rafmacalaba/datause-encoder-data huggingface dataset:
- Pauw, Karl, Thurlow, James. 2022. COVID-19 impacts on food systems, poverty, and diets: Lessons learned from country-level analyses. In J. Swinnen, and J. McDermott eds. _COVID-19 and Global Food Security: 2 years later._ Washington D.C., International Food Policy Research Institute (IFPRI).

- Pople, A., R.H. Hill, S. Dercon, and B. Brunckhorst. 2021. Anticipatory Cash Transfers in Climate Disaster Response. Centre for Disaster Protection Working Paper 6. London: Centre for Disaster Protection.

- Prydz, E.B., D. Jolliffe, U. Serajuddin. 2021. Mind the Gap: Disparities in Assessments of Living Standards Using National Accounts and Household Surveys. Policy Research Working Paper No. 9779. World Bank, Washington, DC.

- Ravallion, Martin. 2003. Measuring Aggregate Welfare in Developing Countries: How well do national accounts and surveys agree? _The Review of Economics and Statistics_ 85 (3): 645-652.

- Robertson, Laura, Phyllis Mushati, Jeffrey W. Eaton, Lovemore Dumba, Gideon Mavise, Jeremiah Makoni, Christina Schumacher, et al. 2013. Effects of Unconditional and Conditional Cash Transfers on Child Health and Development in Zimbabwe: A Cluster-Randomised Trial. _Lancet_ 381 (9874): 1283–92.

- Roelen, K., D. Longhurst, and R. Sabates-Wheeler. 2018. _The role of cash transfers in social protection, humanitarian response and shock-responsive social protection_ : IDS.

- Sumner, Andrew, C. Hoy, E. Ortiz-Juarez, Eduardo. 2020. Estimates of the impact of COVID-19 on global poverty. WIDER Working Paper, No. 2020/43. UNU-WIDER, Helsinki.

- World Bank. 2020. _Poverty and Shared Prosperity: Reversals of Fortune._ Washington, DC: World Bank. World Bank. 2022. _Global Economic Prospects: Slowing Growth, Rising Risks._ Washington, DC: World Bank.

- Yonzan, N., C. Lakner, D. G. Mahler, R. A. C. Aguilar, H. Wu. 2020. _The impact of COVID-19 on global poverty under worsening growth and inequality._ Washington, DC: World Bank. https://blogs.worldbank.org/opendata/impact-covid-19-global-poverty-under-worsening-growthand-inequality.

- Yonzan, N., A. Cojocaru, C. Lakner, D. Mahler, and A. Narayan. 2022. The impact of COVID-19 on poverty and inequality: Evidence from phone surveys. World Bank Development Data Blog. https://blogs.worldbank.org/opendata/impact-covid-19-poverty-and-inequality-evidence-phonesurveys.

- Zeufack, A.G., C. Calderon, G. Kambou, M. Kubota, V. Korman, C.C. Canales, and H.E. Aviomoh. 2021. Africa’s Pulse, No. 23.April _._ Washingto D.C.: The World Bank.

- World Health Organization (WHO), 2022. Africa’s COVID-19 vaccine uptake increases by 15%. <u>https://www.afro.who.int/news/africas-covid-19-vaccine-uptake-increases-15</u>

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where<sup>_y_</sup> _i_<sup>are the different labor statistics like labor force participation, labor supply,</sup> earnings, and occupational choice for the _i_<sup>th</sup> individual,<sup>_T_</sup> _i j_<sup>is an indicator variable for</sup> the _j_<sup>th</sup> respective survey assignment for the _i_<sup>th</sup> individual, _Xi_ is a vector of individual and household characteristics for _i_<sup>th</sup> individual, _V_ is a village fixed effect, and ε _i_ , _j_ is the stochastic error term which is randomly distributed.

