rafmacalaba/datause-encoder-data huggingface dataset:
on unemployment was far less than in the US and UK context.

Compared to the voluminous literature from high-income countries, there is scant evidence from the developing world on the labor market impacts of COVID-19, largely due to lack of data. Studies in high-income countries use a variety of data sources, ranging from surveys to government administrative data sets, from private sector transactions to data from social media and search engine companies. With some exceptions, work in developing countries needs to rely on survey evidence that would have to be collected for the specific purpose of studying the crisis and addresses specific questions. Recent work has found substantial impacts on employment and energy consumption in India (Beyer, Bedoya and Galdo, 2020; Deshpande, 2020; Dhingra and Machin, 2020; Lee, Sahai, Baylis and Greenstone, 2020), on family businesses in Nigeria (Avenyo and Ndubuisi, 2020), and simulated aggregate consumption in Uganda (von Carnap et al., 2020). While these papers consider individual countries and various specific situations in these countries, our data allow us to consider a much wider set of countries and to compare countries to each other.

Several sets of papers offer regional or even global assessments of the impacts of the crisis on different dimensions, often based on firm surveys, simulations, or Google search or mobility data. For example, Adian et al. (2020) find that small and medium enterprises in 13 countries were more affected by the crisis than larger firms. Apedo-Amah et al. (2020) confirm this using data from a wider set of countries, and also find that most adjustments occurred on the intensive margin of hours reductions or temporarily work stoppage. Bachas, Brockmeyer and Semelet (2020) simulate the shock on firms using administrative data from 10 countries and predict an annual payroll reduction of 5%-10%.

A smaller set of global studies explicitly considers distributional effects. Bargain and Aminjonov (2020) document that poorer regions across nine countries in Latin America and Africa were less likely to comply with stay-at-home orders, and therefore more likely to spread the disease. Dang, Huynh and Nguyen (2020) use surveys from China, Italy, Japan, Korea, and the United Kingdom to analyze the unequal effects of the pandemic by income 7
# **TABLE A2.  Afrobarometer (AFR)**

## **A2: Afrobarometer (AFR)**

http://www.afrobarometer.org The Afrobarometer is a joint enterprise of Michigan State University (MSU), the Institute for Democracy in South Africa (IDASA) and the Centre for Democracy and Development (CDD, Ghana). The Afrobarometer Series, launched in October 1999, reports the results of national sample surveys on the attitudes of citizens in selected African countries towards democracy, markets and other aspects of development. The objective of the Afrobarometer is to collect, analyze and disseminate cross-national, time-series attitudinal data for up to a dozen new democracies on the African continent.

In the table below we list the variables included in each of the governance indicators. We use data from the 2000, 2002 and 2005 Surveys. 2003 and 2004 data were drawn from 2002 survey.

|**Table A2: Afrobarometer(18 African**|**count**|**ries)**||||||
|---|---|---|---|---|---|---|---|
||2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**||||||||
|How much do you trust the parliament?|..|X|X|X|..|..|..|
|Overall, how satisfied are you with the way democracy works in your<br>country?|..|X|X|X|X|..|..|
|Free elections|X|..|..|..|..|..|..|
|**Political Stability**||||||||
|NA|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||
|What proportion of the country's problems do you think the government<br>can solve?|..|X|X|X|..|..|..|
|Based on your experiences, how easy or difficult is it to obtain<br>household services (like piped water, electricity or telephone)?|X|X|X|X|..|..|..|
|Based on your experiences, how easy or difficult is it to obtain an<br>identity document (such as birth certificate, driver's license or passport)?|X|X|X|X|..|..|..|
|Government handling of health services|X|..|..|..|..|..|..|
|Government handling of education system|X|..|..|..|..|..|..|
|**Regulatory Quality**||||||||
|NA|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|Over the past year, how often have you or anyone in your family feared<br>crime in your own home?|..|X|X|X|..|..|..|
|Over the past year, how often have you or anyone in your family had<br>something stolen from your house?|..|X|X|X|..|..|..|
|Over the past year, how often have you or anyone in your family been<br>physically attacked?|..|X|X|X|..|..|..|
|How much do you trust the courts of law?|..|X|X|X|..|..|..|
|How much do you trust the police?|..|X|X|X|X|..|..|
|Based on your experiences, how easy or difficult is it to obtain help from<br>the police when you need it?|X|X|X|X|..|..|..|
|**Control of Corruption**||||||||
|How well would you say the current government is handling the fight of<br>corruption in the government?|..|X|X|X|..|..|..|
|How many elected leaders (parliamentarians) do you think are involved<br>in corruption?|X|X|X|X|..|..|..|
|<br>How many judges and magistrates do you think are involved in|X|X|X|X|..|..|..|
|How many government officials do you think are involved in corruption?|X|X|X|X|X|..|..|
|How many border/tax officials do you think are involved in corruption?|X|X|X|X|..|..|..|

