Empirical development economics:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London [u.a.]
Routledge
2015
|
| Ausgabe: | 1. publ. |
| Schriftenreihe: | Routledge advanced texts in economics and finance
24 |
| Schlagworte: | |
| Online-Zugang: | Klappentext Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references (pages 423-430) and index |
| Beschreibung: | XXVIII, 433 S. 25 cm |
| ISBN: | 9780415810487 9780415810494 |
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Datensatz im Suchindex
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| adam_text | 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 children’s 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.
Måns Soderbom is 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, UK and Managing
Editor, Oxford Economic Papers.
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 at
Georgetown University, USA.
DEVELOPMENT STUDIES/ECONOMICS
Cover image: © Shutterstock
Routledge titles are available as eBook éditions in a range of digital formats
Contents^
List of figures xviii
List of tables xx
Notes on authors xxiii
Preface xxv
How to use this book xxvii
PARTI
Linking models to data for development 1
1 An introduction to empirical development economics 3
L 1 The objective of the book 3
1.2 Models and data: the Harris-Todaro model 4
1.3 Production functions and junctional form 6
1.3.1 The Cobb-Douglas production function 6
1.3.2 The constant elasticity of substitution (CES) functional form 10
1.4 A model with human capital 11
1.5 Data and models 13
1.5.1 The macro GDP data 13
1.5.2 Interpreting the data 14
References 14
Exercise 15
SECTION I
Cross-section data and the determinants of incomes 17
2 The linear regression model and the OLS estimator 19
2.1 Introduction: models and causality 19
2.2 The linear regression model and the OLS estimators 20
2.2.1 The linear regression model as a population model 20
2.2.2 The zero conditional mean assumption 21
2.2.3 The OLS estimator 24
The Mincerian earnings junction for the South African data 26
2.3
viii Contents
2.4 Properties of the OLS estimators 28
2.4.1 The assumptions for OLS to be unbiased 28
2.4.2 The assumptions for OLS to be minimum variance 29
2.5 Identifying the causal effect of education 31
References 31
Exercise 32
3 Using and extending the simple regression model 33
3.1 Introduction 33
3.2 Dummy explanatory variables and the return to education 33
3.3 Multiple regression 36
3.3.1 Earnings and production functions 36
3.3.2 The OLS estimators for multiple regression 37
3.3.3 Omitted variables and the bias they may cause 39
3.4 Interpreting multiple regressions 40
3.4.1 How much does investing in education increase earnings?
Some micro evidence 40
3.4.2 How much does investing in education increase productivity?
Some macro evidence 43
References 45
Exercise 45
4 The distribution of the OLS estimators and hypothesis testing 47
4.1 Introduction 47
4.2 The distribution of the OLS estimators 47
4.2.1 The normality assumption 47
4.2.2 Why normality? 48
4.3 Testing hypotheses about a single population parameter 49
4.3.1 The t distribution 49
4.3.2 The/-test 51
4.3.3 Confidence intervals 53
4.4 Testing for the overall significance of a regression 55
4.5 Testing for heteroskedasticity 57
4.6 Large sample properties of OLS 58
4.6.1 Consistency 58
4.6.2 Asymptotic normality 60
References 60
Exercise 61
5 The determinants of earnings and productivity 62
5.1 Introduction 62
5.2 Testing the normality assumption 62
5.3 The earnings function 65
5.3.1 Bringing the tests together 65
5.3.2 Robust and clustered standard errors 65
Contents ix
5.4 The production function 67
5.4.1 Testing the production function 67
5.4.2 Extending the production function 67
5.5 Interpreting our earnings and production functions 72
5.5.1 Can education be given a causal interpretation? 72
5.5.2 How much does education raise labour productivity? 73
References 74
Exercise 74
SECTION II
Time-series data, growth and development 75
6 Modelling growth with time-series data 77
6.1 Introduction: modelling growth 11
6.2 An introduction to the Solow model 78
6.3 A Solow model for Argentina 80
6.4 OLS estimates under the classical assumptions with time-series data 81
6.4.1 Assumptions for OLS to be unbiased 81
6.4.2 The variance of the OLS estimators 83
6.4.3 Testing for autocorrelation 85
6.5 Static and dynamic time-series models 85
