rafmacalaba/datause-encoder-data huggingface dataset:
_and Reduce Poverty Across the Distribution of Smallholders in Africa? Quantile Regression Results from Malawi. Paper presented at the International Association of Agricultural Economists (IAAE) Trienni_ . https://doi.org/https://doi.org/10.22004/ag.econ.126742.

- Shigute, Z., Strupat, C., Burchi, F., Alemu, G., & Bedi, A. S. (2019). Linking Social Protection Schemes: The Joint Effects of a Public Works and a Health Insurance Programme in Ethiopia. _Journal of Development Studies_ . https://doi.org/10.1080/00220388.2018.1563682

- Sibande, L., Bailey, A., & Davidova, S. (2017). The impact of farm input subsidies on maize marketing in Malawi. _Food Policy_ , _69_ , 190–206. https://doi.org/10.1016/j.foodpol.2017.04.001

- Smale, M., Thériault, V., Mason, N. M., & Mason, N. M. (2020). Does subsidizing fertilizer contribute to the diet quality of farm women? Evidence from rural Mali. _Food Security_ . https://doi.org/10.1007/s12571-020-01097-w ORIGINAL PAPER Does

- Snapp, S. S., & Fisher, M. (2015). “Filling the maize basket” supports crop diversity and quality of household diet in Malawi. _Food Security_ , _7_ , 83–96. https://doi.org/10.1007/s12571-0140410-0

- Swindale, A., & Bilinsky, P. (2006). _Household Dietary Diversity Score (HDDS) for Measurement of Household Food Access: Indicator Guide (v.2)_ . Washington, D.C.

- Thome, K., Taylor, E. J., & Filipski, M. (2014). _Local Economy Impacts of FISP. Discussion paper, Evaluation of the 2013/14 Farm Input Subsidy Program, Malawi_ . London.

- Tirivayi, N., Knowles, M., & Davis, B. (2016). The interaction between social protection and agriculture: A review of evidence. _Global Food Security_ , _10_ , 52–62. https://doi.org/10.1016/j.gfs.2016.08.004

- Tiwari, S., Daidone, S., Ruvalcaba, M. A., Prifti, E., Handa, S., Davis, B., ... Seidenfeld, D. (2016). Impact of cash transfer programs on food security and nutrition in sub-Saharan Africa: A cross-country analysis. _Global Food Security_ , _11_ , 72–83. <u>https://doi.org/10.1016/j.gfs.2016.07.009</u>

- Vaitla, B., Coates, J., Glaeser, L., Hillbruner, C., Biswal, P., & Maxwell, D. (2017). The measurement of household food security: Correlation and latent variable analysis of alternative indicators in a large multi-country dataset. _Food Policy_ , _68_ , 193-205

34
understand whether these results are driven by the women’s ownership and decision-making power over the land, we rerun the same analysis but by controlling for the number of parcels the female spouse is controlling and the number of parcels where she makes decisions on agricultural activities.<sup>24</sup> The results remain the same for extension outcomes while there are a few differences in the size and significant level of coefficients from the technology tack-up variables. The two main differences are: (1) we find a positive joint effect on the likelihood of the female spouse trying new technology introduced by the DA, and (2) the likelihood of the male spouse trying new technology on farming method is no longer significant when both spouses are targeted.

Since extension services and technology take-up are defined as the mechanisms to increase agricultural productivity (see section 6.1), we rerun the agricultural productivity outcome variables to see if there is any difference after controlling for land ownership and decisionmaking variables.

When both spouses are targeted, the effect on the size of cultivated land is no longer significant at the midline while the decline by the endline becomes statistically significant at 10%. In addition, the value of harvest at the midline is now statistically significant when both spouses are targeted. Finally, the joint effect of the interventions on the value of harvest is now significant both at the midline and endline.<sup>25</sup>

# **8 Conclusion**

We contribute to the literature by showing that whom you target in a married household for agricultural interventions matters for the type of outcomes achieved. We also show that the reason for these different outcomes is that whom you target also determines the channel through which the impact is generated. Our results indicate that to impact agricultural productivity, it is important to target both spouses, which translates into an increase in take-

> 24 Note that the sample size has declined from 2,428 to 2,355 due to missing values for ownership and decisionmaking variables. Of the women in our sample, 68.5% and 72.7% either alone or jointly own the land and make decisions on the land, respectively.

> 25 The results from these regressions are not in the paper but are available on request.

