rafmacalaba/datause-encoder-data huggingface dataset:
# **6. References**

- Adams, R.H. and A. Cuecuecha. Remittances, household expenditure, and investment in Guatemala. _World Development_ , 38(11): 1626–41.

- Akresh, R., P. Verwimp and T. Bundervoet. 2011. Civil war, crop failure, and child stunting in Rwanda. _Economic Development and Cultural Change_ , 59:  777–810

- Almond, D. and J. Currie. 2010. Human capital development before age five. National Bureau of Economic Research Working Paper No. 15827. NBER: Cambridge MA.

- Bevis, L.E.M, and K. Villa. 2020. Intergenerational transmission of maternal health: Evidence from Cebu, Philippines. _Journal of Human Resources_ (forthcoming).

- Borjas, G.J. and H. Cassidy. (2020). The adverse effect of the COVID-19 labor market shock on immigrant employment. National Bureau of Economic Research Working Paper 27243. NBER: Cambridge MA.

- Camus, M. (Ed.), 2007. _Comunidades en movimiento: La migración internacional en el norte de Huehuetengano_ . _Instituto Centroamericano de Desarrollo y Estudios Sociales (INCEDES)_ and _Centro de Documentación de la Frontera Occidental de Guatemala (CEDFOG)_ , Antigua, Guatemala.

- Carletto, C., K. Covarrubias and J.A. Maluccio. 2011. Migration and child growth in Guatemala. _Food Policy_ 36(February), 16–27.

- Carletto, C. and A. de Brauw. 2007. Measuring Migration Using Household Surveys, Migration Operational vehicles, Operational Note No. 2, The World Bank: Washington, DC.

- Coates J., A. Swindale and P. Bilinsky. 2007. Household Food Insecurity Access Scale (HFIAS) for Measurement of Food Access: Indicator Guide (V3). Food and Nutrition Technical Assistance Project, Academy for Education: Washington DC.

- Cox Edwards, A. and M. Ureta. 2003. International migration, remittances and schooling: Evidence from El Salvador. _Journal of Development Economics_ , 72(2): 429–61.

- de Onis, M., A.W. Onyango, E. Borghi, A. Siyam, C. Nishida and J. Siekmann. 2007. Development of a WHO growth reference for school-aged children and adolescents. _Bulletin of the World Health Organization_ , 85: 660–7.

- Dewey, K.G. and S. Adu-Afarwuah. 2008. Systematic review of the efficacy and effectiveness of complementary feeding interventions in developing countries. _Maternal and child Nutrition_ , 4: 24–85.

- Duflo, E. 2003. Grandmothers and granddaughters: Old-age pensions and intrahousehold allocation in South Africa. _World Bank Economic Review_ , 17(1): 1–25.

- Duryea, S. And M. Morales. 2011. Effects of the global financial crisis on children’s school and employment outcomes in El Salvador. _Development Policy Review_ , 29(5): 527–46.
# **4. Provide instruction on how to activate conscientious behaviors** ( _Duration: max. 40 min_ )

# 4.1. Behavioral activation tasks <u>/ behavioral “experiments” in daily work life</u>

To pursue their specific goals and to perform the four important skills in the context of work, participants have to practice and repeat the goal-related behaviors in the same work context repeatedly so that the contexts may elicit the behaviors [the specific behavioral activation tasks / “behavioral experiments” based on the list in the appendix can be used for the discussion].

# 4.2. Possible barriers to perform the four important skills

Discuss possible barriers that may hinder participants to perform the skills (e.g., individual barriers, work-related barriers).

- What are possible barriers to being _hardworking_ at the workplace? How can these barriers be overcome?

- What are possible barriers to being _punctual_ at the workplace? How can these barriers be overcome?

- What are possible barriers to being _responsible_ at the workplace? How can these barriers be overcome?

- What are possible barriers to being _orderly_ at the workplace? How can these barriers be overcome?

# 4.3. Possible resources to perform the four important skills

Discuss possible resources that may help participants to perform the skills (e.g., social support).

- What are possible resources for being _hardworking_ at the workplace? How can these resources be used?

- What are possible resources for being _punctual_ at the workplace? How can these resources be used?

- What are possible resources for being _responsible_ at the workplace? How can these resources be used?

- What are possible resources for being _orderly_ at the workplace? How can these resources be used?

# 4.4. Identification of goals and goal setting

Participants specify one specific goal for each of the four skills (i.e., industriousness, punctuality, responsibility, and orderliness) they would like to pursue in the next weeks. For each of the four specific goals, participants have to generate one _specific goal_ in the form of an _if-then plan_ (e.g., “If I see a car right in front of the barrier, then I check all the rules before I let the car pass” [industriousness]; “If I have a meeting with my supervisor, then I will do everything I can to be there on time” [punctuality], “If I want to leave the workplace, then I will ask the supervisor first” [responsibility]; “If I start to work, then I will keep things tidy” [orderliness]) [please provide simple examples from the daily life work context]. This task also may help to identify specific situations in which participants can perform specific goal-related behaviors: What are typical work-related situations (“if”) in which participants perform the behaviors and the four skills, respectively (“then”)?

