Determinants of Poverty in Developing Countries.pdf

7/21/2019 Determinants of Poverty in Developing Countries.pdf

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American University in Armenia:

Master of Science in Economics

2015

Student: Vilen Yeretskinyan

Instructor: Vardan Baghdasarya

Determinant of Poverty in Developing Countries



1

Table of Contents

1.

Introduction ...................................................................................................................................... 2

2. Literature Review & References ............................................................................................... 3

3. Data description .............................................................................................................................. 4

3.1 Detailed data description ....................................................................................................... 4

3.2 Data sources ............................................................................................................................ 6

4. Methodology and Econometric Model .................................................................................... 6

4.1 Economic model ..................................................................................................................... 6

4.2 Econometric model ................................................................................................................ 6

5. Results & Conclusion .................................................................................................................. 10

5.1 Results .................................................................................................................................... 10

5.2 Conclusion ............................................................................................................................. 12

6. Appendix ..................................................................................................................... 14

6.1 Graphs for model choosing ................................................................................................. 14

6.2 Graph matrix for all variables ............................................................................................. 15

6.3 Leverage of data ................................................................................................................... 16

6.4 Graphs for normality test ..................................................................................................... 16

6.5 Graph for Heteroskedasticity test ....................................................................................... 17

6.6 List of countries .................................................................................................................... 18



2

1. Introduction

There are significant social and economic differences between developed and developing countries.

Many of the underlying causes of these differences are rooted in the long history of development of

such nations and include social, cultural and economic variables, historical and political elements,

international relations and geographical factors.

According to the UN, a developing country is a country with a relatively low standard of living,

undeveloped industrial base and moderate to low Human Development Index (HDI). This index is a

comparative measure of poverty, literacy, education, life expectancy and other factors for countries

worldwide. The index was developed in 1990 by Pakistani economist Mahbub ul Haq.

One of the causes of developing countries will be observed in this paper which is poverty.

A poverty profile depicts the example of destitution, yet is not basically worried with clarifying its

reasons. Yet an attractive clarification of why a few individuals are poor is crucial on the off chance

that we are to have the capacity to handle the roots of poverty.

Among the key reasons, or possibly connects, of poverty are:

Regional-level characteristics: these include vulnerability to flooding or typhoons; remoteness;

quality of governance; property rights and their enforcement.

Community level characteristics: these include the availability of infrastructure (roads, water and

electricity) and services (health, education), proximity to markets and social relationships.

Household and individual characteristics: Among the most important are:

Demographic: household side, age structure, dependency ratio, gender of head.

Economic: employment status, hours worked, property owned.

Social: health and nutritional status, education, shelter.

Regression analysis is expected to pick the free variables precisely, to make sure that they are in fact

showing correct picture.



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Regression techniques are great at recognizing the quick ("proximate") reasons for poverty, however

are less effective at discovering the “deep” causes; they can show that a lack of education causes

poverty, but cannot so easily explain why some people lack education.

The weakest part of poverty analysis – what Howard White calls the “missing middle” – is

developing a clear understanding of the fundamental causes of poverty. Such an understanding is

needed if one is to develop an effective strategy to combat poverty.

2. Literature Review & References

1. Asian Development Bank (2002) and (2005) Key Indicators of Developing Asian and Pacific

Countries. Manila: The Asian Development Bank.

2. Trade, income distribution and poverty in developing countries: a survey – UNITED

NATIONS

3. Minimum Wages and Poverty in Developing Countries -

http://www.researchgate.net/profile/Darryl_Mcleod/publication/5059750_Minimum_Wages_

and_Poverty_in_Developing_Countries__Some_Empirical_Evidence/links/5474b98a0cf29af

ed60f984e.pdf

4. Grossman, Jonathan. "Fair Labor Standards Act of 1938: Maximum Struggle for a Minimum

Wage". Department of Labor. Retrieved 17 April 2014.

5.

Stone, Jon (1 October 2010). "History of the UK's minimum wage". Total Politics. Retrieved17 April 2014.

6. R. Freeman, “The Minimum Wage as a Redistributive Tool,” The Economic Journal.

7.