30
From this system it can be seen that “collective” models in which utility functions are defined on market consumption and pure leisure drop the household goods yij. Note also that individual demands in (22a-c) are functions of the domestic goods prices and own wage only. The wage of individual j enters only indirectly, through the price of domestic good j and the income share si. Thus the model does not imply symmetry of cross wage effects with respect to each member’s leisure<sup>32</sup> .

If we estimate a version of this model on data for a sample of two-adult households, and assume a single domestic good produced by the time inputs of both partners, the maximization problem for each adult i, i = f, m, reduces to to yield the demand functions where This system is estimated in Apps and Rees (1996) on data for a sample of households containing two adults, drawn from an Australian Bureau of Statistics (ABS) Time Use Survey. The aim of the study is to show that unless domestic production is an integral part of a demand/labor supply model, a wholly distorted picture of the intra-household distribution of income is obtained. This is because a model that assumes that non-market time is pure leisure implies that married women specializing in domestic work produce nothing for other family members. To make this point, results derived for the model above, labeled an “exchange model”, are compared with those obtained for a system that omits household production and therefore permits income transfers only. The latter is labeled a “transfer model”. Not only do the two models give very different results for the

> and the higher, _ceteris paribus_ , the welfare of the household, as discussed more fully below.

> 32 For detailed analysis of the comparative statics of this kind of model, see Apps and Rees (1997).

26
between participation in the garment sector and the years of education of the household head suggests that once the threshold of 5.5 years is reached, additional years of education decrease the likelihood of participation. This is likely related to the fact that an educated individual will probably opt for higher-skilled jobs. Female headship and the age of the household head are positively correlated with the dependent variable, although the employment status of the head does not seem to exert any significant influence. This is not surprising as there is little variation in the employment status of household heads, as only 9 percent of them are unemployed.

Table 2: Propensity Score Estimation

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Head’s years of education|0.132***<br>(0.035)|0.131***<br>(0.035)|0.131***<br>(0.035)|0.129***<br>(0.035)|
|Head’s years of education<sup>2</sup>|-0.012***<br>(0.003)|-0.012***<br>(0.003)|-0.012***<br>(0.003)|-0.012***<br>(0.003)|
|Male headship|-0.249**<br>(0.113)|-0.350*<br>(0.194)|-0.246**<br>(0.113)|-0.243**<br>(0.113)|
|Head’s age|0.015***<br>(0.004)|0.016***<br>(0.004)|0.015***<br>(0.004)|0.015***<br>(0.004)|
|Head’s employed|-0.001|-0.005|-0.001|0.013|
||(0.152)|(0.152)|(0.152)|(0.152)|
|Household size|0.104***<br>(0.024)|0.101***<br>(0.024)|0.104***<br>(0.024)|0.104***<br>(0.024)|
|Urban|-0.180|-0.178|-0.180|-0.234*|
||(0.127)|(0.127)|(0.127)|(0.131)|
|Dependency ratio|-0.006***<br>(0.001)|-0.006***<br>(0.001)|-0.006***<br>(0.001)|-0.006***<br>(0.001)|
|House ownership|-0.816***<br>(0.189)|-0.823***<br>(0.189)|-0.819***<br>(0.189)|-0.796***<br>(0.189)|
|Wall|-0.275**<br>(0.128)|-0.276**<br>(0.128)|-0.277**<br>(0.128)|-0.314**<br>(0.129)|
|Electricity|0.341***<br>(0.117)|0.341***<br>(0.117)|0.344***<br>(0.117)|0.336***<br>(0.118)|
|Head’s married||0.128<br>(0.202)|||
|Khmer ethnicity|||0.150<br>(0.294)||
|Access to water||||0.216**<br>(0.099)|
|Constant|-1.575***<br>(0.334)|-1.595***<br>(0.336)|-1.713***<br>(0.432)|-1.674***<br>(0.338)|
|Observations|3,518|3,518|3,518|3,518|
|Pseudo _R_<sup>2</sup>|0.051|0.051|0.051|0.053|

Notes: Authors’ calculations based on the 2011 Cambodia Socio-Economic Survey. Logit estimates. The dependent variable is a dummy for household participation in the garment sector. Standard errors are given in parenthesis. *, ** and *** denote statistical significance at the 10%, 5% and 1% level respectively. Section 5.1 provides a detailed description of included variables.

19
“synthetic remittance” variable as instruments for the change in population, change in road density and change in remittances between 1995-2004.

What is the “synthetic remittance” variable? Our goal is to identify the effect of remittances on forests. However, as we just discussed, shocks that directly affect forests could well be correlated with remittances, confounding any estimate we may obtain through a fixed effects estimation. In order to deal with this issue, we exploit the plausibly exogenous diversification of migrant destinations at the village level to come up with a measure of the change in the per capita potential remittances between the census years 1991 and 2001. We use this change in potential remittances - a synthetic variable - as an instrument for the change in actual remittances between 1995 and 2004.

