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# Empirical development economics / Måns Söderbom, and Francis Teal ; with Markus Eberhardt, Simon Quinn and Andrew Zeitlin.

##### By: Soderbom, Mans.

##### Contributor(s): Teal, Francis | Eberhardt, Markus | Quinn, Simon | Zeitlin, Andrew.

Material type: BookSeries: Routledge advanced texts in economics and finance.Publisher: London : Routledge, 2015Description: xxviii, 433 p. ; 25 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780415810494 (pbk.); 9780415810487 (hbk.).Subject(s): Development economics | Poverty | Income distribution | Econometrics | BUSINESS & ECONOMICS / General | BUSINESS & ECONOMICS / Development / Business Development | BUSINESS & ECONOMICS / EconometricsDDC classification: 338.9 SOD Online resources: Table of contentsItem type | Current location | Call number | Status | Date due | Barcode |
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Book | Indian Institute for Human Settlements, Bangalore | 338.9 SOD 007455 (Browse shelf) | Available | 007455 |

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Includes bibliographical references (pages 423-430) and index.

Machine generated contents note: pt. I Linking models to data for development

1.An introduction to empirical development economics

1.1.The objective of the book

1.2.Models and data: the Harris

Todaro model

1.3.Production functions and functional form

1.3.1.The Cobb-Douglas production function

1.3.2.The constant elasticity of substitution (CES) functional form

1.4.A model with human capital

1.5.Data and models

1.5.1.The macro GDP data

1.5.2.Interpreting the data

References

Exercise

SECTION I Cross-section data and the determinants of incomes

2.The linear regression model and the OLS estimator

2.1.Introduction: models and causality

2.2.The linear regression model and the OLS estimators

2.2.1.The linear regression model as a population model

2.2.2.The zero conditional mean assumption

2.2.3.The OLS estimator

2.3.The Mincerian earnings function for the South African data

2.4.Properties of the OLS estimators

Contents note continued: 2.4.1.The assumptions for OLS to be unbiased

2.4.2.The assumptions for OLS to be minimum variance

2.5.Identifying the causal effect of education

3.Using and extending the simple regression model

3.1.Introduction

3.2.Dummy explanatory variables and the return to education

3.3.Multiple regression

3.3.1.Earnings and production functions

3.3.2.The OLS estimators for multiple regression

3.3.3.Omitted variables and the bias they may cause

3.4.Interpreting multiple regressions

3.4.1.How much does investing in education increase earnings? Some micro evidence

3.4.2.How much does investing in education increase productivity? Some macro evidence

4.The distribution of the OLS estimators and hypothesis testing

4.1.Introduction

4.2.The distribution of the OLS estimators

4.2.1.The normality assumption

4.2.2.Why normality?

Contents note continued: 4.3.Testing hypotheses about a single population parameter

4.3.1.The T distribution

4.3.2.The t-test

4.3.3.Confidence intervals

4.4.Testing for the overall significance of a regression

4.5.Testing for heteroskedasticity

4.6.Large sample properties of OLS

4.6.1.Consistency

4.6.2.Asymptotic normality

5.The determinants of earnings and productivity

5.1.Introduction

5.2.Testing the normality assumption

5.3.The earnings function

5.3.1.Bringing the tests together

5.3.2.Robust and clustered standard errors

5.4.The production function

5.4.1.Testing the production function

5.4.2.Extending the production function

5.5.Interpreting our earnings and production functions

5.5.1.Can education be given a causal interpretation?

5.5.2.How much does education raise labour productivity?

SECTION II Time-series data, growth and development

Contents note continued: 6.Modelling growth with time-series data

6.1.Introduction: modelling growth

6.2.An introduction to the Solow model

6.3.A Solow model for Argentina

6.4.OLS estimates under the classical assumptions with time-series data

6.4.1.Assumptions for OLS to be unbiased

6.4.2.The variance of the OLS estimators

6.4.3.Testing for autocorrelation

6.5.Static and dynamic time-series models

6.6.Assumptions to ensure the OLS estimators are consistent

6.7.Spurious regression with nonstationary time-series data

6.8.A brief summary

7.The implications of variables having a unit root

7.1.Introduction and motivation

7.2.Testing for a unit root and the order of integration

7.3.Cointegration

7.4.How are growth and inflation related in Argentina?

