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Understanding why so many people across the world are so poor is one of the central intellectual challenges of our time. This book provides the tools and data that will enable students, researchers and professionals to address that issue.

Empirical Development Economics has been designed as a hands-on teaching tool to investigate the causes of poverty. The book begins by introducing the quantitative approach to development economics. Each section uses data to illustrate key policy issues. Part One focuses on the basics of understanding the role of education, technology and institutions in determining why incomes differ so much across individuals and countries. In Part Two, the focus is on techniques to address a number of topics in development, including how firms invest, how households decide how much to spend on their childrens education, whether microcredit helps 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; this book shows how these methods relate to more traditional ones.

Please visit the book's website at www.empiricalde.com for online supplements including Stata files and solutions to the exercises.
List of figures xviii
List of tables xx
Notes on authors xxiii
Preface xxv
How to use this book xxvii
Part 1 Linking models to data for development 1(192)
1 An introduction to empirical development economics
3(14)
1.1 The objective of the book
3(1)
1.2 Models and data: the Harris-Todaro model
4(2)
1.3 Production functions and functional form
6(5)
1.3.1 The Cobb-Douglas production function
6(4)
1.3.2 The constant elasticity of substitution (CES) functional form
10(1)
1.4 A model with human capital
11(2)
1.5 Data and models
13(1)
1.5.1 The macro GDP data
13(1)
1.5.2 Interpreting the data
14(1)
References
14(1)
Exercise
15(2)
Section I Cross-section data and the determinants of incomes
17(58)
2 The linear regression model and the OLS estimator
19(14)
2.1 Introduction: models and causality
19(1)
2.2 The linear regression model and the OLS estimators
20(6)
2.2.1 The linear regression model as a population model
20(1)
2.2.2 The zero conditional mean assumption
21(3)
2.2.3 The OLS estimator
24(2)
2.3 The Mincerian earnings function for the South African data
26(2)
2.4 Properties of the OLS estimators
28(3)
2.4.1 The assumptions for OLS to be unbiased
28(1)
2.4.2 The assumptions for OLS to be minimum variance
29(2)
2.5 Identifying the causal effect of education
31(1)
References
31(1)
Exercise
32(1)
3 Using and extending the simple regression model
33(14)
3.1 Introduction
33(1)
3.2 Dummy explanatory variables and the return to education
33(3)
3.3 Multiple regression
36(4)
3.3.1 Earnings and production functions
36(1)
3.3.2 The OLS estimators for multiple regression
37(2)
3.3.3 Omitted variables and the bias they may cause
39(1)
3.4 Interpreting multiple regressions
40(5)
3.4.1 How much does investing in education increase earnings? Some micro evidence
40(3)
3.4.2 How much does investing in education increase productivity? Some macro evidence
43(2)
References
45(1)
Exercise
45(2)
4 The distribution of the OLS estimators and hypothesis testing
47(15)
4.1 Introduction
47(1)
4.2 The distribution of the OLS estimators
47(2)
4.2.1 The normality assumption
47(1)
4.2.2 Why normality?
48(1)
4.3 Testing hypotheses about a single population parameter
49(6)
4.3.1 The t distribution
49(2)
4.3.2 The t-test
51(2)
4.3.3 Confidence intervals
53(2)
4.4 Testing for the overall significance of a regression
55(2)
4.5 Testing for heteroskedasticity
57(1)
4.6 Large sample properties of OLS
58(2)
4.6.1 Consistency
58(2)
4.6.2 Asymptotic normality
60(1)
References
60(1)
Exercise
61(1)
5 The determinants of earnings and productivity
62(13)
5.1 Introduction
62(1)
5.2 Testing the normality assumption
62(3)
5.3 The earnings function
65(2)
5.3.1 Bringing the tests together
65(1)
5.3.2 Robust and clustered standard errors
65(2)
5.4 The production function
67(5)
5.4.1 Testing the production function
67(1)
5.4.2 Extending the production function
67(5)
5.5 Interpreting our earnings and production functions
72(2)
5.5.1 Can education be given a causal interpretation?
72(1)
