1. Public disclosure of tax information: Compliance tool or social network?. (2022). Vatto, Trine Engh ; Slemrod, Joel ; Reck, Daniel.
   In: Journal of Public Economics.
   RePEc:eee:pubeco:v:212:y:2022:i:c:s0047272722001104.

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2. Taxation officers’ grassroots work experience and tax performance: Evidence from China. (2022). Xing, Fei ; Liu, Feng ; Qi, YU ; Chen, Gao.
   In: Journal of Asian Economics.
   RePEc:eee:asieco:v:80:y:2022:i:c:s1049007822000239.

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3. Water the flowers you want to grow? Evidence on private recognition and donor loyalty. (2021). Dwenger, Nadja ; Bittschi, Benjamin ; Rincke, Johannes.
   In: European Economic Review.
   RePEc:eee:eecrev:v:131:y:2021:i:c:s0014292120302543.

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4. On the determinants and effects of corporate tax transparency: Review of an emerging literature. (2020). Spengel, Christoph ; Muller, Raphael ; Vay, Heiko.
   In: ZEW Discussion Papers.
   RePEc:zbw:zewdip:20063.

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5. Does Cutting the Tax Rate to Zero Induce Behavior Different from Other Tax Cuts? Evidence from Pakistan. (2020). Waseem, Mazhar.
   In: The Review of Economics and Statistics.
   RePEc:tpr:restat:v:102:y:2020:i:3:p:426-441.

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6. Improving Tax Compliance without Increasing Revenue: Evidence from Population-Wide Randomized Controlled Trials in Papua New Guinea. (2020). Sinning, Mathias ; Hoy, Christopher ; McKenzie, Luke.
   In: IZA Discussion Papers.
   RePEc:iza:izadps:dp13407.

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1. Abadie, Alberto, Athey, Susan, Imbens, Guido W, & Wooldridge, Jeffrey. 2017. When should you adjust standard errors for clustering? Tech. rept. National Bureau of Economic Research.

2. Akerlof, George A., & Kranton, Rachel E. 2000. Economics and Identity. Quarterly Journal of Economics, 115(3), 715–753.

3. Albert, Steven M, & Duffy, John. 2012. Differences in risk aversion between young and older adults. Neuroscience and neuroeconomics, 2012(1).
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4. Allingham, Michael G., & Sandmo, Agnar. 1972. Income Tax Evasion: A Theoretical Analysis. Journal of Public Economics, 1, 323–338.

5. Andreoni, James, Erard, Brian, & Feinstein, Jonathan. 1998. Tax Compliance. Journal of Economic Literature, 36(2), 818–860.

6. Average Log Change in Tax Paid 40 60 80 100 120 140 160 180 200 220 240 260 280 300 Rank in Year t 2006−08 2009−11 2012−14 B: Pre-program Vs. Post-program −2 −1 0 1 Average Log Change in Tax Paid 40 60 80 100 120 140 160 180 200 220 240 260 280 300 Rank in Year t Pre−reform Post−Reform Notes: The figure explores the response to the TPHC program. We rank taxpayers in each of the four categories—self-employed, wage-earners, partnerships, and corporations—on the basis of tax paid by them in period t, group them into 20 rank bins, and plot the average log change in tax paid from period t to t + 1 in the bin as a function of the rank in period t. Panel A takes the average over three-year periods; Panel B over the entire pre- and post-program periods. The upper bound of the bin is always included in the bin. For example, the bin indicated by 40 includes 21-40 ranked taxpayers of each category. The vertical line demarcates the eligibility cutoff of the program.
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7. Bø, Erlend E., Slemrod, Joel, & Thoresen, Thor O. 2015. Taxes on the Internet: Deterrence Effects of Public Disclosure. American Economic Journal: Economic Policy, 7(1), 36–62.

8. Bénabou, Roland, & Tirole, Jean. 2010. Individual and corporate social responsibility. Economica, 77(305), 1–19.

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11. Blank, Joshua D. 2014. Reconsidering Corporate Tax Privacy. New York University Journal of Law & Business, 11, 31.
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12. Borghans, Lex, Heckman, James J, Golsteyn, Bart HH, & Meijers, Huub. 2009. Gender differences in risk aversion and ambiguity aversion. Journal of the European Economic Association, 7(2-3), 649–658.

