Decisions in the face of migrant stocks: A disaggregated aid analysis | Kimberly Zhang, Smith College

Decisions in the face of migrant stocks: A disaggregated aid analysis | Kimberly Zhang, Smith College angieliu

Abstract

Poor economic opportunity and stability has created migrant flows -- both forced and voluntary. Consequently, foreign development assistance is an increasingly popular solution to alleviating the increase in global migrant flows. There is no consensus on whether these policies are effective in curtailing migration. A majority of the economic studies that focus on the link between aid and migration use aggregated aid totals, hiding the fact that certain types of aid may be more effective than others. Throughout my paper, I build upon existing literature that explains the link between aid and migration by looking at disaggregated aid types. I examine disaggregated aid by looking at subcategories within aid types. For instance, one such subcategory is Education, which is a form of social infrastructure and services aids. Based on my analysis, Production and Economic sector aid seem to be the most effective in curtailing migration to destination countries. Social sector and Humanitarian aids have mixed effects on migration stock. The relationship of aid on migration can have varying results dependent any many factors such as time period, geopolitical results, origin and destination countries’ characteristics. This paper examines how certain types of aid may change individuals’ migration decisions.

Introduction

Introduction angieliu

Global human migration has become a particularly salient issue. It was reported in 2017 that due to civil wars, climate change, and political instability, the number of refugees has increased to record highs (UNHCR 2017). The rise in migration has placed increasing pressure on destinations such as the European Union (EU), Japan and the United States to manage their migratory flows. Many see foreign development assistance as the solution to deter migration. The EU’s new initiative to draw new private sector funds, in addition to existing public spending, to donate to origin countries to address the drivers of migration, such as poor economic opportunity (Bercetche 2018) is the most recent example of this.

The focus of this paper is on official development assistance (ODA). It is defined as “government aid designed to promote the economic development and welfare of developing countries.” Loans and credits for military purposes are excluded (OECD 2018). Developed countries have been using ODA to curtail migration. This policy tool is predicated on the belief that if developed countries can raise the quality of life of potential migrants in origin countries, then individuals will be less inclined to migrate. Beyond the capital allocation question of whether these billions of dollars in funds are effective, there lies a human motivation. Migration is not easy. Year-to-date in 2018, there have been 2,806 migrant fatalities due to the physical journey alone (International Organization for Migration 2018). Therefore, studying the effectiveness of developmental aid policies can help policy makers to direct funds more efficiently, and improve welfare on a global basis by reducing the percentage of people risking their lives and allowing for greater development in majority sending countries.

Literature review

Literature review angieliu

The relationship between migration and foreign ODA has not been understudied. The existing literature suggests that aid’s capacity to deter migration is small at best, and even shows that programs in formerly-poor countries have led to an increase in emigration. One way scholars look at the aid-migration link is through income and budgetary constraint effects. The income channel suggests that higher incomes, through developmental aid, will reduce emigration, since the opportunity cost of moving increases. The budgetary constraint channel builds off the fact that migration is costly and increased wealth from aid would decrease the relative cost of migrating and will encourage movement. Especially because many poor families view migration as an investment in their futures and an insurance policy against unexpected economic events at home and decrease in migration costs would make migration easier (Clemens and Postel 2018). Others point to a network effect resulting from bilateral aid relations, where the existence of aid decreases the information cost about potential destinations—increasing migration to developed countries (Barthelemy et al. 2009). Studies like these, however, examine this relationship at an aggregate level – lumping all types of aid like humanitarian, social infrastructure, and debt forgiveness together.

The few disaggregated studies have shown a negative relationship between total aid received and migration rates. Studies have focused primarily on the impact of foreign health aid on the emigration rates of physicians (Moullan 2013) and agricultural aids’ impact on urban and rural emigration (Gamso & Yuldashev 2018). The most recent study on the aid-migration builds on Barthelemy’s gravity model using a disaggregated approach incorporating economic and social infrastructure ODA spending. The results point to “a robust negative relationship between aggregate aid received and emigration rates, which can be attributed to the dominance of the public-services channel over the budgetary-constraint channel” (Lanati & Thiele 2018). Using the example of infrastructure aid, they explain the public services channel simply: better roads from ODA yield a positive externality of more foreign direct investment. As firms can use the roads for commercial purposes, more jobs can be created. This is evidence that local services are an important factor in developing migration decisions – even more than household wealth.