47
|Proper foor<br>(7)<br>(8)<br>0.11_∗∗∗_<br>0.10_∗∗∗_<br>(0.02)<br>(0.02)<br>0.03<br>0.04_∗_<br>(0.02)<br>(0.02)<br>0.08_∗∗∗_<br>0.07_∗∗∗_<br>(0.02)<br>(0.02)<br>0.08_∗∗_<br>(0.03)<br>0.02<br>(0.04)<br>_−_0.07_∗_<br>(0.04)<br>0.06_∗∗_<br>(0.03)<br>0.44<br>0.44<br>1,971<br>1,971<br>4,202<br>4,202<br>, World Bank geocoded<br>d control variables from<br>ountry-year fxed efects<br>de the feature reported|
|---|
|ness and Missions<br>Radio/TV<br>(5)<br>(6)<br>0.06_∗∗∗_<br>0.06_∗∗∗_<br>(0.01)<br>(0.01)<br>0.02<br>0.02<br>(0.01)<br>(0.01)<br>0.04_∗∗∗_<br>0.04_∗∗∗_<br>(0.01)<br>(0.01)<br>0.09_∗∗∗_<br>(0.03)<br>0.02<br>(0.02)<br>0.00<br>(0.03)<br>_−_0.06_∗∗_<br>(0.03)<br>0.69<br>0.69<br>1,970<br>1,970<br>4,201<br>4,201<br>d projects from AidData<br> and Health Survey; an<br> at the country level. C<br>s whose dwellings inclu|
|rences: Aid Efective<br>Piped water<br>(3)<br>(4)<br>0.13_∗∗∗_<br>0.12_∗∗∗_<br>(0.03)<br>(0.03)<br>0.07_∗∗∗_<br>0.07_∗∗_<br>(0.02)<br>(0.02)<br>0.06_∗∗∗_<br>0.05_∗∗_<br>(0.02)<br>(0.02)<br>0.11_∗_<br>(0.06)<br>_−_0.03<br>(0.05)<br>0.02<br>(0.06)<br>0.04<br>(0.06)<br>0.30<br>0.30<br>1,971<br>1,971<br>4,202<br>4,202<br>s from Beach (1903); ai<br> from the Demographic <br>dard errors are clustered<br> fraction of respondent<br>_._01|
|Table 8: Triple Dife<br>Electricity<br>(1)<br>(2)<br>Active aid<br>0.11_∗∗∗_<br>0.10_∗∗∗_<br>(0.02)<br>(0.02)<br>Completed aid<br>0.02<br>0.03_∗_<br>(0.02)<br>(0.02)<br>Ever had aid<br>0.06_∗∗_<br>0.05_∗∗∗_<br>(0.02)<br>(0.02)<br>Mission<br>0.11_∗_<br>(0.06)<br>Mission _×_ Active aid<br>0.01<br>(0.05)<br>Mission _×_ Completed aid<br>_−_0.04<br>(0.06)<br>Mission _×_ Ever had aid<br>0.02<br>(0.07)<br>Mean outcome<br>0.29<br>0.29<br>Number of cells<br>1,970<br>1,970<br>Observations<br>4,201<br>4,201<br>_Source_: Authors’ analysis based on mission location<br>research release, version 1.3; development outcomes <br>multiple sources, detailed in appendix A.<br>_Note_: Estimation is by ordinary least squares. Stan<br>are always included. The dependent variable is the<br>above each regression. _∗p < ._1<br>_∗∗p < ._05<br>_∗∗∗p <_|

31
data in that case is model-based EBP estimates derived from the census, which are also subject to bias due to informative sampling when predicting out of sample.

