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Entrepreneurship and Economic Growth

Entrepreneurship and Economic Growth

By Urmi Uppal

Edited by Nidhi Singh, Junior Editor, The Indian Economist

Theoretical Background

Meaning of entrepreneurship

Throughout intellectual history, the entrepreneur has worn many faces and fulfilled many roles.

I focus on three entrepreneurial roles, emphasized by Schumpeter, Kirzner and Knight, respectively.

A first is the role of innovator. Schumpeter was the economist who has most prominently drawn attention to the ‘innovating entrepreneur’. He or she carries out ‘new combinations we call enterprise; the individuals whose function it is to carry them out we call entrepreneurs.

A second is the role of perceiving profit opportunities. We label this role as Kirznerian (or neo-Austrian) entrepreneurship. A third is the role of assuming the risk associated with uncertainty. We label this role as Knightian entrepreneurship. When an individual introduces a new product or starts a new firm, this can be interpreted as an entrepreneurial act in terms of each of the three types of entrepreneurship.

The individual is an innovator, he (assumes that he) has perceived a hitherto unnoticed profit opportunity and he takes the risk that the product or venture may turn out to be a failure.

Based on their study of the history of economic thought about entrepreneurship, Hébert and Link (1989) propose the following ‘synthetic’ definition of who an entrepreneur is and what he does: ‘the entrepreneur is someone who specializes in taking responsibility for and making judgemental decisions that affect the location, form, and the use of goods, resources, or institutions’.

Entrepreneurship and economic growth

When searching for links between entrepreneurship and growth, the above definition does not suffice. The dynamics of perceiving and creating new economic opportunities and the competitive dimensions of entrepreneurship need more attention. The key contribution of entrepreneurship to economic growth might be singled out as being ‘newness’. This includes the start-up of new firms but also the transformation of ‘inventions and ideas into economically viable entities, whether or not, in the course of doing so they create or operate a firm’ (Baumol 1993)

Different types of entrepreneurship: different impacts on economic performance

The simplified version of the Carrying Capacity model by Carree and Thurik gives the broad results of the economic impact of (the lack) of Kirznerian (neo-Austrian) and Knightian entrepreneurship.

The results of the model are as follows:

  • The lack of Kirznerian entrepreneurship that would otherwise have alerted one retailer to make better business decisions leads to lower output.
  • A decrease in the number of individuals prepared to take risks in the marketplace (Knightian entrepreneurs) leads to an output loss.

The effects of the choice between entrepreneurship and employment

The contributions made in three articles are significant: Banerjee and Newman (1993), Iyigun and Owen (1999) and Lloyd-Ellis and Bernhardt (2000). The papers deal with the complicated issue of the two-way interaction between occupational choice and economic development.

On the one hand, both the number of individuals choosing to become self-employed and their entrepreneurial skills affect economic development. On the other hand, the process of development affects the returns to occupations. It transforms the nature of risks and the possibilities for innovation. 

Banerjee and Newman (1993) develop a model in which the distribution of wealth plays a central role. They assume that occupational decisions are dependent upon the distribution of wealth because of capital market imperfections, due to which poor agents can only choose working for a wage and wealthy agents become entrepreneurs. The initial distribution of wealth determines whether in the long run an economy converges to a case of only self-employment in small-scale production (‘stagnation’) or to one where an active labor market and both large- and small-scale production prevail (‘prosperity’).

Banerjee and Newman stress that the model implies that the initial existence of a population of dispossessed whose best choice is to work for a wage, is the condition needed for an economy to achieve the stage of prosperous capitalism.

Whereas Banerjee and Newman focus on financial requirements as the defining characteristic of entrepreneurship, Iyigun and Owen (1999) focus on the element of risk. Iyigun and Owen distinguish between two types of human capital: entrepreneurial and professional. Entrepreneurial activities are assumed to be more risky than professional activities. Entrepreneurs in the model accumulate human capital through a work-experience intensive process, whereas professionals’ human capital accumulation is education-intensive. The models predicts that, as technology improves, individuals devote less time to the accumulation of human capital through work experience and more to the accumulation of human capital through professional training. The allocation of an increasing share of time to formal education continues until a steady state is reached. Hence, entrepreneurs would play a relatively more important role in intermediate-income countries and professionals are relatively more abundant in rich countries.