Based on these studies, there seems to be more to the story than what studies on aggregated development aid data and migration flow show. My analysis will examine the relationship between development aid and migration rates, as an extension of Lanati and Thiele’s research. This will include ODA categories: Humanitarian Aid and Production Aid in addition to Social and Economic Infrastructure and Services from Lanati and Thiele’s model. I also include subcategories of each category such as Education and Water Supply. The Action related to Debt, Program, Multisector and Other aid categories are left out, because these types of aid are case-by-case and difficult to incorporate such categories and interpret their results through the lens of my research question. This research aims to achieve greater clarity on how different types of developmental aid effect recipient countries’ migration flows. I will be using an approach similar to Lanati and Thiele’s gravity model to do my analysis.

Data

Data angieliu

Below I describe the variables used in this analysis along with their sources and explanations of what each variable measures. To build my dataset, I merge 6 different datasets together, which are explained more in-depth below.

 

Table 1: Sectors and subsectors examined

  • Economic infrastructure and services
    • Transport, communications
    • Energy
    • Banking, business and other services
  • Humanitarian
  • Production
    • Agriculture, forestry and fishing
    • Industry, mining and construction
    • Trade and tourism
  • Social Infrastructure and services
    • Education
    • Water supply and sanitation

 

Table 2: Variable Sources and Notes

  • Development Assistance Committee (DAC) bilateral aid: OECD. This dataset comes from the DAC, the world’s richest donor countries from the OECD. The DAC is a forum within the OECD that promotes developmental aid cooperation and other policies to contribute to the Sustainable Development Goals. The countries in the DAC are 30 of the world’s wealthiest donor countries in the OECD2. Member countries, per 2 the OECD’s criteria, need to have existing strategies, policies and institutional frameworks for development cooperation, a history of giving aid and systems that promote accountability for aid given. More importantly, the data are broken out into detailed categories. Data from the Query Wizard for International Statistics (QWIDS), which preselects a general aid dataset to use, are shown below. The query selected the biggest categories and a selection of their subcategories. For example, Education is under the Social Infrastructure and Services umbrella. There are more subcategories of aid, but the QWIDS query only included some selected subcategories. A comprehensive analysis on the remaining subcategories has potential for new research, which will be discussed in the Further Thoughts section of this paper. The data spans from 2007 to 2015 and includes only donor country data. Organizations like the UN or the Bill and Melinda Gates Foundation excluded. To merge this dataset with the others, I define the donor country as the destination and the recipient country as the origin country. Due to this definition, the recipients/origin countries are not DAC countries. DAC countries do offer aid to each other and there is a considerable amount of migration within this group of countries, but my dataset does not capture these migration stocks. Omitting this group of data should not sway the results, as the DAC countries are among the wealthiest in the world and often, the most politically stable.
  • GDP per capita in 2011 PPP: World Bank. Purchasing power parity is a method of standardizing the cost of goods in each country. It is calculated by examining the price of a similar “basket of goods” and comparing that same basket to another country.
  • Migrant Stock by Origin and Destination: UN. The United Nations’ Population Division gathers data on the number of migrants in a given country. In some cases, due to missing data, they will project the number of migrants using trends. The UN defines an international migrant stock as people “born in a country other than that in which they reside.” They compile these estimates every five years, so this dataset only has information on migrant stock by origin and destination at year 2010 and year 2015.
  • Distance between origin and destination, Common language, Colonized by destination country: United States International Trade Commission. This dataset is from the United States International Trade Commission and describes characteristics and relationships between two countries.
  • Free, Partly Free, Not Free Status: Freedom House. Freedom House is an independent organization, which is dedicated to the expansion of freedom and democracy in the world. They analyze the state of political and civil rights around the world and designate scores to reflect the situation at a given country and construct three measures to score and rate countries and territories in the world to capture the extent of freedom. I use their Status variable, which is a composite of a country’s political and civil rights scores. Status has three levels: Free, Partly Free and Not Free. For the purposes of this analysis, I make Status a binary variable in which Free and Partly Free are lumped together under “Free” denoted by 1 and countries that are classified as “Not Free” as denoted by 0.
  • Conflict: Uppsala Conflict Data Program (UCDP). I use the UCDP/PRIO Armed Conflict Dataset. I use the incompatibility variable to capture the existence of conflict using a binary variable. An incompatibility is the use of armed force between two parties, with the government of a state being a counterparty, that results in at least 25 battle-related deaths in a calendar year. The existence of a conflict, as defined by UCDP/PRIO is denoted by 1 and no conflict is denoted by 0.

