Outcomes of Economic Liberalization in Africa

In the race towards development, many African countries have adopted the western conception of modernization. In the west, open market economies fast-tracked nations to modern-hood.  Less barriers to market, and the less influence of government boosted the growth of enterprise in these economies. Private sector led growth has a multiplying effect, boosting growth several times higher state intervention. Private sector, especially small and medium enterprises, provide the majority of jobs, and therefore can improve the livelihood of workers. State intervention and large government spending can crowd out the private sector, limiting opportunities for entrepreneurs sell those services and solutions to the market. It also diminishes the competition to state-owned enterprise, and less competition means a less transformative economy. 

Ethiopian PM at the test-run of a newly developed light rail http://english.gov.cn/premier/news/2015/02/03/content_281475049660622.htm

Whether a country has state-led development or private led development is signaled by their economic markets. If the market is closed, this means heavy spending and intervention is occurring on the part of governments. If the market is liberalized, this means there is an openness to private investment. Many countries began with state intervention in their path to development (South Korea, Brazil, and Tanzania) but ultimately opened their markets once their domestic economy was fully developed. Countries like China, Ethiopia, and Russia continue to have state-led development although this puts limits on their private sectors.

While there is no right or wrong path to develop, one method might lead to greater human development than the other. It seems fast growth rates in many places have not reduced wealth inequality and standard of living for all. Some of the fastest growing nations, like Mozambique, have not been able to comprehensively address poverty and the standard of living. While China has rapidly industrialized it was able to lift a billion people out of poverty. Can the developmental state model ensure that growth is equal and comprehensive? To answer these question I study African countries and the effects economic liberalization has had on their human development.[1]

The two variables of interest for my study are human development and economic liberalization. I use a cross sectional time series (CSTS) with fixed effects model over 1990-2014. This model allows me to measure the effect of economic liberalization on human development over a long period of time and control for time independent effects. Changes in the explanatory variables of this model correlate to the changes in the level of human development. All the countries used in the data are in the sub-Saharan Africa region. CTST method addresses time order bias of the variables of interests. It also addresses confounding bias from things like culture, state identity, and geopolitical factors that I control for.

To operationalize economic liberalization, the dependent variable of this study, I use the Economic Freedom of the World index. This distinguished if the country has a closed market economy versus fully liberal market economy by a score on a scale of zero (closed market) to 100 (open market). To operationalize human development I use real GDP per capita as measurement.I control for the effects of a several variables, Instability, foreign aid, and economic institutional strength. Instability is operationalized by the Fragility Index provided by Fund for Peace. This measures fragility of a state on a variety of factors and gives a score on a scale of 0 to 120 (0 being the most stable, 120 being very unstable). Economic institutional strength is operationalized by the absolute economic institutional quality provided by the Heritage Foundation’s economic freedom index. This measures the strength of economic institutions by a score of 0 (worst) to 100 (best). This controls for the effects of colonial institution legacies on the continent, francophone and anglophone cultural differences in institutions, and the effectiveness of a countries central banks and finance ministers.

Foreign aid is the sum of dollar amounts received, and is distinguished between aid from international organizations and, and aid from other organizations or donors. Levels of stability are influenced by an increasing presence of non-state violent actors that might have a negative effect on a country’s modernization despite democracy, liberalization, or economic industrialization policies. Foreign Aid might influence a country’s economic growth.

Photo of MTN shop in Nigeria. MTN is a private Nigerian telecommunications firm. http://uk.reuters.com/article/2014/03/05/us-safrica-mtn-idUKBREA240DF20140305

So what is the outcome or effect of economic liberalization on human development? My hypothesis is that economic liberalization has zero (or and infinitely small) effect in in the level of human development. In my analysis however, I found my hypothesis was wrong as there is a positive effect, which is statistically significant.

I create a regression model of of the variables I use on in this study (table one).

The regression shows that economic liberalization has a positive effect on human development. For every improved score on the economic liberalization index the Real GDP per capita increases by 1.35 units. This positive effect is also statistically significant at the 0.1% level, which means it is a substantial effect relative to the sum of GDP.

