rafmacalaba/datause-encoder-data huggingface dataset:
# 2 Related Literature

Human capital accumulation on the job, either through learning by doing or formal training does not come for free. Individuals have to invest time, e¤ort and monetary costs in terms of direct training costs and foregone earnings in order to increase their human capital. How does trade in‡uence this individual investment decision?

A standard model which provides basic insights into the human capital investment decision is the Ben Porath Model (Ben Porath (1967)). This model predicts that the optimal amount of human capital investment depends on the wage rate per unit of human capital, the cost of training and the rate of obsolence. Bartel and Sicherman (1998) show that in the Ben Porath Model an increase in the wage rate per unit of human capital unambiguously increases investment in each period. Raising the productivity of human capital may reduce the cost of training and/or increase the value of time in training relative to work. Both changes enhance investment in training. On the other hand, the introduction of new work processes may make existing human capital obsolete. A higher rate of obsolence of human capital may lower investment in human capital.

If opening up to trade induces countries to specialize in the production of goods whose production technology carries a low rate of learning, then human capital accumulation is likely to decrease. But if trade leads to technology transfer then opening up to trade may speed up learning in technologically less advanced countries because implementing and working with the new technology increases the knowledge of workers. This positive e¤ect of trade on human capital accumulation is reinforced if the transferred technology is skill biased by raising the demand for skilled workers relative to unskilled workers permanently. A higher relative demand for skilled workers leads to an increase in the wage rate per unit of human capital, triggering human capital investment. At the same time the transfer of skill-biased technologies may depress investment in human capital if, for example, the introduction of new products or production technologies makes existing skills obsolete at a faster rate.<sup>3</sup> Consistent with the idea that trade increases the demand for skilled labor, there exists empirical evidence of a positive association between trade and relative wages of skilled workers. Using cross-country data, Denny,

> 3 The transfer of new technologies may not only a¤ect the optimal amount of human capital investment, but also the types of skills which workers would like to acquire. Workers may, for example, be more likely to invest in the accumulation of skills that are more highly valued at the world technology frontier.

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largest impact on agricultural employment, income and prices. As the use of high yielding varieties and irrigation technologies has spread, _Boro_ crop production has been increasing in recent years (Jalil and Kabir 2008). With the cultivation of _Boro_ , the early summer lean period (April-May) has significantly reduced. But the autumn lean season (after the plantation of the _Aman_ crop) still affects almost all parts of the country, especially the northwest region of Bangladesh – due to the dearth of alternative sources of employment (Rahman and Hossain 1991).

The lean season for the rural poor is traditionally the months just before the _Aman_ harvest (locally known as _Monga_ ) when rural consumption reaches its lowest annual level (Khandker 2012; Khandker and Mahmud 2012; Menon 2006). On the other hand, food availability is highest during the months just after the _Aman_ harvest (November-December), and also during May-June, just after the harvest of _Boro_ rice (Chowdhury 1989). Cultivation of _Boro_ takes place in March-May (Banglapedia 2003) which contributes more than 55 percent to the total rice production during 2008-09 (Basak 2010). Figure 1 presents the distribution of months by four seasons.

During September-November, no variety of paddy is sown or transplanted, and none is harvested. As noted earlier, the agriculture sector in Bangladesh constitutes only one-fifth of the GDP, yet almost half of the labor force comes from this sector. About 8.93 million households (31.1 percent) depend on agricultural labor, and 13 percent of them are landless (BBS 2008). Most of these people are engaged in rice cultivation. So the two aforementioned periods (November-December and May-June) definitely affect their living and livelihood.

The _Aman_ harvest during November-December creates the greatest demand for agricultural labor. Labor demand is also relatively high in January when the transplantation of Boro HYV takes place. Labor demand is lowest during September-October, just before the harvesting of _Aman_ rice (Muqtada 1975; Hossain 1990). Thus, the strong seasonality of crop production in Bangladesh is bound to affect 6
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**Table 1: Number of subjects, by treatment**

|**Task:**|**Blank**|**Slider**|**Medical**|
|---|---|---|---|
|No mission (no donation)|37|46|48|
|Mission (donation to school)|38|35|44|

After completing all the experimental tasks, subjects completed an extensive survey recording subject demographics. Each subject was informed that the total donation to the school generated by their actions would be put in sealed box and donated to the school at the end of the study period. Donations were contained in sealed plastic boxes typically used for collecting votes during elections. Subject watched as their donations were placed in the sealed container.

Fourth and fifth year medical students from the University of Ouagadougou (N=121) and third year nursing students from the National School of Nursing (École Nationale De Santé Publique; N=131) participated in the experiments. Subjects were recruited by posting flyers and through briefing sessions with representatives of student unions. The flyers indicated that participants would play games and be able to earn money. It did not reveal the nature or purpose of the experiments.