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15. Center for Investigative Reporting in Pakistan. 2012. Representation Without Taxation. Tech. rept.
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16. Chetty, Raj. 2009. Is the Taxable Income Elasticity Sufficient to Calculate Deadweight Loss? The Implications of Evasion and Avoidance. American Economic Journal: Economic Policy, 1(2), 31–52.

17. Cyan, Musharraf R., Koumpias, Antonios M., & Martinez-Vazquez, Jorge. 2017. The effects of mass media campaigns on individual attitudes towards tax compliance; quasi-experimental evidence from survey data in Pakistan. Journal of Behavioral and Experimental Economics, 70, 10 – 22.

18. Del Carpio, Lucia. 2013. Are the Neighbors Cheating? Evidence from a Social Norm Experiment on Property Taxes in Peru Job Market Paper.
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19. Dwenger, Nadja, & Treber, Lukas. 2018. Shaming for tax enforcement: Evidence from a new policy.

20. Dwenger, Nadja, Kleven, Henrik, Rasul, Imran, & Rincke, Johannes. 2016. Extrinsic and Intrinsic Motivations for Tax Compliance: Evidence from a Field Experiment in Germany. American Economic Journal: Economic Policy, 8(3), 203–232.

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22. Fellner, Gerlinde, Sausgruber, Rupert, & Traxler, Christian. 2013. Testing Enforcement Strategies In The Field: Threat, Moral Appeal and Social Information. Journal of the European Economic Association, 11(3), 634–660.

23. Figure A.I: Google Search Interest 0 25 50 75 100 Relative Search Interest Jan12 Jan13 Jan14 Jan15 Jan16 Jan17 Jan18 Time − Month/Year FBR Tax Directory Tax Directory Notes: The figure plots Google Trends data for the monthly search interest in Pakistan for the terms “FBR Tax Directory” and “Tax Directory” from January 2012 to January 2018. The data is normalized by time and location and scaled on a range of 0 - 100 to compare relative popularity. The data point with the highest search queries within the specified time and location is given a score of 100 and other points are scored relative to it. Vertical lines demarcate the months in which the tax directories were released. Directories for tax years 2012, 2013, 2014 and 2015 were released in April 2014, April 2015, September 2016 and August 2017 respectively.
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24. Figure A.II: Special Immigration Counter for TPHC Holders Notes: The figure shows the picture of special immigration counter at the Allama Iqbal International Airport, Lahore. The picture was taken in the summer of 2018.
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25. Glazer, Amihai, & Konrad, Kai. 1996. A Signaling Explanation for Charity. American Economic Review, 86(4), 1019–1028.

26. Gordon, Roger, & Li, Wei. 2009. Tax structures in developing countries: Many puzzles and a possible explanation. Journal of Public Economics, 93(7-8), 855–866.

27. Graham, John R, Hanlon, Michelle, Shevlin, Terry, & Shroff, Nemit. 2013. Incentives for tax planning and avoidance: Evidence from the field. The Accounting Review, 89(3), 991–1023.
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28. Hanlon, Michelle, & Slemrod, Joel. 2009. What does tax aggressiveness signal? Evidence from stock price reactions to news about tax shelter involvement. Journal of Public Economics, 93(1-2), 126–141.

29. Hasegawa, Makoto, Hoopes, Jeffrey L, Ishida, Ryo, & Slemrod, Joel. 2012. The effect of public disclosure on reported taxable income: Evidence from individuals and corporations in Japan. National Tax Journal, 66(3), 571–608.

30. Hoopes, Jeffrey L, Robinson, Leslie, & Slemrod, Joel. 2018. Public tax-return disclosure. Journal of Accounting and Economics, 66(1), 142–162.

31. In columns (6) and (7) we drop the middle part of the distribution: the middle two quartiles in column (6) and the deciles 2-9 in column (7). Panel B reports the results from parallel placebo regressions, where the sample is restricted to tax years 2006 to 2011, with the last two years defined as the post-program years. Standard errors are in parenthesis.
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32. Instead of defining Name Frequency as the number of times a full name appears in the four years of disclosed data (2012-2015), we define it as 4 × the number of times a full name appears in the 2012 disclosed data. We multiply the number of occurrences of a name in 2012 by four to make this alternative definition of Name Frequency more compatible with the one in our baseline specification. Other than this change of definition, the table is constructed exactly similar to Table III. We obtain similar results if we use any other post-disclosure year 2013-2015 in place of 2012 used here to define Name Frequency.
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33. Kettle, Stewart, Hernandez, Marco, Ruda, Simon, & Sanders, Michael. 2016. Behavioral interventions in tax compliance: Evidence from Guatemala. The World Bank.