However, both entrepreneurship and professional activities are important and those countries that initially have too little of either entrepreneurial or professional human capital may end up in a development trap. Iyigun and Owen point at former communist countries as an example of economies that have a highly educated labor force but that still not achieve the high-income steady state due to a shortage of entrepreneurs.

Lloyd-Ellis and Bernhardt (2000) also derive how the scarcity or abundance of entrepreneurial skills is the defining variable behind the equilibrium development process. In their model, individuals may choose between working as entrepreneurs, wage laborers in industry or in subsistence agriculture. Just like in the Banerjee and Newman model entrepreneurs are faced with a limited capital market and (inherited) wealth is needed to permit entrepreneurial activity to expand. The economy in the model goes through four separate stages. An interesting outcome of the model is that the average firm size rises quickly in the first stages of the development process, but then falls in the later stages of the development process. The number of entrepreneurs (outside agriculture) as a fraction of population may rise in each of the stages.

Carree and Thurik, however, present a simple new model of occupational choice in which the impact of entrepreneurial activities is analyzed by considering the consequence of not allowing firms to enter (or exit) or of not allowing firms to expand (or to limit) their activities. They distinguish between three possible economic ‘systems’. In the first system, labelled ‘market economy’, there is complete freedom of entry and exit and of firms adjusting their inputs to maximize profits. In this system there is complete entrepreneurial and managerial freedom. In the second system, labelled ‘semi-planned economy’, there is no freedom of entry or exit. However, firms are free to adjust their input quantities so as to achieve maximum profits. In such an economic system the large incumbent firms are considered as the engines of economic progress. Starting new enterprises is hampered by regulations and by relatively low esteem of business ownership. The third economic system, labelled ‘planned economy’, has also lost its managerial freedom of adjusting inputs to maximize profits. Firms are assigned to produce output using a certain fixed amount of labor even though it may lead some firms to be unprofitable.

Clearly, the three economic ‘systems’ are extremes. However, comparing the economic performance of such virtual systems may enhance our understanding of the total contribution of entrepreneurial activity on the long and short term on economic performance. In addition, the conditions in the three systems may approximate actual conditions in existing economic systems. For example, the market economy of the United States grants (potential) entrepreneurs considerable freedom with little government intervention. In contrast, the economies of Continental Europe, like France and Germany and the Scandinavian countries, have a much larger role for government. In these countries government has actively intervened to support large enterprises in the recent past. The Soviet type of economic systems is the prime example of the planned economy system.

Given the distribution of the abilities,the equilibrium occupational choice and (maximum) total output can be derived. In case of changes in the ability distribution the manner in which equilibrium on the labor market is restored differs across the economic systems. In case of the ‘market economy’ system there will be entry of managers with increased ability and exit of managers with decreased ability, changes in firm sizes and changes in the wage level. In case of the ‘semi-planned economy’ system there will be changes in firm sizes of incumbents and changes in the wage level. The one variable that restores equilibrium in the ‘planned economy’ system is the wage level because of the absence of managerial discretion to adapt labor demand.

It is obvious that due to larger ‘degrees of freedom’ the total output after changes in the ability distribution will be highest for the ‘market economy’ and smallest for the ‘planned economy’. The differences between the performances will be larger, the more the ability distribution changes over time. Hence, in periods of important changes in technological regimes and on the longer term the differences are likely to be largest. This finding is related to that presented by Eliasson (1995) that lack of new entry of firms will adversely impact economic performance not so much on the short term but in the long term.