There are many reasons for this outcome. A factor I’d like to focus on is GDP measurements. I used. I used the measurement of real GDP per capita. This measurement might not have captured larger picture of inequality and human development. There are other measurements created by the UNDP that try to grasp the whole picture of wealth. Private sector can boost a country's GDP (like Mozambique, China) but if this boost is not being realized in all provinces and levels of society, it can mask larger equality and poor standards of living. In a future study, I’d like to isolate the effects further by either switching measurements, or considering a different and more robust time series statistical  model.

---

[1] Using a Hausman test we can see that there is little difference between the fixed effects and random effects estimation. So there is little room for endogeneity. The test says it is okay to use random effects estimation. Although it is okay to use the random effects, I conclude with my model of the fixed effects would or create bias as the results would be similar.

Vital Flows: The Effect of Changes on GDP on Foreign Aid

Wealthy states provide an immense amount of developmental aid to poor. In 2013, members of the Organization of Economic Cooperation and Development’s (OECD), Development Assistance Committee (DAC) provided over $135 billion in aid to poor countries around the world. [1] The OECD, a group of rich countries, promotes this kind of developmental state-to-state aid known as Official Development Aid (ODA). The OECD defines ODA as aid provided by a government that is designed to promote the “economic development and welfare of developing countries” that is concessional in nature.[2]

While changes in developmental aid from year to year likely have a number of causes, what role does economic growth in the donor states play? In this study, I set out to uncover to what extent the variance in development aid is an effect of the past (or present) variance in GDP.

Do better enrollment rates mean better education outcomes? Analysis of a Cash Conditional Transfer in Honduras

When we think about experiments, we tend to picture microscopes and beakers. The advantage of natural lab experiments is that the research is in highly controlled environments where they can isolate and manipulate variables. In contrast, social experiments present more challenges because the research might use the wrong measurements, miss controlling for an important causal factor, or use an incorrect method.  However, in the policy realm it is crucial to continue measuring the effects and impact of different factors in order to provide politicians with information to design better public policies.

 

 

There has been a great deal of discussion among government officials and scholars about the most effective way to break the cycle of poverty, and one of the most popular methods is using Conditional Cash Transfers (CCT) programs. In these programs, money is given to a household that meets certain criteria. These are usually related to the children going to school or regular medical check-ups.  I am especially interested in testing effects in education outcomes. I want to find out if a CCT has an effect on grade progression and if the increased demand in school enrollment led to success in school, which is measured by grade progression.

Honduras is the second poorest country in Central America. In 1990, they implemented the Programa de Asignación Familiar or Family Allowance Program (PRAF) CCT. The program, PRAF II, was redesigned to improve previous weaknesses. To analyze the effect of PRAF on education performance I used the difference-in-difference technique based on panel data collected in a random experiment. To understand the validity of this experiment, we need to understand its various components.

What was the sample framework?

In 2000, the Honduran government conducted a randomized experiment in 70 of the poorest municipalities of the country. The data was collected on every individual in each program household in 2000 and 2002. The survey from 2000 was collected before the program was implemented and the survey from 2002 was collected for the current year.

Who was eligible for the program?

Education transfer was available to families with children ages 6 to 12 who were enrolled and regularly attended grades 1-4.

Health transfer was available to families with children under 3 and pregnant others who regularly attended health centers.

How are treatment and control groups structured? 

In order to have a good treatment group there is a need to administer a large exogenous shock in a short period of time. The PRAF experiment had a total number of 15,350, which consists of 7,675 individuals (each individual having been surveyed in both 2000 and 2002) and 3,935 households.

Ideally, we would like to be able to compare the same treatment group and run the experiment again without giving them the treatment. Since we are not able to do that because of time restrictions, we need a counterfactual or control group that identifies the outcomes that would have happened in the absence of the treatment.  We hope that random selection makes the groups as similar as possible. In this experiment, child height-for-age was used as a poverty proxy to determine which municipalities would be eligible for the CCT program. They employed a system of geographic targeting to identify the communities where the chronically poor lived. The program drew up a list of municipalities based on the findings of the 1997 census and selected those whose chronic malnutrition indices fall below a standard deviation from the mean.