34. Kleven, Henrik J., Knudsen, Martin B., Kreiner, Claus Thustrup, Pedersen, Søren, & Saez, Emmanuel. 2011. Unwilling or Unable to Cheat? Evidence From a Tax Audit Experiment in Denmark. Econometrica, 79(3), 651–692.

35. Kleven, Henrik Jacobsen, Kreiner, Claus Thustrup, & Saez, Emmanuel. 2016. Why can modern governments tax so much? An agency model of firms as fiscal intermediaries. Economica, 83(330), 219–246.

36. Kopczuk, Wojciech, & Slemrod, Joel. 2006. Putting Firms into Optimal Tax Theory. American Economic Review Papers and Proceedings, 96(2), 130–134.

37. Luttmer, Erzo F. P., & Singhal, Monica. 2014. Tax Morale. Journal of Economic Perspectives, 28(4), 149–168.

38. Malik, Rabia. 2019. Transparency, Elections, and Pakistani Politicians’ Tax Compliance. Comparative Political Studies.
    Paper not yet in RePEc: Add citation now
    

39. Mascagni, Giulia. 2018. From the lab to the field: A review of tax experiments. Journal of Economic Surveys, 32(2), 273–301.

40. Perez-Truglia, Ricardo, & Troiano, Ugo. 2015. Shaming tax delinquents: Theory and evidence from a field experiment in the United States.

41. Perez-Truglia, Ricardo. 2019. The effects of income transparency on well-being: Evidence from a natural experiment. NBER Working Paper No. w25622.

42. Scholz, John T., & Pinney, Neil. 1995. Duty, Fear, and Tax Compliance: The Heuristic Basis of Citizenship Behavior. American Journal of Political Science, 39(2), 490– 512.
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43. Schultz, P Wesley, Nolan, Jessica M, Cialdini, Robert B, Goldstein, Noah J, & Griskevicius, Vladas. 2007. The constructive, destructive, and reconstructive power of social norms. Psychological science, 18(5), 429–434.
    Paper not yet in RePEc: Add citation now
    

44. Slemrod, Joel, Blumenthal, Marsha, & Christian, Charles. 2001. Taxpayer Response to an Increased Probability of Audit: Evidence from a Controlled Experiment in Minnesota. Journal of Public Economics, 79(3), 455–483.

45. Slemrod, Joel. 2007. Cheating Ourselves: The Economics of Tax Evasion. Journal of Economic Perspectives, 21(1), 25–48.