World Bank (1995), p. 75. The International Labor Organization has a different view. For the

ILO "...minimum wages are a potentially important labour market policy instrument for

reducing poverty....” (Rodgers ,1995, p. 48). For more on this issue see Lipton (1995), p. 130.

8. Although Carruth and Oswald’s model was meant to analyze the impact of unions, their

analysis applies to minimum wage legislation as well.

9. Wages and equitable growth – http://www.ilo.org/wcmsp5/groups/public/---dgreports/---

dcomm/---publ/documents/publication/wcms_194843.pdf

10. Introduction to poverty analysis (World Bank Institute August 2005) -

http://siteresources.worldbank.org/PGLP/Resources/PovertyManual.pdf

11. Socio-Economic Determinants of Poverty -

http://projekter.aau.dk/projekter/files/17509764/Microsoft_Word_-_Thesis_AAU.pdf

http://www.researchgate.net/profile/Darryl_Mcleod/publication/5059750_Minimum_Wages_and_Poverty_in_Developing_Countries__Some_Empirical_Evidence/links/5474b98a0cf29afed60f984e.pdf




http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_194843.pdf
















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12. The Determinants of Poverty and Inequality -

http://siteresources.worldbank.org/SPLP/Resources/461653-1207162275268/4847412-

1209400266362/Chapter3Determinants.pdf

13. DETERMINANTS OF POVERTY IN LAO PDR -

http://swopec.hhs.se/eijswp/papers/eijswp0223.pdf

14. Economic Determinants of Poverty in Zimbabwe -

http://www.ijeronline.com/documents/volumes/Vol%202%20issue%206/ijer20110206ND(1)

.pdf

3. Data Description

The observing data in this study is for all developing countries in 2012 from. The expected number

of observations will be up to 151. Not all variables have exact 151 observations as some of the

countries do not have data for some variables. You can see list of counters in Appendix 6.6.

The research and literature that I have observed for this study directs me to select variables that are

part of regional; community; demographic; economic and social fields. Studs and my personal

analysis, which I will introduce later, show that this variable’s at least indicates effect on poverty.

From the list I selected: POVERTY (Y), GDP PER CAPITA (ß2), UNEMPLOYMENT (ß3),

INFLATION (ß4), EDUCATION INDEX (ß5), IMPROVED WATER SOURCE (ß6), LAND

UNDER CEREAL PRODUCTION (ß7), ACCESS TO ELECTRICITY (ß8) and ROADS (ß9).

3.1 Detailed data description:*

POVERTY (POV)

Population below $2 a day is the percentage of the population living on less than $2.00 a day.

GDP PER CAPITA** (GDP)

GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross

value added by all resident producers in the economy plus any product taxes and minus anysubsidies not included in the value of the products. It is calculated without making deductions for

depreciation of fabricated assets or for depletion and degradation of natural resources.

http://siteresources.worldbank.org/SPLP/Resources/461653-1207162275268/4847412-1209400266362/Chapter3Determinants.pdf





http://www.ijeronline.com/documents/volumes/Vol%202%20issue%206/ijer20110206ND(1).pdf



http://en.wikipedia.org/wiki/%C3%9F















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UNEMPLOYMENT (UNEMP) (% of total labor force)

Unemployment refers to the share of the labor force that is without work but available for and

seeking employment.

INFLATION consumer prices (annual %) (INF)

Inflation as measured by the consumer price index reflects the annual percentage change in the cost

to the average consumer of acquiring a basket of goods and services that may be fixed or changed at

specified intervals, such as yearly. The Laspeyres formula is generally used.

EDUCATION INDEX (EDU)

The Education Index is calculated from the Mean years of schooling index and the Expected years of

schooling index.

IMPROVED WATER SOURCE (% OF POPULATION WITH ACCESS) (ATW)

Access to an improved water source refers to the percentage of the population using an improved

drinking water source. The improved drinking water source includes piped water on premises (piped

household water connection located inside the user’s dwelling, plot or yard), and other improved

drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells,

protected springs, and rainwater collection).

LAND UNDER CEREAL PRODUCTION**** (HECTARES) (LUCP)

Land under cereal production refers to harvested area, although some countries report only sown or

cultivated area. Cereals include wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat,

and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal

crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are

excluded.

