Title: Impact of local conditions on the FDI-growth relationship
Author name: Rinchen Dorji
Correspondence
Rinchen Dorji
Graduate School of Economics, Nagoya University
Press-Inn Yamate, Aichi-Ken, Nagoya-shi
Postal code: 466-0804
Phone contact: 08037111410
Email address: jalamjalamgi@gmail.com

Impact of local market conditions on the FDI-growth relationship
Abstract
Empirical puzzle regarding FDI-growth relationship has recently been explained in terms of hostcountry’s absorptive capacities. Countries with good absorptive capacity tend to harness
benefits while others experience negative impacts from FDI. Nonetheless, extant literature fails
to identify a variable that measures local market conditions comprehensively, which may havebiased their estimates in explaining the growth productivity of foreign capitals. This paper,
therefore, uses two variables (level of domestic investment and productive capacity) as ameasure of local conditions, and examine if they affect the relationship between growth and FDI
inflows using panel data approaches. It is unveiled that the positive effects of FDI diminisheswith every increase in local market conditions. This may be interpreted as crowding-out effect or
weak impact of FDIs on growth in strong and competitive domestic markets. We propose thatnational leaders must carefully assess MNC’s impact on the local economy before making any
decisions regarding FDI and related policies.
Keywords: economic growth, foreign investment, domestic investment, fixed effects, developing
countries.

1. Introduction
The influx of foreign capital into developing countries is believed to bring in not only advanced
technologies, knowledge, and managerial skills but also cause spillover effects. These spillover
effects can be either positive or negative, largely depending on the home market’s absorptive
capacity; that is its ability to take advantage of foreign capital. Empirical evidence reports thatcountries will grow with foreign investment if they can maintain a higher level of human capital,
advanced physical infrastructures and financial services, liberal trade policies, and stable
macroeconomic conditions. For instance, Borensztein et al. (1998) find foreign direct investment(FDI) to impact growth positively but the magnitude of which depends on the stock of humancapital available in the host economy, suggesting that FDIs are growth productive only with theinteraction with human capital. Similar recognition of human capital in the FDI-growth
relationship has also been offered by Su and Liu (2016) where they discovered human capital asa facilitator for technology transfer. These findings imply that countries performing poorly interms of human capital will not be able to reap benefits from FDI. Similarly, researchers accord
equal importance to infrastructural facilities, financial systems, and legal institutions in the hostcountries for FDI productivity. Alfaro et al. (2002) claim that emerging economies must
maintain a minimum level of financial development to leverage technology diffusion. In specificterms, they estimate an annual growth rate of 0.6 percent higher for the countries with better
financial markets.
Empirical evidence thus far measures absorptive capacity of host economies in terms of specificconditions such as stock of human capital, level of financial sector development, access toquality infrastructures, macroeconomic variables, policy instruments among others in the local
market. For instance, while explaining the importance of local conditions, Borensztein et al.
(1998) defined local conditions by the level of human capital stock in the host countries.
Although other impacts are appropriately controlled in their models, the coefficient of interaction
term “FDI-human capital” does not fully capture the intended interaction effect of local
conditions. This results in underestimation of the interaction effect. The same issue persists in thestudies that use one or few specific variables to define local conditions. This paper therefore,
reinvestigates the impact of local market conditions in the host economies to explain therelationship between FDI and economic growth by redefining local conditions by a more holistic
variable.
Theoretically, countries that can offer adequate favorable business and economic conditions
generally will have a large and persistent domestic investment. In other words, high level ofdomestic investment can mean that an economy is attractive with low business risks andabundant opportunities. Another interpretation can be such that in countries where there exist
large domestic investments, there will be better infrastructures, institutions, health and education,market systems among others, altogether defining a highly competitive/efficient market
economy. Thus, level of domestic investments in the host economies can be regarded as a trueindicator of local market conditions. Similarly, UNCTAD publishes data on productive
capacities of 193 economies. Productive capacity, in their definition measures a country’s ability
to produce goods and services and grow over time.
Therefore, this paper considers domestic investments and productive capacity of host countries
as better indicators of overall local conditions and examines how it influences the relationshipbetween FDI and growth in developing countries. While growth theories posit foreign
investments as a source of capital, the ultimate reality is not always that simple. In the words of
Agosin and Machado (2005), the growth effect of FDI is not always the same, varying fromcountry to country, depending on domestic policy, the kind of FDI the country receives, and thestrength of domestic enterprises.
In fact, literature is not silent on the significance of domestic investment on economic growth.
Alfaro et al. (2002) use domestic investment as a regressor in the growth regression along withvarious other variables. Marc Lautier & Francois Moreaub (2012) evaluates relationship between
domestic and foreign investments for a panel of 68 countries in which domestic investment isfound to serve as a catalyst for FDI attraction. Our study deviates from the existing ones by
examining impact of domestic investments on growth but via its moderation effect. By doing so,
we investigate the impact of domestic conditions and foreign investments on growth
independently and by their interaction. In specific terms, this study examines if the level ofdomestic investments/productive capacity of host countries influence the growth productivity offoreign capital. Based on the data of 102 countries for the period 1996–2020, we find growthproductivity of FDIs in developing countries to decrease with every increase in domestic
conditions. It implies – foreign capital inflows either displaces local enterprises or its impact
becomes negligible as local economy develops.
The next section offers a brief description of the data used for the study followed by thepresentation of methodology in section III. Section IV contains a detailed presentation of theresults obtained from several econometric approaches. Finally, section V presents a brief
conclusion along with a discussion.