46. Slemrod, Joel. 2019. Tax compliance and enforcement. Journal of Economic Literature, 57(4), 904–54.

47. Table A.V: Intensive Margin Response to the Public Disclosure Program – Alternative Definition of Name Frequency Treat: Name Frequency ≤ 10 ≤ 20 ≤ 30 ≤ 40 (1) (2) (3) (4) (5) (6) (7) (8) A: Main Regression (2006-2015) treat × after 0.098 0.094 0.093 0.092 0.092 0.091 0.091 0.088 (0.006) (0.009) (0.005) (0.008) (0.005) (0.008) (0.005) (0.008) Observations 2,394,847 764,796 2,621,675 837,306 2,704,406 863,405 2,792,270 891,420 B: Placebo Regression (2006-2011) treat × after 0.014 0.010 0.018 0.014 0.018 0.014 0.017 0.013 (0.007) (0.008) (0.006) (0.008) (0.006) (0.008) (0.006) (0.008) Observations 1,288,038 723,868 1,406,460 789,856 1,449,905 814,280 1,496,374 840,469 Sample: Balanced Panel No Yes No Yes No Yes No Yes Individual Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Notes: The table reports the estimates from equation (3). We replicate Table III using an alternative definition of the variable Name Frequency.
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48. Table A.X: Intensive Margin Response to the Public Disclosure Program – By Baseline Taxable Income Baseline Taxable Income: ∈ (0, 100k] ∈ (100k, 200k] ∈ (200k, 300k] ∈ (300k, 400k] ∈ (400k, 500k] ∈ (500k, 600k] (1) (2) (3) (4) (5) (6) A: Main Regression (2006-2015) treat × after 0.075 0.083 0.061 0.058 0.014-0.026 (0.059) (0.018) (0.009) (0.010) (0.028) (0.056) Observations 26,071 197,583 575,312 447,856 60,784 14,442 B: Placebo Regression (2006-2011) treat × after 0.058 0.019 0.005-0.029-0.072-0.069 (0.046) (0.010) (0.021) (0.024) (0.036) (0.078) Observations 44,234 760,496 104,403 38,149 21,214 5,214 Individual Fixed Effects Yes Yes Yes Yes Yes Yes Notes: The table explores how the intensive margin response to the public disclosure program varies across the taxable income distribution.
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49. Table A.XIII: Extensive Margin Response to the Public Disclosure Program – Baseline Frequency Treat: Name Frequency ≤ 10 ≤ 20 ≤ 30 ≤ 40 ≤ Median ≤ 1st Quartile ≤ 1st Decile (1) (2) (3) (4) (5) (6) (7) A: Main Regression (2006-2015) treat × after 0.054 0.046 0.043 0.041 0.039 0.070 0.125 (0.012) (0.011) (0.010) (0.010) (0.010) (0.014) (0.022) B: Placebo Regression (2006-2011) treat × after 0.000-0.000-0.000-0.000-0.001 0.005 0.021 (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.010) Notes: The table conducts a robustness check on our extensive margin results. We replicate Table V using an alternative definition of Name Frequency, measuring it as the number of times a full name appears among the tax filers in the six baseline years 20062011.
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50. Table V: Extensive Margin Response to the Public Disclosure Program Treat: Name Frequency ≤ 10 ≤ 20 ≤ 30 ≤ 40 ≤ Median ≤ 1st Quartile ≤ 1st Decile (1) (2) (3) (4) (5) (6) (7) A: Main Regression (2006-2015) treat × after 0.0117 0.0106 0.0101 0.0097 0.0094 0.0163 0.0265 (0.0027) (0.0024) (0.0023) (0.0022) (0.0022) (0.0041) (0.0089) B: Placebo Regression (2006-2011) treat × after 0.0027 0.0027 0.0026 0.0025 0.0024 0.0038 0.0026 (0.0018) (0.0017) (0.0017) (0.0016) (0.0016) (0.0026) (0.0027) Notes: The table reports the estimates from equation (6). The equation is estimated on a sample of all self-employed individuals.
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51. The definition of the treatment variable is provided in the title of each column. The dummy variable takes the value 1 if the Name Frequency of an individual does not exceed the cutoff indicated in the title. To maintain a fixed control group across columns (1)-(4), we drop taxpayers with the Name Frequency between 10 and 40 in columns (1) to (3). In columns (6) and (7) we drop the middle part of the distribution: the middle two quartiles in column (6) and the deciles 2-9 in column (7). Panel B reports the results from parallel placebo regressions, where the sample is restricted to tax years 2006 to 2011, with the last two years defined as the post-program years. Standard errors are in parenthesis.
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52. The dummy variable takes the value 1 if the Name Frequency of an individual does not exceed the cutoff indicated in the title. To maintain a fixed control group across all columns, we drop taxpayers with Name Frequency between 10 and 40 in Columns (1) to (6). Even-numbered columns restrict the sample to a balanced panel of taxpayers, who file in all years included in the sample. Panel B reports the results from parallel placebo regressions, where the sample is restricted to tax years 2006 to 2011, with the last two years defined as the post-program years. Standard errors are in parenthesis, which have been clustered at the individual level.
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53. The outcome variable here is the log number of filers in group g in year t. Panel A estimates the equation on the period 2006-2015.
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54. Waseem, Mazhar. 2019. Does Cutting the Tax Rate to Zero Induce Behavior Different from Other Tax Cuts? Evidence from Pakistan. Working Paper, University of Manchester.
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55. We multiply this measure of Name Frequency with a factor of 4/6 to make it compatible with the definition used in Table V and our other results. The table reports the estimates from equation (6). The equation is estimated on a sample of all self-employed individuals. The outcome variable here is the log number of filers in group g in year t. Panel A estimates the equation on the period 2006-2015. The definition of the treatment variable is provided in the title of each column. The dummy variable takes the value 1 if the normalized value of Name Frequency of an individual does not exceed the cutoff indicated in the title. To maintain a fixed control group across columns (1)-(4), we drop taxpayers with the Name Frequency between 10 and 40 in columns (1) to (3).
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56. We replicate the specification in Column (7) of Table III restraining the sample to taxpayers whose taxable income in the baseline year (2011) was within the interval indicated in the heading of each column. The treatment variable takes the value 1 if the Name Frequency of an individual does not exceed 40. Panel B reports the results from parallel placebo regressions, where the sample is restricted to tax years 2006 to 2011, with the last two years defined as the post-program years. The baseline year for these regression is 2009. Standard errors are in parenthesis, which have been clustered at the individual level.
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1. Gender gap in STEM entrepreneurship: Effects of the Affordable Care Act reform. (2024). Bao, Jiayi.
   In: Strategic Management Journal.
   RePEc:bla:stratm:v:45:y:2024:i:8:p:1567-1596.