̃ Let _Nij_ and _Nij_ be the number of migrants from village _i_ in destination _j_ in 1991 and 2001 respectively. Let _rj_ be time-invariant average remittance from destination _j_ . Then the total change in per capita remittances between 1991 and 2001 can be written as, where _Pi_ and _P_<sup> ̃</sup> _i_ represent population of village _i_ in years 2001 and 1991 respectively. This can be re-written as, ̃ where _δi_ and _δ_<sup> ̃</sup> _i_ are the migration rates for village _i_ in year 2001 and 1991 respectively and _γij_ and _γij_ are the fraction of migrants in village _i_ that are in destination _j_ in 2001 and 1991 respectively. Note that the change in remittances between the two periods is driven by the change in the aggregate village migration rate as well as the change in the composition of migrants. In the synthetic remittance variable we construct, we allow the change to be driven solely by the change in the composition of migrant destinations. Thus the variable we construct and use as instrument is, The key identifying assumption here is that environmental shocks can be correlated with migration but conditional on the level of migration, they are uncorrelated with the destination that is chosen by the migrant. For instance, an accidental forest-fire that razes your local forests and damages the source of your livelihood may make you more likely to leave your village and seek opportunities elsewhere but conditional on your decision to leave, it will have no influence over whether you end up migrating to India or Malaysia or any other destination. To what extent is this argument valid? After all, it is completely imaginable that the sharper the negative environmental shock experienced by the village, the more likely it is for potential migrants to actively seek out destinations that are better paying. However, the cost of migration drives a wedge between the destination a migrant 23
total number of vehicles operating in 1987-88 stands at around 110,000. As Table 4b indicates, the vehicle fleet is expanding rapidly: 11,600 vehicles registered in 1985, 12,200 in 1986, and 14,800 in 1987. Customs data indicate that 9,400 cars were imported in 1987 and 12,100 in the first 9 months of 1988.

**Table 4a.** The Licensed Vehicle Fleet by Type and Size, 1987

|Type|Number|
|---|---|
|Cycles|4,244|
|Cars<br>|39,952<br>|
|Taxis|8,396|
|Buses<br>0-33 seater|12,794|
|34+ seater|2,790|
|Rigid trucks||
|GVW<br>0-7t|2,707|
|8-16 t|2,915|
|17-20 t|359|
|22+ t|247|
|Road tractors||
|GVW<br>0-24 t|652|
|25+ t|625|
|3-axle|261|
|Total|75,942|

Source: Vehicle examination and Licensing Department, July - December 1987.
To estimate the median voter price, we specify the reduced form probability that a household votes yes on the referendum by where yi j = 1 denotes a yes vote and yi j = 0 indicates a no, and αj is a community fixed effect. We use this relationship to implicitly define the median voter price for community j according to To recover values for the median voter prices we first estimate φ in equation 9 using a fixed effects binary logit model. The estimator recovers a consistent estimate for φ by concentrating the fixed effects out of the likelihood function. To recover values for the fixed effects we implement a contraction mapping that calibrates each αj so that the observed fraction of yes votes in community j matches the average predicted probability of a yes vote for sampled community members. With values for φ and αj in hand, we then implement a numerical routine based on equation 10 to recover B<sup>50</sup> j<sup>for each village in our sample.Details on these steps are included in Appendix A.1.</sup>

# 4.4 Summary statistics

The left panel in Figure 1 shows the distribution of per hectare payments actually received by beneficiary communities, or calculations of what would have been received by non-beneficiaries. The mean value of these payments is 412 pesos per hectare. The second histogram shows the distribution of the random offers given in the referendum question. In 65% of the cases, the value offered was less than what the community was or would have been receiving. These distributions are similar for the household survey.