2. DataThis study is based on a dataset consisting of a cross-section of underdeveloped and developing
countries for the period 1996–2020. According to the World Bank database, there are 137
countries in the list of low-income, lower-middle, and upper-middle-income categories.
However, data on some of these countries are limited because of which only 102 countries havebeen included in this study. In short, the choice of countries is primarily based on data
availability. Information on the FDI flows suggests that developing and underdeveloped
countries received an increasing amount of foreign investments only from the later part of the20th century, especially after 1990s. Therefore, the period before 1990s has been excluded.
There are several sources of data. Information on GDP per-capita, net FDI inflows, gross fixed
capital formation, inflation, broad money supply, trade, real exchange rate and gross school
enrollment ratios are extracted from the World Development Indicators, DataBank (2021). Dataon productive capacity is obtained from UNCTAD database.
The output growth rate is measured as the growth of per capita GDP in constant dollars (2000)
obtained from World Development Indicators, DataBank (2021). Inflation, measured by theannual consumer price index proxies macroeconomic stability. Openness to international market
is measured by the ratio of the sum of exports and imports to GDP. The secondary (tertiary)school enrollment ratio is calculated by dividing the total number of students enrolled into
secondary (tertiary) levels by the total population in that age category. Secondary education
enrollment rate has also been considered to measure human capital by Borensztein et al. (1998).
In addition, to capture the country differences in terms of financial sector development, theM2/GDP ratio is included in the model. For the lack of multi-year cross-country data on
domestic investment especially of the developing countries, we use information on gross fixed
capital formation as does Alfaro et al. (2002) and Marc Lautier & Francois Moreaub (2012).
Brief statistics of the data in Table 5 show that the countries included in the sample are varied interms of almost all variables. Concerning average growth during the 25 years from 1996 to 2020,
Eritrea experienced the lowest (-1.00%) growth while China had the highest (8.48%) growth inper-capita GDP. Foreign investment inflows into the developing countries have risen sharply
over the years, increasing from USD 345 billion in 1995 to USD 1530 billion in 2019 (Marc
Lautier & Francois Moreaub, 2012). During the same period, net FDI inflows as a ratio to GDP
were highest in Liberia and lowest in Iraq. Bhutan tops in terms of capital accumulation while
the Solomon Islands experienced the slowest growth in capital during the period. Countries also
differ vastly in terms of human capital accumulation measured by both secondary and tertiary
school enrolment rates.

