rafmacalaba/datause-encoder-data huggingface dataset:
|Belarus Privatization<br>Tatiana Segal|
|---|
|Belarus Privatization<br>Tatiana Segal|
|Belarus Privatization<br>Tatiana Segal|
|Bangladesh Ntional Strategyfor the Development of Statistics<br>Syed Khaled Ahsan|
|Bangladesh Ntional Strategyfor the Development of Statistics<br>Syed Khaled Ahsan|
|Bangladesh Ntional Strategyfor the Development of Statistics<br>Syed Khaled Ahsan|
|Bangladesh Ntional Strategyfor the Development of Statistics<br>Syed Khaled Ahsan|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|National CommunityEmpowerment Program In Urban Areas For 20Evi Hermirasari|
|Armenia - 2011 Census Multi Donor Trust Fund<br>Ramya Sundaram|
|Armenia - 2011 Census Multi Donor Trust Fund<br>Ramya Sundaram|
|FATA Urban Centers Project(FUCP)<br>Shahnaz Arshad|
|FATA Urban Centers Project(FUCP)<br>Shahnaz Arshad|
|FATA Urban Centers Project(FUCP)<br>Shahnaz Arshad|
|FATA Urban Centers Project(FUCP)<br>Shahnaz Arshad|
|FATA Urban Centers Project(FUCP)<br>Shahnaz Arshad|
|FATA Urban Centers Project(FUCP)<br>Shahnaz Arshad|
|FATA Urban Centers Project(FUCP)<br>Shahnaz Arshad|
|FATA Urban Centers Project(FUCP)<br>Shahnaz Arshad|
|Monitoring& CommunicatingResults: Capacity-Buildingfor the OffiMatthew Stephens|
|Monitoring& CommunicatingResults: Capacity-Buildingfor the OffiMatthew Stephens|
|PreparatoryWork for 2014 Georgia National Population Census<br>Nistha Sinha|
|PreparatoryWork for 2014 Georgia National Population Census<br>Nistha Sinha|
|PreparatoryWork for 2014 Georgia National Population Census<br>Nistha Sinha|
|Economic RecoverySupport Operation<br>Afsaneh Sedghi|
|Economic RecoverySupport Operation<br>Afsaneh Sedghi|
|GPE Management StrengtheningProject<br>Susiana Iskandar|
|GPE Management StrengtheningProject<br>Susiana Iskandar|
|GPE Management StrengtheningProject<br>Susiana Iskandar|
|GPE Management StrengtheningProject<br>Susiana Iskandar|
|GPE Management StrengtheningProject<br>Susiana Iskandar|
|GPE Management StrengtheningProject<br>Susiana Iskandar|
|GPE Management StrengtheningProject<br>Susiana Iskandar|
|Mongolia Global Partnershipfor Education EarlyChildhood EducatioPrateek Tandon|
|Mongolia Global Partnershipfor Education EarlyChildhood EducatioPrateek Tandon|
|Mongolia Global Partnershipfor Education EarlyChildhood EducatioPrateek Tandon|
|Mongolia Global Partnershipfor Education EarlyChildhood EducatioPrateek Tandon|
|Mongolia Global Partnershipfor Education EarlyChildhood EducatioPrateek Tandon|
**<mark>Table 6 – Impact of FD Law on Bank Lending to Local Governments and Firms Given Bank Exposure and State Public Debt</mark>** <mark>This table reports OLS estimates of the impact of the FD Law on the lending volumes issued to local governments (Panel A) and private</mark> firms (Panel B) in states of varying ex ante public indebtedness by banks of varying ex ante exposure to local public debt. The variable _Loans-Govts,b,m_ is the total bank lending (in logs) of state government _s_ with bank _b_ in month _m_ . _Loans-Firmsf,b,m_ is the bank lending (in logs) of firm _f_ extended by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public  indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _BankExposureGovs,b,Mar16_ is a measure of the exposure of a bank to local public entities of a state in the month prior to the reform. It is calculated as the ratio of loans extended by bank _b_ to state government _s_ over bank _b_ ’s total lending. _Postm_ is an indicator variable that equals one from April 2016 onwards. In Panel A, observations are at the state-bank-month level. Borrower fixed effects correspond to fixed effects at the state-level. In Panel B, observations are at the firm-bankmonth level.  Borrower fixed effects correspond to fixed effects at the firm-level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels.  Detailed variable definitions are <u>provided in Table A1 in the Appendix.</u>