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2. Product digitization and differentiation strategy change: Evidence from the book publishing industry. (2024). Wang, Richard D ; Miller, Cameron D.
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   RePEc:bla:stratm:v:45:y:2024:i:7:p:1241-1272.

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3. Do age of consent laws decrease teen births?. (2022). Lepage, Louispierre.
   In: Canadian Journal of Economics/Revue canadienne d'économique.
   RePEc:wly:canjec:v:55:y:2022:i:3:p:1431-1459.

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4. Beggar-Thy-Neighbour Tax Cuts: Mobility After a Local Income and Wealth Tax Reform in Switzerland. (2021). Martinez, Isabel.
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5. Welfare Impact of Hosting Refugees in Ethiopia. (2020). Ayenew, Ashenafi Belayneh.
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6. Distinguishing potential and effective additionality to revisit the location bias of REDD+ project. (2020). Le Velly, Gwenolé ; Delacote, Philippe ; Simonet, Gabriela.
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7. Financial sector strategies and financial sector outcomes: Do the strategies perform?. (2020). Melecký, Martin ; Podpiera, Anca Maria.
   In: Economic Systems.
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8. The effect of ability matching and differentiated instruction in financial literacy education. Evidence from two randomised control trials. (2020). de Witte, Kristof ; Iterbeke, Kaat ; Schelfhout, Wouter ; Declercq, Koen.
   In: Economics of Education Review.
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9. Inference in Differences-in-Differences with Few Treated Units and Spatial Correlation. (2020). Ferman, Bruno.
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10. Mobile Phones and Mozambique Farmers: Less Asymmetric Information and More Trader Competition?. (2019). Zant, Wouter.
    In: Tinbergen Institute Discussion Papers.
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11. A free rider problem? The effect of electric vehicles on urban toll prices in Norway. (2019). Krehic, Lana.
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12. Information Source and Cigarettes: Experimental Evidence on the Messenger Effect. (2019). Maclean, Johanna ; Marti, Joachim ; Buckell, John.
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13. Peer Diversity, College Performance and Educational Choices. (2019). Isphording, Ingo ; Lisauskaite, Elena ; Chevalier, Arnaud.
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14. Mobilization Effects of Multilateral Development Banks. (2019). Stucchi, Rodolfo ; Presbitero, Andrea ; Lotti, Giulia ; Maffioli, Alessandro ; Broccolini, Chiara.
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15. Drought in the city: The economic impact of water scarcity in Latin American metropolitan areas. (2019). Rodella, Aude-Sophie ; Desbureaux, Sebastien.
    In: World Development.
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16. Joint culpability: The effects of medical marijuana laws on crime. (2019). Townsend, Wilbur ; Chu, Yu-Wei Luke.
    In: Journal of Economic Behavior & Organization.
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17. The economic impact of universities: Evidence from across the globe. (2019). van Reenen, John ; VanReenen, John ; Valero, Anna.
    In: Economics of Education Review.
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18. NFL Salary Cap Allocation: Matching Theory with Observed Behavior. (2019). Potts, Todd ; Jeffords, Chris.
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19. Macroeconomic Effects of Debt Relief: Consumer Bankruptcy Protections in the Great Recession. (2019). Goldsmith-Pinkham, Paul ; Auclert, Adrien ; Dobbie, Will.
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20. The introduction of formal insurance and its effect on redistribution. (2019). Marsink, Karlijn ; Anderberg, Dan.
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21. Dispelling Misconceived Beliefs about Rent Control: Insights from a Field and a Laboratory Experiment. (2019). Lopez-Mayan, Cristina ; Panades, Judith ; Busom, Isabel ; Brandts, Jordi.
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22. A simple way to assess inference methods. (2019). Ferman, Bruno.
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23. A Nonparametric Dynamic Causal Model for Macroeconometrics. (2019). Shephard, Neil ; Rambachan, Ashesh.
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24. Achievement rank affects performance and major choices in college. (2018). Zölitz, Ulf ; Isphording, Ingo ; Elsner, Benjamin ; Zolitz, Ulf.
