What evidence should social policymakers use?
Andrew Leigh
Policymakers seeking empirical evidence on social policy interventions often find themselves confronted with a mountain of academic studies that are potentially relevant to the question. Without some systematic way to sort through the evidence, there is a risk that analysts will become mired in the research, or simply cherry‑pick those studies that support their prior beliefs. An alternative approach is to test each study against a hierarchy of research methods. This article discusses two hierarchies — one used by US medical researchers, and another used by UK social policymakers — and suggests one possible hierarchy for Australia. Naturally, such a hierarchy should not be the only tool used to assess research, and should be used in conjunction with other factors, such as the ranking of the journal in which a study is published. But used carefully, a hierarchy can help policymakers sort through a daunting body of research, and may also inform governments’ decisions on how to evaluate social policy interventions.

Introduction
Imagine a diligent policymaker decided that before providing advice on a particular social policy question, she was going to read all the relevant academic literature. Being a fast reader, she envisaged spending half an hour on each article that Google Scholar determined to be relevant to the question at hand. How long would this take? 
Reading solidly for 40 hours a week, 52 weeks a year, it would take a policymaker 18 months to get through the 6,000 articles on ‘early childhood intervention’, four years to get through the 16,000 articles on ‘teacher quality’, or five years to get through the 20,000 articles on ‘social housing’. Moreover, given that more articles are being written all the time, this probably underestimates the time that would need to be devoted to understanding even such narrowly defined topics as these.
Across the social sciences, the explosion of research over recent decades shows no signs of abating. The ready availability of working papers, the creation of new journals, and the continued production of new books makes it harder than ever before for the consumers of research to keep up with the burgeoning supply. 
With the exception of those who work in an extremely narrow field, it is now virtually impossible for policymakers to read everything that has been written on their topic. For those who are committed to the notion of ‘evidence‑based policymaking’, this presents a considerable challenge. Good policymakers should consider theory, context and risk (see Wilkie and Grant, this issue). Then they must ask: what is the most efficient way to sift through the available evidence? With such an abundance of evidence, there is a risk that advocates will simply ‘cherry‑pick’ the studies that suit their worldview, conveniently ignoring those that do not. 
In medicine, the generally accepted solution to this problem is to use what is known as an ‘evidence hierarchy’, by which evidence is ranked according to a set of methodological criteria. Doctors are then encouraged to give more weight to high‑quality research, and less weight to low‑quality research. 
This article suggests that when it comes to interpreting impact evaluations, social policymakers may benefit from applying the same approach. Although there is more debate about appropriate methodologies in economics than there is in medicine, it is nonetheless possible to identify a set of broad principles that can help shape an appropriate evidence hierarchy for economic research. Where doubt still remains, journal rankings can also be instructive in assisting policymakers decide how to weight multiple pieces of evidence.
At the outset, a caveat is in order. Although process evaluations and qualitative evidence can also be important, this article will focus on impact evaluations using quantitative evidence. Furthermore, this article focuses solely on policymaking in the social policy field (including education, health, income support and crime). In fields such as defence policy and monetary policy, a different hierarchy may be appropriate.
Climbing the research mountain
A sense of the challenge facing policymakers can be gleaned from the Econlit database, which indexes new economic research. Figure 1 charts the number of new articles published in Econlit over a 30‑year period. In 1977, there were 7,077 articles published in the database. In 2007, there were 31,633 new articles, more than four times as many. In part, this is due to an increase in the number of available journals. For example, the Berkeley Electronic Press has established 19 journals in business and economics in the last decade. This year, the American Economic Association has launched four new journals. With the growing acceptance of journals which publish only online, it is likely that the number of outlets will continue to increase. 