The goal setting process should follow the SMART criteria:

- _Specific:_ Goals must be clearly defined (not vague, but as precise as possible).

24
women who report that they have never had sex is higher than among single men. For age at the first intercourse, table A1 in the appendix shows that, in general, females tend to report having initiated sexual activity later; 19 and 16 years respectively for females and males. There are no large differences across gender in reporting having obtained the results from an HIV/AIDS test prior to the survey. On average, more than half of married people report having discussed AIDS with their spouse and the percentages are very similar between males and females. The knowledge that an HIV positive individual can be asymptomatic is fairly widespread but it is lower among men. Stigmatization for HIV seems to be slightly more common among males; 50 percent of men answer that they would not buy vegetables from an infected vendor.

The analysis of the independent variables shows that a higher percentage of women than men live in urban areas, respectively 23.7 and 21.5 percent. An interesting feature of Lesotho emerges from the education statistics. Education, measured by the highest grade achieved, is higher for females than for males, both at the primary and the secondary levels. This is peculiar to Lesotho since in most other African countries educational achievement is higher for boys. Households in Lesotho seem to allocate more resources in education for girls, since boys are generally in charge of herding cattle (the so called “herdboys”). The variables describing marital status are defined as follows. The omitted category is composed of individuals who have never been married. Marriage is defined as being legally married or living with a partner with the intention of staying together and therefore it covers both formal and informal marriage. Approximately, 52 percent of the women interviewed were married at the time of the survey compared to 42 percent of men. This can be explained either by polygamy, by migration or by the age differences between spouses: men tend to marry younger women. Formerly married includes widowed, divorced and separated individuals. In the sample, 14 percent of women are formerly married compared to 6.8 percent of men. Widowhood is defined as having lost one spouse and not being remarried. The proportion of widows is higher than widowers, either because women get married to older men, or because it is easier for males to remarry after the death of their spouse. The mean for the variable for having been in successive marriages is taken on the entire sample and can apply both to currently and formerly married persons. Approximately 3 percent of men reported having been in more than one marriage and this percentage is slightly higher than among women, confirming that for males it is easier to remarry. Being in a polygamous union is 8
Table 2: Baseline Characteristics by Treatment Status

|Variable|Treat|ment|Con|trol|T=C|N|
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|p-value||
|_Panel A: Household and location_|||||||
|Mud foors|0.04|0.19|0.04|0.19|0.86|7,320|
|Refrigerator|0.91|0.29|0.91|0.29|0.79|7,321|
|Piped water|0.92|0.27|0.93|0.26|0.54|7,321|
|Piped water inside|0.82|0.38|0.82|0.38|0.99|7,321|
|Toilet exclusive for the household|0.82|0.39|0.83|0.37|0.10|7,321|
|Piped water toilet|0.77|0.42|0.77|0.42|0.79|7,321|
|Electricity|0.98|0.15|0.97|0.17|0.20|7,321|
|Blender|0.87|0.34|0.87|0.33|0.60|7,321|
|Gas stove|0.93|0.25|0.93|0.25|0.79|7,321|
|Radio|0.57|0.49|0.56|0.50|0.49|7,321|
|Time to school (hours)|0.17|0.81|0.17|0.81|0.97|7,321|
|Expenditure transportation to school|54.69|63.63|53.19|61.97|0.31|7,321|
|Urban|0.46|0.50|0.46|0.50|0.99|7,321|
|_Panel B: Other characteristics_|||||||
|Income|634.79|496.06|631.09|490.81|0.75|7,321|
|Age|15.26|0.44|15.27|0.44|0.39|7,220|
|Male|0.55|0.50|0.56|0.50|0.40|7,220|
|Spanish score ENLACE 2009|0.01|1.01|-0.01|0.99|0.30|7,321|
|Math score ENLACE 2009|0.01|1.01|-0.01|0.99|0.50|7,321|

Note: p-value derive from a OLS regression on the treatment variable 13
# **Dynamic Panel GMM Estimation**

Cross-section methods are simple and easy to interpret, but have weaknesses. Relationships may be artificially created by unobserved heterogeneity that is caused by a number of factors such as history, mortality rates, geography, or endowments of resources (country effects that are not time varying-in the case of economic institutions). The use of panel data can overcome this problem. We can look at relationships over time (so we can add _within_ country variation to the _across_ country variation), to see whether increases in aid are followed by changes in economic institutions, and to distinguish between short-run and long-run effects. The main estimated equation is the following one: Or alternatively, Where y_it represents the level of the different institutional indicators for country (i) at time (t), y_it-1 is its own lag. t, is a time trend attempting to catch a common co-movement between the regressors and the dependent variable, in the estimates we consider both a linear trend and a set of time dummies but we report results only with time dummies. η _i_ are the country specific effects and ε _i_ , _t_ are the i.i.d stochastic disturbances. X_it are the regressors. Among them we consider the lagged level of the natural log of aid flows.

