighted average of ICRG index of Political Risk. An increase denotes greater political stability.

39
<!-- Start of picture text --> (g) Republic of Korea: Standard Deviation of (h) Republic of Korea: Standard Deviation of<br>Real GDP Growth, % points GDP growth, % points<br>4<br>2<br>3.5<br>East Asian Crisis 1.8<br>3 Global<br>Crisis 1.6<br>2.5<br>1.4<br>2 1.2<br>1.5 1<br>1 0.8<br>0.5 0.6<br>0.4<br>0<br>0.2<br>0<br>1972-1985 1986-2008 2009-2011(iii)<br>(i) France: Standard Deviation of Real GDP (j) Germany: Standard Deviation of<br>Growth, % points Real GDP Growth, % points<br>1 2.5<br>0.9 Global Global<br>Crisis<br>0.8 2 Crisis<br>0.7<br>0.6 1.5<br>0.5<br>0.4 1<br>0.3<br>0.2 0.5<br>0.1<br>0 0<br>1972 Q1 1974 Q1 1976 Q1 1978 Q1 1980 Q1 1982 Q1 1984 Q1 1986 Q1 1988 Q1 1990 Q1 1992 Q1 1994 Q1 1996 Q1 1998 Q1 2000 Q1 2002 Q1 2004 Q1 2006 Q1 2008 Q1 2010 Q1<br>1981 Q4 1983 Q2 1984 Q4 1986 Q2 1987 Q4 1989 Q2 1990 Q4 1992 Q2 1993 Q4 1995 Q2 1996 Q4 1998 Q2 1999 Q4 2001 Q2 2002 Q4 2004 Q2 2005 Q4 2007 Q2 2008 Q4 2010 Q2 1993 Q1 1994 Q2 1995 Q3 1996 Q4 1998 Q1 1999 Q2 2000 Q3 2001 Q4 2003 Q1 2004 Q2 2005 Q3 2006 Q4 2008 Q1 2009 Q2 2010 Q3<br><!-- End of picture text --> 14
39% in 2100. Agricultural area dedicated to cellulosic feedstocks increases by additional 36 million Ha in 2100. All other areas decline. GHG ows increase because of additional conversion of natural lands, and decline due to greater biofuels' osets. The latter eect dominates in the long run, and net GHG accumulation declines by 10 GtCO 2 e in 2100. The consumption of energy services increases, and the consumption of other land based goods and services declines.

<!-- Start of picture text --> Change in Land Use Relative to Baseline Change in GHG Emissions Relative to Baseline<br>40<br>1.5 10 Natural Land<br>30 Food Crops 8 Conversion<br>20 Livestock Feed 1 6 Livestock<br>10 Biofuels Crops 0.5 4 Biofuels<br>Pasture Land 2 Offsets<br>0 Protected Forests 0 0 Use of<br>-10 -2 Fertilizers<br>Managed Forests -0.5 -4 Forest<br>-20 Unmanaged Forests -1 -6 Sequestration<br>-30 -8 Net GHG<br>Accumulation<br>-40 -1.5 -10<br>2005 2030 2055 2080 2105 2005 2030 2055 2080 2105<br>Change in Consumption of Land Based Good Change in Consumption of Biofuels Relative to<br>and Services Relative to Baseline Baseline<br>2 10 6%<br>0 8<br>6 4%<br>-2-4 Processed Livestock 42 2% Consumption of 2gBiofuels<br>Ecosystem Services<br>-6 Processed Timber 0 0% Consumption of 1g<br>-8 Energy Services -2-4 -2% Biofuels<br>-10 Processed Grains Share of Biofuels in<br>-6 -4% Liquid Fuel<br>-12 -8 Consumption<br>-14 -10 -6%<br>2005 2030 2055 2080 2105 2005 2030 2055 2080 2105<br>Million Ha GtCO2e/yr GtCO2e<br>USD Per Person Million toe<br><!-- End of picture text --> Figure E.5: Sensitivity Analysis: 50% Decline in Energy Eciency Growth Rate per annum Figure E.5 shows the eects a 50% decline in energy eciency growth rate. Smaller energy eciency implies increased requirements for energy fuels (here petroleum products and biofuels) to satisfy the demand for energy services. The increased consumption of petroleum and biofuels results in a greater demand for agricultural land. Areas dedicated to biofuels feedstocks add 3 million Ha. All other areas dedicated to commercial land decline in response to demand reduction for land based goods and services due to higher energy prices. Unmanaged forest lands increase by additional 26 million Ha in 2100. The GHG emissions ows from land use decline slightly in the medium term because of avoided de33
6