Because questions on hours, earnings, and sector are identical across assignments, variations in statistics across survey assignments are not due to question wording. However, the response to labor force participation determines whether statistics on those other dimensions are collected at all for the individual (in other words, these statistics are conditional on the individual being classified as employed). In the case of self-respondents, the screening questions which differentiate the start of the short and detailed modules entirely explain variations in selection into employment and therefore variations in hours, earnings, and sector statistics. In the case of proxy respondents, variations in statistics for these other outcomes derive from both the quality of the reporting by proxy respondent on a specific variable (how well does the wife know how many hours her husband works?) and the accuracy of the reporting on the employment status (if the husband does not report that his wife works, then he will not be asked her hours). Only the latter is an issue of selection.

15
considerable variation in yield increase (from no change in Australia to 37 percent increase in India). Large variation was reported in the gross margin as well (from a low of $US 23 per hectare in Argentina to a high of $US 470 per hectare in India).

A more extensive review undertaken by Tripp (2009) covered six countries but was based on broader survey coverage (table 5). His results are remarkably similar to those of Qaim (2009). For example, the average reduction in insecticide costs is 41 percent, with relatively little variation among countries. The average change in yields is 15 percent, ranging from a 2 percent reduction in Australia to a 35 percent increase in South Africa.

Gruère and Sengupta (2011) reviewed 51 estimates based on 23 studies that focused exclusively on India and found even larger benefits. They concluded that, on average, use of biotech cotton reduces the number of chemical applications and pesticide costs by 36 percent each, increases yields by 34 percent, raises net returns by 84 percent, while it increases the costs of production by 15 percent.<sup>**2**</sup> Numerous other models have evaluated the welfare gains from biotech cotton varieties. Depending on assumptions regarding adoption rates and methodology, global welfare gains range from a low of $1.5 to a high of $3.6 billion annually (see Bouët and Gruère 2011). Welfare gains in Africa vary from a low of $20 million annually (Bouët and Gruère 2011) to a high of $214 million (Anderson, Valenzuela, and Jackson 2008). Again, such range depends on numerous factors including modeling framework, country composition, and more importantly, price assumptions.

# **2. The biotechnology controversy**

Despite its benefits, biotechnology remains a highly controversial subject which becomes evident when considering how unevenly countries responded. Some have fully embraced the technology while others have not even introduced the necessary legal and regulatory framework. From the perspective of high-income countries, the United States and Europe have taken different stances with the United States being the leader in both development and use of biotechnology and Europe taking a cautious approach. Other countries fall into one or the other camp with most African countries taking the precautionary approach.

Graff, Hochman, and Zilberman (2009) argued that adoption of biotechnology has been affected by the alignment of rent seeking behavior that influences the policy-making process. They also note that because companies in the

> **2** It should be noted that some studies appear in more than one review.

_— 6 —_
- 40 -

Fraga, A. (1986), "Price Uncertainty And Exchange Rate Risk", Journal of International Economics, 20, pp. 179-185.

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- Averse International Investors:, Journal of Economic Dynamics and Control, 5, pp. 173-185.

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being employed is not significantly different from zero for females. Also, the impact on labor supply does not significantly differ across genders (Table7).

Finally we analyzed how the effect of chronic diseases changes among people employed in the informal sector, where workers do not benefit from any employment protection, disability benefits or health insurance. In our data there is no explicit information on which sector a worker belongs to, though extensive information on health insurance coverage is available. In Egypt only employees of the formal economy are entitled to a health insurance which typically does not extend to other members of the family. Indeed our indicator of informal sector is based on whether people are covered by a health insurance. We attribute individuals to the informal sector when they report that in their household no one is covered by a health insurance and those who report of having a health insurance provided by the school, which covers only the children<sup>9</sup> . According to this classification about 51 percent of the sample belongs to the informal sector, a figure not dissimilar from the 49 percent in 1998 which is estimated using the Egypt Labor Market Survey 1998.

In column (5) of Tables 6-8 we have reported estimates of the specification including the interaction between chronic diseases and the informal sector indicator. Not surprisingly, chronic conditions reduce employment probability much more among workers employed in the informal sector (37 percent compared to a statistically insignificant 13 percent of those belonging to the formal sector) (Table 6). Similarly, labor supply falls by 36 hours per week in the informal sector compared to less than 13 in the formal (Table 7).