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25. Government-made bank distress: Industrialisation policies and the Russian financial crisis of 1899-1902. (2018). Lychakov, Nikita.
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26. Move a Little Closer? Information Sharing and the Spatial Clustering of Bank Branches. (2018). Ongena, Steven ; De Haas, Ralph ; Straetmans, Stefan ; Ongena, S. R. G., ; Qi, Shusen.
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27. Exposición al comercio internacional e ingresos laborales en Bolivia: evidencia al nivel del trabajador. (2018). Zuazo, Rodrigo Gonzales ; Molina, Jose Miguel.
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28. Weather Shocks and Climate Change. (2018). Gourio, Francois ; Fries, Charles.
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29. Notes on Bonds: Illiquidity Feedback During the Financial Crisis. (2018). Musto, David ; Schwarz, Krista ; Nini, Greg.
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30. What Do Workplace Wellness Programs Do? Evidence from the Illinois Workplace Wellness Study. (2018). Reif, Julian ; Jones, Damon ; Molitor, David.
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31. Do Black Politicians Matter?. (2018). Logan, Trevon.
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32. International Migration Intentions and Illegal Costs: Evidence from Africa-to-Europe Smuggling Routes. (2018). Prarolo, Giovanni ; Mendola, Mariapia ; Manchin, Miriam ; Friebel, Guido.
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33. The Impact of Xenophobic Violence on the Integration of Immigrants. (2018). Steinhardt, Max.
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34. The Impact of the Announcement of Temporary Building Sites for Refugees on House Prices in Gothenburg. (2018). van Vuuren, Aico ; Nilsson, Viktor ; Kjellander, Josef.
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35. The effect of a mystery shopper scheme on prescriptions in primary care. (2018). Godager, Geir ; Wang, Jian ; Liu, Rugang ; Cheo, Roland.
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36. Technical education, noncognitive skills and labor market outcomes: experimental evidence from Brazil. (2018). Souza, André ; Fernandes, Andre Portela ; Russo, Flavio Luiz ; Silva, Lycia ; Camargo, Juliana.
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37. Dying to win? Olympic Gold medals and longevity. (2018). Leive, Adam.
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38. Encountering female politicians. (2018). Joo, Hailey Hayeon ; Lee, Jungmin.
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39. Parental human capital and child health at birth in India. (2018). Nazmul, MD ; Maharaj, Riddhi.
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40. Trade in tasks and the organization of firms. (2018). Tarasov, Alexander ; Schymik, Jan ; Marin, Dalia.
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41. The impacts of workers remittances on human capital and labor supply in developing countries. (2018). Azizi, Seyedsoroosh.
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42. Incentives to (not) Disclose Energy Performance Information in the Housing Market. (2018). Dressler, Luisa ; Cornago, Elisabetta.
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43. The Power to Protect: Household Bargaining and Female Condom Use. (2018). Janssens, Wendy ; Morsink, Karlijn ; Bruinderink, Marije Groot ; Cassidy, Rachel.
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44. The Organization of International Trade. (2018). Stähler, Frank ; Stahler, Frank ; Boddin, Dominik.
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45. Digital Disintermediation and Efficiency in the Market for Ideas. (2018). Reimers, Imke ; Peukert, Christian.
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46. Robots and reshoring: Evidence from Mexican local labor markets. (2018). Faber, Marius.
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47. The Impact of Exports on Innovation: Theory and Evidence. (2018). Melitz, Marc ; Lequien, Matthieu ; Bergeaud, Antonin ; Philippe, Antonin Bergeaud.
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48. Is wealth found in the soil or brain? Investing in farm people in Malawi. (2018). Mkondiwa, M.
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49. Farmer Valuation of Improved Bean Seed Technologies: Real Auction Evidence from Tanzania. (2018). Maredia, Mywish ; Mason, Nicole M ; Morgan, Stephen N.
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50. .

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