# II. The Policy Litgrature on Trade RegLaes: A **Review**

The abundant policy literature on trade regimes and economic performance has mainly focused on two issues: (a) defining alternative trade regimes; and (b) determining the relationshlp between trade regimes and growth. In this section I provide a general and selective review of this literature, focusing on these two questions. I also discuss briefly the different measures of "protection" that have been used in studies on tradb reforms.

# I1.1 Defining a Trade Regime

Tho purpose of this subsection is to discuss alternative definitions of trade regimes that have been used ir. the literature. A particularly important objective of the section is to illustrE_e the way in which the meaning of "liberalization" has changed in the last few years.

Little, Scitovsky and Scott (1970) and Balassa (1971) are the pioneering modern studies on trade orientation and economic performance. These influential works discussed the characteristics of two broadly defined trade regimes -- import substitution and exp.rt promotion -- and dealt with some important issues related to the transitional period from one regime to another. No effort was made in these studies, however, to provide a detailed taxonomy of trade regimes.

In the classic NBER study directed by Krueger (1978) and Bhagwati (1978) we find for the first time a systematic devel6pment of concepts that allow a more or less formal classification of trade regimes. In this study two key concepts wero introduced into the discussion: (1) the degree of bias of the trade regime; and (2) the premi that is created by the existence of quantitative restrictions (QRs) in the importables market.
- _ 14

## **Tabl- S: Central** Govornment **Flnances.** 1981-87 (X of CDP)

||1981|1982|1988|1984|1085|1988|1987|
|---|---|---|---|---|---|---|---|
|Current**Revenues**|14.0|18.8|18.0|16.6|10.2|16.6|16.1|
|**Tax Revenuos**|18.1|12.8|12.0|14.0|14.7|18.2|18.7|
|Non-Tax<br>Revonues|0.9|1.0|1.6|1.6|1.6|2.4|2.4|
|Current Expenditures|16.1|16.6|18.8|17.1|18.6|17.9|18.5|
|Current<br>AccountBalance|-1.1|-1.7|-8.2|-1.6|-2.8|-2.8|-2.4|
|**Capiteb**<br>Exoenditures<br>**and Net**||||||||
|Lending|8.8|8.4|7.4|10.1|7.2|6.6|4.9|
|**Overall Balanco**(accrual**basis)**|-7.9|**-10.1**|**-10.6**|-11.7|-9.6|-7.8|-7.3|
|**OverallBalance**<br>**(cash basis)**|-7.9|**-10.1**|-10.6|-11.7|-9.6|-7.8|-6.7|
|NotExtornal<br>Financing|4.7|4.6|**5.1**|**7.8**|4.9|2.7|0.9|
|Foroign<br>**Assistance!/**|**0.0**|**0.6**|**0.8**|1.1|1.7|1.8|1.1|
|**Not**Domestic<br>Financing|**8.2**|**5.0**|**5.2**|**2.8**|2.9|8.8|4.0|
|**Bank**credit|**8.0**|4.4|**8.0**|1.9|1.9|2.8|1.6|
|**Bonds**|0.6|0.8|1.5|1.4|0.6|1.0|1.7|
|**Others**|-0.4|-0.2|0.7|-0.6|0.6|0.0|0.7|
|Change In Arrears<br>on amortizati|on|||||||
|obilgations|0.0|0.0|0.0|0.0|0.0|0.0|0.7|

Source: Central Bank **of** Honduras, **Ministry of** Financo, IMF, Bank Staff **Estimates.** / Mostly USAID grants.