_Table 2: Predictions using geospatial data are much less accurate in non-sampled areas_

|Country|Indicator|Source of survey<br>data|Source of<br>validation data|R<sup>2</sup>for<br>sampled<br>areas|R<sup>2</sup>for non-<br>sampled areas|
|---|---|---|---|---|---|
|Burkina Faso|Poverty|2018 EHCVM|Census-based<br>EBP estimates|0.76|0.21|
|Madagascar|Wealth|2018 census<br>(simulated samples)|Census|0.83|0.62|
|Mexico|Poverty|2014 MCS-ENIGH|EBP estimates<br>using<br>Intercensus|0.74|0.49|
|Mexico|Poverty|2015 Intracensus|Design-based<br>simulation<br>using<br>Intercensus|0.88|0.64|
|Malawi|Wealth|2018 census<br>(simulated samples)|Census|0.79|0.53|
|Malawi|Poverty|2018 census<br>(simulated samples)|Derived from<br>census-based<br>predictions|0.76|0.64|
|Mozambique|Wealth|2019 census<br>(simulated samples)|Census|0.85|0.71|
|Sri Lanka|Wealth|2012 census<br>(simulated samples)|Census|0.90|0.80|

Results from household-level EBP models using geospatial auxiliary data. Sources: Edochie et al. (forthcoming), Newhouse et al. (2022), Merfeld and Newhouse (2023) Pfefferman and Sverchkov (2007) proposed a bias correction for out-of-sample areas when the probability of selection of an area into the sample is known, but first stage sampling probabilities are rarely available to analysts, and this correction has not yet to our knowledge been implemented in any small area estimation software package. Estimating a separate model with inverse probability weighting for out-of-sample areas may have the potential to improve the accuracy of out-of-sample estimates, by giving greater weight to areas that were less likely to be sampled, based on observable characteristics, when estimating model parameters. As discussed in more detail below, tree-based machines learning methods are also more robust to this source of bias and therefore tend to generate more accurate outof-sample predictions. Until either the use of tree-based machine learning or explicit bias correction becomes routine, however, predictions for out-of-sample areas based on publicly available geospatial indicators should be treated with great caution.

11
# **Knowledge and adoption of complex agricultural technologies**

# **– Evidence from an extension experiment –**

Denise Hörner, Adrien Bouguen, Markus Frölich, and Meike Wollni

## **JEL classification** : D83, O13, O22, O33, Q16

**Key words** : Randomized controlled trial, integrated soil fertility management, technology diffusion, agricultural extension systems, rural development

Denise Hörner (corresponding author) is a postdoctoral researcher and Meike Wollni is a professor at the Department of Agricultural Economics and Rural Development, University of Goettingen, Germany; their email addresses are denise.hoerner@uni-goettingen.de and mwollni1@gwdg.de. Adrien Bouguen is assistant professor at the Department of Economics, Santa Clara University, United States; his email address is abouguen@scu.edu. Markus Frölich is a professor at the University of Mannheim and Center for Evaluation and Development (C4ED), Germany, and J-PAL Affiliate; his email address is froelich@uni-mannheim.de. This work was financially supported by the German Research Foundation (DFG) [grant numbers RTG1666 GlobalFood and 390367541]; in particular for data collection, funding of the video intervention and data analysis. The German Agency for International Cooperation (GIZ) provided funding for baseline data collection. Further support for research analysis was provided by DFG through SFB 884 at University of Mannheim. The thank all members of the Integrated Soil Fertility Management Project (GIZ-ISFM+) for their support, and Ibrahim Worku for his excellent field work coordination as well as the whole team of research assistants and enumerators, and farmers who gave their time. The authors are grateful to Eric Edmonds and three anonymous reviewers for their constructive comments and valuable advice that have greatly helped to improve this manuscript. We thank participants at the NBER Transformation of Rural Africa Conference, the Annual International Conference of the Research Group on Development Economics 2019, the Agricultural & Applied Economics Association Annual Meeting 2019, the PEGNet Conference 2019 as well as seminar participants at C4ED in Mannheim and DARE in Göttingen for helpful comments. This study is registered under the AEA RCT ID: AEARCTR-0004406, a previous version has been published in the NBER Working Paper Series (Hörner et al. (2019)). A supplementary online appendix is available with this article at _The World Bank Economic Review_ website.
of the labor force, especially in urban areas, and provides an important -- if not the sole - - income opportunity for growing numbers of the poor. But the debate about its role in economic development continues. There is considerable disagreement aoout whether measures should be taken to promote informal activities as an impetus to economic growth and a strategy for improving the earnings of low-income households.