6.6 Assumptions to ensure the OLS estimators are consistent 87
6.7 Spurious regression with nonstationary time-series data 89
6.8 A brief summary 91
References 92
Exercise 93
7 The implications of variables having a unit root 95
7.1 Introduction and motivation 95
7.2 Testing for a unit root and the order of integration 96
7.3 Cointegration 100
7.4 How are growth and inflation related in Argentina? 101
7.5 The error-correction model 104
7.6 Causality in time-series models 105
7.7 Cross-section and time-series data 106
References 107
Exercise 107
8 Exogenous and endogenous growth 109
8.1 The Solow model and the history of development 109
8.2 Long-term growth and structural change 109
8.3 The Solow model, structural change and endogenous growth 112
8.4 Human capital and the dynamic Solow model 113
8.5 Exogenous and endogenous growth 116
x Contents
8.6 A Solow interpretation of development patterns 118
References 118
Exercise 119
Appendix: deriving the dynamic Solow model 119
SECTION III
Panel data
9 Panel data: an introduction
9.1 In troduc tion 123
9.2 Paneldata 123
9.2.1 The structure of the panel 123
9.2.2 Panel data and endogeneity 124
9.3 Panel production functions 127
9.3.1 A panel macro production function 127
9.3.2 A panel micro production function 130
9.4 Interpreting the fixed effect 134
References 135
Exercise 135
Appendix: matrix notation 135
10 Panel estimators: POLS, RE, FE, FD
10.1 Introduction 140
10.2 Panel estimators 140
10.2.1 The fixed effects and first difference estimators 140
10.2.2 The random effects estimator 142
10.3 Key assumptions for consistency 143
10.4 Model selection 144
10.4.1 Testing for correlation between the q and the explanatory
variables 145
10.4.2 Testing for the presence of an unobserved effect 146
10.5 The micro panel production function extended 147
10.6 What determines the productivity of Ghanaian firms? 148
References 152
Exercise 152
11 Instrumental variables and endogeneity
11.1 Introduction 153
11.2 Sources of bias in the OLS estimates 153
11.2.1 Bias from omitted variables 153
11.2.2 Bias from measurement error 154
11.2.3 Panel data: omitted variables and measurement error 155
11.3 Instrumental variables 156
11.3.1 Valid and informative instruments 157
Contents xi
11.3.2 Interpreting the IV estimator 159
11.4 The properties of the IV estimator 160
11.4.1 The IV and OLS estimators compared 160
11.4.2 Inference with the IV estimator 161
11.5 The causes of differences in world incomes 162
Exercise 167
References 168
SECTION IV
An introduction to programme evaluation 169
12 The programme evaluation approach to development policy 171
12.1 Introduction: causal effects and the counterfactualproblem 171
12.2 Rubin causal model 172
12.2.1 Potential outcomes 172
12.2.2 Assignment mechanism 173
12.2.3 Defining measures of impact 174
12.2.4 From potential outcomes to regression 174
12.3 Selection on observables ill
12.3.1 Ignorability of treatment 177
12.3.2 Overlap 178
12.4 Unconditional unconfoundedness and the experimental approach 179
References 180
Exercise 180
13 Models, experiments and calibration in development policy analysis 182
13.1 Introduction 182
13.2 Empirical estimators under (conditional) unconfoundedness 182
13.2.1 Multivariate regression 183
13.2.2 Panel data methods 184
13.3 A randomised controlled trial (RCT) for conditional cash transfers 185
13.4 Calibrating technology 188
13.5 Education, technology and poverty 190
References 190
Exercise 191
PART 2
Modelling development 193
14 Measurement, models and methods for understanding poverty 195
14.1 Introduction 195
14.2 The causes of poverty 195
14.2.1 Poverty and GDP data 195
14.2.2 Poverty, consumption and incomes 196
xii Contents
14.2.3 Poverty, inequality and GDP 197
14.3 The Mincerian earnings function, the price of labour and poverty 199
14.4 Modelling impacts 201
14.4.1 A generalised Roy model of selection 201
14.4.2 Implications of the Roy model for estimation of treatment
effects 202
14.5 An overview: measurement, models and methods 203
References 204
Exercise 205
SECTION V
Modelling choice
15 Maximum likelihood estimation
15.1 Introduction 209
15.2 The concept of maximum likelihood 209
15.3 The concept of population 211
15.4 Distributional assumptions and the log-likelihood function 211
15.5 Maximising the (log-) likelihood 214
15.6 Maximum likelihood in Stata 215
15.7 Problems and warnings ... 218
15.7.1 Maximum likelihood and endogeneity 218
