rafmacalaba/datause-encoder-data huggingface dataset:
Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, The World Bank, Infrastructure Network, Office of the Vice President (June), Washington, D.C.

Fedderke, Johannes, W., Perkins P., and Luiz, J.M. (2005). “Infrastructural Investment in Long-Run Growth: South Africa 1875-2001,” _World Development_ , forthcoming.

Leipziger, Danny (2001). “The Unfinished Agenda: Why is Latin America Lagging Behind?” _Finance & Development_ , International Monetary Fund, vol. 38, 38-41.

Manuel, Trevor (2005). _Budget Speech 2005_ , Ministry of Finance, Pretoria, South Africa, February 23.

Perkins, P., J.W. Fedderke, and J. M. Luiz (2005). “An Analysis of Economic Infrastructure Investment in South Africa,” _South African Journal of Economics_ , Vol. 73 (2), 211-28.

Reynolds, T, (2005). “Global developments in Telecommunications Pricing,” OECD Presentation 14-15 July, Midrand, South Africa.

Truen, S., and Hodge, J. (2005). “Competitive conditions in international bandwidth markets in South Africa: Pricing and regulatory issues in the SAT-3 cable and satellite market,” Mimeo: GenesisAnalytics.

World Bank (1994). _World Development Report 1994: Infrastructure for Development_ . New York: Oxford University Press.

19
# A Deeper Dive into the Relationship between Economic Development and Migration<sup>*</sup>

## **Maheshwor Shrestha**<sup>†</sup>

**JEL** : F22, J61, O15

**Keywords** : Economic development, migration, growth, inverse-U, migration hump

> * This paper serves as a background paper to the _World Development Report 2023_ . The author thanks Samik Adhikari, Laurent Bossavie, Michael Clemens, Xavier Devictor, Quy-Toan Do, Caglar Özden, and Mauro Testaverde for helpful discussions and feedback. Laura Caron and Narcisse Changom provided excellent research assistance.

> † The World Bank. Contact: mshrestha1@worldbank.org
Annex 10: <u>Senegal - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

|||||**25% increase**|**50% increase**|**25% increase**|**50% increase**|
|---|---|---|---|---|---|---|---|
|||||**Upper**|**Upper**|**Lower**|**Lower**|
|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|<br>**bound**<br>**Impact**<br>**(Cons. only)**|**bound**<br>**Impact**<br>**(Cons. only)**|**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|||||**Senegal National**||||
|Rice|6.8|96.3|**50.8**|52.5|54.2|||
|Huiles végétales|4.5|95.8||51.6|52.7|||
|Sucre|3.0|99.2||51.4|52.1|||
|Bread|4.0|92.7||51.5|52.4|||
|Milk|2.1|79.6||51.1|51.4|||
|All|20.5|99.8||55.9|60.4|||
|||||**Senegal Dakar**||||
|Rice|4.5|95.4|**32.5**|33.8|35.5|||
|Huiles végétales|3.2|95.7||33.3|33.9|||
|<br>Sucre|1.8|99.6||33.1|33.7|||
|Bread|3.9|98.9||33.4|34.6|||
|Milk|2.5|99.4||33.1|33.5|||
|All|15.8|99.9||37.4|41.2|||
||||**Sene**|**gal Other Urban Ar**|**eas**|||
|Rice|6.9|94.2|**38.8**|40.7|42.2|||
|Huiles végétales|4.8|94.7||39.5|41.1|||
|<br>Sucre|3.0|99.3||39.5|40.7|||
|Bread|5.0|96.5||39.9|41.5|||
|Milk|2.6|96.0||39.2|39.5|||
|All|22.3|99.8||43.9|50.2|||
|||||**Senegal Rural**||||
|Rice|9.4|97.4|**61.9**|63.8|65.6|||
|Huiles végétales|5.8|96.2||62.9|64.0|||
|<br>Sucre|4.5|98.8||62.5|63.2|||
|Bread|3.8|88.2||62.5|63.0|||
|Milk|1.4|63.6||62.1|62.4|||
|All|24.9|99.7||67.1|71.4|||

Source: Authors’ estimation using country household survey data 29
# rejecting our identification strategy.

