rafmacalaba/datause-encoder-data huggingface dataset:
- [4] Croppenstedt, A., M. Goldstein, and N. Rosas (2013). Gender and Agriculture: Ineciencies, Segregation, and Low Productivity Traps, World Bank Research Observer 28(1), 79-109.

- [5] Deininger, K., C. Carletto, S. Savastano, and J. Muwonge (2012). Can Diaries Help In Improving Agricultural Production Statistics? Evidence from Uganda, Journal of Development Economics 98, 42-50.

- [6] De Nicola, F. and X. Giné (2013). How Accurate Are Recall Data? Evidence from Coastal India, Journal of Development Economics, available online: doi:10.1016/j.jdeveco/2013/08.008.

- [7] Farnworth, Cathy Rozel (2010). Gender Aware Approaches in Agricultural Programmes: A Study of Sida-supported Agricultural Programmes. Sida Evaluation 2010:3. Stockholm, Sweden: SIDA. Available online at: http://www.oecd.org/countries/burkinafaso/46145893.pdf

- [8] Food and Agriculture Organization of the United Nations (FAO) (2007). Mozambique Factsheet. Available online:

http://www.fao.org/leadmin/templates/tc/tce/pdf/Mozambique_factsheet.pdf

- [9] House, L., J. Lusk, S. Jaeger, W. B. Traill, M. Moore, C. Valli, B. Morrow, and W. M. S. Yee (2004). Objective and Subjective Knowledge: Impacts on Consumer Demand for Genetically Modied Foods in the United States and the European Union, AgBioForum 7(3), 113-123.

- [10] Johnston, D., C. Propper, M. Shields (2009). Comparing Subjective and Objective Measures of Health: Evidence from Hypertension for the Income/Health Gradient, Journal of Health Economics 28, 540-552.

- [11] Kilic, T., A. Zezza, C. Carletto, S. Savastano (2013). Missing(ness) in Action: Selectivity Bias in GPS-Based Land Area Measurements, World Bank Policy Research Working Paper 6490.

- [12] King, M. F. and G. C. Bruner (2000). Social Desirability Bias: A Neglected Aspect of Validity Testing, Psychology and Marketing 17(2), 79-103.

- [13] Knight, A. (2005). Dierential Eects of Perceived and Objective Knowledge Measures on Perceptions of Biotechnology, AgBioForum 8(4), 221-227.

16
# Ammwdix

## Construction of the Variables

- GPPI Rate of change in the Producer Price Index. Source: Cental Statistical Office.

- GCPI Rate of change in the Consumer Price Index. Source: Centa Statistical Office.

- GE Rat of change of L%a exchanp mte.

- Mhe exchange rate is a weighted average index constructed as: (0.5) 0 Fotint/USS + _(0.5)_ * Forint/ ECU. Source: Nadonal Bank of Hungary.

- _GP_ Ratc of change of the index of wholesale prices of Hungary's main triding partners. Source: National Bank of Hungary.

- _GMI_ Rate of change in the stock of currency outside banks, enterprise deposits, household deposits, and other liquid deposits. Source: National Bank of Hungary.

- GWAGE Rate of change in the index of average manufacturing wages. Source Cental Statistical Office.

- DUMMY The dummy variable equals I in January and February of 1990 and 1991. For all other months it equals zero.

The indices, are escalated to December 1990 = 100.

All the series are monthly data collected from the Hungarian Cental Statistical Office and the National Bank of Hungary. However, prior to 1992, wage data was only on a quarterly basis. We have interpolated the quarterly wage data through December 1991 to get monthly wage series for the pre-1992 perod.

36
61 least not decrease) for the rest of the cenitury. Therefore, the assumptions for the 1990s may be conservative.

Finally, there should be a slight preponderance of males for the periods 1980-1985 and 1985-1990. Since the Immigration and Naturalization Service inadvertently destroyed much of the data on the sex of immigrants for FY1980-1982, data on the sex distribution of Cuban immigrants is available only for FY1983-1987. During the latter five years, the sex ratio of the Cuban refugees granted lawful permanent resident status was 127.3 (Immigration and Naturalization Service, 1984-1988). I would suggest a sex ratio of 120 for 1980-1985, 110 for 1985-1990, and 100 (the same as previously proposed by the World Bank) for 1990-2000.

