rafmacalaba/datause-encoder-data huggingface dataset:
- _(1) Please imagine a ladder with steps numbered from zero at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?_

- _(2) On which step do you think you will stand about five years from now?_

Accordingly, the average life evaluation index is a two-item ordinal scale (Cronbach’s alpha of .91), accounting for both current and expected future life evaluation. The current and expected life satisfaction scores are combined, as it is reasonable to expect that not only current evaluations, but also expectations for the future indicate grievances. This is in line with the modernization theory which posits that improvements in living standards raises people’s expectations about their future well-being. As the answers to the Cantril Ladder questions are ordinal, and hence cannot be treated as continuous, we group respondents into three categories and calculate the percentage of people in each category. _Suffering_<sup>_6_</sup> individuals are those with current and future life satisfaction scores that are equal or less than 4. Individuals are considered _struggling_ when their current life satisfaction scores are 5 or 6 and their future life satisfaction scores are equal or greater than 5 and less than or equal to 7. _Thriving_ individuals are those with current life satisfaction scores of 7 or above and future life satisfaction scores of 8 or above.

The country-level SWB index used as a variable in the regression model represents the share of respondents who report to be _suffering_ . Country-level weights are applied to this calculation and a respondent must have answered both questions to be included in the calculation.<sup>7</sup> People who identify themselves as s _uffering_ are likely to lack the capabilities that allow them to make autonomous decisions and to pursue a fulfilling life (Graham & Nikolova, 2015). The percentage of _suffering_ individuals is negatively correlated with GDP per capita

> 6 We also refer to these individuals as the unhappy or dissatisfied people.

> 7 To ensure that the results are not driven by the way the Gallup World Poll categorizes responses into _suffering_ and _struggling,_ we rerun our baseline model with the average life evaluation as obtained with the Cantril Ladder and we confirm the results reported here.

12
||City reside|ntsworkin|g in the su|burbs|Suburban re|sidents Wo|rking in th|e cities|
|---|---|---|---|---|---|---|---|---|
||1960|1970|1980|1990|1960|1970|1980|1990|
|Chicago|6.6|16.1|18.4|22.5|34.6|27.1|22.5|25.6|
|Cleveland|7.7|24.4|28.6|30.3|52.4|43.5|34.8|32.5|
|Detroit|17.3|32.1|34.3|36.4|33.5|24.6|16.9|19.4|
|Minneapolis-St. Paul|6.6|19.7|24.5|29.8|52.1|43.5|31.2|30.5|
|St.Louis|8.3|21.1|24|35.9|36.7|30|25.4|27.9|
elicited expectations are useful in predicting the future outcomes of events of interest, and in predicting economic behaviors. Given all this, Section 6 concludes that these measures should be used more in developing country surveys.

# **2. Methods for collecting expectations data**

In developing countries, most data collection is done in person, that is, with an enumerator visiting the home or workplace of the respondent. In this paper we focus on methods for eliciting expectations that can be collected in the course of a one-on-one interview.<sup>2</sup> These methods have also been used in online surveys, although the use of such surveys is rare in developing countries, and is typically restricted only to surveys of highly educated and/or wealthy individuals.<sup>3</sup> The methods used for eliciting expectations differ depending on whether the researcher is interested in eliciting a point estimate or the whole distribution.

# _2.1. Eliciting a point estimate_

To elicit a point estimate, Likert scales have been used as well as cardinal scales.

# _Likert scale_

Although Likert scales have been widely used in Psychology to assess the degree to which respondents agree with certain statement, they have also been used to elicit the likelihood that an event will occur. In the context of probability elicitation, such a question may read: “How likely is it that it will rain tomorrow?” The responses come from a three, five or seven point scale. The five point scale for a probabilistic question is “Very likely”, “Likely”, “Neither likely nor unlikely”, “Unlikely” and “Very unlikely”. One of the problems with the scale is that different respondents may interpret the scales differently, and that the scale can be related with the “true” probability of rainfall, but

> 2 The interested reader can consult Chapter 10 in Tourangeau, Rips and Rasinski (2000) for a critical comparison of different modes of data collection.

