Evaluating Projections of African Economic Growth

A discussion of the African Development Bank’s report utilizing the Solow Model and examining welfare effects.

Student

Introduction

GDP per capita in Sub-Saharan Africa is the lowest in the world at $1,594 in 2015 (World Bank 2015). Rates of extreme poverty (living on less than $1.90 per person per day) are the highest in the world at 40 percent in 2015 (World Bank 2015). While other countries such as China, India, Vietnam and Cambodia have experienced rapid growth over the last quarter century, bringing millions of their citizens out of poverty and industrializing their economies, most African countries have economies based largely on agriculture and commodities, sectors that are susceptible to shifts in exogenous factors such as international prices and weather. As the continent seeks to alleviate the mass poverty, build its vital infrastructure, and join the ranks of other high-growth countries, the African Development Bank (AfDB) was instituted to “spur sustainable economic development and social progress in its regional member countries (RMCs), thus contributing to poverty reduction” (AfDB).

The AfDB recently held its 52nd annual meeting, at which it released its annual assessment on the economic outlook for Africa. A newspaper article in the Ugandan Daily Monitor, “Africa’s Economic Growth Bright – AfDB,” reported on the document and summarized its projections. The report predicted growth of 3.4 percent in 2017, up from 2.2 percent in 2016. The lower growth in 2016 was “due to low commodity prices, weak global recovery and adverse weather conditions” and the expected rebound in 2017 is “on the assumption that as commodity prices recover, the world economy will be strengthened and domestic macroeconomic reforms are entrenched” (Daily Monitor, June 2017).

Are the Report’s Assumptions Realistic?

The growth rates predicted in the AfDB’s report are higher than growth rates in the United States. This is not surprising, as developing countries typically have higher growth rates than developed countries. While higher than projected growth rates in developed countries, the AfDB report’s rates are lower than those projected in China. China’s growth rate, although slowing somewhat (from 14 percent in 2012 to 7 percent in 2015), remains one of the highest in the world. That the projection in the AFDB report is between that of the United States’ and China seems reasonable.

Although the figure might be within a reasonable range, it is important to evaluate if the assumptions made about the drivers of this growth are reasonable. The projection is based “on the assumption that as commodity prices recover, the world economy will be strengthened and domestic macroeconomic reforms are entrenched.”

The assumption that commodity prices will go up is line with most analysts’ estimates. The World Bank’s Commodity Markets Outlook forecasts higher prices for industrial commodities such as energies and metal and stable prices or a small decrease in the prices of grains and some other agricultural products (World Bank, April 2017). As different African countries contain different natural resources and have varying economic dependence on commodity exports, they will be affected individually by price changes. But the overall impact of price changes is likely to be positive.

It is a strong assumption to say that the world economy will be strengthened next year. There is a great deal of uncertainty in international trade and financial markets being caused by international terrorism and the nationalistic movements in the Europe and the United States. Furthermore, the continued cooling of China’s economy will not only affect international growth, but also will significantly reduce demand for raw materials. This will have a severe impact on Africa, as many African countries’ economies are reliant on exports of raw materials.

Finally, the reinforcement of domestic macroeconomic reforms within Africa depends on African leaders’ willingness to fight corruption and make their markets better for doing business. The incentives align for these reforms to advance and Africa’s economy should continue to become more pro-growth. So, while some of the assumptions in the AfDB’s report are more realistic than others, it is safe to expect economic conditions that are favorable for growth in Africa.

Evaluating African Growth Using Economic Models

Economists use formalized models to derive predictions about future economic growth within one country and differences in growth patterns among countries. One such macroeconomic model is the Solow Model, named after Robert M. Solow, who won the Nobel Prize in Economics for the 1956 paper in which he introduced the model (Solow 1956). It is a neoclassical model in that total output is a function of capital and labor. The model expresses output in per worker terms such that per capita GDP only grows with capital accumulation, which in turn only increases by a higher saving rate or lower rate of depreciation of capital. The implication is that a higher saving rate increases per-capita GDP, but that the growth rate of per capita GDP would return to zero. Later revisions to the Solow model added in a term for human capital to explain sustained economic growth and differences in cross-country differences in per-capita GDP (Mankiw et al, 1992).