# D. Financing of the Central Government Deficit

53. As external assistance contracted, domestic sources of financing gained importance during 1984-87. Net external financing fell from a peak of 7.8Z of GDP in 1984 to 0.9X in 1987, mostly as a result of reduced capital inflows. During this period, the domestic counterpart of USAID assistance provided financing of the fiscal deficit of an amount equal to an annual average of 1.4X of GDP. However, paralleling a reduction in project pipeline, net external financing decreased over the years. An expansion in the domestic sources financed a larger share of the deficit in the last few years.
With ���: Total number of topics covered in both courses � and �, We then computed the average over these pairwise similarity indices within regions, for developing countries as a whole, and for the U.S. departments. They can be found in the last row of Table 2. The results show that the undergraduate development economics courses in developing countries are less similar, in terms of topics covered, than the courses taught in the United States, both in the top 20 U.S. economics departments and in the remaining departments. Whereas the overlap for developing countries is 24 percent at the undergraduate level and 22 at the graduate level, it is 38 percent for the top U.S. schools. The similarity indices are higher on average within MENA, EAP, and ECA for which we have small samples, with the topic coverage particularly diverse in Sub-Saharan Africa in which only 18 percent of the topics overlap on average.

We also used the Jaccard similarity measure to examine the distance to the research frontier by assessing the overlap between each development economics course offered in a developing country with each of the 16 courses offered in the top-20 U.S. economic departments. On average, undergraduate development economics courses taught in developing countries have an overlap in terms of topics of 23 percent with the courses offered in the top U.S. schools. The course with the highest overlap, achieves on average an overlap of about 50 percent with the syllabi of the top U.S. schools, while the course with the lowest overlap on average has an overlap of only 1 percent with the top U.S. schools.<sup>4</sup>

# **4.2 Emphasis on Theory and Empirics**

> 4 The course with the highest overlap has on average a Jaccard measure with a course offered at one of the top 20 U.S. economic departments of 0.4974 and is offered at the Pontificia Universidad Javeriana in Colombia. The course with the lowest average overlap has a mean Jaccard measure with the top 20 U.S. economic departments’ courses of 0.0104 and is offered at the University of Cape Coast in Ghana.

13
# **Distributional effects of educational improvements: Are we using the wrong model?**<sup>**1**</sup>

François Bourguignon Paris School of Economics F. Halsey Rogers<sup>+</sup> Development Research Group, World Bank, 1818 H St., NW, Washington, DC 20433, USA _JEL Classifications:_ I22, I38, H52 _Keywords:_ Educational finance, Economic development, Economic impact, Human capital, Expenditures, Incidence of Public Spending

> + Corresponding author. Tel.: + 1 202 473 6292; Fax: + 1 202 522 1158.

_E-mail addresses:_ Francois.Bourguignon@Parisschoolofeconomics.eu, hrogers@worldbank.org
TABLE 15: OTHER MULTILATERAL NONCONCESSIONAL