 

Table 3: Categorized Summary Statistics, by Period

Period 1

Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max
MigStock 3,235 55,535.210 403,251.200 1.000 495.000 20,630.00 12,168,662.000
distance 3,990 7,339.731 3,546.232 345.373 4,589.460 9,618.853 18,708.700
lnGDPperCap_o 3,904 8.430 0.952 6.487 7.594 9.206 10.728
lnGDPperCap_d 3,990 10.645 0.215 10.194 10.501 10.717 11.491
Bilateral 3,990 48.962 185.723 0.000 1.200 29.457 4,077.910
Economic Infrastructure & Services 821 13.910 104.659 0.000 0.020 2.040 2,387.990
Humanitarian 699 6.293 30.301 0.000 0.130 2.840 446.460
Production Sector 823 3.786 15.218 0.000 0.070 2.195 254.710
Social Infrastructure & Services 1,504 16.366 80.846 0.000 0.140 7.110 1,625.090

Period 2

Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max
MigStock 3,883 62,699,470 433,066.100 1.000 567.00
21,894.000
12,275,876.000
distance 4,836 7,346.041 3,601.207 345.373 4,582.014 9,737.048 18,708.700
lnGDPperCap_o 16,560 9.025 1.165 6.326 8.107 9.864 11.815
lnGDPperCap_d 8,258 9.992 1.088 6.326 9.469 10.682 11.815
Bilateral 28,084 53.251 185.240 0.000 1.103 33.245 4,862.170
Economic Infrastructure & Services 913 16.361 104.482 0.000 0.010 2.100 2,364.600
Humanitarian 882 7.634 35.289 0.000 0.070 3.558 768.230
Production Sector 1,031 4.643 26.046 0.000 0.050 2.390 642.280
Social Infrastructure & Services 1,916 15.209 73.499 0.000 0.120 7.565 1,865.410

 

Table 4: Uncategorized Summary Statistics, by Period

 

Period 1

Statistic N Mean St. Dev. Min Ptctl(25) Ptctl(75) Max
MigStock 9,019 67,395.620 385,492.900 1.000 757.000 31,296.000 12,168,662.000
distance 11,582 7,421.602 3,544.230 345.373 4,666.997 9,845.781 18,215.300
lnGDPperCap_o 11,317 8.233 0.932 6.515 7.386 8.981 10.728
lnGDPperCap_d 11,582 10.610 0.188 10.194 10.497 10.677 11.461
Bilateral 11,582 77.600 243.546 0.000 3.493 55.590 4,077.910
Agriculture, Forestry & Fishing 1,981 4.215 20.074 0.000 0.050 2.020 404.190
Education 3,046 4.907 18.795 0.000 0.060 2.450 307.120
Energy 1,044 9.810 48.366 0.000 0.010 1.320 813.400
Food 615 4.027 7.798 0.000 0.060 4.450 68.460
Industry, Mining and Construction 1,207 1.980 22.074 0.000 0.010 0.520 678.770
Trade & Tourism 957 0.679 3.227 0.000 0.010 0.320 77.080
Transport & Communications 1,149 11.225 57.157 0.000 0.000 0.670 829.650
Water Supply & Sanitation 1,583 5.819 27.407 0.000 0.010 1.320 470.850

Period 2

Statistic N Mean St. Dev. Min Ptctl(25) Ptctl(75) Max
MigStock 19,856 91,270.390 599,836.300 1.000 940.000 38,756.000 12,275,876.000
distance 25,720 7,380.477 3,514.563 345.373 4,688.914 9,739.940 19,314.750
lnGDPperCap_o 37,019 8.749 1.064 6.326 7.879 9.513 11.815
lnGDPperCap_d 29,142 10.439 0.667 6.326 10.485 10.696 11.815
Bilateral 44,524 50.761 185.575 0.000 0.510 28.980 4,862.170
Agriculture, Forestry & Fishing 4,403 3.489 12.815 0.000 0.040 1.950 285.860
Education 7,350 4.045 15.971 0.000 0.050 1.990 485.590
Energy 2,418 12.559 63.122 0.000 0.000 1.320 919.590
Food 888 3.720 7.958 0.000 0.060 3.893 94.520
Industry, Mining and Construction 2,758 1.780 16.226 0.000 0.010 0.530 566.820
Trade & Tourism 2,029 0.813 3.314 0.000 0.010 0.320 70.090
Transport & Communications 2,422 12.749 99.906 0.000 0.000 0.610 2,861.340
Water Supply & Sanitation 3,452 5.571 31.167 0.000 0.010 1.240 900.100