5.5.2 How much does education raise labour productivity?
73(1)
References
74(1)
Exercise
74(1)
Section II Time-series data, growth and development
75(46)
6 Modelling growth with time-series data
77(18)
6.1 Introduction: modelling growth
77(1)
6.2 An introduction to the Solow model
78(2)
6.3 A Solow model for Argentina
80(1)
6.4 OLS estimates under the classical assumptions with time-series data
81(4)
6.4.1 Assumptions for OLS to be unbiased
81(2)
6.4.2 The variance of the OLS estimators
83(2)
6.4.3 Testing for autocorrelation
85(1)
6.5 Static and dynamic time-series models
85(2)
6.6 Assumptions to ensure the OLS estimators are consistent
87(2)
6.7 Spurious regression with nonstationary time-series data
89(2)
6.8 A brief summary
91(1)
References
92(1)
Exercise
93(2)
7 The implications of variables having a unit root
95(14)
7.1 Introduction and motivation
95(1)
7.2 Testing for a unit root and the order of integration
96(4)
7.3 Cointegration
100(1)
7.4 How are growth and inflation related in Argentina?
101(3)
7.5 The error-correction model
104(1)
7.6 Causality in time-series models
105(1)
7.7 Cross-section and time-series data
106(1)
References
107(1)
Exercise
107(2)
8 Exogenous and endogenous growth
109(12)
8.1 The Solow model and the history of development
109(1)
8.2 Long-term growth and structural change
109(3)
8.3 The Solow model, structural change and endogenous growth
112(1)
8.4 Human capital and the dynamic Solow model
113(3)
8.5 Exogenous and endogenous growth
116(2)
8.6 A Solow interpretation of development patterns
118(1)
References
118(1)
Exercise
119(1)
Appendix: deriving the dynamic Solow model
119(2)
Section III Panel data
121(48)
9 Panel data: an introduction
123(17)
9.1 Introduction
123(1)
9.2 Panel data
123(4)
9.2.1 The structure of the panel
123(1)
9.2.2 Panel data and endogeneity
124(3)
9.3 Panel production functions
127(7)
9.3.1 A panel macro production function
127(3)
9.3.2 A panel micro production function
130(4)
9.4 Interpreting the fixed effect
134(1)
References
135(1)
Exercise
135(1)
Appendix: matrix notation
135(5)
10 Panel estimators: POLS, RE, FE, FD
140(13)
10.1 Introduction
140(1)
10.2 Panel estimators
140(3)
10.2.1 The fixed effects and first difference estimators
140(2)
10.2.2 The random effects estimator
142(1)
10.3 Key assumptions for consistency
143(1)
10.4 Model selection
144(3)
10.4.1 Testing for correlation between the c, and the explanatory variables
145(1)
10.4.2 Testing for the presence of an unobserved effect
146(1)
10.5 The micro panel production function extended
147(1)
10.6 What determines the productivity of Ghanaian firms?
148(4)
References
152(1)
Exercise
152(1)
11 Instrumental variables and endogeneity
153(16)
11.1 Introduction
153(1)
11.2 Sources of bias in the OLS estimates
153(3)
11.2.1 Bias from omitted variables
153(1)
11.2.2 Bias from measurement error
154(1)
11.2.3 Panel data: omitted variables and measurement error
155(1)
11.3 Instrumental variables
156(4)
11.3.1 Valid and informative instruments
157(2)
11.3.2 Interpreting the IV estimator
159(1)
11.4 The properties of the IV estimator
160(2)
11.4.1 The IV and OLS estimators compared
160(1)
11.4.2 Inference with the IV estimator
161(1)
11.5 The causes of differences in world incomes
162(5)
Exercise
167(1)
References
168(1)