|||1988|1989|1990|1991|1992|1993|1994|1995|
|---|---|---|---|---|---|---|---|---|---|
||B) Financing:|||||||||
|CBB4|Costof Buyback||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|EFBB4<br>|<br>Externatty Financed<br>||0.00<br>|0.00<br>|0.00<br>|<br>0.00<br>|<br>0.00<br>|<br>0.00<br>|<br>0.00<br>|
|URBS4|Use of Reserves||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|EBBB4|Exit Bonds||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|DEBB4|Debt-Equity<br>Swaps||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||C) Effects<br>on:|||||||||
|AP4bb|Amortization<br>Payments<br>||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|AP4eb<br>|Of which: Exit Bonds<br> <br>||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|IP4bb|Interest<br>Payments|||0.00|0.00|0.00|0.00|0.00|0.00|
|IP4eb|Of which: Exit Bonds|||0.00|0.00|0.00|0.00|0.00|0.00|
||3. ACCUMULA(1ON<br>OF ARREARS:|||||||||
||A) Arrears<br>on:|||||||||
|AR4AP|Amortization<br>Payments||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|AR41P|Interest<br>Payments||0.00|0.00|0.00|0.00|0.00|<br>0.00|<br>0.00|
|GD4ar|B)Capitalization:||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|R4ar|<br>Interest rate|0.08|<br>0.08|0.08|0.08|0.08|0.08|<br>0.08|<br>0.08|
|N4ar|Maturity|20.00|<br>20.00|20.00|<br>20.00|20.00|20.00|<br>20.00|<br>20.00|
|G4ar|Grace Period|4.00|<br>4.00|4.00|4.00|4.00|4.00|<br>4.00|<br>4.00|
||C) Effects on:|||||||||
|GD4ar|Gross Disbursements||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|AP4ar|Amortization<br>Payments||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|IP4ar|Interest Payments|||0.00|0.00|0.00|0.00|<br>0.00|<br>0.00|
||4.WRITE-OFFS:|||||||||
|WOFF4|A) Debt Forgiven||0.00|0.00|0.00|0.00|0.00|<br>0.00|<br>0.00|
||B) Effects on:|||||||||
|AP4wof|f Amortization<br>Payments|||0.00|0.00|0.00|0.00|0.00|0.00|
|IP4wof|fInterest<br>Payments|||0.00|0.00|0 00|0.00|<br>0.00|<br>0.00|
||C. RESTRUCTURED<br>DEBT:|||||||||
|GD4r|Gross Disbursements|130.88|<br>146.95|<br>159.77|<br>143.41|117.19|<br>81.44|51.66|<br>29.24|
|AP4r|Amortization<br>Payments|62.55|<br>57.23|64.69|71.65|82.65|93.63|99.10|<br>106.76|
|ND4r|Net Disbursements|68.33|<br>89.72|95.08|71.75|34.54|-12.19|<br>-47.43|<br>-77.52|
|D4r|Restructured<br>Debt|1099.05|1188.77|1283.85|1355.61|1390.15|1377.96|1330.52|1253.00|
|D4eb|Of which: Exit Bonds||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||Interest Rate:|||||||||
|R4e|on Non-Restructured<br>Debt||10.1X|<br>10.4%|<br>10.3%|<br>10.1%|<br>9.9%|9.8%|<br>9.7%|
|R4r|laplicit||10.1%|<br>10.4%|<br>10.3%|<br>10.1%|<br>9.9%|9.8%|<br>9.7%|
|IP4r|Interest Payments|94.99|111.20|123.60|<br>132.14|137.28|138.'3|<br>134.94|128.75|
-40-

correction because it leads to overtaxation at high rates of inflation, it taxes nominal gains even when in real terms the taxpayer has a capital loss, and it biases investment decisions towards assets that offer returns in terms of asset appreciation ratther than dividends or interest. Similarly, firms are sometimes allowed accelerated depreciation deductions to offset the effect of inflation. This too is an inappropriate adjustment since it is an effective offset only at a particular rate of inflation and for certain firms.