All earnings were expressed in tokens, with an exchange rate of 1.00 CFA per token.<sup>15</sup> All subjects were paid in cash at the end of each session. Subjects were paid according to their decisions in the motivation measure (dictator game); the ability measure with sliders based on their performance (piece rate); the ability measure with medical cases based on their performance (piece rate); and the flat salary for the core effort task (independent of effort exerted). Donations to the school were generated based on the mission motivation measure (dictator game); and effort in the core effort task in the treatments with a mission. The average subject earned a sizeable amount, 5,742 CFA ($11.94, or more than five times daily per capita income in the country). The average payment to charity, per subject, was 978 CFA ($2.03). Table 2 presents the summary statistics for the entire sample.

> 15 We use “tokens” rather than cash to facilitate replication across cultures: currency focal points vary across countries. In implementing tokens, experimental protocols and instructions remain identical even when conducting experiments in different contexts.  Though tokens reinforce the artificiality of the lab, replicability is a more important concern.
Additionally, highly signicant in terms of parental ability to invest in children, exposed women have signicantly less wealth. Combined with having more children, this drastically decreases parents' ability to invest in children during their critical period of development.

# 6 Concluding Remarks

This paper exploits variation in conict at a detailed geographical level to establish causality between early childhood exposure to conict and women's nal stature. Additionally, along with Akresh et al. (2018), this paper is among the rst to document the intergenerational transmission of the impact of early childhood conict exposure on second generation health. Nepal experienced a decade-long violent civil conict between 1996 and 2006, which resulted in more than 13,000 fatal casualties, signicant infrastructure damages, and severe hindrance in delivery of basic services and generated extreme fear, sense of insecurity and stress among its citizens. Considered the most reliable, I use INSEC's database on the conict casualties and create an individual level victims' data set with exact geographical location (village) and dates of the incidents. This allows me to exploit variation in conict intensity at the village level for identication.

Fueled by international remittances, Nepal enjoyed consistent economic growth and poverty reduction (Uematsu et al., 2016) during the period of conict. The country also made significant improvement in other dimensions of development including health (Headey and Hoddinott, 2015) and non-income based multidimensional poverty (OPHI, 2013). These aggregate development trends, however, may mask disparities at a more disaggregated level due to signicant variation in conict intensity. The little research documenting the consequences of the war thus far is focused on district-level disaggregation, and results are mixed - little to zero impact on human capital accumulation (Valente, 2014; Pivovarova and Swee, 2015) and positive impact on women's employment (Menon and van der Meulen Rodgers, 2015). Libois (2016), on the other hand, using conict measurement at a more detailed geographical level (distance from the conict sites) nds signicant negative immediate impact on consumption and income. Failure to capture the substantial conict heterogeneity across villages within district may explain the lack of evidence of conict eects at the district level.

In contrast, I exploit variation in early childhood exposure to conict by birth cohort and village 24
**Table 6:** Restricting the cointegration space

||**Agr**|**Manu**|**Tra**<br>**ns**|**Com**|**Fin**|**Gov**|**Oil**|**Const**|**LR-test**|**p-**<br>**value**|
|---|---|---|---|---|---|---|---|---|---|---|
|**H1**|0|1|*|*|*|*|*|*|2<br>χ (3) = 12.97|0.00|
|**H2**|1|0|*|*|*|*|*|*|2<br>χ (3) = 16.51|0.00|
|**H3**|1|*|0|*|*|*|*|*|2<br>χ (3) = 22.26|0.00|
|**H4**|1|*|*|0|*|*|*|*|2<br>χ (3) = 26.78|0.00|
|**H5**|1|*|*|*|0|*|*|*|2<br>χ (3) = 28.14|0.00|
|**H6**|1|*|*|*|*|0|*|*|2<br>χ (3) = 21.09|0.00|
|**H7**|1|*|*|*|*|*|0|*|2<br>χ (3) = 27.20|0.00|
|**H8**|1|*|*|*|*|*|*|0|2<br>χ (3) = 36.77|0.00|

_Note:_ Table 6 summarises the findings of different hypotheses tests on the coefficients of α and β. A 0 indicates that the coefficient of a variable, i, has been restricted to zero and is equivalent to a test of longrun exclusion, a 1 indicates the variable used for normalisation and a * indicates that a variable has been left unrestricted. All tests are joint tests for long-run exclusion and weak exogeneity, i.e., α _i_ = β _i_ = 0 . All tests are likelihood ratio (LR) tests which are distributed as χ2 , conditional upon the rank and the number of restrictions imposed.

Based on consideration in section 3.2, we interpret the second cointegration vectors is a “petrolization” equation that links oil with the public sector, the financial sector and the transport sector. We attempt to interpret the first cointegration relationship as a “traditional” fiscal relationship that links the public sector with non-oil productive activity. The third cointegration vector is interpreted as a link between agriculture, manufacturing and commerce.