Unfortunately, the Solow Model is not particularly suitable for evaluating the drivers of growth that are projected in the AfDB report. Higher commodity prices are essentially lump-sum payments to commodity exporting countries. While this is going to make those countries better off, changes in the terms of trade do not have an impact on the Solow Model output function or growth rate function. Similarly, a strong recovery in the global economy does not directly factor into the Solow Model. However, these sources of growth will have an indirect effect on the Solow Model output function. A stronger global recovery will give multinational corporations additional capital to invest, higher commodity prices will direct that capital towards commodity-rich countries, and African domestic macroeconomic reforms will encourage these corporations that Africa is business-friendly. This increase in foreign direct investment raises the level of capital. Per the Solow Model, this will lead to a higher level of output and a higher level of growth in the short-term. These results are consistent with the AfDB’s report.

Welfare Implications

            Assuming the projections within the AfDB’s report are accurate, how will this growth impact poverty, inequality and other measures of household well-being? Because the growth is mainly projected to be driven by changes in commodity prices, demand for the unskilled labor that produces these commodities will increase and the remote areas where these resources are located will gain greater access to domestic and international markets. These changes will reduce poverty and improve the standards of living among the rural poor in African countries. Nonetheless, the clear majority of the income that will arise from the increase in commodity prices will likely flow to wealthier portions of the population who provide the capital to produce these commodities. As such, the effect of the growth on measures of inequality such as the Gini Coefficient is unclear.

 

References

“Africas economic growth bright – AfDB.” Daily Monitor. Nation Media Group, 01 June 2017. Web. 06 June 2017.

 

“Industrial Commodity Prices to Rise in 2017.” World Bank. World Bank Group, 26 Apr. 2017. Web. 07 June 2017.

 

Mankiw, N. Gregory, et al. “A Contribution to the Empirics of Economic Growth.” The Quarterly Journal of Economics, vol. 107, no. 2, 1992, pp. 407–437. JSTOR, www.jstor.org/stable/2118477

 

“Mission & Strategy.” African Development Bank. N.p., n.d. Web. 06 June 2017.

 

Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) http://data.worldbank.org/indicator/SI.POV.DDAY?locations=ZG&view=chart

 

Solow, Robert M. “A Contribution to the Theory of Economic Growth.” The Quarterly Journal of Economics, vol. 70, no. 1, 1956, pp. 65–94. JSTOR, www.jstor.org/stable/1884513.

 

Banking on Trust: An Analysis

An in depth analysis of the study entitled “Banking on Trust: How Debit Cards Enable The Poor to Save More”.
Written By: Kaushik Nagarur

Introduction

Financial Institutions have long been the backbone to many countries’ economies. Institutions such as JP Morgan and Bank of America are critical to the economy of America. Economic development is related closely to these financial institutions. The growth of a developing country can directly be linked to these financial institutions. Yet, what is it that drives and causes people to invest in these financial institutions? A study done by Pierre Bachas, Paul Gertler, Sean Higgins, and Enrique Siera entitled “Banking on Trust: How Debit Cards Enable The Poor to Save More”, attempts to answer this question. The study looks into whether or not trust in a financial institution is needed in order for people to use formal financial services (Bachas et al 1).

Background

In order to examine this question, Bachas used a natural experiment that was occurring in Mexico. A natural experiment is an “observational study in which an event or situation that allows for the random assignment of study subjects to different groups is exploited to answer a question” (Messer). In this case the natural experiment was to answer whether or not there was a connection between trust and the use of formal financial services. The experiment had to do with the handing out of debit cards through a Mexican cash transfer program known as Oportunidades. Oportunidades was a cash transfer program that provided bimonthly cash transfers to poor families. The objectives of Oportunidades was to “Increase capacities in health, nutrition, and education of poor families” (Projects). Essentially the program put money into a savings account and gave poor families money conditional on the fact that the families have to send their children to school and must have regular health checkups.