The following section provides details on the variables considered and a discussion of the results. In this section we describe the methodological aspects of estimating (dynamic) models with panel data. There are several econometrics methods which can be used to estimate equations like (2), or equivalently (3) where the number of countries (N) is larger than the number of time periods for which data are available (T). Because of the assumptions on the 21
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# **1. INTRODUCTION**

Sub-Saharan Africa will be undergoing substantial demographic changes in the coming decades with the rising working age share of its population. Recent analyses suggest that demographic dividends in the region could account for 11 to 15 percent of GDP volume growth by 2030, while accounting for 40 million to 60 million fewer poor in 2030 (Ahmed et al. 2016). However, the realization and magnitude of demographic dividends for a given African country would depend on their current demographic profile and their trends in their demographic changes, as well as their ability to create an appropriate enabling environment (World Bank 2015a). An appropriate enabling environment would include improvements in educational attainment and quality, labor productivity, savings and investment, and job creation.

This paper examines how demographic dividends may play out in the economically linked but otherwise diverse Southern African Customs Union (SACU) economies, when presented with similar economic policy outcomes. SACU economies - Botswana, Lesotho, Namibia, South Africa, and Swaziland – have relatively diverse demographic and economic starting points. This diversity is present in terms of their demographic profile, as evidenced by South Africa being further along in the demographic transition process than the other countries. The diversity is also present in terms of their development indicators, as illustrated by the relatively lower educational attainment rates in Lesotho and Swaziland, or the extremely high unemployment rates in South Africa.

Three broad questions are addressed:

- 1) What is the potential contribution of demographic change to growth in the different SACU economies?

- 2) What is the potential effect of demographic change on poverty reduction for SACU economies?

- 3) What policy intervention might have the largest impact on the size of a SACU economy’s demographic dividend?

Several different possible scenarios for demographic change and development in the SACU economies are considered following the approach of Ahmed et al. (2016) where a global dynamic simulation model is applied in tandem with a microsimulation model based on harmonized household and labor force survey data. Demographic changes in the different economies are considered against the backdrops of different policy outcomes in the areas of educational improvement, mobilizing savings, and improving employment rates. When considering the scenarios of individual interventions, Lesotho and Swaziland benefited the most from improvements in educational attainment rates by achieving higher economic growth and lower poverty rates. For other economies, interventions that had greater impacts were those that focused on improving employment ratios, through higher labor force participation, reducing unemployment, or eliminating gender gaps in the labor market. The paper thus provides a comparative analysis across several policies highlighted by the literature (e.g. as in Bloom, 2
consistent with those reported in Table 5-1. The GMM results suggest that homicide rates and an increasing share of public industry capital have a detrimental effect on city growth. For example, a 10% increase in base period homicide rates reduces city growth by 1.1% over the next decade. The findings on public industrial capital accumulation suggest that public investment in industry tends to crowds out private investment (at least in the short term), and the potential inefficiency of state enterprises may also deter economic growth.<sup>12</sup>

# **<u>Decomposing City Growth</u>**

In Table 6, we decompose the city population growth results of Table 5-1 (3) into contributions of each covariate. We focus on the covariates which are statistically significant. The contribution of each covariate is calculated as a fitted value (the mean value multiplied by the estimated coefficient) relative to the sum of all the fitted values. Column 5 shows the overall contributions for all cities. There is a strong negative effect of city size in base period (-83.4%). This effect is compensated by increases in market potential (63.8%) and educational attainment (66.7%), along with base period's educational attainment (46.7%) which affects local technology growth.

The estimated effects of market potential and technology spillovers support the new economic geography emphasis on local markets and the endogenous growth literature emphasis on human capital accumulation. These results are also consistent with cross country findings in Henderson and Wang (2005).<sup>13</sup> Columns 6 and 7 compare city

> 12 La Porta and López-de-Silanes (1999) showed privatization in Mexico in 1980s and 1990s led to a significant improvement in firm performance, as profitability increased 24 percentage points and converged to levels similar to those of private firms.