15.7.2 Maximum likelihood and convergence 219
15.8 Properties of maximum likelihood estimates 220
15.8.1 Consistency 221
15.8.2 Efficiency 221
15.8.3 So what? 221
15.9 Hypothesis testing under maximum likelihood 222
15.10 Overview 224
References 224
Exercise 224
16 Modelling choice: the LPM, probit and logit models
16.1 Introduction 226
16.2 Binary choices and interpreting the descriptive statistics 227
16.3 Estimation by OLS: the linear probability model 228
16.4 The probit and logit models as latent variable models 231
16.4.1 The probit model 232
16.4.2 The logit model 234
16.5 Maximum likelihood estimation of probit and logit models 234
16.6 Explaining choice 235
References 237
Exercise 237
Contents xiii
17 Using logit and probit models for unemployment and school choice 239
17.1 Introduction 239
/7.2 Interpreting the probit model and the logit model 240
17.2.1 A model of unemployment 240
17.2.2 Average partial effects and marginal effects at the mean 240
17.2.3 Age and education as determinants of unemployment in South
Africa 245
17.3 Goodness of fit 245
17.4 Indian private and state schools 248
17.4.1 How well do private schools perform? 248
17.4.2 Who attends a private school? 249
17.4.3 Mother’s education and wealth as determinants of attending
private school in India 250
17.5 Models of unemployment and school choice 250
References 252
Exercise 252
18 Corner solutions: modelling investing in children and by firms 254
18.1 Introduction 254
18.2 OLS estimation of corner response models 255
18.2.1 Investment in Ghana’s manufacturing sector 255
18.2.2 Gender discrimination in India 258
18.3 The Tobit model 260
18.4 Two-part models 262
18.4.1 Truncated normal hurdle model 264
18.4.2 The log-normal hurdle model 265
18.5 Overview 268
References 268
Exercise 269
Appendix: the Inverse Mills Ratio (I MR) 269
SECTION VI
Structural modelling 271
19 An introduction to structural modelling in development economics 273
19.1 Introduction: the challenge of using microeconomic theory in
empirical research 273
19.2 Using a structural model to think about risk-sharing 274
19.3 Building and solving a microeconomic model 276
19.4 Thinking about unobservables and choosing an estimator 281
19.4.1 The model to be estimated 281
19.4.2 Identification in the model 282
19.4.3 Testing the model 282
19.5 Estimating the model 283
xiv Contents
19.5.1 The data 283
19.5.2 Estimation results 283
19.6 Conclusion 284
References 285
Exercise 285
20 Structural methods and the return to education 286
20.1 Introduction: Belzil and Hansen go to Africa 286
20.2 The question 286
20.3 A model of investment in education 287
20.4 Thinking about unobservables and choosing an estimator 292
20.5 Models and data 296
20.5.1 Adolescent econometricians’? 296
20.5.2 Possible applications for structural modelling in development 297
20.6 Structural models: hubris or humility? 298
References 298
Exercise 299
SECTION VII
Selection, heterogeneity and programme evaluation 301
21 Sample selection: modelling incomes where occupation is chosen 303
21.1 Introduction 303
21.2 Sample selection 303
21.3 A formal exposition 304
21.3.1 The regression with sample selection 304
21.3.2 Modelling the correlation of the unobservables 305
21.4 When is sample selection a problem? 308
21.5 Selection and earnings in South Africa 309
21.6 Corner solution and sample selection models 313
References 314
Exercise 314
22 Programme evaluation: regression discontinuity and matching 316
22.1 Introduction 316
22.2 Regression discontinuity design 316
22.3 Propensity score methods 319
22.3.1 Regression using the propensity score 319
22.3.2 Weighting by the propensity score 320
22.3.3 Matching on the propensity score 321
22.4 Food aid in Ethiopia: propensity-score matching 322
22.5 Assessing the consequences of property rights: pipeline identification
strategies 323
22.6 Estimating treatment effects ( the plot so far) 326
Contents XV
References 326
Exercise 327
23 Heterogeneity, selection and the marginal treatment effect (MTE) 328
23.1 Introduction 328
23.2 Instrumental variables estimates under homogeneous treatment effects 328
23.3 Instrumental variables estimates under heterogeneous treatment effects 330
23.3.1 IV for noncompliance and heterogeneous effects: the
LATE Theorem 330
23.3.2 LATE and the compliant subpopulation 332
23.4 Selection and the marginal treatment effect 333
23.4.1 Interpreting the LATE in the context of the Roy model 333