ACCESS TO ELECTRICITY (% OF POPULATION) (ATE)

Access to electricity is the percentage of population with access to electricity. Electrification data are

collected from industry, national surveys and international sources.



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ROADS, TOTAL NETWORK (KM) (ROAD)**

Total road network includes motorways, highways, and main or national roads, secondary or

regional roads, and all other roads in a country. A motorway is a road designed and built for motor

traffic that separates the traffic flowing in opposite directions.

* Data are in U.S. dollars. **Data are divided to 1,000. *** Data are divided to 10,000.

3.2 Data sources

1. www.worldbank.org

2. www.unece.org

3. www.oecd.org

4. www.indexmundi.com

4. Methodology and Econometric Model

The main problem is to find the impact of the data to poverty level in developing countries. I have

selected several variables and will develop regression models to see how well the variables explain

poverty. For that reason I will test correlation in my data, see most suitable model and generate new

variables if needed, compare regression outcomes and do other relevant tests.

4.1 Economic model

POV = ß1 + ß2GDP + ß3UNEMP + ß4INF + ß5EDU + ß6ATW + ß7LUCP + ß8ATE + ß9ROAD

4.2 Econometric model

Econometric model

POV = E (POV) + e = ß1 + ß2GDP + ß3UNEMP + ß4 INF + ß5EDU + ß6ATW + ß7LUCP +

ß8ATE + ß9ROAD + e

This is not my final econometric model, I need to do several testes to clarify the final variables and

see if I need to transform my variables.




















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Correlation test

Here we can see that we have little correlation between variables besides EDU and ATW, EDU and

ATE, ATW and ATE, ROAD and LUCP which is the most highest. In this case I will omit ROAD as

I have 2 other community level characteristics and give more value to LUCP.

New econometric model

POV = E (POV) + e = ß1 + ß2GDP + ß3UNEMP + ß4 INF + ß5EDU + ß6ATW + ß7LUCP +

ß8ATE + e

Regression 1

The regression model shows that only 3 variables are significant. R-squared=0.6973 and Adj R-

squared=0.6775 are high so selected variable’s explain poverty, but most variables are insignificant

and not matches expected signs of coefficients. This can be due to other problems which I will try to

ROAD -0.0545 0.0210 -0.1079 -0.0224 0.0482 0.1146 0.9584 0.1334 1.0000

ATE -0.8083 0.4061 0.1262 -0.1319 0.7473 0.7454 0.1160 1.0000

LUCP -0.0308 0.0424 -0.1668 0.0191 0.0126 0.0800 1.0000

ATW -0.7057 0.2756 0.1533 -0.1638 0.6369 1.0000

EDU -0.7100 0.4634 0.1361 -0.0984 1.0000

INF 0.0748 -0.0487 -0.0923 1.0000

UNEMP -0.1541 -0.0045 1.0000

GDP -0.3401 1.0000

POV 1.0000

POV GDP UNEMP INF EDU ATW LUCP ATE ROAD

_cons 122.153 9.466145 12.90 0.000 103.4212 140.8848 ATE -.4796728 .0781227 -6.14 0.000 -.6342635 -.3250821

LUCP .0011051 .0011884 0.93 0.354 -.0012466 .0034567 ATW -.3616318 .1416332 -2.55 0.012 -.6418983 -.0813654 EDU -37.10315 14.89976 -2.49 0.014 -66.58708 -7.619227 INF -.1792067 .1895201 -0.95 0.346 -.5542329 .1958194 UNEMP -.0831026 .1864863 -0.45 0.657 -.4521253 .2859201 GDP .0637176 .1930226 0.33 0.742 -.3182393 .4456746

POV Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 112667.209 134 840.800068 Root MSE = 16.367 Adj R-squared = 0.6814 Residual 34021.6655 127 267.88713 R-squared = 0.6980 Model 78645.5436 7 11235.0777 Prob > F = 0.0000 F( 7, 127) = 41.94 Source SS df MS Number of obs = 135
















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find out. Our next step is obtaining the model which will better explain impact of independent

variable’s to dependent one.