Section IV An introduction to programme evaluation
169(24)
12 The programme evaluation approach to development policy
171(11)
12.1 Introduction: causal effects and the counterfactual problem
171(1)
12.2 Rubin causal model
172(5)
12.2.1 Potential outcomes
172(1)
12.2.2 Assignment mechanism
173(1)
12.2.3 Defining measures of impact
174(1)
12.2.4 From potential outcomes to regression
174(3)
12.3 Selection on observables
177(2)
12.3.1 Ignorability of treatment
177(1)
12.3.2 Overlap
178(1)
12.4 Unconditional unconfoundedness and the experimental approach
179(1)
References
180(1)
Exercise
180(2)
13 Models, experiments and calibration in development policy analysis
182(13)
13.1 Introduction
182(1)
13.2 Empirical estimators under ( conditional) unconfoundedness
182(3)
13.2.1 Multivariate regression
183(1)
13.2.2 Panel data methods
184(1)
13.3 A randomised controlled trial ( RCT) for conditional cash transfers
185(3)
13.4 Calibrating technology
188(2)
13.5 Education, technology and poverty
190(1)
References
190(1)
Exercise
191(2)
Part 2 Modelling development 193(230)
14 Measurement, models and methods for understanding poverty
195(12)
14.1 Introduction
195(1)
14.2 The causes of poverty
195(4)
14.2.1 Poverty and GDP data
195(1)
14.2.2 Poverty, consumption and incomes
196(1)
14.2.3 Poverty, inequality and GDP
197(2)
14.3 The Mincerian earnings function, the price of labour and poverty
199(2)
14.4 Modelling impacts
201(2)
14.4.1 A generalised Roy model of selection
201(1)
14.4.2 Implications of the Roy model for estimation of treatment effects
202(1)
14.5 An overview: measurement, models and methods
203(1)
References
204(1)
Exercise
205(2)
Section V Modelling choice
207(64)
15 Maximum likelihood estimation
209(17)
15.1 Introduction
209(1)
15.2 The concept of maximum likelihood
209(2)
15.3 The concept of population
211(1)
15.4 Distributional assumptions and the log-likelihood function
211(3)
15.5 Maximising the (log-)likelihood
214(1)
15.6 Maximum likelihood in Stata
215(3)
15.7 Problems and warnings
218(2)
15.7.1 Maximum likelihood and endogeneity
218(1)
15.7.2 Maximum likelihood and convergence
219(1)
15.8 Properties of maximum likelihood estimates
220(2)
15.8.1 Consistency
221(1)
15.8.2 Efficiency
221(1)
15.8.3 So what?
221(1)
15.9 Hypothesis testing under maximum likelihood
222(2)
15.10 Overview
224(1)
References
224(1)
Exercise
224(2)
16 Modelling choice: the LPM, probit and logit models
226(13)
16.1 Introduction
226(1)
16.2 Binary choices and interpreting the descriptive statistics
227(1)
16.3 Estimation by OLS: the linear probability model
228(3)
16.4 The probit and logit models as latent variable models
231(3)
16.4.1 The probit model
232(2)
16.4.2 The logit model
234(1)
16.5 Maximum likelihood estimation of probit and logit models
234(1)
16.6 Explaining choice
235(2)
References
237(1)
Exercise
237(2)
17 Using logit and probit models for unemployment and school choice
239(15)
17.1 Introduction
239(1)
17.2 Interpreting the probit model and the logit model
240(5)
17.2.1 A model of unemployment
240(1)
17.2.2 Average partial effects and marginal effects at the mean
240(5)
17.2.3 Age and education as determinants of unemployment in South Africa
245(1)
17.3 Goodness of fit
245(3)
17.4 Indian private and state schools
248(2)
17.4.1 How well do private schools perform?
248(1)