23.4.2 The marginal treatment effect 336
23.4.3 What does IV identify? 337
23.5 The return to education once again 339
23.6 An overview 341
References 342
Exercise 342
SECTION VIII
Dynamic models for micro and macro data 345
24 Estimation of dynamic effects with panel data 347
24.1 Introduction 347
24.2 Instrumental variable estimation of dynamic panel-data models 348
24.3 The Arellano-Bond estimator 349
24.3.1 No serial correlation in the errors 349
24.3.2 Serially correlated errors 350
24.4 The system GMM estimator 351
24.5 Estimation of dynamic panel-data models using Stata 352
24.6 The general case 355
24.6.1 The regressors are strictly exogenous 355
24.6.2 The regressors are predetermined 356
24.6.3 The regressors are contemporaneously endogenous 357
24.6.4 Implications of serial correlation in the error term 357
24.7 Using the estimators 358
References 358
Exercise 359
Appendix: the bias in the fixed effects estimator of a dynamic
panel-datamodel 359
25 Modelling the effects of aid and the determinants of growth 361
25.1 Introduction 361
25.2 Dynamic reduced-form models 361
xvi Contents
25.2.1 Aid, policy and growth 361
25.2.2 Dynamics and lags 364
25.2.3 Differenced and system GMM estimators 366
25.3 Growth rate effects: a model of endogenous growth 368
25.3.1 Dynamic and growth rate models 368
25.3.2 Is there evidence for endogenous growth? 370
25.4 Aid, policy and growth revisited with annual data 371
25.4.1 Cross section and time-series uses of macro data 371
25.4.2 Growth and levels effects of aid 371
25.5 A brief overview: aid, policy and growth 372
References 373
Exercise 373
SECTION IX
Dynamics and long panels 375
26 Understanding technology using long panels 377
26.1 Introduction 377
26.2 Parameter heterogeneity in long panels 378
26.3 The mean group estimator 379
26.4 Cross-section dependence due to common factors 383
26.5 Conclusion 386
References 386
Exercise 386
27 Cross-section dependence and nonstationary data 388
27.1 Introduction 388
27.2 Alternative approaches to modelling cross-section dependence 388
27.2.1 Country fixed effects and year dummies 389
27.2.2 Estimating unobserved common factors 389
27.2.3 Constructing weight matrices 390
27.3 Modelling cross-section dependence using cross-section averages 390
27.4 Detecting cross-section dependence 393
27.5 Panel unit root testing 394
27.5.1 First-generation panel unit root test 394
27.5.1.1 The Im, Pesaran and Shin test (IPS) 395
27.5.1.2 The Maddala and Wu test (MW) 395
27.5.2 Second-generation panel unit root test 395
27.5.2.1 The PANIC approach 395
27.5.2.2 The CIPS and CIPSM tests 396
27.6 Cointegration testing in panels 396
27.6.1 Residual analysis and error-correction models 396
27.6.2 Tests for panel cointegration 397
Contents xvii
27.7 Parameter heterogeneity; nonstationary data and cross-section
dependence 397
References 399
Exercise 400
28 Macro production functions for manufacturing and agriculture 402
28.1 Introduction 402
28.2 Estimating a production function for manufacturing 403
28.2.1 The homogeneous models 403
28.2.2 The heterogeneous models 405
28.3 Estimating a production function for agriculture 407
28.3.1 Unit roots 408
28.3.2 What determines the productivity of agriculture? 409
28.4 Manufacturing and agriculture and the growth of an economy 412
References 412
Exercise 413
SECTION X
An overview 415
29 How can the processes of development best be understood? 417
29.1 Introduction 417
29.2 A range of answers as to the causes of poverty 417
29.3 Macro policy, growth and poverty reduction 419
29.4 Programme evaluation and structural models 419
29.4.1 Programme evaluation and the ‘failure’ of poverty policies 419
29.4.2 Structural models and understanding the causes of poverty 420
29.5 Skills, technology and the returns on investment 420
29.5.1 The value of skills 420
29.5.2 The role of technology 421
29.5.3 Rates of return on investment 421
29.6 A final word 421
References 422
Bibliography
Index
423
431
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| id | DE-604.BV042320062 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:18:21Z |
| institution | BVB |
| isbn | 9780415810487 9780415810494 |
| language | English |
| lccn | 014012241 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027757011 |
| oclc_num | 899725942 |
| open_access_boolean | |
| owner | DE-29 DE-M382 DE-188 DE-N2 DE-703 DE-739 DE-20 DE-19 DE-BY-UBM |