Chart 1: A growing body of research(a)

Source: Author’s calculations, based on year‑by‑year searches of www.econlit.org

If the sheer volume of research was not daunting enough, today’s research is also more accessible than ever before. Given that most journals can be accessed electronically, one can no longer make the excuse that a highly pertinent article has been overlooked solely because a hard copy was not available in the library. In addition, many economics papers now receive wide circulation prior to being published in a peer‑reviewed journal, which creates its own challenge for the consumers of academic research. Similar trends are evident in other social sciences, with the numbers of journals and articles rapidly increasing in sociology, education policy, political science and health policy. 
How might a hierarchy look?
One way to sift through the available evidence is to devise an evidence hierarchy, borrowing from the approach commonly used by medical researchers. For example, a report from the US government ‘Preventive Services Task Force’ sets out a hierarchy that is routinely followed in the medical profession (see US Preventive Services Task Force 2008, Section 4). 

Box 1: The US Government’s evidence hierarchy for medical research
I: Properly powered and conducted randomised controlled trial (RCT); well conducted systematic review or meta‑analysis of homogeneous RCTs
II‑1: Well‑designed controlled trial without randomisation
II‑2: Well‑designed cohort or case‑control analytic study
II‑3: Multiple time series with or without the intervention; dramatic results from uncontrolled experiments
III: Opinions of respected authorities, based on clinical experience; descriptive studies or case reports; reports of expert committees	

In the social policy context, the UK Cabinet Office has sought to adapt the medical schema for the use of policymakers who are considering interventions that might assist vulnerable individuals. They propose the hierarchy set out below.
Box 2: The UK Government’s evidence hierarchy for policymakers
1. Systematic review — Synthesis of results from several studies 
2. Randomised controlled trial — Population allocated randomly to groups
3. Quasi‑experimental study — Similar populations compared
4. Pre‑post study — Results compared before and after intervention	

One feature that characterises both the US medical hierarchy and the UK social policy hierarchy is the precedence given to systematic reviews. Systematic reviews (also known as meta‑analyses) allow researchers to quickly gain a sense of the preponderance of evidence on a particular topic, without having to read each of the studies in a field. This is particularly valuable if the literature is comprised of many well‑designed studies with small sample sizes. Taken individually, these studies may reach divergent conclusions, but by aggregating them, it is often possible to get above the trees and see the shape of the forest. Another issue is that systematic reviews are only as good as the studies being aggregated (if the individual studies are flawed, then combining them will not solve the problem). Some systematic reviews address this issue by explicitly placing more emphasis on higher‑quality studies.
Another point to note is that the above hierarchies adopt a similar ranking of research types, putting randomised trials above natural experiments, which in turn are placed above before‑after studies. Underlying this classification is the credibility of the counterfactual — what would have occurred in the absence of the intervention. In an ideal study, we would like to be able to compare the treatment group, who received the intervention, with a control group of individuals who did not receive the intervention. 
In a randomised trial of a new pharmaceutical, participants are informed in advance that they will have a 50 per cent chance of receiving the new drug, and a 50 per cent chance of receiving a placebo (such as a sugar tablet). Typically, the study is set up in such a way that neither the participants nor the person administering the experiment is aware of who is in the treatment group and who is in the control group. This is known as a double‑blind randomisation. 
In a randomised policy trial, participants are almost always aware of whether they are in the treatment group or the control group. For example, in a 1999 randomised trial to evaluate the efficacy of the NSW Drug Court, individuals who were awaiting trial on a drug offence were randomly allocated either to a regular court, or to the new Drug Court (Lind et al. 2002). By matching participants to court records over the next year, the researchers were able to see whether the sentencing approach had an impact on recidivism. (It turned out that those who were assigned to the Drug Court were significantly less likely to commit a drug‑related offence in the following year). 