Entrepreneurship in endogenous growth model 

One of the reasons that entrepreneurship disappeared from economic theory is that it played no role in the neoclassical growth model as developed by Solow (1970). An important characteristic of this growth model is that technological improvements are exogenous and therefore independent of economic incentives. Economic growth in the traditional growth models is achieved by capital accumulation and exogenous technological progress, both of which leave little room for any entrepreneurial role whatsoever. The more recently developed endogenous growth models also support the idea that improvements in technology have been the key force behind perpetually rising standards of living. However, this long-term growth process is assumed in many endogenous growth models to be determined by purposive, profit-seeking investment in knowledge (Grossman and Helpman). The act of seeking profits by shifting resources to achieve improvements in technology can be seen as an entrepreneurial act because the outcome of the investments is uncertain. However, it is not common for endogenous growth models to explicitly address the issue of entrepreneurship as driving force of technological and economic development.

I will discuss three exceptions in this section. The first exception is the Aghion and Howitt’s (1992) model of creative destruction. The second exception is the endogenous market structure model by Peretto (1998; 1999a; 1999b) and the third exception is the imitation model developed by Schmitz (1989). Of these three exceptions the model by Aghion and Howitt has been the most influential.

Aghion and Howitt introduce the notion of Schumpeterian ‘creative destruction’ into a growth model by having firms investing resources in research to achieve a new product that renders the previous product obsolete. Capital is excluded from the basic model and growth results from technological progress, being a result from competition among firms that generate innovations.

Firms are motivated by the prospect of (temporary) monopoly rents after a successful innovation is patented. A next innovation will again destroy these rents as the existing good is being made obsolete by the Schumpeterian entrepreneur.

This model shows a direct connection between research in stationary equilibrium and the degree of market power. Some extent of market power to achieve rents is needed for Schumpeterian entrepreneurs to engage into research. The effect of market power attracting entrepreneurial energy shows the importance of imperfect competition for the growth process.

Competition and growth are inversely related in this Schumpeterian model, something usually not supported by empirical evidence. Aghion and Howitt (1997), therefore, extend their model to show that a more competitive market structure may contribute to economic growth. In Howitt and Aghion (1998), the authors add capital to their model of creative destruction. They show that capital accumulation and innovation are complementary processes and equal partners in the growth process. Aghion and Howitt have contributed to the endogenous growth literature by connecting purposive, profit-seeking investment in knowledge to the persons performing this task: entrepreneurs.

In a series of papers Peretto introduces a different kind of endogenous growth model where an endogenous market structure is incorporated. His model has a key role for the number of firms, again in the intermediate sector, determining the returns to investment and R&D. An important difference between his model and the model by Aghion and Howitt is the assumption that monopolistic firms in the intermediate sector set up in-house R&D facilities to produce a continuous flow of cost-reducing innovations. This differs from the independent research firms in Aghion and Howitt (1992). The relation between the number of firms and returns to investment and R&D in the Peretto (1999b) model is determined by a trade-off between external and internal economies of scale. External economies of scale are a result of complementarities across firms because aggregate output is increasing in the number of intermediate goods.A large number of firms in the model therefore leads to high specialization, large investment and R&D programs, and fast growth. On the other hand, the fragmentation of the market due to a large number of firms leads to small investment and R&D programs, and slow growth. An increase in the number of firms increases the market size through the specialization effect whereas each firm’s market share is reduced through the fragmentation effect. As a consequence there is a hump-shaped relation between the number of firms and economic growth.

