rafmacalaba/datause-encoder-data huggingface dataset:
# Insert Boxes

|1|T & V: Seven Underlying Principles . . . . . . . . . . . . . .<br>2|
|---|---|
|2|Women's Time Use .....................<br>. .<br>9|
|3|Education and Farmer Productivity . . . . . . . . . . . . . .<br>10|
|4|Cote d'Ivoire, Togo and Burkina Faso.<br>Income from The "Family Fields" . . . . . . . . . . . . . . . .<br>15|
|5|Nigeria: Diagnosing of Gender Roles in<br>T&V Extension Project .20|
|6|Potential Contributions of Home Economists . . . . . . . . . .<br>28|
|7|Technical Expertise for Rural Income Earning Activities . . .<br>.<br>30|
|8|Economies of Scale Using Groups<br>..<br>31|
|9|Research Agenda for Women Farmers . . . . . . . . . . . . . . . 41|
|10|Reducing Risk for Women Farmers Adopting Technologies.<br>. . .<br>.<br>42|
|Illust|rative<br>Case Boxes|
|Case 1|Malawi: Profile of a Women Farmer<br>. . . . . . . . . . . . . .<br>3|
|2|Nigeria: Women Farmers in Imo State . . . . . . . . . . . . . .<br>4|
|3|Malawi: Women's Agricultural<br>Work and Access to Extension . .<br>.<br>5|
|4|Malawi: Vicious Cycle of Small-holder<br>Agriculture . . . . . . .<br>6|
|5|Cote d'Ivoire: Effect of Expanding<br>Cultivation of Cocoa and Coffee<br>..<br>6|
|6|Gambia: Women's Technology and Productivity . . . . . . . . . .<br>7|
|7|Zimbabwe: Mobilizing Credit for Rural Women<br>. . . . . . . . .<br>8|
|8|Malawi: Women Farmer's Lower Fertilizer Use. . . . . . . . . .<br>9|
|9|Cameroon: Extension Agents and Women Farmers . . . . . . . . .<br>11|
|10|Nigeria: Extension Directed to Men . . . . . . . . . . . . . .<br>11|
|11|Burkina Faso: Extension Information<br>Bypasses Women . . . . . .<br>12|
|12|Kenya: Efficiency of Female Versus Male Farmers . . . . . . . .<br>12|
<!-- Start of picture text --> SAeae waneSe ee<br>i a 2 SSELS<br>ag iat ae<br>ee<br>Cos<br>MR]ra a<br>i acy<br><!-- End of picture text --> <!-- Start of picture text --> 600,000 Rupees per person per year<br>500,000<br>,<br>.<br>0 [] i n A ' [| r i<br>ess soece s s ao aose<br>S 9 os<br>* _ & ¥ “0<br>DThis -. Damage This paper: loss PDNA<br><!-- End of picture text -->

<!-- Start of picture text --> 7, Disaster cost: this paper versus PDNA Bhaktapur<br>6 tone ‘<br>3 nomecing Kavrepalancho Kathmandu<br>Okhall *. Nuwakot<br>¢ mn ang Dolakha<br>0 sched= Housing price: district versus average<br>00 O5 10 15 20 25 #30 35 40<br><!-- End of picture text -->
response to an income support program across these employment schemes is interesting in and of itself, especially in light of the optimal transfer policy discussed in the recent empirical literature.

An additional contribution of our paper is to look at whether the labor supply response will differ across transfer programs. Many analysts look at the labor supply response of one program in isolation. From the developing countries, one such program that has received significant attention is the South African old-age social pension (see Case & Deaton, 1998; Bertrand, Mullainathan & Miller, 2003; Ardington, Case & Hosegood, 2007). There are, however, reasons why looking at more programs is important. It may tell us whether households treat transfer programs differently. Income from one transfer program may be treated as certain income, while another may be treated as “temporary”, which has different implications for household behavior. In developing countries, knowing which of the many transfer programs induce larger negative labor supply responses can also inform prioritization and sequencing of reforms of the transfer programs.