With a sufficiently large sample, assigning individuals to the treatment or control group by randomisation ensures that the two groups are evenly matched. With randomisation, the two groups should have similar observable characteristics (such as education or income), and similar unobservable characteristics (such as motivation or self‑control). This is a major advantage over multiple regression approaches which make it possible to hold constant observable traits, but not unobservables. For this reason, randomised trials are known as the ‘gold standard’ in policy research, and have informed policymaking in areas as diverse as job training, driver education, school vouchers, financial assistance to ex‑prisoners, welfare reform, health insurance and rental subsidies (for a discussion, see Leigh 2003, Farrelly 2008). Yet randomised policy trials remain relatively rare, with 24 medical randomised trials being conducted for each randomised policy trial (The Economist 2002). This may reflect a lack of familiarity with the technique, or a perception of randomised policy trials as being unethical, because those in the control group do not receive a potentially effective intervention.
In the evidence hierarchy, natural experiments are the next category below randomised trials. Also known as ‘quasi‑experiments’, these approaches construct the counterfactual in various ways. ‘Differences‑in‑differences’ identifies a similar population that is not affected by the treatment, and tracks the outcomes of the treated and control groups over time. For example, suppose that a government decided to increase garbage collection fees in order to reduce landfill. In order to assess the impact of the change, we might compare the amount of garbage collected in two neighbouring areas — Town A (which is just inside the affected area) and Town B (just outside the affected area). With measures of the outcome measure (garbage volume) for two cities (treatment and control) in two time periods (before and after) one can estimate the policy impact by comparing the change over time in the control group with the change over time in the treatment group. Unlike a cross‑sectional comparison (comparing Towns A and B after the policy change), differences‑in‑differences is able to account for persistent factors that might confound the analysis (Town A’s residents might be more prone to littering). And unlike a before‑after comparison (looking at Town A before and after the policy change), the strategy is able to account for other time‑specific shocks (for example, there might be seasonal patterns of garbage disposal).
Another commonly‑used natural experiment approach is regression discontinuity. This research method compares individuals who are very close to an arbitrary cutoff, such as an entry score or an eligibility threshold. Inherent in this strategy is that the closer one comes to the cutoff, the more similar those on either side are to one another. For example, suppose an individual must score 90 per cent to be admitted into a selective school. We would probably expect students scoring 50 per cent to be very different from students scoring 99 per cent (on both observable and unobservable characteristics). However, as we come closer to the cutoff, students are likely to be more similar. A regression discontinuity approach might compare those who scored 90 per cent with those who scored 89 per cent. Since only one point separates these individuals, it is plausible to imagine that it was only a matter of luck that one student scored above the threshold and the other below it. The assumption underlying regression discontinuity — that individuals who are very close to an arbitrary threshold are likely to be alike — suggests that students who just fail to meet the cutoff might be a good control group for those who narrowly exceed the cutoff. In this example, one could use regression discontinuity to see whether students who attend a selective school eventually perform better on university entrance exams.
Another set of natural experiments use multiple regression or matching approaches to control for observable differences between the treatment and control groups. For example, an evaluation of pre‑school education programs in the UK (the Effective Provision of Pre‑School Education project) compares the outcomes for children who were enrolled in pre‑school with children who were not enrolled in pre‑school, but who had similar observable characteristics. The limitation of this strategy is that there may be unobservable traits about families who chose not to use pre‑school programs. If these traits also affect child outcomes, then the matched control group will not constitute a valid counterfactual for the treatment group.
Before‑after studies rank below systematic reviews, randomised trials and natural experiments. Implicit in a before‑after study is that if the intervention did not take place, the outcomes in the after period would be precisely the same as they were before the intervention. Put another way, the counterfactual in a before‑after study is what we observe before the intervention. This is a strong assumption, which will be violated if there are other factors affecting outcomes over time (such as rising productivity, other policy changes, or fluctuating economic cycles).
Lowest in the medical hierarchy (and not even rating a mention in the UK Cabinet Office’s hierarchy) are expert opinions and descriptive case studies. From a policymaking perspective, this may include first‑principles analyses, based purely upon theory; or anecdotes about the effectiveness of particular policies. Sometimes this evidence is all that is available; but the above hierarchies suggest that where possible, it should be supplemented by empirical findings.