 

From the summary statistics, I observe that the dataset used for Period 1 (2007 - 2010) has a total of 3,990 number of observations with 23 OECD destination countries and 124 origin countries. Period 2 (2011 - 2015) dataset has 3,698 number of observations using 81 countries’ data with the categorized aid types (Categorized Summary Statistics). When looking at the data in its selected subcategories or the uncategorized analysis, there is a total of 9,019 observations and the same number of OECD destination and origin countries in Period 1. Period 2 has 19,856 observations in uncategorized analysis (Uncategorized Summary Statistics). In my dataset, there is missing data due to unavailability or conditions that may not apply to the specific country-year-aid pair. In the regressions, I omit the missing data. The data covers three years using the 2010 estimate of migrant stock. This way, I can look at the effects of the various types of aid over a course of three years. The standard deviations of Bilateral Aid and all the aid types (Energy, Food Aid, Industry, Mining and Construction, Trade and Tourism, Transport and Communications and Water Supply and Sanitation) are very high due to the highly variable nature of aid that is dependent on relations between the destination and origin country like distance, migrant stock and population (Gurevich and Herman 2018). Overall, migrant stock is higher in all countries in the second period, which is consistent with the increasing pace of migration globally. There is a considerable amount of focus, measured by number of observations, on Social Infrastructure and Services Aid in both periods. The remaining categories have roughly similar observations. 

 

Figure 1: Number of observations by aid type

A bar graph depicting the number of observations of period 1 and 2 by aid type.

 

The following maps show the top donor countries in this dataset which are the United States, Japan and the United Kingdom. Bilateral aid is a summation of the years covered in each period. The darker the shading, the more bilateral aid donated. The top donors are unchanged over the two periods.

 

Figure 2: Map of Top Donor Countries, Period 1 (2007-2010)

A graphic of the top donor countries for 2007-2010, with the United States being first.

 

Figure 3: Map of Top Donor Countries, Period 2 (2011-2015)

A graphic of the top donor countries for 2011-2015, with the United States being first.

 

The following data visualizations provide a more detailed look into the amount of aid received in a specific year during each given time period. Notably, Afghanistan and Iraq show up most frequently in the period from 2007 to 2010. In the second period (2011 to 2015), there does not appear to be any trend in which countries appear in the top 30 recipients by aid type each year. This could be due to factors such as geopolitical events such as Sudan and South Sudan splitting into two countries in 2013 (CIA 2018) and the Crimean Peninsula being annexed from Ukraine by the Russian Federation in 2014 (Treisman 2016).

 

Figure 4: Top Recipients by Aid Type and Amount

A graphic chart depicting the top recipient countries by aid type and amount for 2007-2010.

 

2 Note that there are fewer than 30 donor countries in this dataset, as some countries elect not to report these figures or only participate in other forms of aid types that are omitted here such as Multi Sector or Program aid.

Gravity Model

Gravity Model angieliu

I use a gravity model to determine the relationship between migration and development aid. The gravity model looks at the interaction between migration flow and its drivers, in this case, the types of developmental aid. The gravity model is the best choice for this analysis, because the OECD developmental finance dataset reports bilateral net aid, and gravity models can incorporate paired country time and fixed effects into a regression. The time effects will be particularly helpful in this instance because amounts of bilateral aid vary over time as countries’ situations change. I estimate the following model:

ln Migrant Stocki,j,t = β1 ln (GDP per Capitai,t) + β2 ln (GDP per Capitaj,t) + β3Conflicti,t +  β4Freei,t + β5ln(Distance)i,j + β6Landlockedi + β7Common Languagei,j + β8i Former Colony of ji,j + β9ln(1 + Bilateral Aid)j-->i,t + β10ln(1 + Aid Type)j-->i,t + δi + δj + δt + εi,j,t