The program was being conducted in three different time periods (waves), and the authors used these “waves” in order to create their natural experiment. There were three major groups based on the roll out of these debit cards. There were two treatment groups that received the debit cards one year apart, and one control group that received the debit cards at the end of the study period. The authors used a combination of high frequency administrative data on bank transactions as well as survey data of the beneficiaries. The survey data was to explore whether or not people were learning to trust the bank, people use the banks, and people understand how savings work. The authors analyzed 343,204 accounts at 380 Bansefi branches over a four-year period from November 2007 to October 2011(Bachas et al 8).

Analysis and Results

The first analysis that was done in the paper by Bachas was the effect of the debit cards on the stock of savings. In order to estimate the effect of the debit cards on savings, a difference in difference regression was done. The reason that a difference in difference regression was needed was to control for other shocks including “time effects”, as well as control for general individual differences that might affect the outcome. The following formula (Figure 1) was utilized in order to understand the balance effect and see whether debit cards affect the overall balance in an account (Bachas et al 9).

Screen Shot 2017-04-23 at 6.24.03 PM

Source: Bachas, Pierre, Paul Gertler, Sean Higgins, and Enrique Seira. “Banking on Trust: How Debit Cards Enable the Poor to Save More.” NBER Working Paper Series 23252 (2017): n. pag. Web. 24 Apr. 2017. Formula 1

Balance was the average balance in the account over time period t. The  lI is the account level fixed effect, while d is the fixed time period effect. The Tj is a dummy variable that is 1 if there is treatment, and the last term is a time dummy variable. The most important variable is  fk which measures the average difference in balances between treatment and control group in period K. The following figure V is the result of this regression (Bachas et al 42).

Screen Shot 2017-04-23 at 7.36.45 PMSource: Bachas, Pierre, Paul Gertler, Sean Higgins, and Enrique Seira. “Banking on Trust: How Debit Cards Enable the Poor to Save More.” NBER Working Paper Series 23252 (2017): n. pag. Web. 24 Apr. 2017. Fugure 5 page 42

The figure plots the coefficient  fk and the confidence interval. There are two important conclusions in the figure. The first one is that there is no difference in the pretreatment levels between treatment and control groups. Second is that around 8 months after receiving a debit card, balances start to change. After around 2 years with the card, balances are 1,400 pesos higher for the treatment group over the control group. In wave 2, savings begin to rise much more quickly after receiving the card. This is likely due to information spillovers from wave one (Bachas et al 10).

The second major question is what effect trust has on the flow of savings at the beneficiary level. Whether or not someone trusts the bank can be calculated using a dummy variable. In the surveys, they ask “do you leave part of the monetary support from Oportunidades in your bank?”(Bachas et al). After they answer, the authors ask why they do or do not leave money behind. Based on that they capture lack of trust.  Since trust is endogenous, it is possible to use it as an instrument variable with the date of debit card assignment. The main reason to do this is that it isolates variation in trust that can be explained exogenously by time with the card. What results is the following formula( Bachas et al 10).

Screen Shot 2017-04-23 at 8.02.28 PM.png

Source: Bachas, Pierre, Paul Gertler, Sean Higgins, and Enrique Seira. “Banking on Trust: How Debit Cards Enable the Poor to Save More.” NBER Working Paper Series 23252 (2017): n. pag. Web. 24 Apr. 2017. Formula Pg 56

Essentially this formula is just trying to see what effect trust has on the proportion of average income. When running the 2SLS regression test, a statistical test to see the correlation between two items, the authors came up with the following table.

Screen Shot 2017-04-23 at 8.05.38 PM

Source: Bachas, Pierre, Paul Gertler, Sean Higgins, and Enrique Seira. “Banking on Trust: How Debit Cards Enable the Poor to Save More.” NBER Working Paper Series 23252 (2017): n. pag. Web. 24 Apr. 2017. Table II PG 56

This table shows two major conclusions. The first stage shows that an average of six additional months with the card leads to a 10.3 percent increase in trusting the bank. The IV coefficient in column 2 states that those who trust the bank as a result of this six additional months save an extra 2.8% on their income (Bachas et al 56).