7.5.The error-correction model

7.6.Causality in time-series models

7.7.Cross-section and time-series data

Contents note continued: 8.Exogenous and endogenous growth

8.1.The Solow model and the history of development

8.2.Long-term growth and structural change

8.3.The Solow model, structural change and endogenous growth

8.4.Human capital and the dynamic Solow model

8.5.Exogenous and endogenous growth

8.6.A Solow interpretation of development patterns

Appendix: deriving the dynamic Solow model

SECTION III Panel data

9.Panel data: an introduction

9.1.Introduction

9.2.Panel data

9.2.1.The structure of the panel

9.2.2.Panel data and endogeneity

9.3.Panel production functions

9.3.1.A panel macro production function

9.3.2.A panel micro production function

9.4.Interpreting the fixed effect

Appendix: matrix notation

10.Panel estimators: POLS, RE, FE, FD

10.1.Introduction

10.2.Panel estimators

10.2.1.The fixed effects and first difference estimators

Contents note continued: 10.2.2.The random effects estimator

10.3.Key assumptions for consistency

10.4.Model selection

10.4.1.Testing for correlation between the ci and the explanatory variables

10.4.2.Testing for the presence of an unobserved effect

10.5.The micro panel production function extended

10.6.What determines the productivity of Ghanaian firms?

11.Instrumental variables and endogeneity

11.1.Introduction

11.2.Sources of bias in the OLS estimates

11.2.1.Bias from omitted variables

11.2.2.Bias from measurement error

11.2.3.Panel data: omitted variables and measurement error

11.3.Instrumental variables

11.3.1.Valid and informative instruments

11.3.2.Interpreting the IV estimator

11.4.The properties of the IV estimator

11.4.1.The IV and OLS estimators compared

11.4.2.Inference with the IV estimator

11.5.The causes of differences in world incomes

Contents note continued: SECTION IV An introduction to programme evaluation

12.The programme evaluation approach to development policy

12.1.Introduction: causal effects and the counterfactual problem

12.2.Rubin causal model

12.2.1.Potential outcomes

12.2.2.Assignment mechanism

12.2.3.Defining measures of impact

12.2.4.From potential outcomes to regression

12.3.Selection on observables

12.3.1.Ignorability of treatment

12.3.2.Overlap

12.4.Unconditional unconfoundedness and the experimental approach

13.Models, experiments and calibration in development policy analysis

13.1.Introduction

13.2.Empirical estimators under (conditional) unconfoundedness

13.2.1.Multivariate regression

13.2.2.Panel data methods

13.3.A randomised controlled trial (RCT) for conditional cash transfers

13.4.Calibrating technology

13.5.Education, technology and poverty

pt. 2 Modelling development

Contents note continued: 14.Measurement, models and methods for understanding poverty

14.1.Introduction

14.2.The causes of poverty

14.2.1.Poverty and GDP data

14.2.2.Poverty, consumption and incomes

14.2.3.Poverty, inequality and GDP

14.3.The Mincerian earnings function, the price of labour and poverty

14.4.Modelling impacts

14.4.1.A generalised Roy model of selection

14.4.2.Implications of the Roy model for estimation of treatment effects

14.5.An overview: measurement, models and methods

SECTION V Modelling choice

15.Maximum likelihood estimation

75.7.Introduction

15.2.The concept of maximum likelihood

15.3.The concept of population

15.4.Distributional assumptions and the log-likelihood function

15.5.Maximising the (log-) likelihood

15.6.Maximum likelihood in Stata

75.7.Problems and warnings ...

15.7.1.Maximum likelihood and endogeneity

15.7.2.Maximum likelihood and convergence

Contents note continued: 15.8.Properties of maximum likelihood estimates

15.8.1.Consistency

15.8.2.Efficiency

15.8.3.So what?