Continuous variables are expressed using natural log to allow for interpretation of the results as percent changes from proportional movements in the explanatory variables (World Bank 2018). The dependent variable is the migrant stock that originated in country i and is resident in country j at time t. I use migrant stock instead of migrant flow, because I can control the unique characteristics of specific time periods, destination and origin. Migrant flow measures the number of individuals entering/leaving a country during a specific period, whereas migrant stock captures the number of migrants at a given point in time. The research question is whether aid is effective in reducing the drivers of migration, but the decision to migrate depends on time, origin and available destination countries. Using migrant flow would wash out these variables in this analysis. This migrant stock data has only 2010 and 2015 estimates and the OECD’s aid data covers 2007 to 2015. To extract the most data and acknowledge that some time is needed to see aid’s effects, I split the aid data into two periods: 2007 to 2010 and 2011 to 2015 and use corresponding migrant stock from 2010 and 2015 from the UN as the dependent variable. The time periods are unfortunately unevenly split with period 1 having 3 years while period 2 has 4 years. Thus, the time periods are not meant for comparison, but rather additional information to look at the aid-migration link in two time periods.

To control for each years’ unique characteristics within each time period such as the presence of more conflict or uneven crop yields, I added a fixed effect δt to control for this. I also use fixed effects with origin δi and destination δj. I add to my regression equation to capture the network effects derived from bilateral aid from one country to another. We add ln(1 + Bilateral Aid)j-->i,t to our regression equation to capture the network effects derived from bilateral aid. The terms ln (GDP per Capitai,t) and ln (GDP per Capitaj,t) are added to reflect the abundant literature which says economic potential is a key driver of migration. GDP per capita is a proxy for potential wages migrants may earn at the destination country and reflects the economic condition of countries. I add a constant of 1 to GDP per Capita, Bilateral Aid and Aid type3 to work around the small values of these variables which would turn negative when the log is taken. These aid amounts are expressed in 2016 constant USD millions, so a constant of 1 will affect the results negligibly. I also separate my analysis into the categorized aid groups and selected subcategories within the larger category to further examine the differences between aid types. In addition to the aid type as a regressor, I add traditional variables of a gravity model such as distance between countries, the status of political and civil freedoms in a country and dummy variables to capture the following: conflict, landlocked, commonality in language and colonial history to determine the pull factors of migration. These variables have been shown to be strong determinants of migration. This is further evidenced by the regression tables below that show a strong statistical significance of these variables in predicting migration flows.

I use a quasi-poisson model with a log link or Poisson Pseudo Maximum Likelihood (PPML) model. I use PPML as opposed to an ordinary least square model (OLS), based on evidence that OLS overestimates many determinants of migration such as geographic distance between countries (Silva and Tenreyro 2006). OLS exhibits this behavior because migration data often has unequal variability across the range of variables of the predictors. Migration patterns are dependent on many factors and it is often the case that there is zero migration between a specific country pair.

3 Note that Aid is multiplied by a million to convert it to millions for an apples-to-apples comparison with the rest of aid and population statistics

Regression Results

Regression Results angieliu

Table 5: Categorized Period 1 Regression Results

Categorized Period 1 Regression Result table.

 

Table 6: Uncategorized Period 1 Regression Results

Uncategorized Period 1 Regression Result table

 

Table 7: Categorized Period 2 Regression Results 

Categorized Period 2 Regression Result table

 

Table 8: Uncategorized Period 2 Regression Results

Uncategorized Period 2 Regression Result table

Bilateral Aid

Bilateral Aid angieliu

I ran a regression with the total amount of bilateral aid an origin country receives as a baseline. In this analysis, bilateral aid is the total amount of aid that an origin country receives from all of the OECD countries combined. The results reaffirm the literature’s consensus that aid, as a whole, increases migration. In 2007 to 2010, the model estimates that every one percent increase in total aid received results in a 0.106 percent change in migrant stock. The small coefficient confirms that there is an indirect link between aid and migration. From the time period 2011 to 2015, the relationship is the same with a slightly larger magnitude of 0.173. This positive relationship between migrant stock, as a dependent variable, and bilateral aid given is also seen in Barthelemy et al.’s paper in 2009, however their results show a larger coefficient of 0.31. The larger coefficient is due to a different methodology: they separate the data into different country income groups and use a three-stage least squares model using two equations describing wage differentials and one regarding the cost of migration.