From Table 8 we notice that the absence of relation between chronic diseases and wages persists also within each sector. This result is further confirmed by Figure 3 which plots log hourly wage distribution by sector for the sample of siblings. There is no evidence that chronic conditions influence wages differently in the informal sector.

**Figure 3  Wages in formal and informal sectors**

<!-- Start of picture text --> Kernel Density Hourly Wages - Formal sector Kernel Density Hourly Wages - Informal sector<br>-4 -2 0 2 4 -4 -2 0 2 4<br>Log hourly wages Log hourly wages<br>healthy chronic healthy chronic<br>.8 .8<br>.6 .6<br>.4 .4<br>density<br>.2 .2<br>0 0<br><!-- End of picture text --> 9 Information about health insurance is collected at the household level and it does not vary across household members.

17
14 indicates that the decile distribution ratio is 0.66, indicating that the poorest 40 percent of households earn only two thirds of the earnings of the top quintile. While income levels vary significantly between urban and rural areas, both the analysis of income shares by decile and Gini coefficients indicate that there is no significant difference between them in terms of income distribution (see Table 3). We note that the fact that income inequality is _not_ significantly different between urban and rural areas is unusual for countries at Bulgaria's level of income.

**Table 2: Distribution** or **Annual Household Income Per Capita** (%, Leva)

||Nati<br>|onal<br>|Urb<br>|an<br>|Rur<br>|al<br>|
|---|---|---|---|---|---|---|
|Income<br>Decile|Share<br>(%)|Average<br>(Leva|Share<br>(%)|Average<br>(Leva)|Share<br>(%)|Average<br>(Leva)|
|Bottom|4.2|6,941|4.5|6,816|3.8|7,507|
|Second|5.6|9,361|5.9|8,882|5.6|11,020|
|Third|6.5|10,900|6.7|10,101|6.6|13,037|
|Fourth|7.4|12,387|7.6|11,359|7.5|14,789|
|Fifth|8.3|13,928|8.4|12,654|8.5|16,622|
|Sixth|9.3|15,643|9.4|14,095|9.5|18,663|
|Seventh|10.6|17,778|10.5|15,844|10.5|20,856|
|Eight|12.1|20,380|12.1|18,192|12.0|23,608|
|Ninth|14.3|24,121|14.4|21,598|14.2|28,265|
|Top|21.8|36,653|20.7|31,357|21.8|42,784|
|Average||16,809||15,090||19,151|

Table 3: Indicators of Income Inequality in Bulgaria (National Average, Urban, Rural)

|Indicators of Ineqality|National|Urban|**Rural**|
|---|---|---|---|
|Decile Distribution|0.66|0.70|0.65|
|**Ratio**<br>__<br>|________________|___________________|______<br>_____________________|
|Gini Coefficient<br>j|25.8|24.6|26.5|
|<br>Source (tables 2-3): A|uthors' estimates from**_th_**|**_e_**_1992 Indvidual_**_Budgets_**_o_|_f_**_HiouiseholR_**<br>_Suney, NSI._|
_-_ 21 _-_ Table 1 - Test Statistics for Dynamic Model of Grain Markets in Bolgatanga

