rafmacalaba/datause-encoder-data huggingface dataset:
outcomes between PSNP beneficiary and non-beneficiary households. We employ the following difference-in-difference specification: h 0P P 1P P h ∗P P (1) htt tt tt htt YY = αα + ββ + ββ + εε where stands for food security and related maternal and child diet measures for household _h_ and round htt _t_ . h stands for household fixed effects, which capture all time-invariant differences YY between PSNP beneficiaries and non-beneficiaries. P P h represents households’ access to PSNP αα transfers. P P is a dummy variable, assuming a value of 1 for the phone survey conducted during the COVID-19 pandemic (henceforth post-COVID-19 round) and 0 for the pre-COVID-19 round. tt The parameter associated with this time dummy captures aggregate trends in food security or potential differences in our outcomes of interest driven by differences in survey methods (face-toface or phone survey).

face or phone survey). The key parameter of interest in equation (1) is 1. This parameter identifies potential differences in temporal evolution of food security outcomes of PSNP beneficiaries and nonββ beneficiaries. In the absence of unobservable time-varying variables with differential impact on PSNP beneficiaries, this parameter can be interpreted as the protective impact of the PSNP against income losses caused by the COVID-19 pandemic. To be credible as an estimation strategy, this requires that, in the absence of the pandemic, food security status of PSNP and non-PSNP beneficiaries would follow similar trends. To indirectly probe this assumption, we run the same specification using the two pre-COVID-19 surveys (March 2019 and August 2019) and assess changes in our outcomes of interest. As shown in Table A4, the trends in food gap and diet diversity of mothers and children across these two rounds for PSNP and non-PSNP beneficiaries were similar.

In all our specifications, we control for observable potential time-varying confounders such as the locust invasion that hit parts of the country. We include a categorical variable that measures whether households were affected by locust swarms. The use of alternative definitions of access to PSNP helps us to probe the robustness of our findings to potential time-varying unobserved factors. Our outcome variables are measured as binary and count outcomes. Hence, we estimate both linear and nonlinear specifications.

12
Table 3. Effects of the PEP Program on Access to Government Programs

|_Dep Variable:_|SISBEN [=1]<br>(1)|Subsidized health care [=1]<br>(2)|Transfers [=1]<br>(3)|
|---|---|---|---|
|PEP [=1]|0.492***|0.114***|0.330***|
||(0.021)|(0.016)|(0.020)|
|Observations|3,873|3,959|3,903|
|Wave FE|Yes|Yes|Yes|
|HH FE|Yes|Yes|Yes|
|Department_×_wave|Yes|Yes|Yes|
|Geographic Sampling_×_wave|Yes|Yes|Yes|
|Pre-migration controls_×_wave|Yes|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1) using variables on access to government programs as main outcomes. Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Premigration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a written contract, and the time gap between the last job and the migration episode); (iii) household characteristics (number of children, household size, access to public services, owning dwelling, and having a smartphone); and (iv) networks prior to migration episode (had family and friends in Colombia, knew of job opportunities before migrating, and migrated for health-related reasons). Standard errors clustered at the household level in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

26
**Table 1.  A more complete picture of the PK results when observations are trimmed**

(full sample = 5218, asymptotic t-ratios in parenthesis)

||Trim N<br>consu<br>observ<br>Weight|largest<br>mption<br>ations<br>ed OLS|Symmetr<br>Weight|ically trim N<br>resi<br>ed OLS|largest and N<br>duals<br>Instrument<br>bynonli|smallest<br>al variables<br>near ML|
|---|---|---|---|---|---|---|
|N<br>(observations<br>trimmed)|Female|Male|Female|Male|Female|Male|
||(1)|(2)|(3)|(4)|(5)|(6)|
|16|0.0040|0.0054|**0.0051**|**0.0056**|-.0144|.0133|
||(1.91)|(1.98)|**(2.51)**|**(2.07)**|(-1.00)|(1.14)|
|100|0.0042|0.0043|**0.0089**|**0.0038**|.0193|.0115|
||(2.18)|(1.71)|**(4.88)**|**(1.60)**|(1.26)|(1.37)|
|500|0.0050|0.0041|**0.0207**|**0.0060**|.0292|.0063|
||(2.87)|(1.98)|**(14.52)**|**(3.42)**|(3.56)|(0.81)|
|1000|0.0055|0.0034|0.0306|0.0094|**.0386**<sup>**a**</sup>|**.0141**|
||(3.46)|(1.78)|(28.27)|(7.24)|**(9.85)**|**(2.44)**|

aExogeneity rejected (p=0.034) when N=1000 observations are trimmed from each tail. Consequently, the estimates in bold are the ones that correspond to the PK protocol.