|provided in Table A1 in the Appendix.|Pane<br>|l A. Loans-Go<br>|vts,b,m<br>|Panel<br>|B. Loans-Firm<br>|sf,b,m<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm*DebtStates,16Q1*BankExposureGovs,b,Mar16|-1.87***|-2.89***|-4.61***|0.189*|0.279*|0.734*|
||(0.56)|(0.64)|(1.09)|(0.113)|(0.142)|(0.391)|
|Postm*DebtStates,16Q1|-0.08|0.15||0.016***|0.022***||
||(0.12)|(0.11)||(0.006)|(0.006)||
|Postm*BankExposureGovs,b,Mar16|1.33***|1.65***|2.81***|-0.224|-0.363*|-1.263**|
||(0.29)|(0.35)|(0.44)|(0.200)|(0.199)|(0.539)|
|Observations|7,568|7,512|7,499|1,136,616|1,136,616|469,977|
|R-squared|0.78|0.80|0.85|0.798|0.798|0.897|
|Bank-Borrower FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month FE|Yes|-|-|-|-|-|
|Bank-Month FE|No|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|-|-|-|No|Yes|-|
|Borrower-Month FE|No|No|Yes|No|No|Yes|

53
|With SGRY control<br>6.873_∗∗∗_<br>7.052_∗∗∗_<br>[2.438]<br>[2.202]<br>5.035_∗∗_<br>5.261_∗∗_<br>[2.254]<br>[2.049]<br>59130<br>59130<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>With SGRY control<br>6.621_∗∗∗_<br>7.487_∗∗∗_<br>[2.400]<br>[2.162]<br>3.692<br>4.558_∗∗_<br>[2.284]<br>[2.061]<br>59130<br>59130<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>or SGRY from 2004 to 2007. Program ex-<br>egressions include district and year-quarter<br>dependent variable in panel B is the log of<br>y 2004 to June 2005 and from July 2007 to<br>m the Indian Labour Bureau._NREGAd,t_<br>le equal to one for the frst two quarters of<br>include dummy variables for gender, age<br>tivity, fraction of workers who are catego-<br>District controls that do not vary over time<br>hts. The standard errors in square brackets<br>ployment Guarantee Act.|
|---|
|**Panel A. Dependent Variable: Nominal Earnings**<br>Without SGRY control<br>_NREGAd,t × Dry_<br>4.497_∗∗_<br>4.310_∗_<br>4.173_∗∗_<br>6.794_∗∗∗_<br>[2.218]<br>[2.225]<br>[2.071]<br>[2.441]<br>_NREGAd,t × Rainy_<br>3.540<br>3.590_∗_<br>3.631_∗_<br>4.835_∗∗_<br>[2.234]<br>[2.157]<br>[1.982]<br>[2.322]<br>Observations<br>59130<br>59130<br>59130<br>59130<br>Worker Controls<br>No<br>Yes<br>Yes<br>No<br>District Controls FEs<br>No<br>No<br>Yes<br>No<br>SGRY Control<br>No<br>No<br>No<br>Yes<br>**Panel B. Dependent Variable: Real Earnings**<br>Without SGRY control<br>_NREGAd,t × Dry_<br>4.494_∗∗_<br>4.308_∗∗_<br>4.909_∗∗_<br>6.540_∗∗∗_<br>[2.175]<br>[2.176]<br>[2.016]<br>[2.411]<br>_NREGAd,t × Rainy_<br>2.345<br>2.389<br>3.099<br>3.498<br>[2.257]<br>[2.184]<br>[1.987]<br>[2.351]<br>Observations<br>59130<br>59130<br>59130<br>59130<br>Worker Controls<br>No<br>Yes<br>Yes<br>No<br>District Controls FEs<br>No<br>No<br>Yes<br>No<br>SGRY Control<br>No<br>No<br>No<br>Yes<br>_Source_: Author’s analysis based on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data f<br>penditure data is from the Ministry of Rural Development, Government of India.<br>_Notes_: All estimates are of the order10_−_2. Each column presents results from a separate regression. All r<br>fxed effects. The dependent variable in panel A is the log of nominal daily earnings in casual work. The<br>real daily earnings in casual work. The sample is composed of all adults aged 18 to 60 interviewed from Jul<br>June 2008. Real earnings are defated using the monthly, state-level price index for agricultural labourers fro<br>is a dummy variable equal to one for early districts during July 2007 to June 2008. Dry is a dummy variab<br>the year. Rainy is a dummy variable equal to one for the second two quarters of the year. Worker controls<br>group, education levels, caste, religion and marital status. District controls are rainfall, agricultural produc<br>rized as casual (agriculture) casual (non-agriculture), cultivators, business (non-agriculture), and salaried. <br>are interacted with a dummy for 2007-08 (post-NREGA). All estimates are computed using sampling weig<br>are clustered at the district level. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Em<br>_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p <_0_._01|
**Table 3: Programs covered by National Property Fund, 1993-98 (CZK bn)**