||Model|Test|F-Statistic|
|---|---|---|---|
|Mai|ze Base: Maize prices asaftunc-<br>tionofTechiman maize prices<br>and perioddummyvariabtes.|Significance<br>ofmodel:P<sup>50 </sup>wZa<br>ptEtZ)|F(4,110)<br>*****71.23|
|1:|Inclusionof1lperiod lagged<br>maize prices|0,OiZ<br>r0<br>8<br>2<br>1<br>o<br>(Joint significance<br>relative<br>to base model)|F(2,108)<br>*****49.97|
|2:|Inclusion of 2-period<br>lagged<br>maize prices|8s^Z<br>*<br>0,<br>r<br>(Joint significance<br>relative<br>to Model 1)|FC2,'06)*1.74|
|3:|Inclusion of 4-period<br>Lagged<br>maize prices|0,<br>0"<br>=0.<br>a,<br>2iZa0<br>r t,<br>***ITUZ*** 0,De"X2<br>a0<br>;Joint<br>signiff;ance<br>relative<br>to Model 1)|FC6,102)a<br>3.39|
|4:|<br>Inclusion of 5-period<br>lagged<br>maize prices|a!<br>a0,<br>l"<br>0<br>(Joinc significance<br>relative<br>to Model 3)|F(2.¶00)<br>*<br>0.32|
|5:|Inclusion of 4-period<br>tagged<br>maize prices (same as Model 3)|Rejectionofhypothesis thit<br>_10'Md'J4_**_Ad'._**+*<br>*<br>***4**<br>,2<br>*****e487<sup>2</sup>'<br>**4.**<br>'<br>1|F(1,1023*****0.3|
|_6:_|Inclusion of 4-period tagged<br>maize prices and 4-period<br>lagged<br>tocal millet<br>prices|4"<br>=0,<br>**4t.2**<br>**30,**<br>t<br>**=**0'a<br>0<br>cJointsignificance<br>relative<br>to Model 3)|F(4,98)<br>**a**1.23|
|_7:_|Inclusion of 4-period<br>lagged<br>maize prices and 4-period<br>lagged<br>local sorghum prices|4"?'<br>O<br>*,<br>e * 0,<br>.a,."<br>*<br>*<br>(Joint significance<br>relative<br>to Model 3)|F04,94)<br>*****<br>1.70|
|mil|letBase:Millet<br>prices<br>as a func-<br>tion of Techiman maize prices,<br>lagged<br>tocal mi1let prices and<br>perioddrmayvariables.<br>Corres-<br>ponds to model 3 with millet<br>price<br>as dependent variable.|Significance<br>of model|FC12,103)*****32.08|
|8:|<br>(Sameas Miltet<br>Base)|Rejection of hypothesis that<br>dEs . dsut<br>_+_<br>+s"2<br>4 irsxZ Rtss|F01,103)<br>0.15|
|Sor|ghuk Base:<br>Sorghum prices as e<br>funetion of Techiman maize prices,<br>tagged tocal sorghum prices and<br>period dunnm variables.<br>Corresponds<br>to model 3 with sorghum price as<br>dependent variable.|Significance<br>of model|F(10,98)<br>*****41.56|
|_9:_|(Same as Sorghum Base)|Rejection of hypothesis that<br>**1**<sup>**,r**</sup><br>**.**<br>u2*****<br>*****<br>1<br>*<br>NOWIc 4 4M20C<br>at8M3C, 4<br>OWg_"_|F(1,98)<br>*****<br>0.002|

Note: Superscripts denote the market and real commodity price as follou: BOtZ Soloatnga wholtle _inze; TEN? **a** Techiman wholesele maize; _BCOI_ **=** Botgatanga whotesale millet; BOCC **a** Bolgatano whotlesl sorghum.
can be further simplified: To arrive at a spatial equilibrium, we provide conditions for land market clearing, labor market clearing and a labor mobility condition. For an equilibrium in the land market, total income from land must equal total expenditure on land, where the latter summarizes land expenditure by consumers, _T_ -sector firms, _M_ -sector firms and _S_ -sector firms. Similarly, labor market clearing requires that total labor income earned in one location must equal total labor payments across sectors on goods purchased from that location everywhere. Finally, we assume that workers can freely move across locations within a country but cannot move across countries. Therefore, free mobility of workers across locations within country implies that the wage earned by workers in a given location after correcting for land and goods prices, as well as a location’s amenity value, must be equalized across locations of a same country. The welfare in each location of a same country _c_ is given by: where _Pn,c_ = ( _φ_<sup>_M_</sup> )<sup>_κ_</sup> ( _Pn_<sup>_M_</sup> _,c_<sup>)1−</sup><sup>_κ_+(</sup><sup>_φT_)</sup><sup>_κ_(</sup><sup>_P_</sup> _n_<sup>_T_</sup> _,c_<sup>)1−</sup><sup>_κ_+(</sup><sup>_φS_)</sup><sup>_κ_(</sup><sup>_P_</sup> _n_<sup>_S_</sup> _,c_<sup>)1−</sup><sup>_κ_.</sup> We follow the specification in Moneke (2020) and Michaels et al. (2011) to include the district specific parameter _νn,c_ in the wage so that the welfare can be interpreted as the real income in each location.