Table 3 shows summary statistics for the actual payments and offered prices, as well as the proportion of ‘yes’ votes. The acceptance rates for the contract offers overall were high – 72% for leaders and 79% for households. We note that the lower rate of acceptance among the leaders implies that estimates of WTA made from their responses will be higher than those from the household survey, and thus more conservative (i.e., reflect a higher opportunity cost). The rate 22
|WIFO Taxonomies (continued)|Taxonomy ||Taxonomy II|
|---|---|---|
|NACE rev. 1|factor inputs|labour skills|
|Basic iron and steel, ferro-alloys (ECSC)<br>271|3|1|
|Tubes<br>272|1|1|
|Other first processing of iron and steel<br>273|3|1|
|Basic precious and non-ferrous metals<br>274|3|1|
|Structural metal products<br>281|2|2|
|Tanks, reservoirs, central heating radiators and boilers<br>282|4|2|
|Steam generators<br>283|2|2|
|Cutlery, tools and general hardware<br>286|4|2|
|Other fabricated metal products<br>287|1|2|
|Machinery for production, use ofmech. power<br>291|1|4|
|Other general purpose machinery<br>292|1|4|
|Agricultural and forestry machinery<br>293|1|4|
|Machine-tools<br>294|2|4|
|Other special purpose machinery<br>295|1|4|
|Weapons and ammunition<br>296|1|4|
|Domestic appliances n. e. c.<br>297|1|3|
|Office machinery and computers<br>300|5|4|
|Electric motors, generators and transformers<br>311|1|3|
|Electricity distribution and control apparatus<br>312|5|3|
|Isolated wire and cable<br>313|1|3|
|Accumulators, primary cells and primary batteries<br>314|1|3|
|Lighting equipment and electric lamps<br>315|1|3|
|Electrical equipment n. e. c.<br>316|2|3|
|Electronic valves and tubes, other electronic comp.<br>321|5|3|
|TV, and radio transmitters, apparatus for line telephony<br>322|5|3|
|TV, radio and recording apparatus<br>323|5|3|
|Medical equipment<br>331|5|3|
|Instruments for measuring, checking, testing, navigating<br>332|5|3|
|Opticalinstrumentsandphotographicequipment<br>334|5|3|
|<br><br>Watches and clocks<br>335|4|3|
|Motor vehicles<br>341|5|2|
|Bodies for motor vehicles, trailers<br>342|2|2|
|Parts and accessories for motor vehicles<br>343|3|2|
|Ships and boats<br>351|2|2|
|Railway locomotives and rolling stock<br>352|2|2|
|Aircraft and spacecraft<br>353|5|4|
|Motorcycles and bicycles<br>354|1|2|
|Other transport equipment n. e. c.<br>355|1|2|
|Furniture<br>361|2|2|
|Jewellery and related articles<br>362|2|2|
|Musical instruments<br>363|4|2|
|Sports goods<br>364|4|2|
|Games and toys<br>365|4|2|
|Miscellaneous manufacturing n. e. c.<br>366|4|2|
|Taxonomy |:|Taxonomy Il<br>:||
|Industry clusters:<br>1. Mainstream|1. Low-skill industries||
|2. Labour-intensive industries|2. Medium-skill/blue-coll|ar workers|
|3. Capital-intensive industries|3. Medium-skill/white-col|lar workers|
|4.Marketing-drivenindustries|4.High-skillindustries||
|<br>5.Technology-drivenindustries|||

Source: M. Peneder (2001), Entrepreneurial Competition and Industrial Location, Edward Elgar, Cheltenham, UK.
# **Long Audit Form**

# **Department**

Laboratory Group Respondent: Last name Name Patronymic name Tel.: e-mail

# **1. SUBDIVISION DATA**

- **1.1. Basic fields of subdivision activity**

- **1.2. Methods and tools being used in the subdivision that can be claimed for by exterior users.**

- **1.3. Software support, databases, etc., worked out in the subdivision that can be claimed for by exterior users.**

**2. PROSPECTS OF PROJECTS UNDER ANALYSIS**

(results commercialization)

- **2.1 Which of projects are especially perspective for commercialization?**

- **2.2 Which of your future researches are especially perspective?**

- **2.3 What sources of finance can be used by commercialization?**

- **2.4 Is there a direct availability of financing your researches by industrial enterprises?**

# **3. RESPONDENT DATA**

- **3.1 Basic fields of your scientific interests**

- **3.2 Conferences, exhibitions you have taken part in (3 last years)**

- **3.3 List of scientific works (3 last years)**

- **3.4 Applications and patents where you are an author or an applicant for a patent (proprietor).**

- **3.5 List of works you have taken part in as a consultant (3 last years). Please, name the customer.**

- **3.6 Would you like to take an active part in industrial application of the project achievements or their commercialization?**

(or do you prefer to entrust with this task somebody else in order to concentrate yourself on your researchers)

- **3.7 Would you like to be a co-founder of the company, created for a commercialization of your works?**

82
- a real agricultural market that guarantees the conditions for fair competition through its overall rules, physical conditions, and institutions;

- an agricultural policy that emphasizes production efficiency through the same means applied in the market-oriented, developed countries and that also enforces the traditional objectives of agricultural policy. Income parity of agricultural producers is necessary;

- a fundamental change in the role of government, including the reassessment of the agricultural sector within the macroeconomic framework;

- increased environmental protection and support of the application of environmentally sustainable agricultural production technologies.