> 3 An example is the survey of the top academic achievers from a number of Pacific Islands discussed in Gibson and McKenzie (2008), which used the percent chance formulation to ask expectations questions about migration using online and in-person questionnaires.

- 3 -
parameters. In most cases researchers will not have data on the autocorrelation of the outcome of interest. It is hoped that the data provided in this paper for different outcomes from different surveys will therefore provide a starting point for researchers, and that more researchers will report these autocorrelations in future experiments.

**7.** _Can we just obtain multiple measures more cheaply by recall?_ A cheaper alternative to conducting multiple post-treatment or pre-treatment rounds is to ask multiple measurements in a single survey, with recall. While intuitively appealing, the value of this approach is likely to be limited in practice, since it is precisely the types of variables that are noisiest and hardest to measure in practice for which the data are typically less autocorrelated and for which multiple measures are of most value. For small informal firm owners who do not keep books, asking profits with recall over multiple months is difficult, while asking expenditure month by month or quarter by quarter with recall is likely to be highly inaccurate. Gibson and Kim (2010) show even with wage workers in the United States that retrospective recall is problematic, with workers underreporting transitory variation in earnings, creating non-classical measurement errors. However, such a strategy could be used in cases where good records are available, such as in experiments with larger firms which have accurate books or records.<sup>11</sup>

**8.** _To baseline or not?_ The power calculations above suggest that, in a number of cases, it can be optimal in terms of power to conduct a single post-treatment survey of size _n=K_ than to do a baseline and follow-up with _n=K/2_ . However, there are several other factors to consider in choosing whether or not to use a baseline. A baseline can be used to stratify the randomization on key variables, improving power and providing a basis for examination of treatment effect heterogeneity. It is often used to examine what determines take-up of some intervention. It can be useful for verification of randomization in cases where there is a risk of the randomization not being implemented perfectly, and can also be used to test whether attrition is non-random in terms of baseline characteristics. Finally a baseline, and especially multiple pre-treatment surveys, offer the possibility of matched difference-in-differences as a back-up evaluation strategy in cases

> 11 See e.g. Bloom et al. (2010) who collect daily and weekly production data from plants with over 100 workers each once a month.

24
differences in geographical availability of the different kinds of maize (as opposed to just prices and preferences). If we suppose that where a product is widely available we would typically observe at least _some_ households consuming it, then we can infer that where we find no households consuming a product, it is probably not widely available. The first two columns of Table 1 show the percentage of survey clusters (small geographical areas) where each product was consumed in the week of the survey, broken down by urban/rural. Given that only 64 percent of rural (and 63 percent of urban) clusters consumed loose flour _at all_ , it is highly likely that in at least some of these clusters, this product was not available. The last two columns of the table show the percentage of households overall that consume each kind of maize – this, in comparison with the clusters columns, gives a sense of how the household’s consumption bundle of types of maize varies widely across the country. In addition to what is shown in the table, most households only consumed one (49 percent) or two (35 percent) of the four types of maize during the week of the survey.

**Table 1: Geographical consumption of maize**

||**% clusters consuming**|**% househ**|**old consuming**|
|---|---|---|---|
||Rural<br>Urban|Rural|Urban|
|**Green**|43%<br>80%|14%|26%|
|**Grain**|95%<br>71%|66%|25%|
|**Loose flour**|64%<br>63%|29%|19%|
|**Sifted flour**|55%<br>96%|26%|63%|

_Source: KIHBS 2004/5, author calculations_

# **The fact that many households grow maize for their own consumption and for sale must**

**be taken into consideration for the analysis of the welfare effects.** Roughly half of all Kenyan households grow maize, although only 14 percent sell _any_ of their produce<sup>87</sup> . The left hand panel of Figure 11 shows mean annual sales and consumption of own-production of maize across income deciles. Notice that unlike demand, we have a strong inverse U-shape for supply – those in the middle of the distribution produce the most.  Those that grow and sell are concentrated within rural areas, although there are a few urban households that grow maize.