# **4.2 Calibration of the Model**

We calibrate the model by using some parameters from the literature and by recovering the key productivity parameters and wages to obtain an equilibrium for the current situation. Table A17 in the Appendix reports the parameters from the literature to calibrate the model partly taken from Moneke (2020) whose model is calibrated to Ethiopia. To recover the productivity parameters, we use the labor market clearing conditions, the land market conditions, and the labor mobility conditions. For each location, the model admits three equations for the three endogenous variables in each location—land market clearing, labor market clearing, and labor mobility condition—which allows to solve for a general equilibrium of the model in terms of its core endogenous variables: wages, land rental rates, and population. Moneke (2020) shows the uniqueness of the equilibrium based on a similar work by Michaels et al. (2011). We obtain a series of { _A_<sup>_T_</sup> _n ,A_<sup>_M_</sup> _n_<sup>_,AS_</sup> _n_<sup>}</sup><sup>_n_∈</sup><sup>_N_forwhichthedistributionofpopulation,employment,andlandis</sup> an equilibrium given the current trade costs.

23
Page 28 held in France are used to purchase other currencies, and the anticipated shift to a single European currency.

Yet the fact remains that an urgent objective of all African countries is to dramatically_ increase and diversJify_ =o through economically rational investment projects, and that the commercial banks dt show some willingness to support export projects, prpyis4Ls-gS cash flows are committed to servicing the debt. This is a prudent requirement applied in most other forms of commercial bank lending, both cross-border and domestic, but one that is difficult, timeconsuming and therefore costly to structure in the more regulated foreign exchange regimes within Africa.

Given the apparent opportunity to generate commercial bank support fairly quickly for export development, more open attitudes towards the commitment of export cash flows appear to be an important ingredient in accelerating resumed lending, until a given country has adopted a very open foreign exchange regime or achieved full convertibility. Concerns about potential distortions could best be mitigated by setting a limit on the amount of funds that can be so committed by a given country or borrower, and by improved appraisal of the underlying projects.

# 4. ZTh Exhange s-ak

The exchange risk faced by local currency earning entities (including exporters obliged to repatriate and convert export proceeds) who have obtained a crossborder loan is an important factor that enters into both lending and borrowing decisions. In a number of donor-sponsored projects and credit facilities aimed at the private sector, the borrowing country government has agreed to bear the exchange risk in local currency, partly because the ultimate borrowers were not prepared to accept this risk and the facilities were not being utilized. Similar coverage could be extended within appropriate limits to protect private borrowers seeking support from private banks. Many donors have accumulated substantial counterpart funds in the African countries that could be applied to supporting such arrangements.

## 5. luoed CreditL Informaton

Although not stressed in the interviews, commercial banks are at a major disadvantage in analyzing or projecting the creditworthiness of developing countries. They understandably have little confidence in statistics and public information, given the bewildering array of vastly different numbers and the superficiality of the press reports. They also have virtually no influence on the course of economic policy or foreign exchange management practices.

The in-depth economic analyses of the IMF and the World Bank are the closest that the international community has come to establishing an "independent audit function". However, they are not available to the public unless the member government authorizes their release. A positive development would be for these authorizations to be routinely granted, or at least for basic, accurate macroeconomic information, qualitative and quantitative, to be published more quickly