3. Methodology
The main purpose of this paper is to estimate the impact of foreign capital on growth andexamine if the local conditions play any role in explaining the FDI-growth relationship indeveloping countries. While researchers in the past such as Alfaro et al. (2002), Borensztein etal. (1998) among others examine the same problem, their estimates suffer from incomplete
moderation effect of local conditions. This is likely because they measure local conditions using
some specific indicator variables such as financial institutions, trade regimes, human capital,
availability of natural resources and so on. When done so, the estimates of the effect of local
conditions on the relationship between FDI and economic growth can be biased.
We first adopt a model similar to that of Alfaro et al. (2002).
𝑦!" = 𝛽# + 𝛽$ 𝑦!%!"!&' + 𝛽( 𝐹𝐷𝐼!" + 𝛽) 𝐿𝐶!" + 𝛽* (𝐹𝐷𝐼!" ∗ 𝐿𝐶!" ) + ∑𝛽+ 𝑋!" + 𝑒!" − (1)where 𝑦!" represents per capita GDP for country 𝑖 in period 𝑡, 𝐹𝐷𝐼!" and 𝐿𝐶!" measure net foreign
capital inflows and local conditions respectively. Their inclusion in the growth regression
independently helps test if they affect growth by themselves. 𝑋!" comprises various control andpolicy variables that are frequently included in growth regressions of cross-country nature.
Following existing literature, human capital, financial development, inflation rate, and trade
openness have been included to control for country differences. Also, lagged values of thedependent variable (𝑦!",$ ) are included in the model to capture convergence effects. Finally, 𝑒!"is the error term capturing unobserved country and time-variant effects in the data.
Cross-country estimates of this nature most often are affected by a presence of unobservable
fixed effects. Inability to properly account for these effects would bias the estimates. Hence, we
implement fixed effect estimates of equation (2) which includes individual-specific (𝜆! ) andtime-specific (𝜃" ) effects respectively.
𝑦!" = 𝛽# + 𝛽$ 𝐹𝐷𝐼!" + 𝛽( 𝐿𝐶!" + 𝛽) (𝐹𝐷𝐼!" ∗ 𝐿𝐶!" ) + 𝜆! + 𝜃" + 𝑒!" − (2)To eliminate the country fixed effects, this study employs fixed effect (first difference) estimatorapproach. Each variable is subtracted by its own value of the previous year and we obtainequation (2).
ÿ!" = 𝛽$ ä$!" + 𝛽( ä(!" + 𝛽) (ä$!" ∗ ä(!" ) + ü! − (3)where double dots above the variables indicate their values in first differences, ä$!" and ä(!" arefirst differences of FDI and local condition variable, and ü! error term. Following literature, we
include a set of regressors to control if any, omitted variable biases in the model. Variables suchas human capital, financial development, inflation rate, and trade openness have been included in
the model.

4. Estimation Results
This paper empirically examines the impact of FDI on economic growth and then assesses if theeffectiveness of FDI in enhancing growth depends on the local conditions of developing hosteconomies.
We begin by implementing a simple panel data approach by grouping the sample period into five
five-year periods (1996–2000, 2001 – 20005, … , 2016–2020) and construct the variables foreach period separately. The dependent variable is the growth rate of GDP per-capita during each
sub-period and independent variables are the averages of the same variables for each group. We
treat level of domestic investment to define local conditions. The results are presented in Table 1
with standard errors in parenthesis. The estimated coefficients of the variables of interest appear
as expected. Growth is positively influenced by foreign capital inflows and domestic investment.
In specific terms, a unit increase in net FDI inflows-to-GDP ratio causes per-capita GDP to growby 0.006–0.009 percent.
In column (2) through (4), additional covariates are added in the model for robustness test andresults do not show significant variation from the results in column (1). It is worth mentioningthat the coefficient for FDI is positive and statistically significant throughout these models,suggesting a positive direct impact of FDI inflows on the growth of developing economies.
While the other results are as expected, trade openness appears negative. It is surprising that thecountries that are more liberal/open to international markets seem to register lower growth
contrasting with the existing literature.
Table 1: Panel Regression Results
Models
Initial GDPPC(1)-.273***(.033)(2)-.199***(.035)(3)-.152***(.033)(4)-.167***(.035)(5)-.278***(.033)(6)-.205***(.035)(7)-.153***(.034)