The paper as a whole has two major implications. The first is that the people who received debit cards save more. The second major conclusion is that trust plays an important role in the savings effect. Once people who received these cards started trusting the bank, they started saving much more.

Why does it matter?

The conclusion reached by the authors have policy implications that can be used in order to help financial institutions. First and foremost, building savings has been shown to have positive effects on business investment. This is shown in the experiment Saving Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya, done by Dupas and Robinson. While looking at banking in rural Kenya they found that “Market women use these (savings) accounts to save up to increase the size of their business” (Dupas et al. 185). This means that savings accounts have a direct effect on business which has a direct effect on the economy as a whole. Savings accounts can also help smooth consumption curves. In other words, savings accounts help make sure that your expenditures do not fluctuate too much after an unforeseen emergency such as a health emergency. Savings accounts have also been linked to a way out of Poverty. Mullainathan and Shafer wrote a paper entitled “Savings Policy and Decision Making in Low income households” where they conclude that access to formal savings accounts may “provide an important pathway out of poverty” (Mullainathan et al 9). With all these papers on the importance of savings, it is easy to see that helping the poor in developing countries. Debit cards, encourage people to save in these intuition’s due to the fact that people can see their account balance. This ability lets them trust the institutions more. It is important that governments utilize this information and this technology in order to help others.

 

Works Cited:

Bachas, Pierre, Paul Gertler, Sean Higgins, and Enrique Seira. “Banking on Trust: How Debit Cards Enable the Poor to Save More.” NBER Working Paper Series 23252 (2017): n. pag. Web. 24 Apr. 2017.

Bukari, Jeff. “Using Your Debit Card Might Actually Make You Richer.” Fortune. March 22, 2017. http://www.fortune.com/2017/03/22/debit-card-how-to-get-rich/

Dupas, Pascaline, and Jonathan Robinson. “Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya.” American Economic Journal: Applied Economics 5.1 (2013): 163-92. Web.

Messer, Lynne C. “Natural Experiment.” Encyclopædia Britannica. Encyclopædia Britannica, Inc., 07 Oct. 2016. Web. 24 Apr. 2017.

Mullainathan, Sendhil and Eldar Shafr, Savings policy and decision-making in low-income households, in Insu‑cient Funds: Savings, Assets, Credit and Banking Among Low-Income Households, 121145 (New York City: Russell Sage Foundation Press, 2009).

“Projects & Operations.” Projects : Support to Oportunidades Project | The World Bank. World Bank, n.d. Web. 24 Apr. 2017.

 

 

 

The Bolsa Familia Welfare Program in Brazil and its Impact on Poverty and Inequality

An analysis of the Bolsa Familia program compared to models and theory from 416 – by Erica Ryan

My dear friend and colleague Rafael is from Brazil. For a long time I didn’t quite understand why he was here at the University of Maryland. Not only is Brazil the largest country in South America, it also has the largest population, largest economy, and is home to a vast amount of the natural resources in South America.[1] Even so, the socioeconomic climate in Brazil is far from perfect. For this blog post I am going to focus on two articles that appeared in the Rio Times in early 2017, one titled “Economic Crisis to Push 3.6 Million Brazilians Into Poverty” and the other “Forty Percent of Children Under 14 Live in Poverty in Brazil.”

The first article talks about how the current economic hardships could push up to 3.6 million more Brazilians into poverty by the end of 2017, with most of the new poor living in urban areas. There is a welfare program in Brazil called Bolsa Familia that provides cash transfers to low income families, and the current crisis could add up to 1.16 million families to the program. Currently, there are no plans to increase funding for Bolsa Familia.