Based on this preliminary selection of eligible municipalities, a random statistical sample was then made to assign those which fell into the treatment and control groups. As a result, 40 eligible municipalities were assigned to treatment and 30 into control. Any of those households from the selected treatment communities was considered eligible for the transfer if it complied with the component criteria. The quality of the estimate is mainly based on the similarity of the counterfactual to the treatments group. Otherwise, the estimate will have bias.

How to assess impact in Education performance?

The education system in Honduras faces problems of general service provision, low teacher quality, low enrollment rates and low performance.  Children also tend to delay joining school, and their age on entry exceeds the normal age at that grade level.  Due to the levels of poverty in Honduras, it is common that children have to work instead of going to school. Moreover, the quality of the educational service is very poor.

The cash transfers in the treatment group were available for families for each child age 6-12 who was enrolled in the first four years of primary school and attended regularly. In my test, I restricted the sample so that we are only examining children between the ages of 6 and 12. The total observations were 2,648.

I used the difference-in-difference technique because it allows me to measure the impact with the data from the pre and post implementation of the program. This method mimics an actual experiment by measuring changes in average outcome in the treatment group before and after treatment minus the difference in average outcome in the control group before and after treatment.

The program requires enrollment and attendance in school in order to receive the cash. Therefore, it will be expected that these two variables will increase because of the pre-condition and not because it is a successful program. As a consequence, only measuring these variables (enrolled or attendance) will not actually show that the program is changing behaviors in children and improving the education performance.  In order to measure impact, I used two models. The first one measures the difference in enrollment and the second one that of grade progress.

It is important to include in each model some characteristics that affect enrollment and grade progress.  I controlled for years of education of head of household, distance to primary school, and size of household and household characteristics such as floor composition, availability of electricity.  In addition, I added the name of their municipality and if they received support from welfare program that is not PRAF.

In my first OLS regression using the variable “enrolled” I can prove that in fact there was an increase of 0 054 and it is statistically significant.

However, to find impact I applied the difference-in-difference formula to the “progressed” variable. The results show that the difference was of 0.05 and this was not statistically significant.

We can infer that even though the cash transfer motivated parents to enroll their children in school, when accounting for the original differences between the two groups and tracking those differences over time, we see that there is no significance in the results. In consequence, it appears unlikely that the PRAF program led to considerable improvements in grade progression from 2000 to 2002.

Are my results valid?

The key assumption for any difference-in-difference strategy is that the outcome in treatment and control group would follow similar trajectories in the absence of the treatment.

Common trend assumption is difficult to verify but using pre-treatment data could show that the trends are the same. Yet, even if pre-trends are the same we still have to worry about other policies changing at the same time. In this experiment, I cannot quantitatively demonstrate the common trends assumption because I do not have pretreatment data. However, the years previous to the experiment were not unusual. In other words, there was not a big economic shock or other macroeconomic factors that could have affected the municipalities included in this study. I am aware that PRAF was in place in some of the municipalities since 1990, so even if I had data from previous years the effect was already there. In addition, the number of observations and the randomization effect should help to overcome the lack of pre and post treatment data.

Conclusion:

    PRAF II did not show the increase in performance results as expected. The difference-in-difference is a good estimator, but the problem is that many factors can affect the assumption that our counterfactual is almost equal as to our treatment group. Therefore, it is important to use other techniques to prove our findings. CCTs have provided good results in countries such as Mexico with Progresa and should continue to be monitored and improved; hopefully allowing people to break the poverty cycle and better their lives.


Appendix:

I used fixed effects for municipality in order to control for any differences in the municipalities such as rural vs. urban, culture, weather, agricultural conditions, etc.

 

Economic freedom and corruption in Latin America

Economic freedom and the effects of 

corruption in Latin America

After decades of economic management with solid social progress, Latin America has reached certain levels of development. This improvement of social and economic conditions over time might improve the national level of corruption perception, even though the region consistently performs poorly in the CPI (corruption perception index). Recently much attention has been devoted to testing the relationship between economic freedom and corruption, with the assumption that economic freedom leads to reduce level of corruption. Thus, this paper examines the effect of economic freedom and economic globalization on corruption perception across the Latin American region over the period 1995 to 2013, since I believe that political and trade liberalization lead to a decrease in corruption.