Using a sample of 35 countries that crossed the IDA threshold from below between 1987 and 2010, we find that a one percent increase in the aid to GNI ratio raises the annual real per capita short term GDP growth rate by 0.0312 percentage points. The mean aid to GNI ratio at the crossing is 0.09, so a one percentage point increase in the aid to GNI ratio raises annual real per capita GDP growth by approximately 0.35 percentage points. Our effects are about 1.75 times as large as those reported by Clemens et al. (2012). Using OLS, they find that a one percentage point increase in aid/GDP (at aid levels similar to our sample mean) is followed by at most a 0.2 percentage-point increase in growth of real GDP per capita. We find similar effects of aid on growth to the ones reported by Clements et al. (2012) without instrumenting foreign aid. We also present evidence consistent with the fact that OLS estimates suffer from attenuation bias due to measurement error in aid, which is exacerbated when the variability in the aid to GNI ratios is exploited to identify the effect of foreign aid on economic growth in a fixed effect or first-differenced growth equation commonly used in the literature. Thus, one should expect that 2SLS using a valid instrument produces larger estimates than OLS. This could also be the case if economic growth affects aid levels.

The sizable effect of foreign aid on economic growth we find may also be attributable in part to the fact that our sample consists of a group of similar low-income countries that are financially constrained, where aid could have the largest impact. Prior to crossing the IDA threshold, the countries we study tend to have relatively large amounts of aid and low capital levels. Aid is likely to be relatively effective in this context. Although we focus on a small group of countries, our results have strong policy relevance since the sample is composed of low-income countries. We also show suggestive evidence that our results might have meaningful external validity to the remaining poor countries as they grow closer to the IDA threshold.

4
_Table 7: Effects of VAT reforms on tax rates when high-income income taxes adjust_

||Base rates||Reduce V|AT:|0 FO|OD VAT|
|---|---|---|---|---|---|---|
|||25%|50%|75%|100%||
|Firms|0.128|0.128|0.128|0.128|0.128|0.128|
|h_d0|0|0|0|0|0|0|
|h_d1|0.011|0.011|0.011|0.011|0.011|0.011|
|h_d2|0.024|0.024|0.024|0.024|0.024|0.024|
|h_d3|0.052|0.052|0.052|0.052|0.052|0.052|
|h_d4|0.08|0.08|0.08|0.08|0.08|0.08|
|h_d5|0.095|0.095|0.095|0.095|0.095|0.095|
|h_d6|0.111|0.111|0.111|0.111|0.111|0.111|
|h_d7|0.135|0.135|0.135|0.135|0.135|0.135|
|h_d8|0.169|0.169|0.169|0.169|0.169|0.169|
|h_d91|0.185|0.223|0.259|0.294|0.328|0.221|
|h_d921|0.168|0.203|0.236|0.268|0.299|0.201|
|h_d922|0.16|0.193|0.224|0.255|0.284|0.191|
|h_d923|0.181|0.219|0.255|0.289|0.322|0.217|
|h_d924|0.144|0.173|0.202|0.229|0.255|0.172|

Source: South Africa CGE model simulations _Figure 5: Regressiveness of VAT as VAT rates are reduced_ <!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>h_d0 h_d1 h_d2 h_d3 h_d4 h_d5 h_d6 h_d7 h_d8 h_d91 h_d921 h_d922 h_d923 h_d924<br>BASE 25% 50% 75%<br><!-- End of picture text --> Source: South Africa CGE model simulations

# **5.4b VAT Rate Reforms**

Finally, we consider restructure the VAT rates to make the tax instrument less regressive. We remove the VAT for food and increase the other VAT rates by 16.4 percent to replace the lost government revenue. As seen in Figure 6, the revised VAT structure is no longer regressive: the VAT payments as a percentage of household income increase as income increase, reversing the pattern observed with the initial VAT rates in place.

18
- Chicoine, L. (2019). Schooling with Learning: The Effect of Free Primary Education and Mother Tongue Instruction Reforms in Ethiopia. _Economics of Education Review_ , 69(April):94–2017.

- Clark, D. and Bono, E. D. (2016). The Long-run Effects of Attending an Elite School: Evidence from the United Kingdom. _American Economic Journal: Applied Economics_ , 8(1):150–176.

- Croicu, M. and Sundberg, R. (2015). UCDP GED Codebook version 2.0. Department of Peace and Conflict Research, Uppsala University.

- Finlay, K., Magnusson, L., and Schaffer, M. E. (2013). WEAKIV: Stata Module to Perform Weak-instrumentrobust Tests and Confidence Intervals for Instrumental-variable (IV) Estimation of Linear, Probit and Tobit Models.