Joint hypothesis testing

H0: ß2GDP = ß3UNEMP = ß4 INF = ß5EDU = ß6ATW = ß7LUCP = ß8ATE = 0

H1: At least one of the variables is not equal to zero

First last see if our variables are right selected and test them. We can see that p<α so we reject H0

and accept alternative one that at least one of the variables are not equal to zero.

Second for understanding the model that will better fit, I will use graphical illustration, which

attached in Appendix 6.2 and 6.3. From this graphs I can conclude that log-log model is the one that

will better fit my data also its graphical illustration better explain economical intuition behind it.

Also from graph in Appendix 6.4, we can see that I have leverage in my data. It can be from the fact

that I have countries like China, Brazil and Russia at the same time Niger, Congo and some islands.

This can cause some problems but I will leave data as it is for this paper skipping the leverage

problem.

The next modification in model which will be applied due to grapes and testing of different models

is log-log model.

New econometric model

POVLN = E(POVLN) + e = ß1 + ß2ln(GDP) + ß3ln(UNEMP) + ß4ln(INF) + ß5ln(EDU) +

ß6ln(ATW) + ß7ln(LUCP) + ß8ln(ATE) + e

Prob > F = 0.0000 F( 7, 127) = 41.94

( 7) ATE = 0 ( 6) LUCP = 0 ( 5) ATW = 0 ( 4) EDU = 0 ( 3) INF = 0 ( 2) UNEMP = 0 1 GDP = 0


















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Correlation Test

Here we can see that almost all variables are non-correlated and the highest correlation is 0.6243,

which I will keep for this step to see the impact of other tests.

Goodness-of-Fit and Information Criteria

To see if all variables are right selected and explain poverty and to see if there is irrelevant variable I

will use Akaike and Schwarz criterion test.

Goodness-of-Fit and Information Criteria

Included Variables R 2

_

R 2 AIC SC

POVLN GDPLN 0.3064 0.3016 506.6065 512.6009

POVLN GDPLN UNEMPLN 0.3066 0.2971 508.5482 517.5399

POVLN GDPLN UNEMPLN INFLN 0.3072 0.2927 507.9105 519.8722

POVLN GDPLN UNEMPLN INFLN EDULN 0.3749 0.3568 484.7613 499.5755

POVLN GDPLN UNEMPLN INFLN EDULNATWLN

0.4166 0.3952 474.2427 491.9776

POVLN GDPLN UNEMPLN INFLN EDULNATWLN LUCPLN

0.4918 0.4676 433.8876 454.12

POVLN GDPLN UNEMPLN INFLN EDULNATWLN LUCPLN ATELN

0.4933 0.4649 435.4843 458.6071

Final econometric model

From above test we can see that the best model is:

POVLN = E(POVLN) + e = ß1 + ß2ln(GDP) + ß3ln(UNEMP) + ß4ln(INF) + ß5ln(EDU) +

ß6ln(ATW) + ß7ln(LUCP) + e

For interpretation of results we need to use the Slope = dy/dx =ß2 y/x and Elasticity = ß2.

LUCPLN -0.0631 -0.1968 -0.3259 0.2182 -0.1263 -0.1988 1.0000 ATWLN -0.5600 0.5732 0.1589 -0.2267 0.6243 1.0000 EDULN -0.6066 0.7738 0.2020 -0.1488 1.0000 INFLN 0.1333 -0.1680 -0.0535 1.0000

UNEMPLN -0.1701 0.2570 1.0000 GDPLN -0.5896 1.0000 POVLN 1.0000

POVLN GDPLN UNEMPLN INFLN EDULN ATWLN LUCPLN





















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5. Results & Conclusion

5.1 Results

Summarize the Variables

Normality test

In Appendix 6.4 you can find graphs for normality test. Pnorm is sensitive to non-normality in the

middle range of data and qnorm is sensitive to non-normality near the tails. As you can see, the

results from pnorm show indications of non-normality, while the qnorm command shows a deviation

from normal at the tails. Nevertheless, this seems to be deviation from normality. We gate same

results for 3 test we have gone. We can accept that the residuals are not normal distributed.