Pop-growth-.001(.014)-.001(.013)-.022*(.012)-.049***(.013)

00(.014).001(.013)-.021*(.012).007***(.002).003***(.001).008***(.002).006***(.002).004***(.001).006***(.002).004***(.001).002***(.001).006***(.002).003**(.001)00(.001).009***(.002).009***(.002).003***(.001).029***(.008)-.001***(.00)-.002***(.001).001**(.00)-.002***(.001).001*(.00)-.00***
(.00)

-0.00
(.001).001***(.00)-.001***(.00).008***(.002).004***(.001).022***(.008)-.001**(.00)-.002***(.001).001*(.00).004***(.002).002***(.001).008(.007)-.00(.00)-.002***(.001)
.001*
(.00)
0***
(.00)

DI
HK
FDI
Interaction
Money
Trade OpenInflation(8)-.162***(.035)-.052***(.013).002(.002)00(.001)-.004(.008)
-.00*
(.00)
00
(.001).001***(.00)-.001***(.00)

Constant

1.887***(.234)

R-squared
0.232
Number of obs
491
Prob > F
0.000*** p<.01, ** p<.05, * p<.1

1.374***(.244)

1.122***(.236)

1.271***(.242)

1.857***(.232)

1.364***(.242)

1.122***(.236)

0.263
470
0.000

0.307
440
0.000

0.367
357
0.000

0.245
491
0.000

0.272
470
0.000

0.307
440
0.000

1.265***(.241)
0.374
357
0.000

To examine if the impact of FDI on growth depends on local conditions, we include aninteraction term between FDI and domestic investment in model (5) through (8). The interaction
term appears surprisingly negative and statistically significant. This result is in contrast to thefindings of Le and Suruga (2006), Borensztein et al. (1998), Alfaro et al. (2002), among others
where they find local conditions to intensify positive externalities associated with FDI.
Our results show that foreign investment plays an important role in the growth of developing
economies via the transfer of limited resources. However, the positive impact of foreign
investment on growth falls as the local conditions in the host economy increases. This possibly
suggest that the indirect effect of FDI is negative for developing countries. It may be that a large
influx of foreign investments is displacing domestic enterprises which is known as crowding-out
phenomenon. Primarily, foreign multinationals are not only technologically advanced but also
substantially large making domestic firms incomparable and impossible to compete with them inthe market. Inclusion of additional explanatory variables in the model does not lead to noticeablechange in the estimates shown in models (5) through (8) in Table 1.
Any presence of country-specific fixed effects in the model can be eliminated by using fixed
effect estimators. We run fixed effect first difference estimator given in equation (3), the resultsfrom which are given in Table 2. The abundance of domestic investment in the host economy
negatively affects the growth effect of FDI. In other words, better the host economy is in termsof its local conditions, FDI is less likely to be productive in enhancing growth in developing
countries. It seems like domestic circumstances are the dominant determinants of economic
growth in developing countries with large domestic conditions. Indeed, the coefficient estimatesof FDI and DI explain this.