- Food and Agriculture Organization of the United Nations. FAO GeoNetwork. (2015). Global Administrative Unit Layers (GAUL).

- Fort, M., Schneeweis, N., and Winter-Ebmer, R. (2016). Is Education Always Reducing Fertility? Evidence from Compulsory Schooling Reforms. _Economic Journal_ , 126(595):1823–1855.

- Garcia Hombrados, J. (2018). Child Marriage and Infant Mortality: Evidence from Ethiopia. Working Paper No. 1317. Department of Economics, University of Sussex.

- Geruso, M. and Royer, H. (2018). The Impact of Education on Family Formation: Quasi-Experimental Evidence from the UK. NBER Working Paper No. 24332.

- Heugh, D. K., Benson, C., Bogale, B., and Yohannes, M. A. G. (2007). Study on Medium of Instruction in Primary Schools in Ethiopia. Final Report. Commissioned by the Ministry of Education.

- Holmlund, H., Lindahl, M., and Plug, E. (2011). The Causal Effect of Parents’ Schooling on Children’s Schooling: A Comparison of Estimation Methods. _Journal of Economic Literature_ , 49(3):615–651.

- Kattan, R. B. and Burnett, N. (2004). User Fees in Primary Education. World Bank Education Advisory Service.

- Keats, A. (2018). Women’s Schooling, Fertility, and Child Health Outcomes: Evidence from Uganda’s Free Primary Education Program. _Journal of Development Economics_ . Forthcoming.

- Lam, D. and Duryea, S. (1999). Effects of Schooling on Fertility, Labor Supply, and Investments in Children, with Evidence from Brazil. _Journal of Human Resources_ , 34(1):160–192.

20
-49-

FIGURE **6a.** The Expansion of Bank Deposits **in** Thailand <!-- Start of picture text --> 0.7 __ _0<br>0.80<br>O.5S<br>0.4 7<br>0.3 ° 0 M2Z<br>0.1<br>0<br>¶g6197¶g79?19719719719tl71979197¶Tglgglgglgtg8s98lteelgs<br>0.1 9-0-=1 -2-3-4-5u6 -7-8o-9-0 - 1-2b3-4- 5-6- 7<br><!-- End of picture text --> .UIZ M1AGDP .M2z **/MGDP** INF **Inflanon rae** (CPfl **_SQUMC_** IFS
<!-- Start of picture text --> 2 g<br>5 8 8<br>fe wT<br>z aS<br>°o<br>x=<br>B -" 188<br>‘6 74 oOo<br>fe Yo“ ni€<br>an 2 So<br>0 a 3s<br>@ LO “7 o<br>a w yo az°<br>=<7 7 £o<br>£ wt 2<br>oO Pia ” 2<br>2 a gt<br>c ao °<br>fe aot 2<br>=<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>year<br>——— Migrant Earnings as Share of Household Income<br>—--—-—- Household Migrant Earnings (real)<br>a = °<br>£Qoc& _~ = >> se<br>= ~ 8<br>o we ~<br>o ~ [o)<br>5 an 3 4<br>= © ‘ —-e£<br>o% x £<br>tS ~ =<br>o ‘ Of<br>co) . Beo=<br>a) . “<br>3a8<br>°<br>o XN te]<br>a TM. ‘ =<br>g ‘\<br>o Q<br>Ig a<br>2014 2016 2018<br>year<br>—- Household Income Share from Remittance<br>—---—- Real Remittance<br><!-- End of picture text -->
Table 13: Education-Gender

|Variable||19|93|||19|99||
|---|---|---|---|---|---|---|---|---|
|Edugen|Wage|P>Izl<br>|Productivity|**P>Izl**|Wage|**P>Izl**<br>|Productivity|P>IzI|
|Pooled|0.00|0.82|-0.02|0.00|-0.01|0.11|-0.02|0.04|
|Micro|0.00|0.94|-0.03|0.32|-0.01|0.50|_-0.05_|0.19|
|Small|-0.01|0.13|0.01|0.63|0.01|0.38|-0.07|0.00|
|Medium|0.01|0.10|0.00|0.78|-0.01|0.43|0.00|0.83|
|Large|0.00|0.72|-0.03|0.00|0.00|0.93|0.00|0.92|

_Source:_ Author's calculations based on ENTRAM-ENESTYC.