**Relative to their incomes, people across most of the lower and middle of the distribution produce a significant volume of maize.** The right hand panel of Figure 11 shows the value of own-production maize consumed and sold, as a percentage of total expenditure, across expenditure deciles.  Notice here how much higher the bars are in the poorer end of the distribution (as compared to the left hand panel of Figure 11) – even though these households do not produce that much maize, their production is significant relative to the size of their total consumption levels.

> 87 Estimated from the KIHBS 2005/6, as the percentage of household’s with non-zero annual incomes from maize of maize, accounting for household weights.

24
**_Energy demand models for policy_** **_<u>formulation</u>_**

T = fiscal regime SF = seasonal factors Energy demand by fuel is considered to a function of exogenous macro and sectoral variables (such as GDP, private consumption, industrial production, etc.) and real energy prices.

Df = f(Q, Pf, Ps, DD, SF, ..) (7) Where Df = demand for fuel f Q = economic activity Ps = price of competing fuel DD = Climatic conditions (degree-days) The dynamic adjustment process is introduced generally through Kyock lags, although the lag system is redesigned for each re-estimation of the model.

Fuel substitution is captured through relative prices of fuels and technological progress/ structural change is captured through a trend.

# **5.1.2 Generic energy forecasting models**

# **MAED model**

This is a widely used bottom-up model for forecasting medium to long-term energy demand. MAED (acronym for Model for Analysis of Energy Demand) falls in the MEDEE family of model developed by B. Chateau and B. Lapillonne (IAEA (2006), Lapillonne (1978)) but has been modified now to run on PCs and using EXCEL.

The earlier versions of the model were built around a pre-defined set of economic activities and end-uses. Manufacturing industry was broken into four sub-sectors while 82
MULTILINGUAL CHILD ASSESSMENT

- Peña, E. D., Bedore, L. M., & Kester, E. S. (2015). Assessment of language impairment in bilingual children using semantic tasks: two languages classify better than one. _International Journal of Language and Communication Disorders_ , _51_ , 192–202.

Piper, B., & Miksic, E. (2011). Mother tongue and reading: Using early grade reading assessments to investigate language-of-instruction policy in East Africa. In A. Gove & A. Wetterberg (Eds.), _The early grade reading assessment: Applications and intervention to improve basic literacy_ (pp.139–182). Research Triangle Park, NC: RTI Press. Retrieved from <u>http://www.rti.org/pubs/bk–0007–1109-wetterberg.pdf.</u> Piper, B., Schroeder, L., & Trudell, B. (2016). Oral reading fluency and comprehension in Kenya: Reading acquisition in a multilingual environment. _Journal of Research in Reading_ , _39_ (2), 133–152.

- Piper, B., Zuilkowski, S. S., Kwayumba, D., & Oyanga, A. (2018). Examining the secondary effects of mother-tongue literacy instruction in Kenya: Impacts on student learning in English, Kiswahili, and mathematics. _International Journal of Educational Development_ , _59_ , 110–127.

- Piper, B., Zuilkowski, S. S., & Ong’ele, S. (2016). Implementing mother tongue instruction in the real world: Results form a medium-scale randomized controlled trial in Kenya. _Comparative Education Review, 60_ (4), 776–807.

Potgieter, A. P., & Southwood, F. (2016). A comparison of proficiency levels in 4-year-old monolingual and trilingual speakers of Afrikaans, isiXhosa and South African English across SES boundaries, using LITMUS-CLT. _Clinical Linguistics and Phonetics_ , _30_ (2), 87–100.