Hence, the paper goes on to explore the impact of _household_ receipts from NE and oldage pension on labor supply of _work-eligible individuals_ at the extensive and intensive margins,<sup>2</sup> using the Albanian Panel Survey (APS). The panel regression models used also control for household receipts from work invalidity pension, and “other”<sup>3</sup> transfer programs. However, the coefficients on these variables are not reported, considering our focus on the NE program and the old-age pensions. Given distinct gendered and urban/rural differences in labor market outcomes,

> 2Traditionally, the empirical studies analyzing labor supply response on the intensive margin have used cross sectional data, and defined the outcome of interest as hours or weeks worked _conditional_ on labor force participation. In the case of panel data, if regressions of hours or weeks of labor supply are conditioned on labor force participation, this is likely to lead to an unbalanced panel and loss of information, since labor force participation status of individuals does not necessarily stay constant over time. Given this complexity, this study refers to labor supply at the intensive margin simply as hours or weeks worked, without conditioning the outcome on labor force participation.

> 3We define household work invalidity pension receipts to include disability, unemployment, illness and maternity benefits received by household members. Household receipts from “other” social protection programs is the sum household member receipts from survivor, special merit, war veteran, social care/services pensions.

4
23

**Flgure** 6 WELFARE, REAL EXCHANGE RATE, AND TERMS OF TRADE

<!-- Start of picture text --> 114<br>112<br>3 10/ For Offtwt Value d S1gm-(K)<br>108 ~~~~~~~~~~~~~~~~~~~(a. 0)<br>ioo * 100 _(0.2. 1.0)<br>`1034 * _ _-(50. 1.0)<br>1 02*_<br>9I a<br>hi--<br>907<br>60 80 100 120 140 160 0 02 04<br>Teems d Trade (we/ om) Imoorts/Absovption(M/6Q)<br><!-- End of picture text --> <!-- Start of picture text --> 14-c - * * -<br><!-- End of picture text -->
2020). The central government launched a US$275 billion plan to improve air quality throughout the country, and the Beijing municipal government promised an additional US$160 billion toward that goal (Vaitla et al. 2015b).

# **Confidentiality**

When data are not confidential, subjects may be identifiable. This violates core principles of privacy and increases the likelihood that harm can be done to the identified subjects. Deidentifying individuals is not always enough to maintain confidentiality. In the 1990s, for example, the Governor of Massachusetts approved making de-identified medical records of state employees available for researchers. Though the data had key identifiers such as name and addresses removed, by triangulating the information available with other public information, a researcher was able to identify the medical records of the governor. Other individuals could likewise be identified (Heffetz and Ligett 2014).

Beyond re-identification, data breaches also pose a threat to confidentiality because they raise doubts as to whether personal information is safe. Aadhar, the unique 12-digit identity number of India mentioned before, has suffered from several data leaks. In one instance, more than 200 government websites accidentally made personal data including demographic data, names, phone numbers, religion, bank account numbers and more, available publicly on the internet.<sup>5</sup> Though the data were quickly removed, such leaks often have permanent implications on confidence in data systems.

# **Appropriateness**

For data to generate value, they need to be generated and used to serve a legitimate policy purpose. This means the data must contain information relevant to the policy problem at hand. Critically, this also means that the amount of information collected and used should not be excessive but appropriate for the particular needs. As such, appropriateness is key to preventing data misuse, such as surveillance or exclusion.

Governments’ tracking and contact tracing of infected and at-risk individuals through smartphone apps or phone location data during the COVID-19 pandemic shed light on the relevance of appropriateness. Some of these tracking efforts have been large-scale, data, and technology enabled extensions of manual contact tracing, with the aim to identify and isolate possibly contagious individuals to contain the spread of the virus. In principle, tracking and contact tracing measures thus serve an important policy function (though their effectiveness is still subject to debate (Almagor and Picascia (2020); Fraser et al. 2020)). Yet, the risk of surveillance and other kinds of overreach are high. To minimize the risk of surveillance beyond what is needed to minimize the spread of the virus, it is imperative that only data strictly necessary to carry out contact tracing are collected.

> 5 <u>https://www.firstpost.com/tech/news-analysis/aadhaar-security-breaches-here-are-the-major-untowardincidents-that-have-happened-with-aadhaar-and-what-was-actually-affected-4300349.html.</u>

21
_Decentralization: The Way Forward for Rural Developrnent?_ centralizing tendency and premature bureaucratization of the Latin American political systems, the prospects in general are not very favorable for the successful implementation of forms of political and administrative decentralization that are based on the devolution of power from the national to the local level" (1983: 198).

The importance of the overall political environment can be seen in subsequent decentralization initiatives that have been introduced in many Latin American countries following the transition from autocratic to democratic forms of government that took place during the 1980s. Political transition provided a window of opportunity to introduce important constitutional reforms that permitted the introduction of more decentralized systems of government in Argentina (World Bank 1990), Chile (World Bank 1992b), Colombia (World Bank 1989), and Venezuela (World Bank 1992a).