> 13 Henderson and Wang (2005) analyzes how urbanization in a country is accommodated by increases in numbers versus population sizes of cities. Using a worldwide dataset on all metro areas over 100,000 population from 1960-2000, they show market potential, educational attainment, and the degree of democratization strongly affect growth in both city numbers and individual city sizes.

19
it follows that conditional on the residuals observed in the survey data, the error _eaj_ is normally distributed with mean and variance given by (see e.g. van der Weide, 2014): where _γa_ = _σv_<sup>2</sup><sup>_/_(</sup><sup>_σ_</sup> _v_<sup>2+</sup><sup>_σ_</sup> _ε_<sup>2</sup><sup>_/ma_),with</sup><sup>_ma_equaltothenumberofsurveyhouseholdssam-</sup> pled into the area _a_ , and where: If there are sampling weights these will be absorbed by (and modify) the weights _αah_ (see van der Weide, 2014). Conditioning on the survey data when drawing the errors is known as Empirical Bayes (EB) prediction (see e.g. Molina and Rao, 2010). Note that EB prediction only concerns the drawing of the area errors, and only those areas that have been sampled into the survey. The gain in precision resulting from conditioning on the survey data for these areas is most notable when the variance _σv_<sup>2and the number</sup> of sampled households _ma_ are relatively large.

The small area estimates of poverty and corresponding standard errors are obtained by means of bootstrapping. Let _R_ denote the number of simulations. The estimator for poverty then takes the form: where _h_ ( _y_ ) is a function that converts the vector _y_ with (log) consumption expenditures for all households into the corresponding poverty rate, and where _y_ ̃<sup>(</sup><sup>_r_)</sup> denotes the _r_ -th simulated vector with elements: ̃ ̃ ̃ ̃ where the model parameters _β_<sup> ̃(</sup><sup>_r_)</sup> _, σ_<sup>(</sup> _v_<sup>_r_)</sup> and _σε_<sup>(</sup><sup>_r_)</sup> and the errors _va_<sup>(</sup><sup>_r_)</sup> and _ε_<sup>(</sup> _ah_<sup>_r_)aredrawn</sup> ̃ ̃ from their estimated distributions. We draw _β_<sup> ̃(</sup><sup>_r_)</sup> _, σ_<sup>(</sup> _v_<sup>_r_)</sup> and _σε_<sup>(</sup><sup>_r_)</sup> by re-estimating the model parameters using the r-th bootstrap version of the survey.<sup>3</sup> The error term _v_ ̃ _a_<sup>(</sup><sup>_r_)+ ̃</sup><sup>_ε_(</sup> _ah_<sup>_r_)is drawn from a normal distribution with mean and variance given by eq. (1)-</sup>

> 3Alternatively, the model parameters can be drawn from their estimated asymptotic distribution.

7
cropping allows the landlord and the tenant to share the inherent risk in agricultural production; in contrast, the tenant bears the entire risk under fixed rent (Cheung, 1969; Stiglitz, 1974; Ghatak and Pandey, 2000). Second, sharecropping can reduce the impacts of the tenant’s limited liability (Shetty, 1988; Basu, 1992; Laffont and Matoussi, 1995; Sengupta, 1997; Ray and Singh, 2001). Third, the control over the tenant in sharecropping might allow the landlord to ensure a more sustainable use of the land (Reddy, 1996). Fourth, sharecropping provides an incentive for the landlord to provide inputs, such as, pesticides, fertilizers and managerial support, to the tenant (Newbery and Stiglitz, 1979; Eswaran and Kotwal, 1985).

Motivated by anecdotal evidence and this last argument, we develop a theoretical framework which combines prior models (Eswaran and Kotwal, 1985) with the idea that when landlords migrate away from the village, the opportunity cost of providing managerial inputs increases. This results (for those who do not opt for land sales) in a preference for fixed over sharecropping arrangements.

To test this proposition we construct a district-level data set covering the first decade of the 21st century. The choice of time period is dictated by both data availability (we have detailed migration data only for the decades preceding 2001 and 2011) and contextual considerations: liberalization in the 1990s strengthened property rights, allowing for migration of the landed class (Giles and Mu, 2018). This period also saw an acceleration in the trend towards fixed rent arrangements: from 1992 to 2012, fixed rent agreements increased from 45% to 58% while sharecropping decreased from 45% to 31% (as a percentage of total rented in acreage). The largest drop happened in the last decade under consideration (Table 1).

Establishing a causal link from migration to rental contracts is challenging because of the presence of potential confounding factors. They include a major rural bank branch expansion program (Burgess and Pande, 2005) which could have simultaneously increased fixed rent arrangements (following Laffont and Matoussi (1995) and evidenced by Das, de Janvry and Sadoulet (2019) in Bangladesh) and migration (Shonchoy, Fujii and Raihan, 2018; Foltz, Guo 4