26
**Table 14. Allowing for Persistence in the Breakdown of Expenditures**

|Dependent variable:|Consumption<br>expenditure/GDP (%)|Capital<br>expenditure/GDP (%)|Wage<br>expenditure/GDP (%)|
|---|---|---|---|
|Constant|-0.368<br>(-0.68)|0.768*<br>(1.75)|0.167<br>(1.02)|
|Lagged dependent<br>variable|0.925***<br>(77.59)|0.888***<br>(20.92)|0.922***<br>(23.98)|
|Dummy fuel > 25%<br>exports|0.0067<br>(0.03)|0.429**<br>(2.45)|-0.0044<br>(-0.06)|
|Dummy ores and<br>metals > 10%<br>exports|-0.148<br>(-0.98)|0.296**<br>(2.25)|0.0609<br>(0.93)|
|Hard peg dummy|-0.394*|0.271*|-0.0953*|
||(-1.94)|(1.83)|(-1.66)|
|Political stability|0.0336***|-0.0046|0.0061*|
|(0 to 100)|(3.58)|(-0.89)|(1.64)|
|Time trend|0.0316*|0.0007|0.0042|
|(=0 in 2000)|(1.69)|(0.07)|(0.60)|
|n|1,244|1,186|1,082|
|R-squared|0.915|0.812|0.903|
|RMSE|2.64|1.74|0.982|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

27
Table 13: Relationship of endline work hours and post-training outcomes

|Total hrs per week<br>per rubber plot<br>hectare|Planter hrs per week<br>per rubber plot<br>hectare|Spouse hrs per week<br>per rubber plot<br>hectare|HH hrs per week per<br>rubber plot hectare|Hired labor hrs per<br>week per rubber plot<br>hectare|
|---|---|---|---|---|
|Panel A: Individual training observations|with spouse tasks as a|main explanatory variab|le (449 observations)||
|Spouse tasks with sole responsibility<br>3.512|0.787|0.440|-0.340**|2.787*|
|(2.26)|(0.67)|(0.40)|(0.14)|(1.51)|
|Ind. TrainingMean<br>18.900|5.791|2.284|2.211|7.899|
|Panel B: Couples training observations|with spouse tasks as a m|ain explanatory variabl|e (478 observations)||
|Spouse tasks with sole responsibility<br>0.339|-0.086|0.023|-0.044|0.548**|
|<br>(0.40)|(0.09)|(0.07)|(0.05)|(0.26)|
|Couples Training Mean<br>22.094|6.277|2.862|2.304|9.683|
|Ind Training=Couples Training (p-val)<br>014|017|028|004|012|
|. <br>.<br>Panel C: Individual training observations with<br>Planter willing to delegate<br>3.421|.<br>planters’ opinion on sp<br>-0.803|.<br>ouse ability to make de<br>-0.015|.<br>cisions (267 observations<br>-0.479|.<br>)<br>3.486|
|<br>(4.62)|(1.37)|(0.81)|(0.81)|(2.75)|
|Ind. Training Mean<br>19.528|5.907|2.475|2.212|8.197|
|<br>Panel D: Couples training observations with|planters’ opinion on spo|use ability to make dec|isions (311 observations)||
|Planter willing to delegate<br>5.913|2.501**|0.962|1.320|0.287|
|<br>(4.33)|(1.25)|(0.74)|(0.80)|(2.88)|
|Couples Training Mean<br>23.700|6.501|3.116|2.542|10.709|
|Ind Training=Couples Training (p-val)<br>067|005|033|008|038|
|. <br>.<br>Panel E: Individual training observations with|.<br>planter opinion on who|.<br>should control rubber i|.<br>ncome (280 observations|.<br>)|
|Who should control rubber income<br>-0.952|-0.595**|-0.266|-0.224|0.081|
|(0.88)|(0.28)|(0.17)|(0.30)|(0.56)|
|<br>Ind. Training Mean<br>19.159|<br>5.971|<br>2.441|<br>2.175|<br>7.869|
|<br>Panel F: Couples training observations with|planter opinion on who|should control rubber in|come (315 observations)||
|Who should control rubber income<br>2.722*|0.693|0.387*|0.488|0.871|
|(1.62)|(0.45)|(0.22)|(0.34)|(0.93)|
|<br>Couples Training Mean<br>23.159|<br>6.281|<br>3.094|<br>2.513|<br>10.632|
|<br>Ind Training=Couples Training (p-val)<br>003|001|001|009|043|
|. <br>.<br>Robust and clustered standard errors in parentheses. * p<0.1 ** p<0.05 *<br>which were recoded as zeros. Baseline control variables in all regression<br>Married planter, Polygamous relationship, Matrilineal ethnicity, Planter<br>Number of plots, Average plot size.|.<br>** p<0.01. Note: For all va<br>s: Age of household head,<br>has IGA, Asset index, Cred|.<br>riables, individuals who d<br>Household size, Number o<br>it constrained, Share of pl|.<br>id not take part in the traini<br>f adults, Number of childre<br>ots owned, Average distanc|.<br>ng had missing values,<br>n, Dependency ratio,<br>e to plots (minutes),|