The second article talks about how seventeen million children under 14 are currently living in low income households, which is approximately 40% of the population in this age group. The North and Northeastern regions of Brazil are in a more critical state and have much higher percentages than the average (approximately 60% and 54% respectively). Around 5.8 million of these children are living with a per capita income of less than 25% of the minimum wage.

Income inequality and poverty are really big problems facing Brazil today. According to World Bank Data, the top 10% of the population holds 40.7% of the total income while the lowest 20% only holds 3.6% of the total income as of 2014.[2] Since 2014, the economy has continued to shrink, many political leaders have been accused of corruption, and the president of Brazil was impeached, leading to the poverty and suffering discussed in the two articles.

The Bolsa Familia program is very important for improving conditions for the poor people in Brazil, particularly for poor children. The cash transfers it provides are conditional on the children of the family going to school and receiving regular health checkups.[3] The cash transfer it provides should assist in reducing poverty simply because the poor now have more money, but it is important to look at both the income and the welfare of the poor when determining the effectiveness of the program. The conditionality of the transfer will do more in the long run to further reduce poverty and inequality and increase welfare.

The cash transfers make it easier for kids to attend school longer. When the decision of whether or not a child should continue attending school is being made, the expected utility of the education has to be compared to the opportunity cost (the wage they could earn if they worked instead) of that time spent in school. Because of time inconsistent preferences, people tend to discount the future benefit of education, which can lead to under consumption of education. For poor families this can be especially true because they need the income now, rather than later. So without the transfer, many children may end up working instead of going to school. With the transfer, the utility of education is increased, making the benefit of sending children to school higher.

The 1992 paper by Mankiw, Romer, and Weil shows that the accumulation of human capital is an important factor for economic growth in a country in the Solow model.[4]  The current recession in Brazil is caused by a variety of factors unrelated to the Solow model; however, in order for Brazil to move past their recession and work towards positive economic growth, accumulation of human capital is vital. Because Bolsa Familia requires that children attend school in order for the family to receive the transfer, this program is promoting education which, according to theory, should contribute to rising economic growth. Studies have also shown that increased education leads to higher wage profiles later in life.[5] Requiring that these poor children continue their education could lead to a reduction in inequality if, because of their education, they are able to obtain higher paying jobs as adults and break the trend of generational poverty.

The health checkup requirement of the transfer can also lead to higher human capital and increased productivity. Being healthy means that kids don’t have to miss as much school and will be able to learn more. Regular health checkups can also help to identify and correct health issues that might otherwise become major problems later on in their lives, limiting their future productivity. Studies of the Bolsa Familia program have shown that most of the transfer is used to purchase food, clothing, and school supplies for the children.[6] The nutritional component of food consists of quality and quantity, and malnutrition is linked to higher rates of mortality and stunting. The transfer would effectively relax the budget constraint. This could help families satisfy their nutritional needs while also allowing them to re-optimize and select foods with greater variety and nutritional content, which should have positive effects on welfare.

The current sociopolitical climate in Brazil is certainly less than optimal, and it is absolutely terrible that so many more people, including many children, are going to be pushed into poverty as a result of this recession. The Bolsa Familia program will be vital not only in the current alleviation of poverty, but also for sustained reduction of poverty. The investments the program makes in children will help to improve the health and welfare of poor children, while also allowing them to increase their human capital. According to the Solow model augmented with human capital, this should lead to higher growth rates, which should help Brazil to escape their recession. The increase in human capital should also help poor children to escape generational poverty, which will lead to a reduction in inequality. The Bolsa Familia program is important not only for improving the lives of the poor, but also for moving Brazil as a whole towards a better socioeconomic climate.