Table 2: First Difference Estimates
Models(2).014***(.0024).339***(.0087)(3).014***(.0024).323***(.0094)(4).011***(.0022).414***(.0104)-.003***(.0003)-.002***(.0003).001*(.0004).023***.022***(.0022)(.0022)R-squared
0.428
0.455
Number of obs
2158
2143
Prob > F
0.000
0.000
*** p<.01, ** p<.05, * p<.1.023***(.0022)
0.404
2032
0.000

-.002***(.0003).0001(.0005)-.0002**(.0021).019***(.0021)
0.491
1905
0.000

FDI
DI

(1)
.012***(.0024).334***(.0088)Interaction
Trade OpenFinancial
Inflation
Constant(5).012***(.0024).338***(.0091)-.015**(.0069)(6).015***(.0023).344***(.0089)-.014**(.0067)-.003***(.0003)(7).015***(.0024).326***(.0094)-.023**(.0072)-.002***(.0003).001(.0004).023***(.0022)
0.429
2158
0.000

.023***(.0022)
0.456
2143
0.000

.023***(.0022)
0.407
2032
0.000

(8)
.012***(.0023).419***(.0104)-.035**(.0098)-.002***(.0003).001(.0004)-.0002**(.0001).019***(.0022)
0.493
1905
0.000

Is the indirect effect of FDI same for all countries? To answer this, we perform subsample
analyses for different groups of countries based on income level and location. Results areinteresting – the negative interaction effect is persistent only for upper-middle income countries.
Intuitively, this can be true because low-income countries may not have sufficient domestic
investments for crowding-out effect to happen. It is important however to note that theinteraction effect is neither positive for the low-income countries. When analyzed by country
location, the negative indirect effect is apparent in European and Oceania and Middle-Easterncountries. While the same effect is inconclusive in Sub-Saharan African and Latin Americancountries, Asian countries seem to experience differently. In the Asian developing countries, thelocal conditions seem to be positively moderating the impact of FDI on growth. In other words,
the Asian countries that offer better business and economic climate reap higher benefits from
FDI. The results of Asian, European and Oceania, and Middle Eastern country groups are given
in Table 5.

Endogeneity Issues
While the main intention is to assess the direction of effect from FDI and local conditions on
growth, it is plausible that higher growth would attract more FDI and foster DI or create better
local conditions in the developing countries. This would lead to endogeneity problem andproduce biased estimates. To deal with this problem, we adopt two different approaches – (1)instrumental variable approach and (2) a five-year forward-moving average method proposed by
Le & Suruga (2005). In the latter approach, dependent variable is the average of five-years
forward values per-capita income.
First, we construct instrumental variable approach for which identification of valid instruments
becomes a necessity. As per Wheeler & Mody (1992), current investment decisions in theforeign markets are largely dependent on the previous decisions. It implies – larger theinvestment in the previous period, more it will be in the current period. Similar intuition can beapplied to argue that current domestic investment will be high in countries where previous period
domestic investments are large. Thus, we use lagged values of the FDI and domestic investment
as instruments. Alternatively, as Froot & Stein (1991) suggest, FDI can also be instrumented byreal exchange rate since variation in real exchange rate affect the relative wealth of firms andtheir investment decisions in the foreign markets. We use the two instruments alternatively andpresent results in Table 3. Models (1) through (3) offer results from lagged values of FDI and DI
as instruments, models (4) and (5) considers real exchange rate as instrument for FDI andModels (6) through (8) presents findings from forward moving average approach.
Table 3: Endogeneity controlled estimates
Models
FDI
DI

Lagged values as instruments
(1)
(2)
(3)
.008**.008**.005(.0034)(.0034)(.0041).066***.086***.112***(.0126)(.0149)(.0211)

RER as instrument(4)(5)

5-year Forward Moving Average
(6)
(7)
(8)
.046***.028***.025**(.0064)(.005)(.008).032***.023***.017***(.0023)(.002)(.002)

Real Ex. Rate
Interaction-.021**(.0095)-.070***(.0126)-.000(.0004)-.000(.000).0425**(.0095)
0.023
2073
0.000

.045***(.0031)
0.036
1902
0.000

Trade OpenInflation
HK
Financial
development
Constant
R-squared
Number of obs
Prob > F.029(.019)-.001(.0004)-.000(.000)-.002*(.0013).000(.001).049***(.0044)
0.051
1074
0.000