22
Figure 10a: China’s role in developing East Asia’s participation in GVCs China’s share in the total FVA embodied in a A country’s share in DVA embodied in China’s gross country’s gross exports (percent) exports (percent) <!-- Start of picture text --> 35 50<br>KHM TWN<br>45<br>30<br>KOR<br>40<br>HKG<br>25 35 HKG<br>PHL JPN<br>30 MYS<br>20 CHL<br>THA<br>AUS<br>VNM 25<br>CRI VNM<br>1510CZERUS PHL MEXCOL MYS SGPAUS USATWNPER IDN JPNBRN THA INDKOR 201510 BRAISRPERCHDSGPSAU E ARGUZAFINDCANUSA KHM NZL IDN<br>BRA<br>RUS<br>5<br>5<br>0 0<br>0 5 10 15 20 0 5 10 15 20 25 30<br>1995 1995<br>2011<br>2011<br><!-- End of picture text --> Source: OECD TiVA and authors’ calculations. Notes: 45‐degree line shown as dotted line. FVA stands for “foreign value added”; DVA stands for: “domestic value added.” Labels indicate developing East Asian countries and other selected countries. Note: Trade refers to both goods and services.

Figure 10b: China’s role in developing East Asia’s participation in GVCs: focus on the electrical and optical equipment industry China’s share in the total FVA embodied in a country’s gross exports of electrical and optical equip. (percent) A country’s share in DVA from electrical equip. sector embodied in China’s gross exports (percent) <!-- Start of picture text --> 30 60<br>TWN<br>KOR<br>MYS<br>HKG<br>25 50 PHL<br>JPN THA JPN<br>VNM<br>KHM<br>20 MEX KORUSA 40 CRI VNM<br>CZECOL IDNTHA MLT<br>15 CAN AUSARG PERTWN 30 IDN SGP<br>BRA MYS ISR<br>10HUN PHL POLRUSZAFCHLGBRNZL TURIND 20 IRL CHENORDEU USACAN AUS<br>SGP KHM GBR FRAINDCHLFINNZL HKG<br>SWE<br>ROW<br>5 10 RUS<br>ESP<br>LUX ROU<br>0 0<br>0 2 4 6 8 10 12 14 0 5 10 15 20 25 30<br>1995 1995<br>2011<br>2011<br><!-- End of picture text --> Source: OECD TiVA and authors’ calculations. Notes: 45‐degree line shown as dotted line. FVA stands for “foreign value added”; DVA stands for: “domestic value added.” Labels indicate developing East Asian countries and other selected countries.

19
**Table 14: Asset index weights**

||Dir<br>affect<br>|ectly<br>ed areas<br>|Non-<br>affecte<br>|directly<br>d areas<br>|
|---|---|---|---|---|
|Variable|Weight|Inertia(%)|Weight|Inertia(%)|
|Water Source|||||
|Tap water|-0.494|0.055|-0.802|0.075|
|Private water|0.845|0.122|1.093|0.092|
|Well water|-0.511|0.013|0.615|0.021|
|Water Filtration|||||
|Filtration product*|-0.354|0.025|-0.294|0.009|
|Rudimentary method|0.102|0.000|-0.057|0.000|
|Cooking Fuel|-0.232|0.026|-0.234|0.021|
|Lighting|||||
|Electricity|0.405|0.049|1.094|0.131|
|Lamp|-1.273|0.218|-0.803|0.166|
|Toilet|||||
|Latrine|-0.134|0.007|0.221|0.010|
|WC|1.467|0.095|1.391|0.017|
|Oven|1.369|0.086|1.066|0.006|
|Heater|0.003|0.000|0.196|0.009|
|Hot water tank|-0.133|0.009|-0.235|0.020|
|Television|0.402|0.050|1.112|0.115|
|Radio|0.102|0.004|0.411|0.029|
|Cell phone|0.018|0.000|0.113|0.003|
|Bicycle|0.215|0.003|0.933|0.039|
|Motorcycle|0.373|0.004|1.04|0.026|
|Flatiron|0.155|0.008|0.172|0.006|
|Fan|0.597|0.069|1.359|0.093|
|Car|1.092|0.029|0.758|0.007|
|Sewing machine|0.308|0.004|0.555|0.012|
|<br>Tools/Material|-0.951|0.122|-0.588|0.090|
|Small business stocks|0.089|0.001|0.068|0.001|
|Partial inertia contribution(%)|14.78||19.16||