75.9.Hypothesis testing under maximum likelihood

15.10.Overview

16.Modelling choice: the LPM, probit and logit models

76.7.Introduction

16.2.Binary choices and interpreting the descriptive statistics

16.3.Estimation by OLS: the linear probability model

16.4.The probit and logit models as latent variable models

16.4.1.The probit model

16.4.2.The logit model

76.5.Maximum likelihood estimation of probit and logit models

76.6.Explaining choice

17.Using logit and probit models for unemployment and school choice

17.1.Introduction

17.2.Interpreting the probit model and the logit model

17.2.1.A model of unemployment

17.2.2.Average partial effects and marginal effects at the mean

17.2.3.Age and education as determinants of unemployment in South Africa

Contents note continued: 17.3.Goodness of fit

17.4.Indian private and state schools

17.4.1.How well do private schools perform?

17.4.2.Who attends a private school?

17.4.3.Mother's education and wealth as determinants of attending private school in India

17.5.Models of unemployment and school choice

18.Corner solutions: modelling investing in children and by firms

18.1.Introduction

18.2.OLS estimation of corner response models

18.2.1.Investment in Ghana's manufacturing sector

18.2.2.Gender discrimination in India

18.3.The Tobit model

18.4.Two-part models

18.4.1.Truncated normal hurdle model

18.4.2.The log-normal hurdle model

18.5.Overview

Appendix: the Inverse Mills Ratio (IMR)

SECTION VI Structural modelling

19.An introduction to structural modelling in development economics

19.1.Introduction: the challenge of using microeconomic theory in empirical research

Contents note continued: 19.2.Using a structural model to think about risk-sharing

19.3.Building and solving a microeconomic model

19.4.Thinking about unobservables and choosing an estimator

19.4.1.The model to be estimated

19.4.2.Identification in the model

19.4.3.Testing the model

19.5.Estimating the model

19.5.1.The data

19.5.2.Estimation results

19.6.Conclusion

20.Structural methods and the return to education

20.1.Introduction: Belzil and Hansen go to Africa

20.2.The question

20.3.A model of investment in education

20.4.Thinking about unobservables and choosing an estimator

20.5.Models and data

20.5.1.`Adolescent econometricians'?

20.5.2.Possible applications for structural modelling in development

20.6.Structural models: hubris or humility?

SECTION VII Selection, heterogeneity and programme evaluation

Contents note continued: 21.Sample selection: modelling incomes where occupation is chosen

21.1.Introduction

21.2.Sample selection

21.3.A formal exposition

21.3.1.The regression with sample selection

21.3.2.Modelling the correlation of the unobservables

21.4.When is sample selection a problem?

21.5.Selection and earnings in South Africa

21.6.Corner solution and sample selection models

22.Programme evaluation: regression discontinuity and matching

22.1.Introduction

22.2.Regression discontinuity design

22.3.Propensity score methods

22.3.1.Regression using the propensity score

22.3.2.Weighting by the propensity score

22.3.3.Matching on the propensity score

22.4.Food aid in Ethiopia: propensity-score matching

22.5.Assessing the consequences of property rights: pipeline identification strategies

22.6.Estimating treatment effects (the plot so far)

Contents note continued: 23.Heterogeneity, selection and the marginal treatment effect (MTE)

23.1.Introduction

23.2.Instrumental variables estimates under homogeneous treatment effects

23:3.Instrumental variables estimates under heterogeneous treatment effects

23.3.1.IV for noncompliance and heterogeneous effects: the LATE Theorem

23.3.2.LATE and the compliant subpopulation

23.4.Selection and the marginal treatment effect

23.4.1.Interpreting the LATE in the context of the Roy model

23.4.2.The marginal treatment effect

23.4.3.What does IV identify?