As part of the globalization process and as a response to the Latin American debt crisis, the region moved toward political and trade reform during the 80s and 90s. The economic effects of economic reforms have been subject of complex and holistic studies. However, the analysis of the effect on components of corruption perception is limited. Even though economists have argued that lack of competition fosters corruption and empirical findings confirm that democracy and economic freedom alone does reduce corruption; there is no cross-country study which captures the interaction effect of these two factors in Latin America over time. Therefore, this paper pretends to fill this gap of analysis due to the fact that economic freedom and corruption are factors that undermine the development growth of the region.

The theory and literature suggest that among the most corrupt nations, greater economic freedom does not reduce corruption and may even exacerbate it, because nations respond differently to levels of economic freedom, depending on their level or stage of development conditions (Billger et al, 2009). Thus, the effect of economic freedom on corruption may depend on how efficient government intervention is in countries which are more highly “regulated” or “have high levels of economic freedom”. In line with this argument, Giavazzi and Tabellini (2005), who use panel data to look at the relationship between economic and political liberalizations and their effects on growth, investment, inflation, institutional quality, and corruption find that in isolation, increased economic and political liberalization decrease corruption. Therefore, greater openness may reduce corruption, but the more corruption there is, the more rent-generating trade barriers there will be (Treisman, 2000). The degree to which a country is open to trade, then, is clearly endogenous; however, whether or not a country is open to trade, as well as whether the country decides to liberalize or not, is less likely to depend on the perceived levels of corruption.

Now focusing on Latin America, in order to examine the effect of economic freedom on corruption perception, I use data from the World Bank and the Quality of Governance Institute for the 20 countries of the Latin America region for the period 1995 to 2013. In this case, corruption perception (CPI) is the depended variable of the model and the main independent variables are economic freedom and economic globalization. Also, since I’m using the CPI I might include measurements of political participation and income size (GDP per capita) because they can cause a variation on CPI and economic freedom .(for more details see table B).

Furthermore, in order to have some idea of the probable relation with corruption, Figure A shows the Economic Freedom index and the CPI corruption index across the 20 countries of the Latin American region for the year 2000, and illustrates the clear positive association between the indices, implying that higher economic freedom is associated with lower corruption

Additionally, I examine the relationship of economic freedom and their interaction effect on corruption using Pooled Time-Series Cross-national Regressions with Fixed Effects[1]. I regress corruption (log) on prior corruption and prior scores of the Independent variables.  Table A. presents the results. I found that using previous years of corruption perception, on average and controlling for Trade freedom index (column 2) 1% increase on economic freedom is associated with a reduction of 17% of corruption perception index. The results is statistical significant. However, even though the results confirm the hypothesis that Economic freedom reduces corruption perception; the results are not always significant. Column 1 shows that controlling for other variables such as income, political participation and economic globalization, economic freedom index does not have statistical significant on corruption perception. The only variable that remains significant is the Income. In this sense, it may be suggested that economic size (Income) is also associated with a decrease on levels of corruption perception with statistical significance.

Table A. Pooled Time-Series Cross-national Regressions with Fixed Effects. 

(1 year Lag)

DV: Corruption Perception (log),  from 1995 to 2013	(1)	(2)	(3)
Corruption Perception (Log)	0.61***	0.62***	0.64***
	(13.91)	(15.40)	(15.68)
Economic Freedom Index (Log)	-0.17	-0.17*	-0.17*
	(-1.80)	(-2.46)	(-2.37)
Income (GDP) (Log)	-0.049*
	(-2.01)
Political Participation (Log)	0.034
	(0.73)
Economic Globalization	-0.00039
	(-0.31)
Trade Freedom Index (Log)	0.24**	0.18**
	(2.96)	(3.28)
Constant	0.55	0.41	1.13***
	(1.21)	(1.14)	(3.91)
R-squared	0.41	0.43	0.41
Observations	276	325	325

Note: Absolute value of t statistics in parentheses. ^* p < 0.05, ^** p < 0.01, ^*** p < 0.001

It is important to note that while cross-country time series regressions of economic freedom and corruption give results in line with the argument that corruption is reduced by an increase in economic freedom, this may hide heterogeneity at sectoral level, particularly in the degree and nature of government intervention (Graeff et al, 2003). In other words, I notice an ambiguous correlation among the components of economic freedom and corruption. This suggests that the weight of the components of the aggregate index influences the effect on corruption, implying that the results are sample-sensitive.