By mixing the variables of training and gender, we found that training generally increases men's wages above that of women's (see Tables 14 and 15). In 1993 external training increased men's wages slightly more than in-house training did. In-house training, however, increased men's wages more in 1999 than in 1993 in large firms. External training increased men's wages by a higher percentage in 1999 than in 1993 in small firms. This generally shows a trend that training-especially external trainingincreased men's wages more than women's by a higher percentage over time.

In terms of productivity, training also increased men's productivity more than women's. In 1993 external training increased men's productivity by slightly more than in-house training did in large firms. Productivity in the case of men's in-house training in large firms in 1999 increased from 2 to 26 percent in just 6 years. Thus, over time training seems to increase productivity of men more than productivity of women, but we cannot make any definite conclusions as to whether productivity increases more with inhouse or external training.

||||**Tabl**|**e14:In-h**|**ousetrai**|**ing-Gender**||||
|---|---|---|---|---|---|---|---|---|---|
|Variable||**1**|**993**||||**1**|**999**||
|Trigen|Wage|P>Jzl<br>|Prod|uctivity|**P>Izl**|Wage|P>jzl<br>|Productivity|**P>jzl**|
|ooled|0.03|0.00||0.01|0.14|0.05|0.11|0.22|0.01|
|Micro|0.05|0.11||-0.17|0.00|-0.09|0.73|0.02|0.98|
|Small|0.02|0.15||0.00|0.97|0.13|0.16|0.62|0.00|
|Medium|0.02|0.04||0.04|0.00|-0.05|0.29|-0.13|0.29|
|ILarge<br>1|<br>0.02|0.00||0.02|0.09|0.10|0.04|0.26|0.05|
|<br>_Source:_Auth|or'scalculatio|nsbasedon|ENT|RAM-ENE|STYC.|||||

17
analyze the characteristics of adults using formal and informal financial services and identify the barriers to formal account ownership. For the first time we can analyze the segments of the population served by formal and informal financial services in Africa. Then, using Enterprise Surveys data, we look at how SMEs in Africa compare with enterprises in developing regions in terms of account ownership and availability of a line of credit. All key indicators are presented in Appendix 1. We conclude with a few key messages.

# **2. Africa’s Financial Sector**

Financial systems in Africa generally lag behind those in other developing economies, despite the fact that many significant improvements were implemented within the past decades. An international comparison of private credit to GDP - a main indicator of financial depth - shows a gap with other developing economies (World Bank, 2012). For example, the ratio of private credit to GDP averaged 24% of GDP in Sub-Saharan Africa in 2010 and 39% in North Africa, compared with 77% for all other developing economies, and 172% for high income economies.

The nonbank segments of Africa’s financial systems show an even lower degree of development than banking. Less than half of African countries have stock markets and only a few of these are liquid (Beck, et. al. 2011). With the exception of South Africa, African stock exchanges are small as measured by the ratio of market capitalization to GDP—only 38% on average, as compared to 44% in all other developing economies and 62% including high-income economies (World Bank, 2012). In addition, African stock markets are among the most illiquid in the world as measured by the ratio of traded to listed stocks (Beck et al., 2011).

The depth of African financial systems has improved within the past two decades, but the gap with other developing economies still remains. In the following two sections we investigate what is behind the macroeconomic indicators and examine financial inclusion from the perspective of the users of these financial services.

# **3. Individuals’ Access to Finance**

Until now, in Africa and elsewhere, little had been known about the reach of the financial sector—the extent of financial inclusion and the degree to which such groups as the poor, 3
associated with domestic business groups, are subsidiaries of multinational corporations, or are independent firms. 8 For reasons of space we do not report the detailed results. However, it is interesting to note that the cash flow coefficient is significant only for independent firms (.41 with a t statistic of 3.3), and, more importantly from our point of view, the coefficient of the change in maturity variable is significant and positive only for members of domestic groups (.11 with a t statistic of 3.08). It is insignificant and minuscule for the other firms. This result is somewhat surprising because one would have expected that an increased access to long term (possibly subsidized ) debt would be associated with greater investment, especially for financially constrained independent firms.

# **5. Conclusions**

What are the main conclusions we can derive from the empirical investigation of the determinants and consequences of the maturity structure of debt for our panel of UK and Italian firms? One of the most robust results for both countries is that, in choosing the maturity structure of debt, firms tend to match assets and liabilities as suggested both by the conventional wisdom and by some recent theoretical models (Hart and Moore (1995)). A second general conclusion is that more profitable firms

> 18 All the firms in **the** UK panel are quoted and represent a relatively more homogeneous set relative to the Italian fims in our panel.