In Peretto (1998) entrepreneurs play a more visible role. His model seeks to explain a shift in the locus of innovation from R&D undertaken by inventor-entrepreneurs (‘competitive capitalism’) to R&D undertaken within established firms in close proximity to the production line (‘trustified capitalism’). In the model the economy converges to a stable industrial structure where entrepreneurial R&D and the formation of new firms peter out, while growth is driven by corporate R&D undertaken by established oligopolists.While it is true that from about 1870 till 1970 the corporate laboratories affiliated with large manufacturing firms have been increasingly responsible for commercial R&D, the disappearance of entrepreneurial energy as important determinant of economic growth is an unrealistic feature of the model. In Peretto’s setup entrepreneurs must develop new differentiated products since entering an existing product line in Bertrand competition with the incumbent is bound to lead to losses because of sunk entry costs. Entrants are net creators of knowledge, as “they create a new product and the knowledge necessary to run manufacturing operations.” Although in more developed stages the economy in Peretto’s model experiences a transition from entrepreneurial to corporate R&D, entrepreneurship plays a vital role in economic development: only when a critical number of firms have entered the market, established firms begin investing in R&D. A key result of Peretto’s models is that “there is an inverted-U relationship between the number of firms and steady-state growth.”

 Schmitz (1989) was the first to present an endogenous growth model that relates entrepreneurial activity and economic growth. However, his entrepreneurs are more ‘passive’ than in the other models because their role is restricted to that of ‘imitation’. This may have contributed to the Schmitz model being less influential than the Aghion and Howitt model. His model implies that the equilibrium fraction of entrepreneurs in an economy is lower than the social optimal level, providing a rationale for policies stimulating entrepreneurial activity.

Empirical Evidence of the hypothesized relationships       

Studying the impact of entrepreneurship on the Total GDP of the Economy

It is hypothesized that an increase in entrepreneurial activity (as measured by the number of new businesses registered) will have a positive impact on the Total GDP.

Model 1: OLS, using observations 1-252 (n = 118)

Missing or incomplete observations dropped: 134

Dependent variable: l_Total_GDP

  Coefficient Std. Error t-ratio p-value
l_No_of_new_bus 2.73458 0.0393719 69.4550 <0.00001 ***
Mean dependent var  24.68358 S.D. dependent var  2.283269
Sum squared resid  1716.876 S.E. of regression  3.830685
R-squared  0.976321 Adjusted R-squared  0.976321
F(1, 117)  4823.995 P-value(F)  5.93e-97
Log-likelihood -325.4118 Akaike criterion  652.8236
Schwarz criterion  655.5942 Hannan-Quinn  653.9485


The regression[1] performed above provides strong evidence for the hypothesis. As the number of new businesses registered increases by 100%, the Total GDP increases by 273%. The coefficient of the explanatory variable is significant and the Adjusted R-squared of the model is 0.976 which is very high, indicating that the model has a very good fit.

I further divided the data set into developed and developing countries[2] in order to investigate whether entrepreneurship has a more significant role to play in developing countries versus developed countries.

The following is the regression performed for Developed Countries:

Model 3: OLS, using observations 1-75 (n = 39)

Missing or incomplete observations dropped: 36

Dependent variable: l_Total_GDP

Coefficient Std. Error t-ratio p-value
l_No_of_new_bus 2.69077 0.0418716 64.2625 <0.00001 ***
Mean dependent var 25.65169 S.D. dependent var 1.993728
Sum squared resid 235.3617 S.E. of regression 2.488721
R-squared 0.990882 Adjusted R-squared 0.990882
F(1, 38) 4129.667 P-value(F) 2.23e-40
Log-likelihood -90.39106 Akaike criterion 182.7821
Schwarz criterion 184.4457 Hannan-Quinn 183.3790

The following is the regression performed for Developing Countries:

Model 1: OLS, using observations 1-140 (n = 73) (developing countries)

Missing or incomplete observations dropped: 67

Dependent variable: l_Total_GDP

Coefficient Std. Error t-ratio p-value
l_No_of_new_bus 2.76398 0.0594957 46.4567 <0.00001 ***
Mean dependent var 24.20961 S.D. dependent var 2.270686
Sum squared resid 1393.264 S.E. of regression 4.398965
R-squared 0.967716 Adjusted R-squared 0.967716
F(1, 72) 2158.229 P-value(F) 2.00e-55
Log-likelihood -211.2190 Akaike criterion 424.4380
Schwarz criterion 426.7285 Hannan-Quinn 425.3508


It can be seen that both the coefficient have the same sign and are significant and the fit of the model (Adjusted R squared) is quite high, but the coefficient of the explanatory variable for the developing countries regression is greater than that for the developed countries regression. This may indicate that a %age increase in the number of businesses in developing countries leads to a larger %age increase in total GDP in developing countries than in developed countries (276% versus 269% for a 100% increase) 

What determines entrepreneurship?