||1993|1994|1995|1996|1997|1998|
|---|---|---|---|---|---|---|
|Financing environment rehabilitation|0.01|0.1|0.8|1.0|1.4|2.1|
|Financing the development of railway route||||0.1|0.01|0.2|
|Support to state-owned enterprises (including liability<br>clean up)|2.1|_0.5_|0.9|0.3|0.2|0.3|
|Support to agricultural businesses||6.1|1.0||||
|Bond Interest|2.1|_1.5_|1.6|_0.5_|0.4|_0.5_|
|National Property<br>Fund's<br>"hidden"<br>fiscal deficit<br>(excluding transfers to KOB, CI and CF and transfers<br>according to state budget law)|4.2|8.2|4.3|1.9|2.0|2.9|
|Others, already included in hidden deficit calculation:|||||||
|Health insurance companies (through KOB)'||||0.8|0.4|0.4|
|Support to aviation companies (through KOB)'|||||0.1|0.02|
|Provisions to Ceska Inkasni (CI) '|||3.4|10.3|_5.5_|6.0|
|Stabilization program of CF (through KOB)'|||||0.6|1.8|
|Others, included in the reported budget deficit:|||||||
|Transfers according to state budget law|9.5|19.4|10.7||||

'These items are excluded from the 'true" deficit calculation. National Property Fund's expenditures related to KOB, Cl and CF are accounted for as financing items of these institutions.

Source: National Property Fund's Annual Reports The impact of guarantees on the hidden deficit is estimated as the net implicit subsidy provided through guarantees in a given year from the portfolio of guarantees issued in that year, or the potential fiscal cost of government obligations, which will emerge from the guarantee in the future. If the amount of this subsidy had been transferred to a guarantee reserve fund the same year the guarantee was issued, it would have served to cover potential future claims emerging from the guarantee. The cost of default would be paid from the guarantee reserve fund and thus would not affect the budget and the deficit.

spending by the special institutions and through guarantees issued directly by the state, but not guarantees issued by special institutions.

12
<mark>Palmisano, F., & Peragine, V. (2022). Inequality of Opportunity: Theoretical Considerations and Recent Empirical Evidence. In</mark> _<mark>Advances in Economic Measurement: A Volume in Honour of DS Prasada Rao</mark>_ <mark>(pp. 349386). Singapore: Springer Nature Singapore.</mark> <mark>Roemer, J. E. (1993). A Pragmatic Theory of Responsibility for the Egalitarian Planner.</mark> _<mark>Philosophy & Public Affairs</mark>_ <mark>, 146-166.</mark> <mark>Roemer, J. E. (1998). Equality of Opportunity. Harvard University Press, Cambridge, MA.</mark> Tetteh-Baah, S., K. Harttgen, R. Lahoti, I. Gunther. (2024). Horizontal Inequalities in Africa. _Review of Income and Wealth_ (Early View).