Social Infrastructure and Services

Social Infrastructure and Services angieliu

The analysis of both periods show that Social Infrastructure and Services aid is not statistically significant. The subcategory data, however, show that there are aid types that fall under Social Infrastructure and Services that are statistically significant. From 2007 to 2010, Education aid is not statistically significant, but Water exhibits a statistically significant negative relationship with a coefficient of 0.014 with migrant stock. From 2011 to 2015, both Education and Water have negative relationships with migrant stock. Holding all other variables constant, a one percent increase in Education aid, on average, would decrease migrant stock by 0.019 percent. For Water aid, a one percent increase in aid would decrease migrant stock by 0.012 percent. Lanati and Thiele’s paper argues that better public services may outweigh aid’s reduction of migration costs, decreasing migration from the origin country. My analysis shows that this may not be the case, or at least aid directed at building public services has varying impacts. This provides us with mixed initial results of the relationship between public services aid and migrant stock.

Humanitarian Aid

Humanitarian Aid angieliu

I was curious whether the presence of humanitarian aid in conflict situations makes an impact. In period 1, humanitarian aid and the interaction of conflict and humanitarian aid were not statistically significant. In period 2, both are statistically significant, but show different relationships with migrant stock. Humanitarian aid in a conflict zone increases migrant stock in destination countries by 0.094 percent given a one percent increase in aid. In conflict zones, which are defined to have more than 25 battleground deaths and the state is a counterparty, it decreases migration by 0.092 percent — an opposite effect. In period 1, there were 1773 conflicts during this period, whereas in period 2, there were 361 conflicts. Donor countries gave a total of $4.4 bn of humanitarian aid in period 1, whereas in period 2 they gave $6.6 bn of aid and there were less conflicts. Just an indicator, assuming donors only gave humanitarian aid to conflict zones and donated aid evenly, in period 1 each conflict received roughly $2.48m in aid whereas in period 2 each conflict received almost $18.31mm in aid. Of course, equal distribution of aid is an oversimplifying assumption to have and this does not reflect the severity of conflict, but the existence of conflict. I suspect the differences in geopolitical events in these two periods may explain the relationships but cannot conclude from this data analysis. I suspect that humanitarian aid reduces the cost of migration in non-monetary ways and increases migration. Figure 1 shows that refugees, the subset of migrants that receive the most humanitarian aid, move the least distances to destination countries. Their needs and preferences are more about safety than economic opportunity, unlike high-skilled emigrants (World Bank 2018)

Humanitarian aid like food, emergency response and reconstruction relief decreases the short-term cost of rebuilding after conflict and reduces the cost of migration. In other words, if migrants are provided with basic services and care during instability, then they can pursue safety and a better life elsewhere. In connection with this finding, food aid is estimated to be statistically significant and inversely related to migrant stock in period 1 -- further supporting the hypothesis that aid decreases the cost of migration. In period 2, however, food aid is not statistically significant. This shows that aid can have differing effects on migration depending on factors like time, origin, destination and other factors that may not be shown in my analysis.

Economic Infrastructure and Services

Economic Infrastructure and Services angieliu

I will discuss Economic and Production Aid together. This is often done when analyzing aid data because these two categories of aid are usually given to well-governed origin countries (Akramov 2012). In the first period, both Economic and Production sector aid show a negative relationship with migrant stock. A one percent increase in Economic or Production aid, on average, all else held constant, will yield an approximately 0.04 percent decrease in migrant stock. The subcategories in period one loosely affirm this effect as both Transport and Communications and Agriculture, Forestry and Fishery aid show a negative relationship with coefficients of 0.012 and 0.038 respectively. These results are in line with the majority of empirical work that determine that economic opportunity is a large driver of migration (World Bank). Opportunity of more economic activity in origin countries may change individuals’ inclinations to migration.

In period 2, both Economic and Production Sector aid are not statistically significant. The majority of sub-categories with the exception of agriculture, forestry and mining aid shows no statistically significance. Agriculture, forestry and mining aid depicts an opposite relationship than expressed in period 1. As this aid type increases by one percent, my model estimates that migrant stock in destination countries increases 0.012 percent, holding all else constant. This estimate of a positive relationship between migrant stock shows an alternative hypothesis. Instead of a decrease in migration when economic opportunities are better at home, individuals may choose to migrate due to a relative decrease in migration cost to find better wages elsewhere. This scenario is what the majority of economists agree with, including Clemens and Postel.