These actions constitute a package that should be implemented in an expedited, coordinated manner. Exj, erience in the transformation of agriculture in other formerly socialist countries indicates that consistent reform packages implemented quickly lead to faster and more visible positive results, versus partial, stop-and-go actions. Furthermore, political transformation-the introduction of a democratic, multiparty political structlire-is an essential precondition of a successful move toward a market economy in any formerly socialist country.

# _Landed Propeny: Repnivatization_

One of the most debated political and economic questions in the region concerns landed property. The creation of marketable landed property, the rehabilitation of land as the valuable means of production of agriculture, is unavoidable. Adjusting the farming structure to market economy conditions is also necessary. There are several ways to do this.

First, landed property relations in Central-Eastem Europe should be surveyed. Land generally was not nationalized after World War II, although it was later collectivized. In addition to state property, cooperative landed property also arose, and, in various forms, private landed property also existed. Over the years, proprietary rights became a formality. With the abolition of a land market, land lost its cnaracter of valuable means.<sup>2</sup> In Central-Eastern Europe the most important current proposals on landed property are to:

> 2 In Central-Eastem Europe the value of **land cannot** be found in **the** registry of **agscultural** implements, nor is the price of land calculated in the various expenses.
|_Obs._<br>(5)||6042|6042|6042|6042|3808|the sq||
|---|---|---|---|---|---|---|---|---|
|_Normalized_<br>_Dif._(_a_)<br>_No. _<br>(4)||0.018|-0.033|0.035|-0.071|0.017|work members divided by||
|_Dif=0_<br>_(p-value)_<br>(3)||0.456|0.167|0.141|0.003|0.573|ents and their net||
|_Mean_<br>_network_<br>_members_<br>(2)||0.300|-0.158|0.189|-0.367|0.107|main respond||
|_n main_<br>_ndents_<br>(1)||.141|.007|.029|.022|.023|means of||
|_Mea_<br>_respo_||0|0|-0|sky sexual behavior<br>-0|or (sex act)<br>0|sample means at baseline.<br> is the diference in the sample|riances.|
||||||ri|i|ts<br>ce|a|
|||ge|s||ards|behav|repor<br>iferen|mple v|
|||led|ude|g|tow|ual|able<br>ed d|e sa|
||_s_|now|tit|stin|des|sex|he t<br>aliz|of th|
||_xe_|k|at|te|tu|y|T<br>rm|m|
||_Inde_|HIV|HIV|HIV|Atti|Risk|Notes:<br>(a) No|the su|

xxix
**Assessing the Adjustment Implications of Trade Policy Changes Using TRIST (Tariff Reform Impact Simulation Tool)**<sup>1</sup>

Authors: Paul Brenton, Christian Saborowski, Cornelia Staritz and Erik von Uexkull 1 We would like to thank Ian Gillson, Anneke Hamilton, Bernard Hoekman, Jamus Jerome Lim, Juan Sebastian Saez, Philip Schuler, David Tarr and Peter Walkenhorst for valuable comments. TRIST is a product of the World Bank’s International Trade Department. It was initially developed by Mombert Hoppe and Erik von Uexkull under the leadership of Paul Brenton. The development and design of the revised version was led by Paul Brenton and coordinated by Christian Saborowski and Erik von Uexkull. Olivier Jammes contributed to the design of the revised version and was responsible for its technical implementation. Cornelia Staritz contributed to the extension of the tool to output and employment analysis. The recently launched revised version of TRIST has benefited from valuable inputs from colleagues both within and outside the World Bank, including Ian Gillson (World Bank Africa Region), Soamiely Andriamananjara (World Bank Institute), Themba Munalula (COMESA secretariat), Caesar Cheelo (University of Zambia), Peter Walkenhorst (AfDB), discussions with TRIST seminar participants in Bolivia, Ethiopia, Seychelles, Syria, Tanzania, Malawi, Mauritius, Nigeria and Zambia, and presentations at the COMESA secretariat (Lusaka), the EAC secretariat (Arusha), UNECA (Addis Ababa), and at the World Bank. The TRIST team acknowledges financial support for TRIST from the governments of Finland, Norway, Sweden and the United Kingdom through the Multidonor Trust Fund for Trade and Development. The views expressed here are those of the authors and should not be attributed to the World Bank. All correspondence regarding TRIST or related activities should be directed to Christian Saborowksi at csaborowski@worldbank.org.