25
# Figure 2: PRICE AND WELFARE EFFECTS<sup>GIVEN CHANGESINMEXICAN SUGAR POLICIES</sup>

<!-- Start of picture text --> Figure 2A<br><!-- End of picture text --> <!-- Start of picture text --> cP~~~~~~~~s<br>Cp TIn<br>CPP<br>WP h~~~~~~~~~~~~~~~~~~~~~~~~~<br>L TI<br>c'j. C' J~otX j t O'<br><!-- End of picture text --> <!-- Start of picture text --> DD<br>TX<br>~~~L TX J J<br><!-- End of picture text -->
# **Table 8. Testing the effects of fiscal capacity, and related hypotheses**

||Full|Full-<br>High<br>War<br>Casualty|Full|Full-<br>High<br>War<br>Casualty|
|---|---|---|---|---|
|Likin per 1000 sq. km|-0.049***|-0.052***|-0.052***|-0.052***|
||[0.009]|[0.009]|[0.009]|[0.009]|
|Early Taiping * post 1880|-0.396***|-0.548***|-0.372***|-0.391***|
||[0.108]|[0.135]|[0.105]|[0.109]|
|Early Taiping *number of decades since end-Taiping|-0.014|0.003|-0.014|-0.011|
||[0.011]|[0.013]|[0.010]|[0.011]|
|Late Taiping * post 1880|-0.388*|-0.521***|-0.593***|-0.918***|
||[0.199]|[0.177]|[0.179]|[0.234]|
|Late Taiping * number of decades since end-Taiping|0.023|0.033*|0.038**|0.068***|
||[0.021]|[0.019]|[0.019]|[0.025]|
|Taiping * post 1880 * standardized distance to NanjingTaiping|0.230***<br>[0.079]|0.795***<br>[0.232]|||
|Taiping * number of decades since end-Taiping * standardized|-0.01|-0.069***|||
|distance to NanjingTaiping|[0.008]|[0.019]|||
|Early Taiping * post 1880 * standardized Distance to|||0.296***|0.296***|
|NanjingEarly|||[0.106]|[0.106]|
|Early Taiping * number of decades since end-Taiping *|||-0.025***|-0.026***|
|Standardized Distance to NanjingEarly|||[0.008]|[0.008]|
|Late Taiping * post 1880 *standardized Distance|||0.264***|1.586***|
|to NanjingLate|||[0.058]|[0.574]|
|Late Taiping * number of decades since end-Taiping *standardized|||-0.005|-0.138**|
|Distance to NanjingLate|||[0.006]|[0.574]|
|Observations|1,330|1,300|1,330|1,300|
|R-squared|0.461|0.457|0.468|0.457|
|Number of prefectures|266|260|266|260|
|Control, prefecture FE, year FE|Y|Y|Y|Y|

Note: The full sample contains the prefecture-year panel for 266 prefectures and seven time periods (from 1820 to 2000). The “full – high war casualty” prefectures contain the prefecture-year panel for 260 prefectures (after dropping 6 cities, Wuchang, Anqing, Songjiang, Jiangning, Suzhou, and Hangzhou which were heavily affected by the Taiping Rebellion) and seven time periods (from 1820 to 2000).

The time-varying control variables are the duration of treat ports, the duration of concessions and leased territories, the frequency of wars since year 1776. Also included are time dummies interacted with the following time-invariant controls: the distance to Yangzi River, to the coastline, and to the Grand Canal; the number of neighboring provinces; the pre-war level of land taxes per mu; the total number of palace graduates per million people from 1793-1820; silk and tea prefectures dummy; the four post designations classified by the Qing government in 1820.

Standard errors are robust, clustered at the prefecture level.