[1] https://blog.oup.com/2016/08/10-facts-economy-brazil/

[2] http://databank.worldbank.org/data/reports.aspx?source=poverty-and-equity-database

[3]http://web.worldbank.org/WBSITE/EXTERNAL/NEWS/0,,contentMDK:21447054~pagePK:64257043~piPK:437376~theSitePK:4607,00.html

[4] http://eml.berkeley.edu/~dromer/papers/MRW_QJE1992.pdf

[5] https://www.brookings.edu/blog/up-front/2012/09/17/education-is-the-key-to-better-jobs/

[6]http://web.worldbank.org/WBSITE/EXTERNAL/NEWS/0,,contentMDK:21447054~pagePK:64257043~piPK:437376~theSitePK:4607,00.html

 

The two articles I analyzed are as follows:

Economic Crisis to Push 3.6 Million Brazilians Into Poverty

Forty Percent of Children Under 14 Live in Poverty in Brazil

 

FDI and Financial Development: A New Answer to an Old Question

An analysis of new evidence suggesting a critical linkage between financial development and FDI and the need for strong financial institutions across the world.
Griffin Riddler

When discussing poverty and economic growth in developing countries, the topic of foreign direct investment (FDI) often comes up. Multiple empirical studies show that FDI helps to spur economic growth and reduce poverty by increasing opportunities for wage employment and improving technology and productivity, among other factors. In “The Effects of Financial Development on Foreign Direct Investment”, Rodolphe Desbordes and Shang-Jin Wei investigate how financial development in both source and destination countries impact different varieties of FDI.

Linking FDI to Development

One case study in Senegal found that even in what was considered “a worst-case scenario” with a single multinational enterprise (MNE) controlling the entire supply chain, FDI in the agro-industry had “robust, significant, and large positive effects on income and poverty reduction” (Maertens, Colen, & Swinnen, 2011). Researchers in Bolivia likewise determined that “FDI inflows enhance economic growth and reduce poverty” (Nunnenkamp, Schweickert, & Wiebelt, 2007). Deng Xiaoping famously opened China to FDI as part of his larger economic reforms, hoping to use foreign capital to kick the Chinese economy into high gear. Clearly, both economists and policy makers view FDI as another tool in the quest to boost growth and reduce poverty around the world.

A Literature Review of Financial Development and FDI

When studying FDI, researchers noticed something rather obvious: the level of FDI flows dropped worldwide in 2008 and 2009, the same time period as the global financial crisis. As FDI is reliant upon external financing from banks and other institutions, it made sense that financial development should affect FDI levels. However, until recently, studies into the effects of financial development, whether it be in source countries (SFD) or destination countries (DFD), on FDI have been stymied by shortcomings in the research design.

The authors of this working paper, Desbordes and Wei, identified three key problems with previous studies. First, by using balance of payments (BOP), other studies do not include external financing from within destination countries which prevents a proper comparison of DFD to SFD. This is compounded by a risk of bias due to imprecise estimates of control variables and country fixed effects. That, in turn, makes it very difficult to definitively show that financial development has long-run effects on FDI.

Finally, the studies that do attempt to limit omitted variable bias through the use of restricted data sets thereby limit the scope of their findings. The results: the vast majority of studies do not cover the total effects of SFD and DFD on FDI. These shortcomings drove Desbordes and Wei to construct a differences-in-differences model which utilized “variations in both country-specific financial development and sector-specific financial vulnerability” (Desbordes & Wei, 2017) to determine the impacts of both SFD and DFD.

Building a Model

After briefly postulating that SFD and DFD have net positive effects on FDI, whether a direct “external finance effect” or indirect effects on overall production, Desbordes and Wei established their definitions of the different types and margins of FDI that they measured. The different types roughly group into two categories, initial investment, represented by greenfield FDI and M&A FDI, and expansion FDI. For those unfamiliar with the first two terms, greenfield simply means establishing an entirely new foreign branch of the enterprise, while M&A is the acquisition of an extant firm in the destination country. As for the margins, the paper measures two kinds: the extensive margin, or the number of FDI projects in a given sector, and the intensive margin, or the average size of said projects.

The data used in “The Effects of Financial Development on Foreign Direct Investment” comes from two sources. Desbordes and Wei use the fDI Markets database complied by the Financial Times to measure greenfield and expansion FDI. The data does not distinguish between sources of external financing, making it superior to BOP measurements, and allows for the breakdown of FDI by sector. For M&A FDI flows, not included in the fDI Markets data, the authors use the Zephyr database, which includes comprehensive measurements of cross-border M&A deals by country and sector since 2003.