# Mexico

The most crucial and controversial estimates in this region are for Mexico. Since I have supported a rather high figure for net immigration to the United States overall and since Mexico is the single largest source country for immigrants to the United States, the proposed assumptions for Mexico will obviously have to be quite large throughout the twenty-year period under consideration. The inflow of foreigners to Mexico is quite small (Garcia y Criego, 1987), so we can focus on the outflow of Mexican citizens and their return.

Since Mexican data cannot be relied upon to produce accurate estimates of the extent of movement to the United States, it is better to rely on U.S. data sources in this case. Between 1980 and 1985 (fiscal years), 330,690 persons born in Mexico legally immigrated to the United States. Kraly and Warren (1988) have recently estimated that legal immigration from Mexico might have to be increased by 8.5 percent to fit the U.N.'s definition of long-term immigration, but most of that increase would be compensated for by higher estimates of emigration in future years. There is some additional Mexican emigration to countries other than the United States, but the magnitude is small -- for example, just a few hundred per year to Canada (Employment and Immigration Canada, 1987).

We should add to gross legal immigration about 450,000-600,000 undocumented immigrants from Mexico (net). Next it is necessary to subtract emigration to Mexico of both Mexican natives and foreign-born persons. Various estimates of emigration to Mexico abound, but the range of plausible figures is quite wide. One estimate is that emigration from the United States was 19.5 percent as large as legal immigration during the decade 1970-1980 (Warren and Kraly, 1985). An earlier estimate suggested that somewhere between 15.6 percent and 56.2 percent of the FY1971 cohort of legal immigrants from Mexico to the United States may have emigrated by January 1979 (Jasso and Rosenzweig, 1982). Given this background, the World Bank's previous estimate of -750,000 for 1980-1985 seems eminently reasonable.

The rapid decline in net emigration in the next three five-year periods, however, cannot be supported. Both legal and illegal immigration from Mexico to the United States have remained high in the last three years and show no
* 17 -

Type of Farner

# Implications for Extension

# Women as Farm Partners or Workers

Men and women responsible for separate crops/livestock Information on women's crops, especially food crops (for both consumption and sale) and other women's activities should be included in technical messages.

Men and women produce the same crops but in separate fields.

EAs should visit both women's and men's fields.

Separate tasks assigned by gender during the cropping cycle or for livestock care.

Technical messages should also be developed for women's tasks and delivered directly to them, not through the male farmer.

Men and women undertake the same tasks on the same crops/ livestock.

Technical messages should be given to both men and women.

## Women as Farm Managers

Women-managed farms where men are temporarily away or rarely present.

Agricultural support systems, including extension, must recognize women as primary decision-makers and deliver services to women farm managers.

# 2. Collecting Information on Women Farmers

2.10 Diagnostic Data. To determine what extension messages female farmers need, extension agents should collect both quantitative and qualitative information on women's cash and subsistence agricultural operations, the constraints women face, their access to<sup>resources, and their</sup> control over _ncome. To understand the importance of gender in<sup>the farming</sup> situation, researchers should use a combination of methods,<sup>including farming</sup> systems research, such anthropoligical techniques as unstructured surveys, and direct observation by social and biological scientists.
Table A.15: Total number of firms in the universe covered by the survey

|**Country**|**Total**||**Sector**|||**Size**||
|---|---|---|---|---|---|---|---|
|||**Agri.**|**Manu.**|**Serv.**|**Small**|**Medium**|**Large**|
|Bangladesh|15,358||15,358||4,164|3,425|7,769|
|Brazil*|23,364|392|4,758|18,214|12,771|8,955|1,638|
|Burkina Faso|57,328|4,808|7,493|45,027|40,189|13,284|3,855|
|Cambodia|8,172||1,890|6,282|5,842|1,287|1,043|
|Chile|104,854|7,419|11,943|85,492|65,425|30,071|9,358|
|Croatia|22,350|524|5,387|16,439|17,038|4,381|931|
|Ethiopia|144,583|3,670|6,553|134,360|105,038|36,798|2,745|
|Georgia|14,839|313|2,194|12,332|10,815|3,259|765|
|Ghana|42,165|880|10,284|31,001|30,133|10,070|1,962|
|India**|616,833|71,464|233,684|311,685|624,452|70,928|13,514|
|Kenya|74,255|3,680|5,407|65,168|50,584|16,676|6,995|
|Korea, Rep.|545,515|1520|167,466|376,529|450,264|82,403|12,848|
|Poland|244,983|3,021|52,340|189,622|198,107|37,799|9,077|
|Senegal|9,583|1,051|4,069|4,463|7,805|1,414|364|
|Vietnam|179,713|1,080|45,805|132,828|135,046|33,107|11,560|
|**Total**|**2,103,895**|**117,070**|**567,829**|**1,432,427**|**1,756,723**|**350,188**|**75,612**|