Drawing this together, the following hierarchy might be used by social policymakers in Australia.
Box 3: A possible evidence hierarchy for Australian policymakers
1. Systematic reviews (meta‑analyses) of multiple randomised trials 
2. High quality randomised trials
3. Systematic reviews (meta‑analyses) of natural experiments and before‑after studies 
4. Natural experiments (quasi‑experiments) using techniques such as differences‑in‑differences, regression discontinuity, matching, or multiple regression
5. Before‑after (pre‑post) studies
6. Expert opinion and theoretical conjecture
All else equal, studies should also be preferred if they are published in high‑quality journals, if they use Australian data, if they are published more recently, and if they are more similar to the policy under consideration.	

Other relevant considerations
The principal value of an evidence hierarchy is as a rule‑of‑thumb, which can help simplify the process of classifying a large body of empirical evidence. However, one limitation of an evidence hierarchy in the social sciences is that some methodologies are better‑suited to answering different types of questions. In particular, while randomised policy trials are an effective way of testing the impact of an intervention on a small scale, randomisation is often unable to provide estimates of the ‘general equilibrium’ impact of a policy change. For example, the Moving to Opportunity rental assistance experiments in the US (Kling, Liebman and Katz 2007) were designed to test the impact on individuals of moving out of a high‑poverty neighbourhood. As a randomised experiment, it has provided credible estimates of the impact of moving to a better neighbourhood. But because of the way the study was designed, it does not measure the impact of mobility on the families who are left behind. It is therefore possible that some of the gains for movers are offset by losses for the old friends and neighbours that they left behind.
Medical researchers are typically less concerned about general equilibrium effects. If a new pharmaceutical is effective in a small sample, then it will most likely ‘scale up’ to the full population. But economists are often concerned about spillover and scale effects, and in such cases, it may be valuable to be able to have evidence from both a randomised trial and a natural experiment. In other cases, randomisation may be unfeasible — either for practical or ethical reasons — in which case, it is necessary to opt for other evaluation methods.
What other factors should be borne in mind when assessing research evidence? All else equal, policymakers will typically give greater weight to more recent studies, to Australian studies, and to evaluations of policies that are most similar to those under consideration. Additionally, some may find it useful to refer to the 13‑question checklist prepared by the UK Cabinet Office for evaluating randomised trials, natural experiments and qualitative studies.
A final consideration in the case of published studies is that policymakers may also wish to give more weight to research that is published in more highly‑ranked journals. Although journal rankings are not a perfect guide to the quality of an individual article, those studies that use rigorous methodologies are more likely to find their way into the best journals. One such ranking, compiled by Kalaitzidakis, Mamuneas, and Stengos (2003), ranks 159 journals using citation data from 1994‑98, including three Australian journals, the Economic Record (58th), the Australian Economic History Review (82nd) and the Australian Journal of Agricultural and Resource Economics (103rd). (The citation database that they used omitted some Australian journals, including the Australian Economic Review, Australian Economic Papers, and Economic Papers.) While reasonable economists might disagree on the margins, most would concur that an article published in a top‑20 journal should be given greater weight by policymakers than an unpublished working paper, or a study published in a journal ranked below 100. The full ranking is provided in the Appendix. 

Conclusion
On most topics, social policymakers cannot hope to thoroughly read all the available studies. The question therefore is not whether they should rank them, but how such a ranking should be done. This article suggests one possible ranking, which gives systematic reviews precedence over single studies; and ranks methodologies as: randomised trials, natural experiments, before‑after studies, and expert opinion. 
Naturally, decision‑making in the real world does not always allow the luxury of neatly sorting all the available research papers into a hierarchy. In some cases, policymakers must spread their attention across a broad range of issues, or rapidly arrive at a solution. Yet even in such cases, a hierarchy of evidence can be used as a rule of thumb, for example by helping to choose between two studies that arrive at different conclusions. In instances where decisions must be made in the absence of high‑quality evidence, the use of a hierarchy may prompt more rigorous evaluation methodologies, laying the groundwork for a better evidence base.