-.001***(.0001)-.001***(.0001)-.001(.0007).019***(.0007)

6.75***(.0518)
0.497
1183
0.000

6.244***(.0641)
0.126
1823
0.000

-.001**(.0002)-.001(.0007).016***(.001)-.002***(.0003).019***(.001)
5.389***(.0833)
0.121
1029
0.000

*** p<.01, ** p<.05, * p<.1

Note: FDI and DI are in their lag forms in IV models.
The second approach is the one suggested by Le and Suruga (2005) in which five-year forward
moving average growth in per-capita income is regressed with the following model:
𝑖𝑛𝑐𝑜𝑚𝑒!(".$,".0) = 𝛽# + 𝛽$ 𝐹𝐷𝐼!" + 𝛽( 𝐿𝐶!" + 𝛽) (𝐹𝐷𝐼!" ∗ 𝐿𝐶!" ) + ∑𝛽+ 𝑋!" + 𝑒!"− (2)where t = 1996, 1997, …. , 2020, 𝑔𝑟𝑜𝑤𝑡ℎ!(".$,".0) is the five years forward moving average of
per-capita GDP for country i, while the rest are same as in equation (1). Local condition is stilldefined by the level of domestic investments. The number of observations for each country
reduces by five years (1996 – 2015) in this model. Fixed effect regression of this model produces
results very similar to those from previous models. The interaction term is negative andstatistically significant as shown in models (6) through (8) in Table 3.
All results above confirm that in contrast to the previous findings in literature, growth effect of
FDI is negatively moderated by business and economic conditions in the local market. Hostcountries with better or efficient local conditions seem to derive lower growth effects fromforeign investments. Some plausible explanations are (1) crowding-out effect – where inflow offoreign investments displaces domestic enterprises and (2) countries with efficient local markets
experience growth more from its own means than from foreign investments. Our results partly
conform to the findings of Agosin & Machado (2005) and Adams (2009).
Given these findings, we posit that developing countries must be extra careful while framing
policies related to foreign investments. While there are certainly some direct benefits from FDI
to growth, its magnitude of impact is far from the widespread belief. Transfer of technology andrelated resources may be occurring with FDI but it comes at the cost of local enterprises. Our
analysis confirms that there are negative indirect impacts from FDI to developing economies.
Hence, countries planning for liberal FDI policies and most importantly the countries that use
distortionary policy instruments in favor of foreign investments must rethink and carefully weighthe impact of MNCs on the local economy.

5. Conclusion and Discussion
The existing literature suggests that developing countries must offer adequate local conditions so
as to leverage the increasing inflows of foreign capital. In a similar vein, measuring the local
conditions by the level of domestic investments and productive capacity of host countries, we
examine the impact of local conditions on the FDI-growth relationship. Based on the data from1996 to 2020 for 102 countries, panel data estimates confirm that a large influx of foreign capital
generally leads to a growth in per-capita income in developing economies, however, this positive
impact disappears with increase in local conditions. In specific terms, the positive externalities
associated with FDI decreases with host country local conditions.
Overall, the results suggest that to fully reap benefits from FDI, developing countries must adopt
screening policies to ensure that FDI doesn’t displace domestic firms. There are a few
possibilities to realize this – (i) allow foreign investments in areas/sectors in which domestic
investment is scarce, (ii) ensure that foreign investment leads to the production of new goods thatcomplement the existing domestic investment, (iii) study its local economic opportunities andchallenges, and then go after specific foreign companies that fit well in its growth process as
suggested by Agosin & Machado (2005). Alternatively, developing countries may emphasize on
its own local market since FDI’s impact is smaller in comparison to the impact of its own local
conditions. Moreover, this study finds that the impact of FDI is decreasing as the local economy
progresses in its own domestic terms.
NOTE: This is an incomplete report. I could not complete examining two approaches mentioned
in the report – (1) treating productive capacity as an indicator of domestic market conditionsand (2) considering real exchange rate as an instrument for FDI. However, these results will
also be reported on 4th June during the presentation. Thank you for your kind consideration.