The place of the informal sector in development is all the more imnortant because of the severe economic crisis in most third world economies. As Cornia (1987) discusses, households faced with sharply reduced employment and incom3 prospects in the formal (or modern) sectors -- manufacturing, services, mining, and government - - tend to seek employment and income opportunities in the informal economy. The first to shift are those who have lost jobs in the formal sector. Next, employed formal sector workers, especially government employees, resort to moonlighting activities, most of which are informal. As household incomes decline, married women and children who previously did not work in the market are drawn into informal market activities, and soon new entrants to the labor force begin to find jobs in the informal rather than the formal sector. 3 The rapid growth of the informal sector has led international aid agencies and governments to explore policies to improve the profitability of such businesses. This surge of interest, however, is not based on much empirical evidence about the underlying determinants of the performance of the firms.

Informals perform a remarkable array of activities, ranging from vending foodstuffs and prepared foods to consumer goods and services, including 3 Several recent studies have identified these general patterns. See Cornia (1987), IDB (1987b), PREALC (1985), van der Gaag, Stelcner and Vijverberg (1989), and Tokman (1986).

2
Panel C compares household characteristics of students in the South and in the North. Because of the missing schools at baseline, we take characteristics measured in the rst follow-up among students in the control schools. A few interesting patterns emerge. Households in the South are poorer but have fewer children and better educated heads (and more prominently so for the families of female students).

Finally, Panel D considers the characteristics of projects being undertaken by schools with the school grant funds. This information comes from a survey conducted in treatment schools which asked principals about the project for which they got funding. We conducted two of these surveys, one at rst follow-up, and one at second follow-up. We report estimates from the second follow-up survey when, presumably, data about the project is more mature and complete.

In the South, students were much more frequently named as participants in the drafting of the proposal. Although it is not clear what input students may have had, this could indicate that principals were more sensitive to the needs of the students in the South. It is also signicant that projects in the South started later. By the end of year 2 of the study projects in the North had been running 7.6 months longer than in the South. If results faded out quickly this could explain why we observe eects of the more recent projects than in the earlier projects but this is unlikely to be the case, given our previous results about the sustainability of program impacts (although those are not very precise). If, on the other hand, a project needed time before it started to inuence children's learning (as in the case of activities that take time, such as training a teacher, or building a library), we would expect larger impacts for more mature projects, which goes against what we nd in terms of the South - North comparison.

Some of the most remarkable dierences relate to the components of the project. The schools in the North were more likely to have components involving the purchase of textbooks/manuals and in particular computer related materials, while schools in the South were much more likely to have components related to training of teachers, building courses, managerial training, spending on the principal and the teachers. At the same time the Northern schools reported more spending on students. Thus there are clear dierences in the characteristics of projects in schools in the North and the South, as stated by the principals of these schools. Schools in the South seem to be investing more in the teaching and management abilities of their human resources, while schools in the North invest more in equipment. This may well be a force behind the large dierences in program impacts in these two sets of schools.

22
existence of pre-trends.

We define treatment status at the community-cohort level. Children from a given cohort are considered treated if a pre-primary existed in their community when they were aged 6. We measure impacts of access to pre-primary education on two outcomes: enrollment and adequate progression rates. We restrict our sample to include cohorts born between 1988 and 1996 to observe outcomes during the whole relevant age range (7-12) for each included cohort. This restriction ensures that differences in estimated effects by age are not driven by heterogeneity in cohort composition, but rather by average dynamic effects of pre-primary access.

For both measures, the denominator corresponds to population counts by communitycohort-age. To construct these counts, we use data on population by community and age from the 1994 and 2002 Population Census and assume that cohort sizes remain constant over time. For example, the number of students aged 6 in 2000 in a given community is assumed to be equal to the number of students aged 8 in 2002 in that community. We compute counts for cohorts 1988-1989 using the 1994 Population Census. For the 19901996 cohorts we use the 2002 Census. We favor the use of the 2002 Census because it is closer to the period of analysis (1995-2008). However, the assumption of constant cohort sizes by community over time becomes problematic when focusing on children aged 13 and older in the 2002 Population Census: in these rural communities students might migrate to attend secondary education. This is why we compute cohort sizes for the 1988 and 1989 cohorts from the 1994 Population Census.<sup>6</sup> The numerator of the enrollment rate is the count of primary school students by community-cohort-age. The numerator for the progression rate corresponds to the number of students progressing adequately given their age. For example, for those aged 8, this includes all students in second or higher grades. The unit of observation of the resulting data set is at the community-cohort-age level. This structure of the data makes it possible to estimate impacts at ages 7 to 12, though we mainly concentrate on impacts at age 12 – the medium-run Our empirical analysis is based on comparing changes in outcomes between later (exposed) and earlier cohorts in treatment communities versus changes in comparison communities. Note that our analysis is performed at the community level and that the treatment indicator equals one if a cohort-community had access to a pre-primary and zero if not. Moreover, in our baseline analysis we restrict the sample to communities that did not have access to a pre-primary prior to the pre-primary construction program. Consequently, preprimary enrollment rates for all treatment and comparison communities are expected to

> 6The results presented below remain robust when using the 2002 Population Census for these cohorts instead. Dropping them from the sample also leaves our findings unaltered.