2
# **1. Introduction**

Significant economic growth experiences for countries rarely occur in isolation. More typically, countries do well if their neighbors do well. The best known example is the industrial revolution. Originating in England, it quickly (by the standards of the 18<sup>th</sup> and early 19<sup>th</sup> century) spread to continental Europe in an almost contagion-like process. More recently, the “East Asian miracle” saw Japan’s dynamic growth pull many of its neighbors along to middle or even high income status. Conversely, in world regions where no initial growth poles emerge and where mechanisms for transmission of spillover benefits are absent, entire regions may remain stagnant for long periods.

In this paper we aim to contribute to the analysis of spillovers between countries in different regions of the world, with a particular emphasis on growth in Sub-Saharan Africa (SSA). We take as a starting point studies by Easterly and Levine (1998) and Collier and O’Connell (2007) and extend them in two main ways. First, we look at spillovers across contiguous countries in a spatial econometric framework, but our main focus is on interaction processes that spread through regional trade agreements. Secondly, we assess a mechanism for the transmission of benefits, namely through infrastructure investments which facilitate interaction between countries through trade and communication of ideas. We are particularly interested to see if spillovers spread in a spatially homogenous process, as Easterly and Levine (1998) assume; or whether these effects are likely to be spatially heterogeneous on account of differences in the degree of spatial or institutional integration, which is closer to the assumptions in Collier and O’Connell (2007).

1
Policy Research Working Paper 10425

# **Abstract**

Liberian women make significant economic contributions yet are constrained from contributing even more due to their exclusion from productive opportunities. This study develops a macrosimulation model of the Gender Dividend that estimates the economic contributions of women and the societal costs incurred by excluding them. Using macroeconomic, demographic, and survey data from Liberia, the analysis finds that women were responsible for 39 percent of market-based output produced annually in 2020, equal to US$1.08 billion, and contributed another US$530 million in non-tradable sources of production, namely, housework and domestic chores. Using the macrosimulation model, the study estimates that if the gender gaps in labor force participation, intra-sectoral wages, and sector of employment were closed, gross domestic product would be 11.5 percent higher. If further reforms were undertaken to equalize education and reduce fertility rates to a net-reproduction rate, gross domestic product would be 23.7 percent higher. Finally, if the model also accounts for the value of non-tradable production, gross domestic product would be US$5.89 billion, or 45.3 percent higher than today’s estimates, with women being responsible for 53 percent of the labor market output. These estimates reinforce the need for a unified policy agenda that actively invests in women’s human capital and work-related opportunities simultaneously.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at wcunningham@worldbank.org, sgupta@worldbank.org, and mdelbono@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Policy, Research, and External Affairs<br>Debt and International Finance<br><!-- End of picture text --> WPS 585 This paper - a product of the Debt and International Finance Division, International Economics Department-is part of a larger effort in PRE to look for better structural models for developing countries. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 204 33. Please contact Sheilah King-Watson, room S8-025, extension 31047 (42 pages).

Traditional estimates of money demand are often characterized by periods of "missing money," unstable parameters, and autocorrelated errors.

Typically, these problems are resolved by changing specifications for the regressions once the shifts are identified. The shifts are usually associated with financial innovation.

Arrau and de Gregorio provide an alternative approach for dealing with the unobservable process of financial innovation. They derive from first principles a money demand that is consistent with many traditional models but that explicitly includes financial innovation. In their model, financial innovation is treated as shocks that have a permanent effect on demand for money.

They estimate the model using data for Chile (1975-89) and Mexico (1980-89).

The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and External Affairs Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

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