Lanati and Thiele use a similar three stage least squares model as Barthelemy et al. in 2009, but use migrant flow as the dependent variable instead. They find a general negative relationship between emigration rates and each type of aid: Social, Economic and Production with coefficients of -0.119, -0.046, -0.065 respectively. This is not exactly an apples to apples comparison, but the differences estimate the relationship between aid and migration. In my analysis, Economic and Production aid also exhibit a negative relationship with migration, however Social aid’s impact is not statistically significant in both periods. In the subcategories of each category, the negative relationship is clearer, as discussed above. The amount of variability explained in their model is very close to mine, at around 90 percent of variability explained. They use residuals squared (r-squared) values to describe the variability explained, while I use deviance squared (d-squared), but the interpretations are the same.

Discussion of Endogeneity

Discussion of Endogeneity angieliu

A key assumption in this paper is that aid is given by donor countries completely exogenously, implying that there is nothing that origin countries can do to receive more aid. Immigrants in destination countries can lobby their governments to give out more aid in their origin countries. This presents a reverse causality problem where large migrant stock can actually increase foreign development aid, as opposed to developmental aid increasing migrant stock. My analysis does not control for this, so the results of the analysis may overstate the magnitude of migrant stock in destination countries. Another source of endogeneity are the omitted variables that are related to the error term. My analysis also assumes that the OECD countries allocate aid in countries and sectors where it is most needed. This is not necessarily the case as aid is often used as a foreign policy tool. The underlying incentives of the aid given is an omitted variable and one that is hard to measure. This creates a bias in my analysis by understating the relationship between migration and aid because the allocation of aid is imperfect.

Conclusion

Conclusion angieliu

My paper investigates a sample of the disaggregated data on developmental aid from the DAC countries. Adding all of the subcategories to the data analysis may give a better picture of how specific types of aid may change individuals’ decisions to migrate. Notice that some of the results in the Categorized and Uncategorized regressions are inconsistent. For example, in period 2, Social Infrastructure and Services aid is not statistically significant. However, its subcategories, Education and Water, depict a statistically significant negative relationship with migrant stock. The incorporation of more subcategories say, Healthcare, may provide more evidence about the most effective types of aid in this category.

My analysis looks at potential factors which can be effective in promoting welfare in origin countries in curtailing migration over many countries and aid types but does not offer evidence on how to tailor solutions to specific situations, an area where more work can be done. A theme throughout my analysis is the heterogeneous nature of aid. All aid does not work the same way in all countries and aid does not necessarily always improve the quality of life in origin countries. An interesting extension of this research could be to continue to investigate specific sectors in the context of geographic regions.

Another avenue of research is examining production and economic sectors more in-depth. These two aid types are similar in that they are often distributed to middle income countries. The hypothesis being that perhaps aid cannot help low income countries in the long run, but maybe middle-income countries can use aid to stimulate growth and decrease migration.

The effectiveness of recent policies to manage migration flows has been debated and my paper aimed to explore disaggregated aid further in relation to migration. Social aid effectiveness on curtailing migration was mixed in this analysis. The results show only Water aid decreases migration stocks in destination countries, as Lanati and Thiele predicted. Further an investigation into public services should be done, as there seems to be high variability in the social infrastructure and services category. Production and Economic sector aid are estimated to be the best at reducing migration stocks, but the effects seem varied. These two types of aid are often deployed to well-governed origin countries , so the aid may be deployed more effectively. They also directly contribute to economic opportunity in origin countries, tackling one of the main drivers of aid. Humanitarian aid appears to most frequently have a positive effect on migration stock in destination countries and, in one case, does not have a statistically significant effect. This paper has shown the potential for more research into the aid and migration link, especially at the disaggregated level in shaping migration policy decisions.

References

References angieliu

Akramov, Kamiljon T. “Introduction.” Foreign Aid Allocation, Governance, and Economic Growth, University of Pennsylvania Press, 2012, pp. 1–4. JSTOR, www.jstor.org/stable/j.ctt3fhm24.8.

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