ROAD 149 134.5465 534.4064 .008 4689

ATE 151 71.76291 31.95859 5.1 100 LUCP 140 403.7305 1205.214 0 9710 ATW 150 84.23667 16.19384 30 100 EDU 147 .529932 .150531 .2 .8

INF 151 7.960927 12.05758 -.9 100 UNEMP 151 9.754305 8.210942 .2 60 GDP 151 6.902649 11.75861 .2 94.2 POV 148 36.08514 28.23566 .1 95.2 CountryCod 0

Variable Obs Mean Std. Dev. Min Max

LUCPLN 139 0.0000 0.0659 20.80 0.0000 ATWLN 150 0.0000 0.0004 38.38 0.0000 EDULN 147 0.0001 0.6508 13.84 0.0010 INFLN 150 0.1308 0.0294 6.62 0.0365 UNEMPLN 151 0.0001 0.0003 22.31 0.0000 GDPLN 151 0.8737 0.0836 3.07 0.2157 POVLN 148 0.0000 0.0045 31.89 0.0000

Variable Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 joint

Skewness/Kurtosis tests for Normality



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Heteroskedasticity Test (white test)

The graph In Appendix 6.5 and test illustrates that the data have heteroskedasticity problem. We will try to

solve the problem by running regression with robust.

Total 91.48 34 0.0000

Kurtosis 4.93 1 0.0263 Skewness 28.18 6 0.0001 Heteroskedasticity 58.37 27 0.0004

Source chi2 df p



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5.2 Conclusion

“Results of various specifications”

M1 M2 M3

coef/se coef/se coef/se

GDP 0,064

(0,193)

UNEMP -0,083

(0,186)

INF -0,179

(0,190)

EDU -37,103*

(14,900)

ATW -0,362*

(0,142)

LUCP 0,001

(0,001)

ATE -0,480***(0,078)

GDPLN -0,373* -0,373*

(0,145) (0,155)

UNEMPLN -0,174 -0,174

(0,141) (0,139)

INFLN 0,074 0,074

(0,137) (0,170)

EDULN -1,124* -1,124*

(0,535) (0,474)

ATWLN -2,296*** -2,296***

(0,663) (0,596)LUCPLN -0,118*** -0,118**

(0,034) (0,036)

_cons 122,153*** 13,264*** 13,264***

(9,466) (3,143) (2,740)

Number of observations 135 133 133

R2 0,698 0,492 0,492

Adjusted R2 0,681 0,468 0,468

note: .001 - ***; .01 - **; .05 - *;

My final regression model gives us following results:

1% change in GDP will decrease POV by 0.373%, 1% change in EDU will also decrease POV by

1.124, 1% change in ATW will decrease POV by 2.296% and 1% change in LUCP will decrease

POV by 0.118.



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Also I have variables which are not significant: UNEMP and INF. Taking in facts the problems

which I have detected during my study: data leverage, missing data, normality problem and

heteroskedasticity I can except to get insignificant variables but in my opinion important once.

In case of inflation we can expect to see more long term effect not immediate one. For

unemployment it must have direct affect but if we take Armenian example that we can have high

unemployment, due to migration and finding job oversees which allows to covers family expanses

and there is no need for external employment in family. This can direct us that in some cases

unemployment also can have long term effect.

It is clear that more variables and more data analysis could help us to indicate more relationships and

impact on poverty. But not all data are available and it is one of the most harming problem for

research work.

Also review of literature and similar papers lead me to the conclusion that this work can identify

poverty superficial. We can find different significant effects on poverty but in general it will not

cover the overall effect. For example to detect the better result for ATW we need to detect dipper

problem why in exact country they have low level of ATW which are one of the main problems.

From the results and overall review I can conclude that for each variable we can make separate

researcher and do deleted observation to understand the problem from root.