* ** Note : Brazil refers to state of Ceará; States of Tamil Nadu, Uttar Pradesh, Gujarat, and Maharashtra in India. The survey does not cover agriculture or services in Bangladesh, nor agriculture in Cambodia. In India, only the states of Gujarat and Maharashtra have agriculture included in the survey. Table 2 provides the distribution of the number of firms sampled in each country, by sector and firm size group.

We exclude micro-firms with fewer than 5 employees. Micro firms, particularly in developing countries, are more likely to be informal (Ulyssea, 2018), making them less likely to be captured in the sampling frame; and this would require further adjustment in the survey instrument and sampling design.<sup>30</sup> This size threshold is aligned with other firm-level standardized surveys with comparability across countries. The World Bank Enterprise Survey (WBES) also uses a threshold of 5 employees. The World Management Survey (WMS) uses a threshold of 50 employees.

We stratify the universe of establishments by firm size, sector of activity, and geographic regions. Our sample is representative across these dimensions. In the firm size stratification, we have three strata: small firms (5-19 employees), medium firms (20-99 employees), and large firms (100 or more employees). Regarding sector, for all countries, we stratified at least for agriculture (ISIC 01), food processing (ISIC 10), Wearing apparel (ISIC 14), Retail and Wholesale (ISIC 45, 46 and 47), other manufacturing (Group C, excluding food

> 30In addition, establishments below this threshold often lack the organizational structure to respond to some of the questions.

99
# ACKNOWLEDGEMENTS

This paper is a summary of a larger LAC regional study, with substantial contributions from PHRWD and largely funded by the Norwegian Trust Fund. The results presented here draw from individual country papers prepared by Shahidur Khandker, Indermit Gill, Carolyn Winter, Eduardo Velez, Maurice Stelchner, James Tenjo, Timothy Gindling, George Jakubson, Jill Tiefenhaler, Ying Ng, Katherine Scott, Thiery Magnac, Diane Steele, Hongyu Yang and Mary Arends.

We are grateful to Barbara Herz for her continuous encouragement throughout this project, and for commenting on earlier drafts of this paper.

George Psacharopoulos Senior Human Resources Advisor LATDR
# **Table S7: GLS Estimates (Robustness for Expenditures)**

||All Providers|Pharmacy|EMP|Providers|Providers|
|---|---|---|---|---|---|
|Panel A: Nonlinear Least Squares||||||
|Estimates of Expenditures|(1)|(2)|(3)|(4)|(5)|
|6 Month Subsidy|0.043|0.049|11.267|2.107*|-0.130|
||(0.115)|(0.113)|0.000|(1.243)|(0.155)|
|6 Month Subsidy*Ineligible|-0.562***|-0.327*|-11.267|-2.107|-1.167***|
||(0.213)|(0.184)|0.000|0.000|(0.366)|
|Ineligible|-0.298**|-0.538***|-13.000|-23.506|0.102|
||(0.143)|(0.125)|0.000|0.000|(0.270)|
|Observations|2,996|2,996|2,996|2,996|2,996|
|R-squared|0.152|0.154|0.001|0|0.071|
|Panel B: Tobit Estimates of Expenditures|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-15.987|20.026|-|-|-158.181|
||(241.807)|(182.123)|-|-|(150.165)|
|Parent Enrolled*Ineligible|-945.9498***|-461.187|-|-|-1171.151***|
||(458.040)|(306.545)|-|-|(417.655)|
|Ineligible|172.445|71.945|-|-|212.1187*|
||(141.860)|(100.934)|-|-|(123.173)|
|Observations|2996|2996|-|-|2996|
|P-value of Enrolled + Enrolled*Ineligible|0.019|0.102|-|-|0.001|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Panel A present estimated coefficients from non-linear least square specifications where "6 Month Subsidy" is the parent's random assignment status and 6 Month Subsidy*Ineligible is the interaction of these two variables. Panel B presents estimates from an IV Tobit specification where "Parent Enrolled" is instrumented with parent's random assignment status and "Parent Enrolled*Ineligible" is instrumented with the interaction of the parent's six month subsidy and Ineligible. The dependent variable is the expenditures at various providers over the past year. EMP and public health sector facilities are omitted from Panel B because estimates would not converge. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