Crook and Manor's (1994, hereafter C&M) comparative analysis of decentralization in South Asia and West Africa is one of the most thorough recent reviews of decentralization. Governments in Bangladesh, Cote d'Ivoire, Ghana and the Indian state of Karnataka all committed themselves to introducing systems of democratic decentralization. It was hoped that by bringing decision-making closer to communities and encouraging their direct involvement in the political process, the prospects for sustained RD would be significantly enhanced. Political decentralization was therefore associated with the devolution of specified powers and responsibilities from central government to lower-level political entities. However, the motivation and aims of central governments in devolving political power can vary widely. In the Indian state of Karnataka, a state government in opposition to the national government wanted to demonstrate that it was more imaginative and democratic than its rival to promote its fortunes nationally, and hoped to develop grassroots support by putting in place a system of locally elected councils (C&M).

Ironically, giving life to local political institutions may increase the opportunity for collusion between locally-based elites and the center. This is the political legitimization of the "urban alliance" Lipton refers to in his description of urban 24
21 _p_ We estimate equation (III) for _p_  1,2,3  using OLS and then test whether _H_ 0 :  0 _bh_  0. Table 5 reports the results of this exercise. We always strongly reject the null hypothesis of no impact of exploration spending on discoveries at the 1% significance level.

**Table 5: The Impact of Exploration Spending on Natural Resource Discoveries**

||**P Value o**|**f Wald Test (H0: E**|**ffect is 0)**|
|---|---|---|---|
|Dependent variable|1 Lag|2 Lags|3 Lags|
|Number of Discoveries|0|0|0|
||**Point Est**|**imate (Sum of coe**|**fficients)**|
|Dependent variable|1 Lag|2 Lags|3 Lags|
|Number of Discoveries|0.013|0.013|0.013|

**Table 6: The Impact of Liberalization on Exploration Spending (2SLS)**

|VARIABLES|(1)<br>(log) Real<br>Exploration<br>Spending|(2)<br>(log) Real<br>Exploration<br>Spending|(3)<br>(log) Real<br>Exploration<br>Spending|(4)<br>(log) Real<br>Exploration<br>Spending|(5)<br>(log) Real<br>Exploration<br>Spending|
|---|---|---|---|---|---|
|SW/Wacziarg Openness, lagged|1.230***|0.903**|1.074**|1.079***|1.804***|
||(0.424)|(0.365)|(0.407)|(0.405)|(0.641)|
|(log) Price, lagged||0.930***<br>(0.193)|0.968***<br>(0.192)|0.966***<br>(0.192)||
|Stock of Discoveries, lagged|||0.0424***<br>(0.0119)|0.0641***<br>(0.0169)|0.223***<br>(0.00724)|
|Stock of Discoveries squared, lagged||||‐0.000325** <br>(0.000136)|‐0.00237***<br>(9.36e‐05)|
|Country FE|/|/|/|/|YES|
|Year FE|YES|YES|YES|YES|/|
|Natural Resource FE|/|/|/|/|/|
|Country by Natural Resource FE|YES|YES|YES|YES|/|
|Year by Natural Resource FE|/|/|/|/|YES|
|Observations|3,706|3,467|3,467|3,467|3,747|
|R‐squared|0.931|0.941|0.940|0.940|0.755|
|Estimation|2SLS|2SLS|2SLS|2SLS|2SLS|
||Year by|Year by|Year by|Year by|Year by|
|Error Clustering|Natural|Natural|Natural|Natural|Natural|
||Resource|Resource|Resource|Resource|Resource|
Figure 5: Change in employment relative to course start: completers and non-completers, UB registrants <!-- Start of picture text --> UB registrants<br>Course<br>-18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18<br>Months relative to start of employment training<br>Non-completers Completers<br>1.2<br>1.1<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>Parameter estimate with 95% CI .2<br>.1<br>0<br>-.1<br><!-- End of picture text --> Notes: Figure depicts the set of coefficients for course completers and noncompleters corresponding to ( _αk_ + _βk_ ) and ( _αk_ ), respectively, from equation 2 estimated for employment [0/1]. 95% confidence intervals constructed from heteroskedasticity-consistent standard errors clustered by individual and month.