A social policy evidence hierarchy is not only useful for consumers of research, but also for producers. Although randomised trials are generally acknowledged to be superior to before‑after studies, it is the case in Australia (and in many other developed countries) that before‑after studies are more common than randomised trials. 
There is a natural human tendency in all of us to prefer empirical studies whose results accord with our prior beliefs. Using an evidence hierarchy can help avoid such selective use of research, and simplify the task of classifying large bodies of literature. Ultimately, this should help ensure that ‘evidence‑based policy’ means identifying the best evidence where it is available, and using the most rigorous evaluation tools to improve the quality of the evidence base in the long‑run.

References
Borland, J, Tseng, Y and Wilkins, R 2005, ‘Experimental and Quasi‑Experimental Methods of Microeconomic Program and Policy Evaluation’ Melbourne Institute Working Paper 2005‑08, Melbourne Institute of Applied Economic and Social Research, University of Melbourne.
Boruch, R and Rui, N 2008, ‘From randomized controlled trials to evidence grading schemes: current state of evidence‑based practice in social sciences’ Journal of Evidence‑Based Medicine 1(1): 41–49.
Clark, D 2007, ‘Selective Schools and Academic Achievement’, IZA Discussion Paper 3182, IZA, Bonn, Germany.
Cobb‑Clark, D and Crossley, T 2003, ‘Econometrics for Evaluations: An Introduction to Recent Developments’ Economic Record, 79(247): 491‑511. 
Donohue, J 2001, ‘The Search for Truth: In Appreciation of James J. Heckman’, John M. Olin Program in Law and Economics Working Paper No. 220, Stanford Law School, Stanford, CA.
The Economist 2002, ‘Try It and See’, Economist, 2 March, 73‑74.
Farrelly, R 2008, ‘Policy on Trial’, Policy, 24(3): 7‑12.
Kalaitzidakis, P, Mamuneas, T. P and Stengos, T 2003, ‘Rankings of Academic Journals and Institutions in Economics’, Journal of the European Economic Association 1(6): 1346‑1366.
Kling, J, Liebman, J and Katz, L 2007, ‘Experimental Analysis of Neighborhood Effects’ Econometrica 75(1): 83–119.
Leigh, A 2003, ‘Randomised Policy Trials’, Agenda: A Journal of Policy Analysis and Reform 10(4): 341‑354.
Lind, B, Weatherburn, D, Chen, S, Shanahan, M, Lancsar, E, Haas, M and De Abreu Lourenco, R 2002, NSW Drug Court evaluation: cost‑effectiveness, NSW Bureau of Crime Statistics and Research, Sydney. Available at
www.courtwise.nsw.gov.au/lawlink/bocsar/ll_bocsar.nsf/vwFiles/L15.pdf/$file/L15.pdf
Mays, N and Pope, C 1995, ‘Qualitative research: rigour and qualitative research’, British Medical Journal 311: 109‑112.
Smith, G, and Pell, J 2003, ‘Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials’, British Medical Journal, 327: 1459‑1461.
Social Exclusion Task Force 2008, ‘Think Research: Using Research Evidence to Inform Service Development for Vulnerable Groups’, London, UK Cabinet Office. 
Ref: 288096/0608. Available at www.cabinetoffice.gov.uk/social_exclusion_task_force/think_research.aspx.
Spencer, L, Ritchie, J, Lewis, J, and Dillon, L 2003, ’Quality in Qualitative Evaluation: A framework for assessing research evidence’, Government Chief Social Researcher’s Office, Cabinet Office, London.