Berrard and Hayn (1986) have argued that allowing nominal interest cost deductions (in addition to accelarating tax depreciation and allowing investment tax credits) while not indexing depreciation may be mutually offsetting and, in the case of the U.S., may have kept constant the real tax burden on corporations taken as a group. If this is so it is indeed fortuitous but such ad hoc measures cannot be the basis for policy in economies subject to high rates of inflation. Even if it were the case in the aggregate, such a crude offset would conceal tremendous disparity in the effect of inflation on individual firms and industries. In fact, the Bernard-Hayn study confirmed that the dispersion of real effective tax burdens across industries was magnified under these ad hoc adjustments. Aaron and Galper (1985) noted that a partial solution which indexes some and not all of these items runs the risk of introducing new distortions. As an example they cited the case where capital gain is indexed but interest expenses are not, thereby creating an infinite tax loophole. Borrowing to finance the purchase of appreciating assets is made attractive since the nominal interest expense is tax-deductible and only the real capital gain is taxed.
|**Ln sales**|**(8)**|0.511***<br>[0.152]<br>0.044***<br>[0.017]<br>-0.614<br>[0.399]<br>-0.112<br>[0.155]<br>0.963***<br>[0.139]<br>0.345***<br>[0.130]<br>0.017<br>[0.029]<br>0.043<br>[0.212]<br>-0.125<br>[0.196]<br>-6.793*<br>[3.559]<br>98<br>0.76<br>0.94<br>e basic set of<br>tional|
|---|---|---|
|**Ln total**<br>**assets**|**(7)**|0.450***<br>[0.166]<br>0.047***<br>[0.018]<br>-0.777*<br>[0.433]<br>-0.13<br>[0.156]<br>0.963***<br>[0.145]<br>0.367***<br>[0.117]<br>0.02<br>[0.023]<br>0.044<br>[0.247]<br>-0.498**<br>[0.213]<br>-4.911<br>[3.881]<br>98<br>2.07<br>0.72<br>umns (1) – (6) th<br>nclude two addi|
|**Ln sales**|**(6)**|0.501***<br>[0.151]<br>0.046***<br>[0.018]<br>-0.671<br>[0.409]<br>-0.112<br>[0.156]<br>0.959***<br>[0.138]<br>0.348***<br>[0.127]<br>0.017<br>[0.029]<br>0.045<br>[0.213]<br>-0.113<br>[0.201]<br>-6.307*<br>[3.606]<br>98<br>0.61<br>0.74<br>ls presented in col<br>umns (7) and (8) i<br>regressions.|
|**Ln total**<br>**assets**|**(5)**|0.463***<br>[0.165]<br>0.045**<br>[0.018]<br>-0.709*<br>[0.428]<br>-0.13<br>[0.155]<br>0.968***<br>[0.143]<br>0.363***<br>[0.115]<br>0.02<br>[0.024]<br>0.039<br>[0.246]<br>-0.511**<br>[0.211]<br>-5.489<br>[3.838]<br>98<br>1.67<br>0.43<br>at 1%. In mode<br>ifications in col<br>r the first stage|
|**Ln sales**|**(4)**|0.412**<br>[0.167]<br>-0.153<br>[0.190]<br>-0.091<br>[0.126]<br>0.964***<br>[0.113]<br>0.447***<br>[0.131]<br>-0.001<br>[0.025]<br>0.082<br>[0.184]<br>-0.356**<br>[0.158]<br>-9.078***<br>[1.535]<br>98<br>0.26<br>0.88<br>*** significant<br>ort costs). Spec<br>l. See table 4 fo|
|**Ln total**<br>**assets**|**(3)**|0.370**<br>[0.180]<br>-0.197<br>[0.175]<br>-0.109<br>[0.127]<br>0.974***<br>[0.121]<br>0.463***<br>[0.120]<br>0.002<br>[0.019]<br>0.077<br>[0.222]<br>-0.751***<br>[0.176]<br>-8.315***<br>[1.808]<br>98<br>0.28<br>0.87<br>ificant at 5%;<br>sity and passp<br>women’s trave|
|**Ln sales**|**(2)**|0.326**<br>[0.153]<br>0.076<br>[0.295]<br>-0.084<br>[0.118]<br>0.976***<br>[0.111]<br>0.511***<br>[0.121]<br>-0.003<br>[0.024]<br>0.124<br>[0.186]<br>-0.449***<br>[0.152]<br>-11.684***<br>[2.556]<br>98<br>1.42<br>0.49<br>at 10%; ** sign<br>populations den<br>restrictions on|
|**Ln total**<br>**assets**|**(1)**|0.284*<br>[0.162]<br>-0.003<br>[0.279]<br>-0.103<br>[0.124]<br>0.983***<br>[0.119]<br>0.526***<br>[0.111]<br>0<br>[0.018]<br>0.118<br>[0.225]<br>-0.837***<br>[0.176]<br>-10.536***<br>[2.834]<br>98<br>1.53<br>0.47<br>* significant<br>ck in the EU,<br>igration and|
|||Ln total migration to US<br>Ln tertiary migration to US<br>Share of tertiary migration to<br>US<br>Ln distance<br>Conflict in 1990s<br>Ln GDP per capita in US dollars<br>Ln population<br>Inflation average<br>Governance<br>Dummy for 1990<br>Constant<br>Observations<br>Hansen J statistic<br>Chi-sq p-value<br>Robust standard errors in brackets<br>instruments is used (migration sto<br>instruments: legal restrictions on m|