Source: Own computations using June 2010 (EFSA II) surveys. Note: *Filtration products are generally used in relatively poor regions so that it can explain the negative weight.

Using retrospective data on assets from the June survey, Figure 4 presents the asset index distributions before the earthquake, in February and in June. Using this index, we can notice that the inequality of household wealth (as measured by the Gini coefficient) has decreased after the earthquake due to higher losses among the wealthiest. This is particularly true among households living in camps (Gini is 0.2446 before the earthquake and 0.1970 in February). Then, between February and June, inequality of household wealth has increased—from 0.3267 to 0.3325 among non-camp households and from 0.1970 to 0.2183 among camp households.

39
While numerous studies have explored the roles of personality traits and gender role attitudes on labor market outcomes, most of these studies have focused on Western societies, and a few on Asia. However, there is notable absence of such studies in the Ghanaian context, rendering this paper crucial for Ghana and, by extension, Africa. This study explores the respective contributions of gender role attitudes and personality traits to labor market outcomes and examines their interactions within the context of a developing country.

Ghana is an interesting country to study for a number of reasons. Firstly, Ghanaian society is predominantly patriarchal, charactarized by strong cultural influences that govern daily life. Secondly, there are substantial disparities in labor force participation between men and women, with men’s participation being approximately 8 percentage points higher than that of women, largely attributed to cultural and gender role attitudes (DTDA, 2020). Thirdly, women in Ghana are disproportionately engaged in family and own-account work, with 31% of women involved in own-account work compared to 11% of men (GSS, 2014). They often opt for low-productivity jobs that provide flexibility for domestic chores (Owoo & LambonQuayefio, 2022). Lastly, the availability of household-level data, including genderdisaggregated information on labor market indicators, as well as personality traits and gender role attitudes, provides a vital context for this study. This dataset covers two independent regions in Ghana with diverse cultural background and gender perspectives.

We employ both descriptive and econometric approaches to achieve the study’s objectives. To understand the drivers of labor market participation decision, we use the multinomial logit (MNL) model with the dependent variable comprising three mutually exclusive categories: not in the labor force, employed, and unemployed. The choice between wage and self-employment is examined using the linear probability model (LPM).

The empirical results suggest that both gender role attitudes and personality traits significantly influence individuals’ decisions regarding labor market participation and employment choices in Ghana. In most cases, gender role attitudes tend to mask the effect of personality traits. The gender-disaggregated analysis shows that having at least secondary education is critical for selection into employment of a male, but not for females. Once the decision to participate in the labor market has been made, having at least secondary education becomes relevant for securing a wage job, irrespective of the individual’s gender.

The remainder of the paper is structured as follows: Section 2 presents a review of relevant literature, while Section 3 presents the data used in the analyses. In Section 4, we 3
**Appendix Table 2:** Determinants of regional enrollment growth (1999-2003) – alternative specification, OLS estimates

|total female enrollment in 1999 in the region, grade 6-10 (in logs)|-0.293<br>(40.54)**|
|---|---|
|Female enrollment in 1999 in the region, grade 6 (in logs)|--|
|Female enrollment in 1999 in the region, grade 10 (in logs)|--|
|# of madrasas in the region, 1999|0.028<br>(8.81)**|
|# of schools in the region, 1999|0.060<br>(22.82)**|
|Δin # of madrasa (1999-2003)|0.075<br>(3.53)**|
|Δin # of school (1999-2003)|0.092<br>(7.37)**|
|Mean age of schools|-0.001<br>(4.29)**|
|Fraction of madrasas in the region being “converts” (established pre-1980)|0.024<br>(2.68)**|
|Urban|-0.016|
||(1.03)|
|Govt (=1 if a government secondary school present)|0.041|
||(2.32)*|
|Constant|1.882|
||(47.01)**|
|N|4784|
|R-squared|0.32|
|Fixed-effects|Thana|

Note: Absolute value of t statistics in parentheses. + significant at 10%; * significant at 5%; ** significant at 1%.