| owner_facet | DE-29 DE-M382 DE-188 DE-N2 DE-703 DE-739 DE-20 DE-19 DE-BY-UBM |
| physical | XXVIII, 433 S. 25 cm |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Routledge |
| record_format | marc |
| series | Routledge advanced texts in economics and finance |
| series2 | Routledge advanced texts in economics and finance |
| spelling | Söderbom, Måns Verfasser (DE-588)1059755394 aut Empirical development economics Måns Söderbom and Francis Teal 1. publ. London [u.a.] Routledge 2015 XXVIII, 433 S. 25 cm txt rdacontent n rdamedia nc rdacarrier Routledge advanced texts in economics and finance 24 Includes bibliographical references (pages 423-430) and index BUSINESS & ECONOMICS / General bisacsh BUSINESS & ECONOMICS / Development / Business Development bisacsh BUSINESS & ECONOMICS / Econometrics bisacsh Wirtschaft Development economics Poverty Income distribution Econometrics BUSINESS & ECONOMICS / General BUSINESS & ECONOMICS / Development / Business Development BUSINESS & ECONOMICS / Econometrics Ökonometrie (DE-588)4132280-0 gnd rswk-swf Wirtschaftswachstum (DE-588)4066527-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Wirtschaftsentwicklung (DE-588)4066438-7 gnd rswk-swf Entwicklungstheorie (DE-588)4121207-1 gnd rswk-swf Wirtschaftsentwicklung (DE-588)4066438-7 s Wirtschaftswachstum (DE-588)4066527-6 s Entwicklungstheorie (DE-588)4121207-1 s Ökonometrie (DE-588)4132280-0 s Zeitreihenanalyse (DE-588)4067486-1 s Statistik (DE-588)4056995-0 s DE-604 Teal, Francis J. 1949- Verfasser (DE-588)170174093 aut Erscheint auch als Online-Ausgabe 978-0-203-07092-5 Routledge advanced texts in economics and finance 24 (DE-604)BV037241432 24 Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027757011&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027757011&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Söderbom, Måns Teal, Francis J. 1949- Empirical development economics Routledge advanced texts in economics and finance BUSINESS & ECONOMICS / General bisacsh BUSINESS & ECONOMICS / Development / Business Development bisacsh BUSINESS & ECONOMICS / Econometrics bisacsh Wirtschaft Development economics Poverty Income distribution Econometrics BUSINESS & ECONOMICS / General BUSINESS & ECONOMICS / Development / Business Development BUSINESS & ECONOMICS / Econometrics Ökonometrie (DE-588)4132280-0 gnd Wirtschaftswachstum (DE-588)4066527-6 gnd Statistik (DE-588)4056995-0 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Wirtschaftsentwicklung (DE-588)4066438-7 gnd Entwicklungstheorie (DE-588)4121207-1 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4066527-6 (DE-588)4056995-0 (DE-588)4067486-1 (DE-588)4066438-7 (DE-588)4121207-1 |
| title | Empirical development economics |
| title_auth | Empirical development economics |
| title_exact_search | Empirical development economics |
| title_full | Empirical development economics Måns Söderbom and Francis Teal |
| title_fullStr | Empirical development economics Måns Söderbom and Francis Teal |
| title_full_unstemmed | Empirical development economics Måns Söderbom and Francis Teal |
| title_short | Empirical development economics |
| title_sort | empirical development economics |
| topic | BUSINESS & ECONOMICS / General bisacsh BUSINESS & ECONOMICS / Development / Business Development bisacsh BUSINESS & ECONOMICS / Econometrics bisacsh Wirtschaft Development economics Poverty Income distribution Econometrics BUSINESS & ECONOMICS / General BUSINESS & ECONOMICS / Development / Business Development BUSINESS & ECONOMICS / Econometrics Ökonometrie (DE-588)4132280-0 gnd Wirtschaftswachstum (DE-588)4066527-6 gnd Statistik (DE-588)4056995-0 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Wirtschaftsentwicklung (DE-588)4066438-7 gnd Entwicklungstheorie (DE-588)4121207-1 gnd |
| topic_facet | BUSINESS & ECONOMICS / General BUSINESS & ECONOMICS / Development / Business Development BUSINESS & ECONOMICS / Econometrics Wirtschaft Development economics Poverty Income distribution Econometrics Ökonometrie Wirtschaftswachstum Statistik Zeitreihenanalyse Wirtschaftsentwicklung Entwicklungstheorie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027757011&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027757011&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV037241432 |
| work_keys_str_mv | AT soderbommans empiricaldevelopmenteconomics AT tealfrancisj empiricaldevelopmenteconomics |