US Preventive Services Task Force 2008, Procedure Manual, AHRQ Publication No. 08‑05118‑EF, Agency for Healthcare Research and Quality, Rockville, MD. Available at www.ahrq.gov/clinic/uspstf08/methods/procmanual.htm.
Wilkie, J, and Grant, A 2009, ‘Using evidence well’ Economic Roundup, Issue 1, Treasury, Canberra.

Appendix: A ranking of journals by Kalaitzidakis, Mamuneas, and Stengos (2003)
Rank	 Journal  	
 1  	 American Economic Review  	
 2  	 Econometrica  	
 3  	 Journal of Political Economy  	
 4  	 Journal of Economic Theory  	
 5  	 Quarterly Journal of Economics  	
 6  	 Journal of Econometrics  	
 7  	 Econometric Theory  	
 8  	 Review of Economic Studies  	
 9  	 Journal of Business and Economic Statistics  	
 10  	 Journal of Monetary Economics  	
 11  	 Games and Economic Behavior  	
 12  	 Journal of Economic Perspectives  	
 13  	 Review of Economics and Statistics  	
 14  	 European Economic Review  	
 15  	 International Economic Review  	
 16  	 Economic Theory  	
 17  	 Journal of Human Resources  	
 18  	 Economic Journal  	
 19  	 Journal of Public Economics  	
 20  	 Journal of Economic Literature  	
 21  	 Economics Letters  	
 22  	 Journal of Applied Econometrics  	
 23  	 Journal of Economic Dynamics and Control  	
 24  	 Journal of Labor Economics  	
 25  	 Journal of Environmental Economics  	
 26  	 Rand Journal of Economics  	
 27  	 Scandinavian Journal of Economics  	
 28  	 Journal of Financial Economics  	
 29  	 Oxford Bulletin of Economics and Statistics	
 30  	 Journal of International Economics  	
 31  	 Journal of Mathematical Economics  	
 32  	 Journal of Economic Behavior and Organization  	
 33  	 Social Choice and Welfare  	
 34  	 American Journal of Agricultural  	
 35  	 International Journal of Game Theory  	
 36  	 Economic Inquiry  	
 37  	 World Bank Economic Review  	
 38  	 Journal of Risk and Uncertainty  	
 39  	 Journal of Development Economics  	
 40  	 Land Economics  	
 41  	 International Monetary Fund Staff Papers  	
 42  	 Canadian Journal of Economics—Revue Canadienne d’Economique  	
 43  	 Public Choice  	
 44  	 Theory and Decision  	
 45  	 Economica  	
 46  	 Journal of Urban Economics  	
 47  	 International Journal of Industrial Organization  	
 48  	 Journal of Law Economics and Organization  	
 49  	 Journal of Law and Economics  	
 50  	 National Tax Journal  	
 51  	 Journal of Industrial Economics  	
 52  	 Journal of Economic History  	
 53  	 Oxford Economic Papers  	
 54  	 Journal of Comparative Economics  	
 55  	 World Development  	
 56  	 Southern Economic Journal  	
 57  	 Explorations In Economic History  	
 58  	 Economic Record  	
 59  	 Journal of Banking and Finance  	
 60  	 Contemporary Economic Policy  	
 61  	 Journal of Population Economics  	
 62  	 Journal of Financial and Quantitative Analysis  	
 63  	 Journal of Institutional and Theoretical Economics  	
 64  	 Applied Economics  	
 65  	 Scottish Journal of Political Economy  	
 66  	 Journal of Economics‑Zeitschrift fur Volkwirtshaft und Socialpolitik	
 67  	 Journal of Macroeconomics  	
 68  	 Review of Income and Wealth  	
 69  	 Oxford Review of Economic Policy  	
 70  	 Europe‑Asia Studies  	
 71  	 Journal of Health Economics  	
 72  	 Regional Science and Urban Economics  	
 73  	 Journal of Economics and Management Strategy  	
 74  	 World Economy  	
 75  	 Small Business Economics  	