Theoretically, factors such as access to capital, corruption/bribery, level of development of the economy and other institutional factors should affect the level of entrepreneurship in the economy. I have used the following variables in the regression:

  1. Log of the number of new businesses started: as a dependent variable measuring the level of entrepreneurial activity
  2. Informal payments made to officials (% of firms): as an independent variable to measure corruption or bribery
  3. Log of total GDP: as an independent variable as a measure of the level of development of the economy
  4. Domestic credit to the Pvt sector (% of GDP): as an independent variable as a measure of access to capital
  5. Ease of doing business Rank (by World Bank): as a measure of the institutional and non institutional factors that affect how easy or difficult it is to start a business in an economy. A higher number indicates a more difficult set up.

Model 16: OLS, using observations 1-252 (n = 39)

Missing or incomplete observations dropped: 213

Dependent variable: l_No_of_new_bus

Coefficient Std. Error t-ratio p-value
Infomal_payment -0.0579133 0.0307289 -1.8847 0.07900 *
l_Total_GDP 0.495372 0.0329464 15.0357 <0.00001 ***
Ease_of_Doing_b -0.0193336 0.00598633 -3.2296 0.00561 ***
Domestic_credit -0.0312135 0.00881015 -3.5429 0.00295 ***
Mean dependent var 8.179144 S.D. dependent var 2.080496
Sum squared resid 15.46466 S.E. of regression 1.015371
R-squared 0.988536 Adjusted R-squared 0.986243
F(4, 15) 323.3624 P-value(F) 2.34e-14
Log-likelihood -25.00396 Akaike criterion 58.00792
Schwarz criterion 61.78567 Hannan-Quinn 58.64726


The model above has a strong fit (Adjusted R squared of 0.98) and all the coefficients are significant. They can be interpreted as follows:

  • As Total GDP increases by 100%, the number of new businesses increases by 49%
  • If the percentage of firms who pay informal payments to officials increases by 1%, the number of businesses registered decreases by 5%
  • If the ease of doing businesses rank increases by 1 (indicating a relative worsening of factors affecting the entrepreneurship setup) the number of new businesses registered decreases by nearly 2%
  • A 1 unit increase in the domestic credit to private sector as a %age of GDP leads to a 3% decrease in the number of new businesses started. This may be because as credit expands, there is greater investment into expanding existing businesses as opposed to starting new ones.

Does entrepreneurship impact inequality?

I have again divided the data set into Developed and Developing Countries and attempted to study the impact of entrepreneurship on Inequality, as measured by the Gini Coefficient. A value of 100 in the Gini Coefficient means perfect inequality, a value of 0 means perfect equality.

The following is the regression for developing countries:

Model 5: OLS, using observations 1-140 (n = 22)

Missing or incomplete observations dropped: 118

Dependent variable: Gini_Index

Coefficient Std. Error t-ratio p-value
l_No_of_new_bus 4.36628 0.254857 17.1323 <0.00001 ***
Mean dependent var 39.85318 S.D. dependent var 9.283359
Sum squared resid 2453.900 S.E. of regression 10.80983
R-squared 0.933231 Adjusted R-squared 0.933231
F(1, 21) 293.5154 P-value(F) 8.08e-14
Log-likelihood -83.07495 Akaike criterion 168.1499
Schwarz criterion 169.2409 Hannan-Quinn 168.4069

 The following is the regression for developed countries:

Model 1: OLS, using observations 1-75 (n = 26)