17.4.2 Who attends a private school?
249(1)
17.4.3 Mother's education and wealth as determinants of attending private school in India
250(1)
17.5 Models of unemployment and school choice
250(2)
References
252(1)
Exercise
252(2)
18 Corner solutions: modelling investing in children and by firms
254(17)
18.1 Introduction
254(1)
18.2 OLS estimation of corner response models
255(5)
18.2.1 Investment in Ghana's manufacturing sector
255(3)
18.2.2 Gender discrimination in India
258(2)
18.3 The Tobit model
260(2)
18.4 Two-part models
262(6)
18.4.1 Truncated normal hurdle model
264(1)
18.4.2 The log-normal hurdle model
265(3)
18.5 Overview
268(1)
References
268(1)
Exercise
269(1)
Appendix: the Inverse Mills Ratio ( IMR)
269(2)
Section VI Structural modelling
271(30)
19 An introduction to structural modelling in development economics
273(13)
19.1 Introduction: the challenge of using microeconomic theory in empirical research
273(1)
19.2 Using a structural model to think about risk-sharing
274(2)
19.3 Building and solving a microeconomic model
276(5)
19.4 Thinking about unobservables and choosing an estimator
281(2)
19.4.1 The model to be estimated
281(1)
19.4.2 Identification in the model
282(1)
19.4.3 Testing the model
282(1)
19.5 Estimating the model
283(1)
19.5.1 The data
283(1)
19.5.2 Estimation results
283(1)
19.6 Conclusion
284(1)
References
285(1)
Exercise
285(1)
20 Structural methods and the return to education
286(15)
20.1 Introduction: Belzil and Hansen go to Africa
286(1)
20.2 The question
286(1)
20.3 A model of investment in education
287(5)
20.4 Thinking about unobservables and choosing an estimator
292(4)
20.5 Models and data
296(2)
20.5.1 'Adolescent econometricians'?
296(1)
20.5.2 Possible applications for structural modelling in development
297(1)
20.6 Structural models: hubris or humility?
298(1)
References
298(1)
Exercise
299(2)
Section VII Selection, heterogeneity and programme evaluation
301(44)
21 Sample selection: modelling incomes where occupation is chosen
303(13)
21.1 Introduction
303(1)
21.2 Sample selection
303(1)
21.3 A formal exposition
304(4)
21.3.1 The regression with sample selection
304(1)
21.3.2 Modelling the correlation of the unobservables
305(3)
21.4 When is sample selection a problem?
308(1)
21.5 Selection and earnings in South Africa
309(4)
21.6 Corner solution and sample selection models
313(1)
References
314(1)
Exercise
314(2)
22 Programme evaluation: regression discontinuity and matching
316(12)
22.1 Introduction
316(1)
22.2 Regression discontinuity design
316(3)
22.3 Propensity score methods
319(3)
22.3.1 Regression using the propensity score
319(1)
22.3.2 Weighting by the propensity score
320(1)
22.3.3 Matching on the propensity score
321(1)
22.4 Food aid in Ethiopia: propensity-score matching
322(1)
22.5 Assessing the consequences of property rights: pipeline identification strategies
323(3)
22.6 Estimating treatment effects (the plot so far)
326(1)
References
326(1)
Exercise
327(1)
23 Heterogeneity, selection and the marginal treatment effect (MTE)
328(17)
23.1 Introduction
328(1)
23.2 Instrumental variables estimates under homogeneous treatment effects
328(2)
23.3 Instrumental variables estimates under heterogeneous treatment effects
330(3)
23.3.1 IV for noncompliance and heterogeneous effects: the LATE Theorem
330(2)
23.3.2 LATE and the compliant subpopulation
332(1)
23.4 Selection and the marginal treatment effect
333(6)
23.4.1 Interpreting the LATE in the context of the Roy model
333(3)
23.4.2 The marginal treatment effect
336(1)
23.4.3 What does IV identify?
337(2)
23.5 The return to education once again
339(2)
23.6 An overview
341(1)
References
342(1)
Exercise
342(3)
Section VIII Dynamic models for micro and macro data
345(30)
24 Estimation of dynamic effects with panel data
347(14)
24.1 Introduction
347(1)
24.2 Instrumental variable estimation of dynamic panel-data models
348(1)
24.3 The Arellano-Bond estimator
349(2)
24.3.1 No serial correlation in the errors
349(1)
24.3.2 Serially correlated errors
350(1)
24.4 The system GMM estimator
351(1)
24.5 Estimation of dynamic panel-data models using Stata
352(3)
24.6 The general case
355(3)
24.6.1 The regressors are strictly exogenous
355(1)
24.6.2 The regressors are predetermined
356(1)
24.6.3 The regressors are contemporaneously endogenous
357(1)
24.6.4 Implications of serial correlation in the error term
357(1)
24.7 Using the estimators
358(1)
References
358(1)
Exercise 359
Appendix: the bias in the fixed effects estimator of a dynamic panel-data model
359(2)
25 Modelling the effects of aid and the determinants of growth
361(14)
25.1 Introduction
361(1)
25.2 Dynamic reduced-form models
361(7)
25.2.1 Aid, policy and growth
361(3)
25.2.2 Dynamics and lags
364(2)
25.2.3 Differenced and system GMM estimators
366(2)
25.3 Growth rate effects: a model of endogenous growth
368(3)
25.3.1 Dynamic and growth rate models
368(2)