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6. Appendix

6.1 Graphs for model choosing



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6.2 Graph matrix for all variables



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6.3 Leverage of data

6.4 Graphs for normality test



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Pnorm Qnorm

6.5 Graph for Heteroskedasticity test



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6.6 List of counties

1 Afghanistan AFG 77 Macedonia MKD

2 Albania ALB 78 Madagascar MDG

3 Algeria DZA 79 Malawi MWI

4 Angola AGO 80 Malaysia MYS

5 Antigua and Barbuda ATG 81 Maldives MDV6 Argentina ARG 82 Mali MLI

7 Armenia ARM 83 Marshall Islands MHL

8 Azerbaijan AZE 84 Mauritania MRT

9 Bahamas BHS 85 Mauritius MUS

10 Bahrain BHR 86 Mexico MEX

11 Bangladesh BGD 87 Federated States of Micronesia FSM

12 Barbados BRB 88 Moldova MDA

13 Belarus BLR 89 Mongolia MNG

14 Belize BLZ 90 Montenegro MNE

15 Benin BEN 91 Morocco MAR

16 Bhutan BTN 92 Mozambique MOZ17 Bolivia BOL 93 Namibia NAM

18 Bosnia and Herzegovina BIH 94 Nepal NPL

19 Botswana BWA 95 Nicaragua NIC

20 Brazil BRA 96 Niger NER

21 Brunei BRN 97 Nigeria NGA

22 Bulgaria BGR 98 Oman OMN

23 Burkina Faso BFA 99 Pakistan PAK

24 Burma BMU 100 Palau PLW

25 Burundi BDI 101 Panama PAN

26 Cambodia KHM 102 Papua New Guinea PNG

27 Cameroon CMR 103 Paraguay PRY28 Cape Verde CPV 104 Peru PER

29 Central African Republic CAF 105 Philippines PHL

30 Chad TCD 106 Poland POL

31 Chile CHL 107 Qatar QAT

32 China CHN 108 Romania ROU

33 Colombia COL 109 Russia RUS

34 Comoros COM 110 Rwanda RWA

35 Democratic Republic of the Congo COD 111 Saint Kitts and Nevis KNA

36 Republic of the Congo COG 112 Saint Lucia LCA

37 Costa Rica CRI 113 Saint Vincent and the Grenadines VCT

38 Côte d'Ivoire CIV 114 Samoa WSM39 Croatia HRV 115 São Tomé and Príncipe STP

40 Djibouti DJI 116 Saudi Arabia SAU

41 Dominica DMA 117 Senegal SEN

42 Dominican Republic DOM 118 Serbia SRB

43 Ecuador ECU 119 Seychelles SYC

44 Egypt EGY 120 Sierra Leone SLE

45 El Salvador SLV 121 Solomon Islands SLB



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46 Equatorial Guinea GNQ 122 Somalia SOM

47 Eritrea ERI 123 South Africa ZAF

48 Ethiopia ETH 124 South Sudan SSD

49 Fiji FJI 125 Sri Lanka LKA

50 Gabon GAB 126 Sudan SDN

51 The Gambia GMB 127 Suriname SUR

52 Georgia GEO 128 Swaziland SWZ

53 Ghana GHA 129 Syria SYR

54 Grenada GRD 130 Tajikistan TJK

55 Guatemala GTM 131 Tanzania TZA

56 Guinea GIN 132 Thailand THA

57 Guinea-Bissau GNB 133 Timor-Leste TLS

58 Guyana GUY 134 Togo TGO

59 Haiti HTI 135 Tonga TON

60 Honduras HND 136 Trinidad and Tobago TTO

61 Hungary HUN 137 Tunisia TUN

62 India IND 138 Turkey TUR

63 Indonesia IDN 139 Turkmenistan TKM

64 Iran IRN 140 Tuvalu TUV

65 Iraq IRQ 141 Uganda UGA

66 Jamaica JAM 142 Ukraine UKR

67 Jordan JOR 143 United Arab Emirates ARE

68 Kazakhstan KAZ 144 Uruguay URY

69 Kenya KEN 145 Uzbekistan UZB

70 Kiribati KIR 146 Vanuatu VUT

71 Kyrgyzstan KGZ 147 Venezuela VEN

72 Lao PDR LAO 148 Vietnam VNM

73 Lebanon LBN 149 Yemen YEM

74 Lesotho LSO 150 Zambia ZMB

75 Liberia LBR 151 Zimbabwe ZWE

76 Libya LBY

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