7
Tarr, David (1999) ―Design of Tariff Policy for Russia‖ in Harry Broadman (ed.), _Russia’s Trade Policy: Reform for WTO Accession_ , Washington D.C.: The World Bank. Available at www.worldbank.org/trade/russia-wto.

- World Bank (2012), _Assessment of Costs and Benefits of the Customs Union for Kazakhstan, Report No. 65977-KZ_ , Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. Available at: http://imagebank.worldbank.org/servlet/main?pagePK=64140839&piPK=460 7554&theSitePK=501889&lang=&sType=2&query=65977&cntry=&isLeftNa v=N&pageSize=10&docType=0&sortOrderby=DOCDT&siteName=IMAGE BANK&menuPK=64154240&callBack=null&sortDesc=ORASCORE&dAtts =ORASCORE%2CDOCDT%2CDOCNA%2CREPNB%2CDOCTY%2CLA NG%2CVOLNB%2CREPNME%2CVOL_TITLE

- World Trade Organization (2011), ―Working Party Seals the Deal on Russia’s Membership Negotiations,‖ November 10. Available at: http://www.wto.org/english/news_e/news11_e/acc_rus_10nov11_e.htm

- World Trade Organization (2007), ―Draft Guidelines for the Conversion of Non-Ad Valorem Duties of Non-Agricultural Products into Ad Valorem Equivalents,‖ Report TN/MA/20, January 16.

- World Trade Organization (2005), ―Technical Note on the Accession Process, Note by the Secretariat,‖ November 28, WT/ACC/10/Rev.3. Available at: http://www.wto.org/english/thewto_e/acc_e/acc_e.htm, and the accessions section of the <u>www.wto.org.</u>

23
Financial Regulation and Development Table 2: Branches and New Towns: Branch Map of Ethiopia

|Variables|(1)<br>Number of<br>Branches|(2)<br>Number of<br>Rural Branches|
|---|---|---|
|Policy _×_ Big Banks|10.19***<br>(2.640)|2.504***<br>(0.525)|
|Bank FE|Yes|Yes|
|Non-Param. Time FE|Yes|Yes|
|Obs.|330|330|
|Adjusted _R_<sup>2</sup>|0.959|0.892|
|Mean Dep. Var.|53.87|5.348|
|SD Dep. Var.|33.11|4.335|

_Note:_ This table reports OLS estimates. The unit of observation is bank level, and bank and non-parametric month fixed effects are included. Robust standard errors are reported in parentheses. The number of branches is defined as the cumulative number of branches installed by a bank, while the number of rural branches is defined as the cumulative number of branches opened in towns previously unbranched. Their means and standard deviations are reported in the final two rows. These variables are regressed over the interaction of a policy dummy taking unit value after April 2011 and a Big Bank dummy taking unit value for the Awash and Dashen Banks, while small banks are BOA, CBB, NIB, and United. The adjusted _R_<sup>2</sup> of these regressions is also reported. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

and the number of employees grow more for larger banks than for smaller banks: Here, as usual, _viy_ is a variable under observation for bank _i_ in year _y_ (note that the subscript _t_ that refers to months is dropped as we deal with annual observations); this variable is regressed over the interaction, _Policyy · Big Banki_ , which accounts for the differential effect of the policy dummy in big banks. The variable _Policyy_ takes unit value for the fiscal years during which the policy has been in place (i.e., 2012 and 2013), while _Big Banki_ takes unit value for the big banks, as presented in Figure 4. These results are reported in Table 3 and show that big banks’ profits grow more rapidly after the policy and the number of employees expands at a similar faster rate.

However, because difference-in-difference regression may present severe limitations in absence of parallel trends, we take a requires a test of the parallel trend hypothesis before the policy. For this reason, we present a second test by running the following regression, where real profits and number of employees are run over a series of yearly dummy variables, _ιy_ , which describe the evolution of profits and number of employees for small banks through the coefficients _by_ and the interaction of yearly dummies with the big bank dummy, describing through the coefficients 27