Thorbecke, E. (2023). The Interrelationships Among Growth, Inequality and Poverty in Africa. _Journal of African Economies_ 32, ii81–ii86.

<mark>Van De Gaer, D. (1993). Equality of Opportunity and Investment in Human Capital.</mark> <mark>Van der Weide, R., Lakner, C., Mahler, D. G., Narayan, A., & Gupta, R. (2024). Intergenerational Mobility Around the World: A New Database.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>166</mark>_ <mark>, 103167.</mark> World Bank. (2022a). _Poverty and Shared Prosperity 2022: Correcting Course_ . The World Bank.

<mark>World Bank. (2022b).</mark> _<mark>Inequality in Southern Africa: An Assessment of the Southern African Customs Union</mark>_ <mark>. The World Bank.</mark> <mark>World Bank. (2023).</mark> _<mark>Expanding Opportunities: Toward Inclusive Growth.</mark>_ <mark>The World Bank.</mark> <mark>Wu, H., Atamanov, A, Bundervoet, T. and Paci, P. (2024).</mark> _<mark>The Growth Elasticity of Poverty: Is Africa Any Different?</mark>_ <mark>Policy Research Working Paper Series 10690. The World Bank.</mark> 22
- 20 -

composition and growth rates--as in Figure 2--disguise the fact that only active tonnage is registered. Adding laid-up vessels would result in different indicators.

30. As the decline of seatrade volumes continued in the mid 1980s, a number of owners camne around to realize that eliminating some of their shipping assets through demolition would be commercially prudent. Thus the tonnage sola for scrapping increased to over 30 million dwt between 1985 and 1986. But as soon as the market seemed to give positive indications through impressive advances in the Baltic Freight Index--which reflects developments in the freight futures market--during 1987, the number of demolition sales shrank. At the same time the number of newbuilding contracts increased sharply (see Figure 4 on below). These attitudes and practices point to the fact that the ocean transport industry distinguishes itself from other industries through very speculative behavior. But the high level of periodic investments in shipping, the value of existing assets, and its debt servicing obligations make ocean transport one of the most capital-intensive of all industries. Substantial freight revenues are needed to keep the ocean transport industry viable and thus in a position to provide the type of services which are required by the changing level and structure of international demand for the sea carriage of goods.

<!-- Start of picture text --> The World Merchant Fleet<br>Newcomers and Departures<br>Newbuilding Contracts Demolitlon Sales<br>nillmon dwt millon dwt<br>40 - - 40 v<br>35 35<br>30 30JO<br>25 - 25 l<br>General Caroo Ships<br>20 -20 B ulk CarrtIer<br>(~Tanker.<br>15 - 15<br>10. 10<br>1985 1988 1987 198R 1989 1990 1991 1992 1985 1986 1987 1988 1989 1990 1991 1992<br>Source: Institute of Shipping Economics and Logistics. and Fearnleys<br>Figure 4<br><!-- End of picture text --> 31. However freight rates have been highly volatile since the early 1980s. When seatrade declined and essentially the same number of ships remained available for lower overall volumes of cargo, both phenomena combined to spark intense competition which provoked freight rates to contract substantially.
of men upon migration are higher than those of women, but also more volatile. The difference between men’s and women’s expected wages is even larger if they remain in the home village, while the difference in variance remains similar. In terms of covariance between migrant’s and spouse’s wages, men’s migration is associated with higher risk. Finally, when combining the expected wages and the associated risk as in expressions (1) and (2), the difference between the two portfolios becomes much less clear with male migration being slightly less remunerative and riskier.