7
**Table 3: Respondents Primary Sources of Information**

||_tv_|Rely o<br>_newspaper_|n Source<br>_radio_|for Any COV<br>_soc. med_|ID-19 Inf<br>_sms_|ormation<br>_pers._<br>_health_|_pers other_|
|---|---|---|---|---|---|---|---|
|Apr-20|89%|11%|15%|10%|16%|12%|10%|
|May-20|92%|3%|8%|9%|25%|34%|19%|
|Jun-20|96%|1%|7%|6%|29%|40%|27%|
|Jul-20|96%|1%|16%|5%|20%|43%|25%|
|Aug-20|95%|2%|11%|5%|20%|39%|23%|
|Sep-20|94%|2%|17%|4%|15%|36%|27%|
|Oct-20|97%|1%|14%|4%|16%|38%|28%|
|Nov-20|96%|4%|16%|5%|20%|39%|32%|
|Dec-20|97%|1%|8%|2%|26%|43%|30%|
|Jan-21|98%|1%|9%|3%|18%|40%|25%|
|Total|96%|2%|12%|5%|21%|39%|26%|

As presented in Table 4, reports of the respondent personally having heard official announcements and guidance provided by government in the week preceding the interview were less common than relying on information sources overall. A maximum of 40 percent of respondents reported receiving official guidance on TV in the previous week, falling to about 33 percent overall. One of the key variables of interest in this study, reporting having received an official SMS message in the week preceding the interview, peaked at about 5 percent of respondents in May 2020. This is a rather strict definition of “treatment,” as it requires the respondent personally received the message in the specific window preceding the interview, and a separate survey question established that at least one member received messages in more than 90 percent of households. The timing of the L2T interviews did not by design correspond to the message-sending schedule of the Mobile Engage initiative. This was due to the decision of the project team to send messages as soon as technically feasible, given the urgency of the situation, rather than wait for ideal measurement conditions. Nonetheless, the survey identified 152 cases in which respondents reported these messages in the week immediately preceding their interview.

**Table 4: Having Seen Official Information on COVID-19 in the Past Week (by Source)**

|||Re<br>|ceived Offi<br>|cial Informati<br>|on in Past 7<br>|Days<br>||
|---|---|---|---|---|---|---|---|
||_tv_|_newspaper_|_radio_|_soc. med_|_sms_|_pers. health_|_pers other_|
|Apr-20|40%|6%|7%|3%|4%|16%|5%|
|May-20|40%|0%|2%|1%|5%|19%|6%|
|Jun-20|26%|0%|1%|0%|4%|14%|3%|
|Jul-20|27%|1%|1%|1%|1%|9%|2%|
|Aug-20|25%|0%|1%|0%|1%|6%|2%|
|Sep-20|34%|0%|1%|0%|1%|6%|5%|
|Oct-20|33%|0%|1%|0%|0%|6%|5%|
|Nov-20|34%|0%|1%|0%|0%|6%|5%|
|Dec-20|35%|0%|1%|0%|0%|6%|3%|
|Jan-21|38%|0%|0%|0%|0%|7%|1%|
||33.2%|0.3%|1.0%|0.3%|1.4%|8.9%|3.4%|

8
17 per cent or so. Inclusion of the Lesotho savings rate in the above equation did not add significantly to the explanatory power of the equation.

The statistical evidence thus suggests that market forces, in the form of the competitive rate in RSA, do play the determining role in the movements of non-administered deposit interest rates in Lesotho, but that there remains a persistent international differential between the average levels of these rates.

# 5 Concluding remarks

By joining currency unions, some. fifteen African countries have erjoyed an otherwise hard-to-achievre degree of credibility for policies of monetary stability. The conclusion to be drawn from the empir:ical analysis in this paper is that membership of a currency union does provide considerable discipline in monetary and price developments. While the members of the African currency unions do not have identical inflation or interest rates, the evidence discussed above implies that the long-term trend of inflation converges to that in the core country (France or South Africa). Transitory deviations are apparently caused more by fiscal policy than by monetary deviations. More limited data on interest rates also suggests convergence of the movements of uncontrolled interest rates to those of the core country. Excessive credit expansion, rather than stimulating the economy, tends to leak out into the balance of payments.