In summary, this paper assesses the effect that economic freedom has on corruption perception across Latin America. I found a linear relationship that supports the hypothesis that trade liberalization leads to a decrease on perception corruption since the results are statistical significant. Therefore, I might think that, after the trade and openness reforms in the 80’s and 90’s, the region trends to reduce the corruption perception. However, the historic trend of high corruption perception on countries of this region may cause the slowly change on corruption perception. It is likely that as long as in the region enters into the global economy, the level of corruption would decrease.

Bibliography

* Pieronia, L.; d'Agostinob, G. (2013). Corruption and the effects of economic freedom. European Journal of Political Economy. Volume 29, March 2013, Pages 54–72. Retrieved from: http://www.sciencedirect.com/science/article/pii/S0176268012000456?np=y

* Costa Tavares, Samia (2007). Do rapid political and trade liberalizations increase 

corruption?. European Journal of Political Economy. Volume 23, Issue 4, December 2007, Pages 1053–1076. Retrieved from: http://www.sciencedirect.com/science/article/pii/S017626800600053X

Appendix

Table B. Descriptive Statistics

Variables	Mean	Std. Dev.	Min.	Max.	Detail
Economic Freedom Index	57.80	9.94	19.5	85.3	Index score range from 0 to 100, where 100 represents the maximum economic freedom
Corruption perception index (CPI)	3.44	1.39	1.4	7.9	10-point scale in which a score of 10 indicates very little corruption and a score of 0 indicates a very corrupt government.
Economic Globalization	57.80	9.94	19.5	85.3	Scale from 0 for non-flows of trade and investments and 100 for highly integration to global economy
Trade freedom	70.51	8.29	44.4	88.0	Index scores range from 0 for minimum degree of trade freedom to 100 for maximum degree of trade freedom.
Income	3928.10	2707.84	314.2	13559.1	GDP per capita in current US$
Political participation	0.41	6.00	-99.0	1.0	Index scores range from 0 for completely absent or disrespected political participation rights to 1 for their full presence and respect
Observations	277

Table C. Hausman Test

	(b)	(B)	(b-B)
	Fixed	Random	Difference
Economic freedom (log)	-.1652047	-.0019991	-.1632057
Income (GDP) (log)	-.0492979	.0146905	-.0639884
Political Participation (log)	.0335217	.0788459	-.0453242
Economic Globalization	-.0003927	.0004396	-.0008323
Trade freedom (log)	.23913	.0992944	.1398356
Chi2(6) =	79.23
Prob>Chi2 =	0.0000

**Luz Angela Serrano

** SIS-750- Spring 2015

**Blogpost 2

**********

* Preamble

            set more off

            ssc install estout

            cd "G:\"

           

**What is the effect of trade freedom and economic globalization on corruption across LA?

** ID: Economic freedom index: eco_freedom        

** ID: Economic globalization: dr_eg

** DV: Corruption perception: corrup_index

** Z1: Income: gdp_pp

** Z2: Political participation: political_p      

           

           

*Test for skew:

            sktest corrup_index eco_freedom dr_eg gdp_pp political_p eco_freedom

            sum corrup_index eco_freedom dr_eg gdp_pp political_p, detail  

* Transforms variables,

                        gen log_gdp = ln(gdp_pp)

                        gen log_trade = ln(fr_trade)

                        gen log_politics = ln(political_p)

                        gen log_corrup = ln(corrup_index)

                        gen log_eco_freedom = ln(eco_freedom)

**Use panel data

encode country, gen(country1) 

xtset country1 year                

sort country1 year

           

***Estimate the CSTS model w/FE estimator (with lag DV)

xtreg log_corrup l1.log_corrup l1.log_eco_freedom l1.log_gdp l1.log_politics l1.dr_eg l1.log_trade, fe   

           

           

* Test for the appropriateness of RE vs FE (Hausman test)              

                        xtreg log_corrup l1.log_corrup l1.log_eco_freedom l1.log_gdp l1.log_politics l1.dr_eg l1.log_trade, fe        

                                   

                                    estimates store fixed

                       

                        xtreg log_corrup l1.log_corrup l1.log_eco_freedom l1.log_gdp l1.log_politics l1.dr_eg l1.log_trade, re

           

                                    estimates store random

                                   

                                    hausman fixed random // note that p<0.05.  MUST USE FE!!! RE BAD!