Missing or incomplete observations dropped: 49

Dependent variable: Gini_Index

Coefficient Std. Error t-ratio p-value
l_No_of_new_bus 3.65969 0.483295 7.5724 0.00064 ***
Mean dependent var 36.20833 S.D. dependent var 7.876081
Sum squared resid 655.7842 S.E. of regression 11.45237
R-squared 0.919796 Adjusted R-squared 0.919796
F(1, 5) 57.34081 P-value(F) 0.000637
Log-likelihood -22.59585 Akaike criterion 47.19170
Schwarz criterion 46.98346 Hannan-Quinn 46.35809


The regression indicates that whether the country is developed or developing, an increase in entrepreneurial activity results in increased inequality. The coefficients are significant and the fit of the model is strong. For a 100% increase in the Number of new businesses, the Gini Coefficient increases by 3.6 (for developed countries) and 4.36 (for developing countries). This indicates that that for a given %age increase in the number of new businesses registered, inequality increases by more in developing countries than in developed countries.

Further, I regressed the log of number of new businesses started on the Gini Coefficient to study the reverse causation. The results were as follows:

Model 1: OLS, using observations 1-252 (n = 29)

Missing or incomplete observations dropped: 223

Dependent variable: l_No_of_new_bus

Coefficient Std. Error t-ratio p-value
Gini_Index 0.219359 0.0115713 18.9572 <0.00001 ***
Mean dependent var 9.063791 S.D. dependent var 1.394231
Sum squared resid 176.1388 S.E. of regression 2.508121
R-squared 0.927719 Adjusted R-squared 0.927719
F(1, 28) 359.3744 P-value(F) 1.64e-17
Log-likelihood -67.30688 Akaike criterion 136.6138
Schwarz criterion 137.9811 Hannan-Quinn 137.0420


The regression indicates that inequality leads to a rise in entrepreneurship as well. For a unit increase in the Gini Coefficient (increase in inequality), the number of new businesses increased by 21%.

Note: All regression results presented above are free from multicollinearity and heteroscedasticity.


It can be concluded that empirical evidence exists pertaining to theory. The positive relationship and reverse causation between entrepreneurship and economic growth/ level of economic development has been established. It can also be established that economic inequality increases entrepreneurship and the reverse causation is true as well. Further, we have seen the differences that exist in these relationships between developing and developed countries.


  • World Bank
  • Impact of Entrepreneurship on Economic Growth, Carree and Thurik


The appendix is the data set obtained from world bank used in the regression.

[1] The software used for regression is Gretl.

[2] The division between developed and developing countries has been done according to the status awarded by World bank. “High Income” Countries are Developed Economies whereas all other are developing.