17
The agricultural production section of the survey collects information on agricultural income and assets; land, livestock and equipment; harvest and disposal of crops; seasonality of sales and purchases (key staples only); and other agricultural income (both in cash and kind). Information on the production of agricultural food is collected at a different frequency. Information on household produce sales during the last 12 months is collected for certain items, such as staple grains, field crops and cash crops, including the value of sales from hunting, honey, fruit/berries, milk, other dairy products, eggs, hides, wool and skin, and mushrooms output. On the other hand, for roots, fruits, vegetables, and other crops harvested piecemeal, respondents are asked how much the household sold in the past two weeks. We converted these two week estimates to a yearly value of sales.<sup>18</sup>

**Table 1: Household per capita expenditure on food and non-food by zone**

||Per Capita<br>Food<br>Expenditure|Per Capita<br>Non- Food<br>Expenditure|Total Per<br>Capita<br>Expenditure|
|---|---|---|---|
|South South|17,287|19,199|36,486|
|South East|22,314|22,902|45,216|
|South West|16,533|26,696|43,229|
|North Central|14,740|15,067|29,806|
|North East|15,364|12,171|27,535|
|North West|16,907|11,176|28,083|
|National|17,094|18,506|35,600|

Source: National Bureau of Statistics (2004)

# **5. Empirical framework**

We use these indices in the regression framework to measuring the impact of price shocks on conflict. The basic specification reads as follows: where C is a measure of conflict (e.g. number of conflict episodes, number of violent episodes, number of conflict-related fatalities), _P_<sup>_oil_</sup> is the oil price index, _d2009_ is a post-2009 dummy which captures the period after the amnesty deal between the state and the militant groups in the Niger Delta; Z and X are vectors of time invariant and time varying state-level covariates of conflict respectively, α are region-time fixed effects, which capture any time varying effect in each of the six macro-regions in Nigeria.

> 18 One way of converting this is to multiply the two week estimate by 26 to get a total of 52 weeks’ value of sale. However, inconsistency in the values reported for cassava, yam and plantain, which reported data on both two weeks and annual sales, shows that multiplication of the two weeks value by 26 is not a consistent estimate of the yearly value.  We therefore elected to predict the yearly value produced by each household, by applying an average of the relationships between the yearly value and the two weeks value reported for cassava, yam and plantain to the other items.

13
2 national level, labor-exporting countries do not typically keep track either of the number or the skill characteristics of migrants. And while some labor-receiving countries do keep track these issues, they often use different definitions of immigration. As a result of these data problems, a host of key policy questions remain unanswered. Exactly how pervasive is the brain drain? Which countries or regions of the developing world are most affected? Does international migration deprive labor-exporting countries of a sizeable fraction of their "best and brightest," or are their numbers too small to worry about?

This paper proposes to answer these, and similar questions, by developing a new data set composed of 24 large, labor-exporting countries.<sup>2</sup> This data set includes all those developing countries which received more than $500 million dollars in official worker remittances in the year 2000.3 To ensure representativeness, the data set includes countries drawn from each major region of the developing world: Latin America and the Caribbean, Middle East and North Africa, Europe and Central Asia, East Asia, South Asia and Sub-Saharan Africa.

The paper uses this new data set to do the following three tasks. First, in order to provide some perspective on the importance of international migration, the paper shows how the level of worker remittances received by these 24 labor-exporting countries has increased over time. Second, using data collected by the two main labor-receiving

> 2 The 24 study countries include: Brazil, Colombia, Dominican Republic, El Salvador, Guatemala, Janaica, Mexico, Peru (Latin America and Caribbean); Egypt, Morocco, Tunisia (Middle East and North Africa); Albania, Armenia, Croatia, Turkey (Europe and Central Asia); China, PR, Indonesia, Philippines (East Asia); Bangladesh, India, Pakistan, Sri Lanka (South Asia); and Nigeria and the Sudan (Sub-Saharan Africa).