**** Table  (regression).

quietly xtreg log_corrup l1.log_corrup l1.log_eco_freedom l1.log_gdp l1.log_politics l1.dr_eg l1.log_trade, fe // 

           

eststo clear

                        eststo: quietly xtreg log_corrup l1.log_corrup l1.log_eco_freedom l1.log_gdp l1.log_politics l1.dr_eg l1.log_trade, fe

                        eststo: quietly xtreg log_corrup l1.log_corrup l1.log_eco_freedom l1.log_trade, fe

                        eststo: quietly xtreg log_corrup l1.log_corrup l1.log_eco_freedom, fe

            esttab using file_name1.rtf, replace label /// creates a table in the file "file_name1.rtf"

                                    stats(r2_a N, fmt(2 0) labels("R-squared" "Observations")) /// adds R-sq and N

                                    legend varlabels(_cons Constant) /// 

                                    addnote("Note: Absolute value of t statistics in parentheses.") ///

                                    b(a2) t(2) ///  "b(a2)" will allow the slope coefficient to go to 2 significant digits if necessary. **only 2 decimals for each number. 

                                    title({\b Table 1. Pooled Time-Series Cross-national Regressions, with Fixed Effects})

            eststo clear

           

           

***Table: Descriptive statistics         

eststo clear

            estpost tabstat dr_eg eco_freedom corrup_index gdp_pp political_p, ///

                        listwise statistics(mean sd min max) columns(statistics)

            esttab using ps4sum.rtf, replace label ///

                        cells("mean(fmt(2) label(Mean)) sd(fmt(2) label(Std. Dev.)) min(fmt(1) label(Min.)) max(fmt(1) label(Max.))") ///

                        nonumbers extracols(5) ///

                        title({\b Table 1. Summary Statistics})

            eststo clear

           

           

           

*** Question: Does economic freedom impact in corruption perception (2000)?

* Inspect the variables           

            codebook corrup_index eco_freedom

* Is there evidence of covariation?

            twoway (scatter corrup_index eco_freedom) (lowess corrup_index eco_freedom) (lfit corrup_index eco_freedom)

            *Is the line a good representation of the non-parametric regression? NO!

           

            twoway (scatter log_corrup log_eco_freedom) (lowess log_corrup log_eco_freedom) (lfit log_corrup log_eco_freedom)

           

* Estimate the regression of log(Y) on log(X): log(Yi) = alpha + beta(log(Xi)) +ui (reg logY logX)

            * H0: beta=0; HA: beta!=0

                        reg log_corrup log_eco_freedom     

* Display your estimates!       reg logY logX; pred yhat; gen ylin=exp(yhat); (line ylin X, sort)

            reg log_corrup log_eco_freedom

            predict mort_hat

            gen mort_lin = exp(mort_hat)

            twoway (scatter corrup_index eco_freedom) (lowess corrup_index eco_freedom) (line mort_lin eco_freedom, sort)

                       

twoway (scatter corrup_index eco_freedom, jitter(2) msize(small) mcolor(black)) ///

                        (lowess corrup_index eco_freedom, lcolor(blue) lpattern(dash)), ///

                        title("Figure A. Effect of Economic Freedom Index on Corruption perception", size(medium) color(black)) legend(off), ///

                        (line mort_lin eco_freedom, sort clcolor(red)), ///

                        ytitle("Corruption Perception Index (CPI)", size(small)) ///

                        xtitle("Economic Freedom Index", size(small)) ///

                        note("Source: World Bank World Development Indicators and Quality of Governance", size(vsmall)) ///

                        caption("Note: Dashed line is lowess estimate (bandwidth = 0.7). Solid line is OLS prediction", size(vsmall)) ///

                        graphregion(fcolor(white) lcolor(white)) ///

                        plotregion(fcolor(white) lcolor(black)) //

---

[1] I used the Hausman test to determine the appropriateness of Fix Effects over Random Effects.  The model includes country fixed-effects (p<0.05) to allow any trends in corruption perception to vary from country to country. (without fixed-effects, the results are unchanged)