*** p<0.01, ** p<0.05, * p<0.1

53
**<u>Table 2: Scale and Inequality</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Discoveries|Discoveries|Patents|Patents|
|ln(GDP per capita)|3.653|4.161|-0.523|-0.165|
||(3.38)***|(2.11)**|(0.24)|(0.07)|
|ln(GDPpercapita)<sup>2</sup>|-0.227|-0.251|0.056|0.030|
||(3.39)***|(2.29)**|(0.45)|(0.20)|
|Gini of innovations|-3.802|-2.767|-1.539|-2.039|
||(8.56)***|(4.75)***|(1.98)**|(3.15)***|
|ln(historical<br>performance)|||0.135|0.045|
||||(2.51)**|(1.22)|
|ln(Population) (a)|-0.074|-0248|0.601|0.629|
||(1.17)|(1.35)|(5.73)***|(4.99)***|
|ln(Poverty) (b)|0.378||0.315||
||(2.30)**||(2.07)||
|(a)x(b)|-0.025||-0.023||
||(2.36)**||(2.36)**||
|Labor force with<br>secondary ed’n (c)||-0143||-0.072|
|||(2.35)**||(1.55)|
|(a) x (c)||0009||0.005|
|||(2.36)**||(1.83)*|
|ln(R&Dpercapita)|||0.423|0.712|
||||(3.33)***|(7.16)***|
|ln(exports to the US<br>per capita)|||0.282|0.231|
||||(4.24)***|(3.64)***|
|ln(previous period<br>patents)|||0.005|-0.024|
||||(0.16)|(1.07)|
|Constant|-8.257|-8.432|-7.758|-8.996|
||(1.90)*|(0.85)|(0.75)|(0.79)|
|Observations|70|44|132|115|

Absolute value of z statistics in parentheses. Period dummies were also included for patent estimations (columns 3 and 4). Ln(historical performance) was not found to be significant for either (1) or (2) and did not affect the other coefficients so it was excluded to increase sample size.

* significant at 10%; ** at 5%; *** at 1%. Notes:

23
# **A.3 Table A3: Data discrepencies.**

|**Indicator**|**Coverage/Source**|**Number of countries**|
|---|---|---|
||Whole country|27|
|**Employment**|Onlyurban|6|
||Unclear/Largelyurban|4|
||Onlyformal|3|
|**Elt**|Allpersons|28|
|**mpoymen**|Unknown<br>Total|7<br>37|
||LFS/HHS|29|
|**Employment**|Establishment survey|8|
||Total|37|
||Industry/Non agriculture|9|
|**Earnings**|All sectors|19|
||Unknown|3|
||Total|31|
||LFS/HHS|7|
||Establishment survey|22|
|**Earnings**|Legislated<br>Total|2<br>31|

Note: Excludes Bosnia and Herzogovina, Montenegro, and Tajikistan due to lack of GDP data.

# **Table A4: Regression results from elasticity regressions**

||**F**|**E**|**PO**|**LS**|**R**|**E**|
|---|---|---|---|---|---|---|
||Pre Crisis|Crisis|Pre Crisis|Crisis|Pre Crisis|Crisis|
|**VARIABLE**<br>**S**|Employmen<br>t Growth|Employmen<br>t Growth|Employmen<br>t Growth|Employmen<br>t Growth|Employmen<br>t Growth|Employmen<br>t Growth|
|**GDPgrowth**|0.160*|0.252***|0.0230|0.199***|0.0897|0.225***|
|**Constant**|0.726|0.282***|1.719***|0.205|1.190**|0.200|
|**Observations**|244|144|244|144|244|144|
|**R-squared**|0.035|0.276|0.001|0.247|||
|**Number of**<br>**countries**|37<br>**Robus**|37<br>**t standard err**|**ors ***p<0.01,**|****p<0.05, ***|37<br>**p<0.1**|37|
# **Table 6 Summary Statistics**

|**Variable**|**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
||**1980**|**-1989**||||
|**Log of GDP per capita**|71|8.52|0.95|6.05|10.09|
|**Openness Index**|71|50.91|31.11|7.53|142.98|
|**Log of Distance to the US**|71|8.45|0.55|6.98|9.15|
|**English**|71|0.41|0.50|0.00|1.00|
|**Military conflict**|71|0.38|0.49|0.00|1.00|
|**Communism index**|71|0.14|0.35|0.00|1.00|
||**1970**|**-1979**||||
|**Log of GDP per capita**|72|8.34|0.93|6.20|10.01|
|**Openness Index**|72|51.10|36.09|7.16|189.99|
|**Log of Distance to the US**|72|8.43|0.56|6.98|9.15|
|**English**|72|0.42|0.50|0.00|1.00|
|**Military conflict**|72|0.40|0.49|0.00|1.00|
|**Communism index**|72|0.07|0.26|0.00|1.00|

41