Aruba 25354.78247 2584463687 57.41257925
Albania 2067 82 4109.082012 12959563902 6 39.25173998
United Arab Emirates 7538 26 39057.84011 3.48595E+11 7 63.98565866
Antigua and Barbuda 66 12420.16276 1094862188 4.8 8 79.62155475
Bahrain 47 22466.94518 29044457920 7 68.91196454
Bahamas, The 76 21490.35708 7872584000 19.1 7 84.44368456
Bermuda 85973.15842 5550771000
Barbados 84 15503.32855 4368900000 14.7 8
Brunei Darussalam 79 40244.31182 16359795686 15 31.84306111
Channel Islands
Cayman Islands
Cyprus 18306 38 29206.5106 24851264943 6 296.4593355
Czech Republic 21782 68 20580.17767 2.16011E+11 9 55.63410691
Micronesia, Fed. Sts. 150 3000.150055 310287519.3 7 19.56217902
Greece 89 25630.79451 2.89627E+11 11 121.8769129
Guatemala 5111 93 3242.690796 47688885121 6.3 12 23.55545534
Guyana 113 3258.048188 2576731667 18.4 8 37.87540433
Honduras 125 2261.649191 17588097150 6.1 13 47.99754762
Haiti 177 732.2093375 7346156703 12 15.01423587
India 60450 131 1533.661307 1.87284E+12 12 49.92559983 33.9
St. Kitts and Nevis 201 97 13198.36438 699130559.6 6.1 7 69.88044634
Kuwait 101 51496.92715 1.60913E+11 12 61.7101463
Liechtenstein 606
Lithuania 5399 25 14148.39115 42872072871 6 53.50291765
Latvia 12039 24 13837.60556 28480338368 4 82.0233692
Macao SAR, China 67359.47356 36796998498 56.20445558
St. Martin (French part)
Monaco 163025.859 6074506533
Malta 2678 100 21963.8116 9151793161 11 129.8672499
Northern Mariana Islands
New Caledonia
Oman 3165 44 23132.9389 69971912138 5 39.98599971
Puerto Rico 37 26733.76117 98757000000 6
Qatar 45 89735.68187 1.71476E+11 8 38.89506884
Romania 58130 73 8874.315178 1.89776E+11 6 42.82031716 24.24
Rwanda 4091 54 570.1668828 6354119344 2
Sudan 143 1537.598665 63997129027 10 11.38352723
Solomon Islands 92 1610.924286 866672433.3 7 23.1278052
Seychelles 77 12289.2563 1074584860 10 25.59622089
Chad 189 1006.319771 12156380062 11 4.848502221
Tunisia 4469 49 4350.335976 46434616144 10 75.46774139 36.06
United States 4 49853.68234 1.55338E+13 6 184.7697091
Vietnam 98 1543.02695 1.35539E+11 10 101.7989695
Australia 92396 10 62125.75523 1.38689E+12 3 123.4444826
Austria 3321 28 49338.76066 4.15612E+11 8 120.346705
Belgium 19054 32 46422.12222 5.1286E+11 3 92.75386653
Canada 174000 17 51554.05921 1.77779E+12 1
Switzerland 12902 27 83087.05284 6.57418E+11 6 169.8493744
Chile 40118 34 14512.61129 2.51191E+11 0.7 7 70.43823134
Germany 73247 19 44314.96616 3.62486E+12 9 103.813058
Djibouti 172 11
Dominican Republic 4592 112 5492.654983 55737254720 10.1 7 24.00585708 47.2
Estonia 7199 21 16808.94864 22522780938 5 83.33537717
Ethiopia 1327 124 335.002886 29946934090 9
Fiji 58 4324.685529 3753485389 9 75.8419856
Faeroe Islands
Georgia 10940 9 3219.569963 14434619972 2 32.68121065 42.1
Grenada 102 7766.473216 816054406.7 7.1 6 80.98624734
Ireland 13767 15 49343.5439 2.25833E+11 4 199.7340274
Iceland 1983 13 44030.57988 14046371410 5 97.19746917
Israel 33 33250.0909 2.58217E+11 5 89.46342008
Italy 68034 67 36147.6461 2.19501E+12 6 122.4187287
Japan 87578 23 46134.56824 5.89679E+12 8 174.8172636
Korea, Rep. 60968 6 22388.39597 1.11447E+12 5 149.0394283
Luxembourg 2448 56 111812.9683 57957916667 6 174.3545731
Netherlands 34867 30 49841.61229 8.3201E+11 6 199.3294349
Norway 14346 7 99143.16512 4.91065E+11 5
New Zealand 45234 3 36918.78818 1.62635E+11 1
Poland 14434 48 13382.07215 5.15667E+11 6 54.81762251 33.75
Portugal 27759 29 22513.5266 2.37675E+11 4 192.0979955
Slovenia 5676 31 24478.34954 50250208507 2 90.06636238
Sweden 34298 14 56755.33193 5.36293E+11 3 136.6386509
Swaziland 120 3274.387294 3969078027 12 27.17885685 51.49
Uzbekistan 14544 156 1544.730285 45324317482 6


The author just completed her graduation studies in Economics at St. Stephen’s College, Delhi University, and is set to join a reputed international consultancy firm. She is also the co-founder of the NGO Care for Bharat. You can contact her at [email protected]

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