**Table 7.** Expected Returns and Risk from Migration Portfolios (Log).

||**M**<br>|**en**<br>|**Wo**<br>|**men**<br>|||
|---|---|---|---|---|---|---|
||(1)<br>Full<br>sample|(2)<br>Restricted<br>sample|(3)<br>Full<br>sample|(4)<br>Restricted<br>sample|(5)<br>Difference<br>(1)-(3)|(6)<br>Difference<br>(2)-(4)|
|(<br>)<br>_d_<br>_i_<br>_E w_|16.218|16.205|15.945|16.067|0.273***|0.138***|
||(0.516)|(0.403)|(0.361)|(0.353)|||
|(<br>)<br>_h_<br>_i_<br>_E w_|16.056|15.964|15.641|15.647|0.415***|0.317***|
||(0.494)|(0.490)|(0.614)|(0.604)|||
|(<br>)<br>_d_<br>_i_<br>_Var w_|16.390<br>(0.881)|16.345<br>(0.766)|15.912<br>(0.556)|15.934<br>(0.529)|0.478***|0.411***|
|(<br>)<br>_d_<br>_i_<br>_Var w_|16.133|16.007|15.723|15.779|0.41***|0.228***|
||(0.731)|(0.734)|(0.766)|(0.834)|||
|(<br>,<br>)<br>_d_<br>_h_<br>_i_<br>_i_<br>_Cov w_<br>_w_<br>|<sup>32.051</sup>|<sup>32.094</sup>|32.030|31.928|0.021***|0.166***|
||(1.235)|(1.110)|(0.903)|(0.947)|||
|(<br>)<br>_i_<br>_E R_|16.722|16.719|16.739|16.755|-0.017***|-0.036*|
||(0.408)|(0.365)|(0.347)|(0.336)|||
|_σi_|16.870<br>(0.788)|16.905<br>(0.728)|16.799<br>(0.567)|16.744<br>(0.562)|0.071***|0.161***|
|_N_|13507|337|13457|337|||

**Notes:** Standard deviations reported in parenthesis. *** p<0.01, ** p<0.05, * p<0.1

The first stage specification for selection into female migration will include relative expected returns of the two migration portfolios as suggested in proposition 1, but also relative risk of the two portfolios and risk aversion of the household. The latter will be included as a control variable on its own and then as an instrumental variable when interacted with the level of risk: 17
<!-- Start of picture text --> A. 1993 B. 1998 A<br>, BR V a5 ° St - : Nae: . xs ° St _<br>PAR¥y¥ Yee ~ ZL? I alAED)Ve . Pesa, PmZAVG<br>C. 2003 D. 2012<br>“2 Wasea Nd<br>oy five o : ¢ Le : ‘ (ame<br>——— Multi-Lanesi-| _=<to og 4 a Ai ; . " 5 iDeeww sOss /.A<br>Two Lanes 4 fc 7A 4 ¢ ,<br>— Divided ~ wae Bay dn <8<br>Pavement Ny ~~<br><!-- End of picture text -->
- Jihad, D., Gottschalk, J., and Portillo, R. (2012). Oil windfalls in ghana: A dsge approach. _Journal of African Economies_ .

- Kamps, C. (2004). The dynamic macroeconomic effects of public capital. _Berlin, Bermany: Springer_ .

- Kopoin, A., Moran, K., and Pare, J. P. (2013). Bank capital, credit market frictions and international shocks transmission. _Mimeo, Universit ́e Laval_ .

- Lartey, E. K. (2008). Capital inflows, dutch disease effects, and monetary policy in a small open economy. _Review of International Economics, 16_ , pages 971–89.

- Lundgren, C., Thomas, A., and York, R. (2013). Boom, bust, or prosperity? managing subsaharan africa’s natural resource wealth. _International Monetary Fund, Washington DC_ .

- Maliszewski, W. (2009). Fiscal policy rules for oil-producing countries: A welfare-based assessment. _Working Paper No. 09/126, International Monetary Fund_ .

- Matsen, E. and Torvik, R. (2005). Optimal dutch diseas. _Journal of Development Economics, 78_ , pages 494–515.

- Obstfeld, M. and Rogoff, K. (2000). New directions in stochastic open economy models. _Journal of International Economics_ , pages 117–153.