References
Adams, S. (2009). Foreign Direct investment, domestic investment, and economic growth in
Sub-Saharan Africa. Journal of Policy Modeling, 31(6), 939–949.
https://doi.org/10.1016/j.jpolmod.2009.03.003
Agosin, M. R., & Machado, R. (2005). Foreign Investment in Developing Countries: Does it
Crowd in Domestic Investment? Oxford Development Studies, 33(2), 149–162.
https://doi.org/10.1080/13600810500137749
Alfaro, L., Chanda, A., Kalemli-Ozcan, S., & Sayek, S. (2002). FDI and Economic Growth: The
Role of Local Financial Markets. Journal of International Economics.
Borensztein, E., Gregorio, J. D., & Lee, J.-W. (1998). How does foreign direct investment affecteconomic growth? Journal of International Economics, 21.
Froot, K., & Stein, J. C. (1991). Exchange Rates and Foreign Direct Investment: An Imperfect
Capital Markets Approach. The Quarterly Journal of Economics, 106(4), 1191–1217.
Marc Lautier, & Francois Moreaub. (2012). Domestic investment and FDI in developing
countries: The missing link. Journal of Economic Development, 37(3), 1–23.
https://doi.org/10.35866/CAUJED.2012.37.3.001
Su, Y., & Liu, Z. (2016). The impact of foreign direct investment and human capital on
economic growth: Evidence from Chinese cities. China Economic Review, 37, 97–109.
https://doi.org/10.1016/j.chieco.2015.12.007
UNCTAD. (2020). Foreign Direct Investment. Unctadstat.
https://unctadstat.unctad.org/wds/TableViewer/tableView.aspx

Vu Le *, M., & Suruga, T. (2005). Foreign direct investment, public expenditure and economic
growth: The empirical evidence for the period 1970–2001. Applied Economics Letters,
12(1), 45–49. https://doi.org/10.1080/1350485042000293130
Wheeler, D., & Mody, A. (1992). International investment location decisions: The case of U.S.
firms. Journal of International Economics, 33(1), 57–76. https://doi.org/10.1016/00221996(92)90050-T
World Development Indicators | DataBank. (2021).
https://databank.worldbank.org/source/world-development-indicators#

Appendices
Table 4: Descriptive Statistics
Variable
Growth rate
initial gdppc
DI
fdi
Pop growth
money
sec
infla
Trade Open

Obs
98
97
102
102
102
102
102
99
102

Mean
.829
2304.973
21.9973.683
1.705
43.16758.97229.02575.068Std. Dev.
1.157
1898.743
6.234
3.494
1.047
29.49726.52962.55732.473Min-1
221.386
7.727
-.124
-.736
10.74510.9422.132
23.456Max
8.477
9061.76
48.39925.044
3.661
198.624102.917381.429202.167

Table 5: Country-group differences
ModelsFDI
DI
Interaction

Asia.020***(.005).416***(.021).006(.0218)Trade OpenFinancial
Inflation.031***(.0041)R-squared
0.557
Number of obs
388
Prob > F
0.000*** p<.01, ** p<.05, * p<.1
Constant.014**(.005).443***(.021).038*(.022)-.001**(.0005)-.0001(.0007).002**(.0006).029***(.0042)
0.613
367
0.000

Europe and Oceania.009*.011**(.0055)(.0056).484***.464***(.021)(.0225)-.069**-.062**(.0253)(.0258)-.002**(.0005).002**(.001)-.0002**(.000).022***.026***(.0047)(.0052)
0.560
0.569
462
400
0.000
0.000

Middle East
.008
.016
(.0116)(.0111).342***.381***(.0369)(.0349)-.225***-.187**(.0614)(.0623)-.001(.0011)-.002*(.0013)-.002*(.0007).035***.034***(.0088)(.0082)
0.459
0.659
128
104
0.000
0.000