23.5.The return to education once again

23.6.An overview

SECTION VIII Dynamic models for micro and macro data

24.Estimation of dynamic effects with panel data

24.1.Introduction

24.2.Instrumental variable estimation of dynamic panel-data models

24.3.The Arellano

Bond estimator

24.3.1.No serial correlation in the errors

24.3.2.Serially correlated errors

Contents note continued: 24.4.The system GMM estimator

24.5.Estimation of dynamic panel-data models using Stata

24.6.The general case

24.6.1.The regressors are strictly exogenous

24.6.2.The regressors are predetermined

24.6.3.The regressors are contemporaneously endogenous

24.6.4.Implications of serial correlation in the error term

24.7.Using the estimators

Appendix: the bias in the fixed effects estimator of a dynamic panel-data model

25.Modelling the effects of aid and the determinants of growth

25.1.Introduction

25.2.Dynamic reduced-form models

25.2.1.Aid, policy and growth

25.2.2.Dynamics and lags

25.2.3.Differenced and system GMM estimators

25.3.Growth rate effects: a model of endogenous growth

25.3.1.Dynamic and growth rate models

25.3.2.Is there evidence for endogenous growth?

25.4.Aid, policy and growth revisited with annual data

25.4.1.Cross section and time-series uses of macro data

Contents note continued: 25.4.2.Growth and levels effects of aid

25.5.A brief overview: aid, policy and growth

SECTION IX Dynamics and long panels

26.Understanding technology using long panels

26.1.Introduction

26.2.Parameter heterogeneity in long panels

26.3.The mean group estimator

26.4.Cross-section dependence due to common factors

26.5.Conclusion

27.Cross-section dependence and nonstationary data

27.1.Introduction

27.2.Alternative approaches to modelling cross-section dependence

27.2.1.Country fixed effects and year dummies

27.2.2.Estimating unobserved common factors

27.2.3.Constructing weight matrices

27.3.Modelling cross-section dependence using cross-section averages

27.4.Detecting cross-section dependence

27.5.Panel unit root testing

27.5.1.First-generation panel unit root test

27.5.1.1.The Im, Pesaran and Shin test (IPS)

27.5.1.2.The Maddala and Wu test (MW)

Contents note continued: 27.5.2.Second-generation panel unit root test

27.5.2.1.The PANIC approach

27.5.2.2.The CIPS and CIPSM tests

27.6.Cointegration testing in panels

27.6.1.Residual analysis and error-correction models

27.6.2.Tests for panel cointegration

27.7.Parameter heterogeneity, nonstationary data and cross-section dependence

28.Macro production functions for manufacturing and agriculture

28.1.Introduction

28.2.Estimating a production function for manufacturing

28.2.1.The homogeneous models

28.2.2.The heterogeneous models

28.3.Estimating a production function for agriculture

28.3.1.Unit roots

28.3.2.What determines the productivity of agriculture?

28.4.Manufacturing and agriculture and the growth of an economy

SECTION X An overview

29.How can the processes of development best be understood?

29.1.Introduction

29.2.A range of answers as to the causes of poverty

Contents note continued: 29.3.Macro policy, growth and poverty reduction

29.4.Programme evaluation and structural models

29.4.1.Programme evaluation and the `failure' of poverty policies

29.4.2.Structural models and understanding the causes of poverty

29.5.Skills, technology and the returns on investment

29.5.1.The value of skills

29.5.2.The role of technology

29.5.3.Rates of return on investment

29.6.A final word

References.

"Understanding why so many people across the world are so poor is one of the central intellectual challenges of our time. What explains a pattern of extreme destitution for billions combined with plutocratic levels of income for a tiny minority? This book offers a novel approach to addressing those issues, not by providing answers, but seeking to provide the tools and data that will enable the student, the researcher and the professional working in this area to investigate the questions for themselves. Empirical Development Economics has been designed as a hands-on teaching tool to investigate the causes of poverty. The book begins by introducing the basics of the quantitative approach to development economics. All the topics are presented through data that addresses some important policy issue. In Part 1 the focus is on the basics of understanding why incomes differ so much. What is the role of education, technology and institutions in ensuring that where you are born is so important in determining whether you are poor? In Part 2 the focus is on techniques that allow us to address questions which include how firms invest, how households decide how much to spend on education of their children, whether microfinance does help the poor, whether food aid works, who gets private schooling and whether property rights enhance investment.A distinctive feature of the book is its presentation of a range of approaches to studying development questions. Development economics has undergone a major change in focus over the last decade with the rise of experimental methods to address development issues. One of our objectives has been to show how such methods relate to more traditional ones. "--

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