The currency unions have not been without their episodes of financial stress. In particular excessive government spending in some countries has resulted in heavy problems of indebtedness. Furthermore, wage levels, Trhich have not been studied in this paper are thought by some to have adjusted less well to 27 external pressures . Nevertheless, the evidence provided in this paper points to real stabilizing benefits of membership in a currency union.

27 See Levy and Newman (1989) for a discussion of wage adjustment in Cote d'Ivoire based on micro data. A general discussion of the adjustment problems of the franc zone is in Devarajan and de Melo (1987). A companion paper, Honohan (1989), is also relevant in this context.
In order to characterize the local densities of the intervention (in the neighborhoods), we combine information from the program administration, indicating which localities were eligible for the program at the end of 1998 and 1999, with information from the 2000 population census and the annual school census. The population census provides the geographical coordinates (latitudes and longitudes) for all the rural localities in Mexico while the school census provides the coordinates of all secondary schools. The georeferenced data further allows us to identify the locations of the evaluation localities.<sup>6</sup> As in many rural regions of Latin America and elsewhere, the topography of the area covered by the program consists of clusters of villages with a quasi-continuum of dwellings rather than isolated villages. On average, there are 22 localities with an overall population of roughly 6,400 inhabitants within an area defined by a five-kilometer radius from each evaluation village. This proximity favors the interactions between inhabitants of neighboring villages.

Looking now at the intervention, figure 1 depicts the geographic scope of the _Progresa_ penetration during the first two years of program roll-out in the seven central states where the evaluation took place. The rural localities targeted by the program in 1998 and 1999 are shown in light and dark grey respectively, while treatment and control localities are reported in red and blue. In order to provide a more in-depth depiction of the areas surrounding evaluation villages, the map features a smaller-scale view of a region in the State of Michoacan in which circles of a five-kilometer radius are drawn around each evaluation village.

As both maps reveal, beneficiary and evaluation villages tend to be geographically clustered with more deprived areas featuring a higher program frequency. These patterns are confirmed by descriptive statistics of the areas surrounding the evaluation sample, 6. We have used official information on the listing of all rural localities receiving the program (broken down by each program component) at the closing of each fiscal year in 1998 and 1999 in order to verify which localities were receiving the program in late 1998 and 1999. A fraction (about 20 percent) of control localities started receiving the program’s food stipends by November 1999, but none of those villages had received any scholarship by that date. We thus continue to treat those observations as belonging to the control group in November 1999.

9
<!-- Start of picture text --> average treatment effects. First, it is difficult to summarize the impact of an intervention<br>with any single statistic. In particular, average effects often hide significant heterogeneity.<br>Second, we gain important insights from the changes in the shape of the distribution. Quan-<br>tile analysis and other DIA methods allow us to address a broader range of policy concerns<br>than means alone under minimal assumptions. Third, conditional analysis is a complement<br>and not a substitute for unconditional approaches. Each method reveals a different dimen-<br>correcting for multiple testing.<br>In this section, we use the methods in this toolkit, focusing on those in Part 4, to re-analyze<br>500 grant to help cover costs associated with implementing new initia-<br>tives based on the training. To disentangle the impact of the WSDP training from the impact<br>without any additional training. In the third arm, 89 schools served as the control group and<br>test scores is approximately zero and statistically insignificant when looking at impacts sepa-<br>pact or with a program having heterogeneous impacts, benefitting some schools and hurting<br>others. In order to distinguish between these two cases we use the methods discussed in<br>Part 4 to estimate the variance of treatment effects and, relying on strong assumptions, the<br>entire distribution of treatment effects. The results of these exercises are important for poli-<br>cy: If there is treatment effect heterogeneity the program could become effective if better<br>targeted at those with positive treatment effects. This is obviously not possible if the treat-<br>The WSDP is a good application to demonstrate the methods discussed in Part 4 because<br>high quality panel data were collected for the evaluation. However, we do not find evidence<br>taset, based on the features of the data collected for this RCT, using the Simulation 1 data<br>generating process described in Appendix 1.<br><!-- End of picture text -->
<!-- Start of picture text --> median NDVI<br>0.113 - 0.468<br>0.469 - 0.517<br>Mm 0.518 - 0.562<br>Ml 0.563 - 0.618<br>ME 0.619 - 0.829<br><!-- End of picture text --> <!-- Start of picture text --> PET<br>1314.5- 1505.5<br>1505.6 - 1557.6<br>M§) 1557.7- 1599.5<br>MMB 1599.6- 1636.5<br>MM 1636.6- 1912.9<br><!-- End of picture text -->