47
female to male. The percentage of HIV positive women is greater compared to infected men in almost all age groups. The HIV infection rate is higher for men from 40 years and older. The prevalence of HIV is considerably higher among young women than among young men. Recent studies in Africa (Dupas, 2006) suggest an explanation for this pattern: unprotected sex between teenage girls and adult men. Men involved in sexual relationships with younger women are more likely to be HIV infected compared to younger boys because they have been sexually active for longer. Moreover, older men generally have a higher income and, therefore, have more power to negotiate unprotected sex.

# [Insert table 1]

The LDHS includes specific variables describing the coverage of HIV testing for 7140 individuals randomly selected to be tested. Among these eligible individuals, HIV tests were conducted for about 75 percent of women and men. Among the sample of all individuals eligible to be tested, about 11 percent were not interviewed in the DHS. Table 1 reports the coverage rate for HIV testing among eligible people selected for the HIV test as well as the test coverage among interviewed individuals, both for women and men. Women are more likely to have been tested than men: 86.2 percent of interviewed women and 81.4 percent of interviewed men took the HIV test respectively. About 15 percent of individuals refused testing when asked for consent, 0.37 percent who were interviewed in the survey were not at home at the time testing was conducted in the household, and others were missing test results for other reasons (e.g. technical problem prevented taking blood).

Table A1 in the appendix presents summary statistics by gender for the variables used in the empirical analysis. All the variables are weighted with the sample weights given by the data provider. In addition to HIV infection and HIV test, we used behaviors and attitudes related to HIV prevention as dependent variables. 40 percent of females tend to use condoms during extramarital intercourse, while 11 percent use this preventive method within the marriage. Unfortunately, the data provides this information only for women. As expected, males always report higher levels of extra marital sex. This could reflect actual behavior or could be due to overreporting by males and/or under-reporting by females. There is a limited discrepancy across gender regarding abstinence in the last 12 months, but the proportion of single 7
coefficient is positive in both regression (with and without rent sectors) but interestingly much higher in the regression without the rent sectors, confirming the value of the signaling effect when the nature and size of economic rents is harder to figure out.

Since the model allows for the computation of an estimate of the impact on probability of success of investment and constructing a metric of this impact at any given measurement for each significant variable, it becomes possible to construct an aggregate metric of the probability of successfully attracting FDI for each sector _at the level of performance observed_ for each country. We derive from these results a simple metric that produces an overall score for countries (Annex 1), reflecting their overall performance across the significant variables and how these relate to the likelihood of attracting investment (a high score representing higher likelihood).

# Revealed comparative advantage

Analogous to the FDI analysis, we first examine the overall classification performance of the empirical model using the receiver operating characteristic (ROC) curve. Figure 6 shows very good empirical performance of the estimated model for the trade flows too. The ROC curve is close to the top left corner and AuROC's value of 0.88 is close to the maximum attainable level. Figure 7 confirms that the model is highly informative about sector’s empirical performance, as all AuROC values are strongly above 0.5.

_Figure 6. ROC curve: RCA data_ Note: The figure reports estimated receiver operating characteristic (ROC) curve. Total positive rate is the share of model's correct classification of RCA in total number of sectors with RCA. False positive rate is the share of model's incorrect signals of RCA in total number of sectors without RCA. The curve is plotted for varying values of threshold probability (threshold probability determines whether the observation is classified as a signal of positive or zero RCA). Estimation and classification are performed over the in-sample 2015-19 period. Estimated area under the ROC curve (AuROC) equals 0.885 and significantly differs from 0.5.

21
**Table 1: Indicative burden on the public purse from recent events**

|**Country**|**Year**|**Disaster**||**Damage a**|**nd Loss**||**Estimated**<br>**Recovery &**<br>**Reconstruction**<br>**Requirements**|
|---|---|---|---|---|---|---|---|
||||**Damage**<br>**(US$m)**|**Loss**<br>**(US$m)**|**Total**<br>**(US$m)**|**Public**<br>**share**<br>**of**<br>**Total**<br>**(%)**|**Total (US$m)**|
|Seychelles|2013|Flood|5.2|3.1|8.3|70|30.3|
|Fiji|2012|Cyclone|67.2|41.2|108.4|17|67.0|
|Samoa|2012|Cyclone|103.3|100.6|203.9|55|206.0|
|Malawi|2012|Flood|1.4|1.5|2.9||7.3|
|Bhutan|2011|Earthquake|||24.5||22.63|
|Pakistan|2011|Flood|3,208.0|522.0|3,730.0||2,747|
|Japan|2011|Earthquake||210,200||||
|Thailand|2011|Flood|2,046|2,604.0|4,650.0|10|50,000|
|Djibouti|2011|Drought|51.7|157.3|209|67|318|
|Kenya|2011|Drought|805.6|11,300|12,105.6||1,770|
|New<br>Zealand|2010-<br>2011|Earthquake||15,950||15<sup>8</sup>||