Desbordes and Wei, in an attempt to isolate the causal effects of SFD and DFD, created a model focusing on the interactions between SFD or DFD and a specific sector’s financial vulnerability (FV). In the first exponential regression, SFD and DFD were measured as the private credit to GDP ratio at time t-1, while country-pair and sector fixed effects were measured at time t, with FV (fraction of capital expenditures not financed by cash flows from operations) remaining a time-invariant measure. The coefficients of the interaction terms, β1 and β2, find the total effects of financial development on the different types of FDI.

FDIijst = exp(β1[ln(SFDit−1) · FVs] + β2[ln(DFDjt−1) · FVs] + αijt + αstijst

The next piece of the model expanded upon the first regression by controlling for the pre-sample size of the manufacturing sectors in both countries. This change meant that now the coefficients only captured the direct effects of SFD and DFD on FDI. (β1γ1) and (β2γ2) therefore represented the indirect effects on FDI, which the authors predicted to be positive. For both regressions, Desbordes and Wei restricted sample data to the period of 2003 to 2006 in order to avoid spillover effects from the financial crises that started in 2007.

FDIijst = exp(γ1[ln(SFDit−1) · FVs] + γ2[ln(DFDjt−1) · FVs] + γ3ln(Yis) + γ4ln(Yjs) + αijt + αstijst

Results and Conclusions

t1

As shown by Table 1, the regressions run on the first model show that even with a variety of control variables, the results are unchanged: across all countries, DFD and SFD are found to have significant positive effects on FDI. These results show a conclusive link between financial development and bilateral FDI, but the first set of regressions only measures the total effects.

t2.png

Table 2 shows the differences-in-differences approach the authors took to measure the indirect effects of financial development. In column (3), the differences shown at the bottom are those measurements: SFD and DFD appear to have both positive direct and indirect effects on FDI. Columns (5), (7), and (9) detail the effects of financial development on the extensive (5 & 7) and intensive (9) margins of FDI. Overall, SFD and DFD have net positive effects on greenfield FDI, with the primary driving mechanism being a strong effect on the average size of FDI projects.

t3.png

Table 3, a series of regressions involving expansion FDI, tells a different story. While both SFD and DFD have significant positives effects on expansion FDI, where those effects occur differs for the different sources. The vast majority of SFD’s effects (~75%) translate into an increased presence of FDI (more projects), while DFD tends to lead to greater average size of said projects. In addition, the total effects of financial development on expansion FDI are only 66-75% the size of the effects on greenfield FDI.

t4.png

The final form of FDI, M&A, is shown to be strongly and positively affected by SFD and DFD, at an even higher level than greenfield or expansion FDI. In summary, the results of all three models lead to the validation of the authors’ hypothesis that SFD and DFD have both direct and indirect impacts on all forms of FDI.

Takeaways

This paper makes one thing absolutely clear: countries wishing to attract FDI or spur an international expansion of their own MNEs must implement policies designed to secure the financial sector. The global financial crisis showed that, as FDI flows across the world plummeted due to instable credit markets. Developing countries in particular should include financial development in their growth strategies: by creating financial institutions with a strong foundation, they can attract more FDI to their nation and spur faster growth and reductions in poverty.

Sources

Desbordes, R., & Wei, S.-J. (2017). The Effects of Financial Development on Foreign Direct Investment. Cambridge: National Bureau of Economic Research.

Maertens, M., Colen, L., & Swinnen, J. F. (2011). Globalisation and poverty in Senegal: a worst case scenario? European Review of Agricultural Economics, 38(1), 31-54.

Nunnenkamp, P., Schweickert, R., & Wiebelt, M. (2007). Distributional Effects of FDI: How the Interaction of FDI and Economic Policy Affects Poor Households in Bolivia. Development Policy Review, 25(4), 429-450.

Cover image courtesy of: http://www.investopedia.com/video/play/foreign-direct-investment/