> 3The source of all data on official worker remittances in this study is: International Monetary Fund, Balance of Payments Statistics Yearbook.
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- Salvanes, K. (2023). What are the Drivers Intergenerational Mobility? The Role of Family, Neighborhood, Education, and Social Class: A Review of Bukodi and Goldthorpe's Social Mobility and Education in Britain. Journal of Economic Literature. Forthcoming.

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- Tilak, J. (1993). East Asia. In Hiil, A. and King, E., editors, Women's Education in Developing Countries: Barriers, Benets, and Policies, chapter 7, pages 247279. World Bank.

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36
same question must be asked of every respondent. While it seems obvious to ask all respondents exactly the same question, in practice interviewers often deviate from the survey script. Fowler and Mangione (1990, p.35) note four studies that tracked interviewer-respondent interactions found that interviewers deviated from their script on 20 to 40 percent of the questions on the survey.

It is not possible to ensure that all interviewers follow the script to the letter all the time. Indeed, turning interviewers into little more than programmed question-readers could have unintended consequences by preventing them from interacting properly with the respondent. Nonetheless, survey designers can take steps to minimize this source of error. Specifically, the survey designer should recognize that the more likely a respondent is to interrupt the question, the more likely the interviewer is to change the question wording. The survey writer should design questions that need little additional explanation. Pretests can measure which questions respondents tend to interrupt for clarification or which questions seem most likely to lead to adlibbing by the respondent. The researcher can use the pretest information to change the questionnaire to minimize opportunities for the interviewer to change the written question.

The issues discussed above are general ones to keep in mind when developing and implementing a survey. Many more specific issues are inherent to most surveys. Those issues are discussed in the sections below.

# **Response scales**

Many surveys ask respondents to rate issues on some scale (say, **1** to 5). In World Bank surveys, such questions typically ask about infrastructure, regulation, or competitors. If phrased properly, such questions may tell us what a firm views as problematic relative to other issues presented in the questionnaire. Such methods, however, should be used with caution. First, they are inherently subjective and respondent- (rather than firm-) specific, making interpretation difficult. Second, respondents have little incentive to answer truthfully. Indeed, they may have an incentive to provide answers they perceive as beneficial to themselves. A firm whose production process generates a great deal of pollution, for example, has an incentive to report that 26
|Figure 12. Hidden costs|26|
|---|---|
|Figure 13. Hidden costs|27|
|Figure 14. Botswana’s Internet market benchmarked against its southern African peers|30|
|Figure 15. Botswana’s infrastructure spending needs are among the lowest in the region, relative to GDP|32|
|Figure 16. Botswana’s existing infrastructure spending level is adequate|33|
|Figure 17. Botswana’s pattern of capital investment in infrastructure differs from that of comparator countries|34|
|Figure 18. Many African countries capture more private investment than Botswana|34|

# List of tables

|Table 1. Achievements and challenges in Botswana’s infrastructure sectors|6|
|---|---|
|Table 2. Botswana’s road indicators benchmarked against Africa’s low- and middle-income countries|11|
|Table 3. Botswana’s governmental investments in roads|12|
|Table 4. Railway indicators for Botswana and select other countries, 2000–05|14|
|Table 5. Benchmarking air transport indicators for Botswana and select other countries|16|
|Table 6. Botswana’s irrigation potential|19|
|Table 7. Benchmarking water and sanitation indicators|20|
|Table 8. Evolution of operational indicators associated with the Botswana Water Utility Corporation|22|
|Table 9. Benchmarking power indicators|24|
|Table 10. Performance of the electricity sector in select African countries|25|
|Table 11. Evolution of hidden costs associated with the Botswana Power Corporation (BPC)|25|
|Table 12. Benchmarking ICT indicators|28|
|Table 13. Illustrative investment targets for infrastructure in Botswana|31|
|Table 14. Indicative infrastructure spending needs in Botswana, 2006–15|31|
|Table 15. Financial flows to Botswana’s infrastructure, average 2001 to 2007|33|
|Table 16. Botswana’s historical losses due to operational efficiency (as of 2008)|35|

iv