**Source: Government of Seychelles, 2013; Government of Fiji ,2012; Government of Samoa, 2012; Government of Malawi, 2012; Royal Government of Bhutan, 2011; Government of Pakistan, 2011; Government of Japan, 2012; Government of Thailand, 2011; Government of Djibouti, 2011; Government of Kenya, 2011;  and New Zealand Treasury, 2011.**

Caution should be used when comparing PDNAs across countries, events and years. While an agreed framework for PDNAs have been developed the content can vary significantly depending on the sectors analyzed and the timeframe allocated for the assessment. The issue of timing is of great importance as they should not be conducted during the initial relief phase, first responders need time to ensure that the public are safe and have adequate shelter and basic provisions such as food and water. However, if the PDNA is conducted several weeks after an event it becomes difficult to assess the damage as the clear up will have begun; this is particularly true for the housing and infrastructure sectors where the cleanup begins almost immediately. As a rule of thumb PDNAs are normally conducted two to four weeks after the event. There are two distinct groups of people who conduct the PDNAs those with expertise of the PDNA framework and those with the in-depth country knowledge. PDNAs often involve an international team arriving in country to provide technical expertise and training to their incountry counterparts who hold the country expertise (and often sources of data) but have limited awareness on how to apply the PDNA framework. The quality of the PDNA is dependent on the strength of the relationship between these two distinct groups.

> 8 For losses only no damage figure obtained.

19
Table 1 Poverty and inequality estimates using survey-to-survey imputations compared to 2018/19 NLSS

||Poverty rate<br>US$1.90<br>(percent)|Poverty rate<br>US$3.20<br>(percent)|Gini<br>coefficient|Mean Annual<br>Consumption<br>(US$2011PPP)|Median Annual<br>consumption<br>(US$2011PPP)|
|---|---|---|---|---|---|
|2010/11 GHS|43.54|72.88|35.65|81.49|64.25|
|2012/13 GHS|42.49|72.12|35.51|82.67|65.41|
|2015/16 GHS|40.75|70.44|35.88|85.62|67.44|
|2018/19 GHS|41.88|72.28|34.94|82.39|65.86|
|2018/19 NLSS|39.09|70.98|35.13|85.13|68.77|

Note: the table shows the results of the survey-to-survey imputation in each round of the GHS. We develop a consumption model using data on 23 non-monetary indicators and household consumption available in the 2018/19 NLSS. Using the estimated parameters, we then impute in each round of the GHS using the same non-monetary indicators and 100 imputations. The imputed consumption vector is then converted to 2011 PPP and poverty estimates are reported at the US$1.90 and US$3.20 poverty lines. Gini coefficients are calculated as the average of 100 estimates resulting from each imputation.

Table _1_ (and Table _9_ in the Appendix) shows the results of the survey-to-survey imputations in each round of the GHS. The imputed poverty headcount rate at the US$1.90 poverty line is 43.5 percent in 2010/11, decreasing to 41.5 percent in 2012/13 and to 40.5 percent in 2015/16. The imputed consumption vector can also be used to estimate the poverty rate at the higher poverty line of US$3.20 per person per day and to produce measures of inequality (Table _1_ ). The latter show that inequality has barely changed between 2010/11 and 2018/19, which reinforces the assumption of distribution-neutral pass-through rates adopted in the backcasting exercise.

Overall, therefore, we find that the results are robust to using two completely different methodologies. While the imputed poverty headcounts rates are on average 2 percentage points higher than their backcasted counterparts, they show a poverty trend that runs parallel to the backcasted series (see Figure _6_ in section 7). The next section presents additional sensitivity checks that confirm these findings.

# 6. Robustness and sensitivity analysis

# _<u>6.1. Backcasting</u>_

While it is not possible to relax all the assumptions behind the backcasting exercise, we can test the sensitivity of the results to different assumptions about how much of the growth in national accounts is passed onto growth in household consumption. Extensive literature shows that growth in national 15