25.3.2 Is there evidence for endogenous growth?
370(1)
25.4 Aid, policy and growth revisited with annual data
371(1)
25.4.1 Cross section and time-series uses of macro data
371(1)
25.4.2 Growth and levels effects of aid
371(1)
25.5 A brief overview: aid, policy and growth
372(1)
References
373(1)
Exercise
373(2)
Section IX Dynamics and long panels
375(40)
26 Understanding technology using long panels
377(11)
26.1 Introduction
377(1)
26.2 Parameter heterogeneity in long panels
378(1)
26.3 The mean group estimator
379(4)
26.4 Cross-section dependence due to common factors
383(3)
26.5 Conclusion
386(1)
References
386(1)
Exercise
386(2)
27 Cross-section dependence and nonstationary data
388(14)
27.1 Introduction
388(1)
27.2 Alternative approaches to modelling cross-section dependence
388(2)
27.2.1 Country fixed effects and year dummies
389(1)
27.2.2 Estimating unobserved common factors
389(1)
27.2.3 Constructing weight matrices
390(1)
27.3 Modelling cross-section dependence using cross-section averages
390(3)
27.4 Detecting cross-section dependence
393(1)
27.5 Panel unit root testing
394(2)
27.5.1 First-generation panel unit root test
394(1)
27.5.1.1 The Im, Pesaran and Shin test (IPS)
395(1)
27.5.1.2 The Maddala and Wu test (MW)
395(1)
27.5.2 Second-generation panel unit root test
395(1)
27.5.2.1 The PANIC approach
395(1)
27.5.2.2 The CIPS and CIPSM tests
396(1)
27.6 Cointegration testing in panels
396(1)
27.6.1 Residual analysis and error-correction models
396(1)
27.6.2 Tests for panel cointegration
397(1)
27.7 Parameter heterogeneity, nonstationary data and cross-section dependence
397(2)
References
399(1)
Exercise
400(2)
28 Macro production functions for manufacturing and agriculture
402(13)
28.1 Introduction
402(1)
28.2 Estimating a production function for manufacturing
403(4)
28.2.1 The homogeneous models
403(2)
28.2.2 The heterogeneous models
405(2)
28.3 Estimating a production function for agriculture
407(5)
28.3.1 Unit roots
408(1)
28.3.2 What determines the productivity of agriculture?
409(3)
28.4 Manufacturing and agriculture and the growth of an economy
412(1)
References
412(1)
Exercise
413(2)
Section X An overview
415(8)
29 How can the processes of development best be understood?
417(6)
29.1 Introduction
417(1)
29.2 A range of answers as to the causes of poverty
417(2)
29.3 Macro policy, growth and poverty reduction
419(1)
29.4 Programme evaluation and structural models
419(1)
29.4.1 Programme evaluation and the 'failure' of poverty policies
419(1)
29.4.2 Structural models and understanding the causes of poverty
420(1)
29.5 Skills, technology and the returns on investment
420(1)
29.5.1 The value of skills
420(1)
29.5.2 The role of technology
421(1)
29.5.3 Rates of return on investment
421(1)
29.6 A final word
421(1)
References
422(1)
Bibliography 423(8)
Index 431
 Måns Söderbom is a Professor of Economics at the Department of Economics, School of Business, Economics and Law, University of Gothenburg, Sweden.



Francis Teal is Research Associate, CSAE, University of Oxford and Managing Editor Oxford Economic Papers, UK.



Markus Eberhardt is Assistant Professor in Economics, School of Economics, University of Nottingham, UK.



Simon Quinn is Associate Professor in Economics and Deputy Director of the Centre for the Study of African Economies, Department of Economics, University of Oxford, UK.



Andrew Zeitlin is Assistant Professor at the McCourt School of Public Policy, Georgetown University, USA.
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