- Pieschacn, A. (2012). The value of fiscal discipline in oil-exporting countries. _Journal of Monetary Economics, 59_ , pages 250–68.

- Pieschacon, A. (2011). The value of fiscal discipline in oil-exporting countries. _Journal of Monetary Economics, vol. 59_ , pages 250–268.

- Pritchett, L. (2000). The tyranny of concepts: Cudie (cumulated, depreciated, investment effort) is not capital. _Journal of Economic Growth, 5_ , pages 361–84.

- Sachs, J. and Warner, A. (1999). The big push, natural resource booms and growth. _Journal of Development Economics_ .

53
Benin, S., Nkonya, E., Okecho, G., Pender, J., Nahdy, S., Mugarura, S., Kato, E., Kayobyo, G. (2007). _Assessing the Impact of the National Agricultural Advisory Services (NAADS) in the Uganda Rural Livelihoods_ . IFPRI Discussion Paper 00724.

Birkhaeuser, D., Evenson, R. E., & Feder, G. (1991). The Economic Impact of Agricultural Extension: A Review. _Economic Development and Cultural Change_ , _39_ (3), 607–650.

Bindlish, V., & Evenson, R. (1993). _Evaluation of the Performance of T&V Extension in Kenya_ (Vol. 23). Washington, DC: World Bank.

Cole, S., & Fernando, A. N. (2012). _The value of advice: evidence from mobile phone-based agricultural extension_ (No. 13-047). Harvard Business School Working Paper.

Conley, T. G., & Udry, C. R. (2010). Learning about a New Technology: Pineapple in Ghana. _The American Economic Review_ , _100_ (1), 35–69.

Davis, K. (2008). Extension in sub-Saharan Africa: Overview and assessment of past and current models and future prospects. _Journal of International Agricultural and Extension Education_ , _15_ (3), 15–28.

Davis, K., Nkonya, E., Kato, E., Mekonnen, D. A., Odendo, M., Miiro, R., & Nkuba, J. (2012). Impact of Farmer Field Schools on Agricultural Productivity and Poverty in East Africa. _World Development_ , _40_ (2), 402–413.

Dercon, S, Daniel O. Gilligan, John Hoddinott, and Tassew Woldehanna. (2009) The Impact of Agricultural Extension and Roads on Poverty and Consumption Growth in Fifteen Ethiopian Villages. _American Journal of Agricultural Economics_ , _91_ (4) , 1007–21.

Evenson, R. E. (2001). Chapter 11 Economic impacts of agricultural research and extension. In Bruce L. Gardner and Gordon C. Rausser (Ed.), _Handbook of Agricultural Economics_ (Vol. Volume 1, Part A, pp. 573–628). Elsevier. Retrieved from http://www.sciencedirect.com/science/article/pii/S1574007201100149 FAO (Ed.). (2011). _Women in agriculture: closing the gender gap for development_ . Rome: FAO. Gautam, M. (2000). _Agricultural extension: the Kenya experience : an impact evaluation_ . Washington, D.C.: World Bank.

Gautam, M., & Anderson, J. R. (1999). _Reconsidering the evidence on returns to T&V extension in Kenya_ (Vol. 2098). World Bank Publications Jack, B. K. (2013). _Constraints on the adoption of agricultural technologies in developing countries_ (Literature Review). Agricultural Technology Adoption Initiative, J-PAL (MIT) and CEGA (UC Berkeley).

Kondylis, F., Mueller, V., & Zhu, J. (2017). Seeing is believing? Evidence from an extension network experiment. _Journal of Development Economics_ , _125_ , 1–20.

Kondylis, F., Mueller, V., Sheriff, G., & Zhu, S. (2014). _Policy experiment in Mozambique highlights importance of gender in dissemination of sustainable land management techniques_ (MozSSP Working Paper 7). IFPRI.

Krishnan, P., & Patnam, M. (2014). Neighbors and Extension Agents in Ethiopia: Who Matters More for Technology Adoption? _American Journal of Agricultural Economics_ , _96_ (1), 308–327.

13