 76  	 Economic History Review  	
 77  	 Cambridge Journal of Economics  	
 78  	 World Bank Research Observer  	
 79  	 Energy Journal  	
 80  	 Weltwirtschaftliches Archiv  	
 81  	 Kyklos  	
 82  	 Australian Economic History Review  	
 83  	 Ecological Economics  	
 84  	 Review of Industrial Organization  	
 85  	 Geneva Papers On Risk and Insurance  	
 86  	 Journal of Transport Economics and Policy  	
 87  	 Economics and Philosophy  	
 88  	 Journal of Accounting and Economics  	
 89  	 Resource and Energy Economics  	
 90  	 Journal of the Japanese and International Economies	
 91  	 Journal of Agricultural and Resource Economics  	
 92  	 Brookings Papers On Economic Activity  	
 93  	 Economic Development and Cultural Change  	
 94  	 Communist Economies and Economic Transformation  	
 95  	 Journal of Regulatory Economics  	
 96  	 Journal of Housing Economics  	
 97  	 Manchester School  	
 98  	 Economic Modelling  	
 99  	 Journal of Policy Modeling  	
 100  	 Developing Economies  	
 101  	 Journal of Productivity Analysis  	
 102  	 Canadian Journal of Agricultural Economics  	
 103  	 Australian Journal of Agricultural and Resource  Economics  	
 104  	 Journal of Risk and Insurance  	
 105  	 Japan and The World Economy  	
 106  	 Review of Black Political Economy  	
 107  	 Journal of Economic Psychology  	
 108  	 Journal of Economic Issues  	
 109  	 Economics of Education Review  	
 110  	 Open Economies Review  	
 111  	 Journal of Agricultural Economics  	
 112  	 Journal of Economic Education  	
 113  	 Journal of Post Keynesian Economics  	
 114  	 Journal of Real Estate Finance and Economics  	
 115  	 European Review of Agricultural Economics  	
 116  	 Jahrbucher Fur Nationalokonomie  	
 117  	 Journal of Evolutionary Economics  	
 118  	 History of Political Economy  	
 119  	 Food Policy  	
 120  	 Real Estate Economics  	
 121  	 Health Economics  	
 122  	 Post‑Soviet Affairs  	
 123  	 China Economic Review  	
 124  	 Insurance Mathematics and Economics  	
 125  	 Review of Social Economy  	
 126  	 Defence and Peace Economics  	
 127  	 Bulletin of Indonesian Economic Studies  	
 128  	 Revue Economique  	
 129  	 Post‑Soviet Geography and Economics  	
 130  	 International Review of Law and Economics  	
 131  	 Work Employment and Society  	
 132  	 Economic Geography  	
 133  	 Economics of Planning  	
 134  	 Eastern European Economics  	
 135  	 Journal of World Trade  	
 136  	 Futures  	
 137  	 Applied Economics Letters  	
 138  	 Energy Economics  	
 139  	 Journal of Developing Areas  	
 140  	 Agricultural and Resource Economics Review  	
 141  	 Hitotsubashi Journal of Economics  	
 142  	 American Journal of Economics and Sociology  	
 143  	 New England Economic Review  	
 144  	 Economy and Society  	
 145  	 Revue d’Etudes Comparatives Est‑Ouest  	
 146  	 Politicka Ekonomie  	
 147  	 Japanese Economy  	
 148  	 Betriebswirtschaftliche Forschung  	
 149  	 Desarrollo Economico  	
 150  	 Economic and Social Review  	
 151  	 Economic Development Quarterly  	
 152  	 Ekonomicky Casopis  	
 153  	 Journal of Media Economics  	
 154  	 Journal of Taxation  	
 155  	 Nationalokonomisk Tidsskrift  	
 156  	 Problems of Economic Transition  	
 157  	 South African Journal of Economics  	
 158  	 Tijdschrift Voor Economische en Management  	
 159  	 Trimestre Economico