Guided by these assumptions, we impose different restrictions on the cointegration space. The basic test is formulated in the following way: This corresponds to a test that excludes agriculture and oil from the first cointegration relationship; agriculture, manufacturing and commerce from the second cointegration relationship; and transport, public services and oil from the third 12
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The involvement of the private sector has improved the financial viability of the Port of Dakar—handling costs have fallen substantially, particularly due to the reduction of dwell times in the port. Time to export and import is just one-third of the level of other Sub-Saharan African countries and about the level of the Organisation for Economic Co-operation and Development (OECD) countries. Costs of importing to Senegal are lower than the regional average, but 1.6 times the costs of

**Table 6. Costs of exporting and importing**

|Indicator|Senegal|SSA|OECD|
|---|---|---|---|
|Cost to export ($ per container)|1,098|1,942|1,090|
|Cost to import ($ per container)|1,940|2,365|1,146|
|Time to export (days)|11|34|11|
|Time to import (days)|14|39|11|
|Documents to export (number)|6|8|4|
|Documents to import (number)|5|9|5|
|_Source:_World Bank 2011a.<br>_Note:_SSA= Sub-Saharan Africa; OEC<br>operation and Development|D = Organisation|for Econom|ic Co-|

importing to an OECD country, whereas costs of exporting from Senegal are now 25 percent lower than the Sub-Saharan African average and at the same level as those faced by OECD countries (table 6).

The sector has also benefited from greater levels of traffic between the Port of Dakar and Port of Ziguinchor. Passenger and freight services increased from two times per week in 2000 to three times per week in 2008. Over the same period, the number of passengers increased from 500,000 to 504,000 and tonnes of freight from 100,000 to 300, 000.

# **Challenges**

Transportation services within the Port of Dakar—from the ships to the warehouses—present a mixed performance record. On the one hand, the average container dwell time was 7 days as of 2006—the lowest among African competitors—and the average truck turnaround time, at 5 hours, was second only to Lomé, Togo. On the other hand, general-cargo performance indicators (vessel stay, preberth waiting time, and handling charges) rank very poorly when compared with neighboring ports (table 7).

Moreover, multimodal coordination remains an issue of concern, and the lack of a multimodal strategy for transportation in Senegal has seriously hindered the capacity of the port to realize its potential as a regional trade hub. The port has failed to fully capitalize on the consequences of the political crisis and instability in Côte d’Ivoire (whose vessels have needed alternate routes to deliver their freight in the region).

16
Appendix Table 11b examines the balance of the household level measures: a wealth index (included because it is a key targeting criterion of the program), whether the household head is Muslim and whether the household belongs to an indigenous ethnic group (included to account for differences in education outcomes across population groups), whether the household head ever attended school (included because it is commonly considered as a determinant of the welldocumented intergeneration link between parents and children’s life outcomes, e.g. Chevalier, 2004), whether the household is engaged in agricultural activities (included because most child labor (62%) in the Philippines takes place in agriculture according to Understanding Children’s Work, 2016) and household size and demographic composition (number of members aged 0 to 5, 6 to 14, and 15 to 17, included because the program is partly targeted based on the number of children in these age ranges). The variables for Muslim household head and indigenous household are based on the follow-up data, all remaining household indicators were measured at baseline in the Proxy Means Test survey. The wealth index is defined as a normalized measure with weights from the first principal component of the following dwelling and asset characteristics: electricity, strong roof, strong walls, dwelling owned by the household, the household has no access to toilet facilities, the household's main source of water is located in the household's own dwelling or plot, and ownership of the following assets: TV, video, stereo, refrigerator, washing machine, air conditioning, living room furniture set, dining room furniture set, car, phone, PC, microwave, and motorcycle.

Appendix Table 11c explores the balance of the two key village level characteristics: whether the distance from the village hall to nearest public primary or secondary school, respectively, is more than 2 kilometers, which is the 95<sup>th</sup> percentile of distance to primary school. We include these variables as the cost of commuting to school is an important component of the overall cost of education (see also Appendix 4).

Appendix Table 11d explores the balance of the child labor recall data measures across treatment and control villages, separately for the years 2007, 2008, and 2009. Importantly, these 43
Pr( _sfpdt_ = 1) = _φ_ ( **x** _fpdtβ_ + _δi_ + _δcd_ + _δt_ + _ufpdt_ ) (2) where _φ_ is the probit function and _ufpdt_ is an error term. Our specification includes industry fixed effects at HS2 ( _δi_ ), origin-destination fixed effects ( _δcd_ ), and spell-start year fixed effects ( _δt_ ). The vector of regressors **x** _fpdt_ includes measures of the firm’s scale and scope as well as proxies for agglomeration and market attractiveness. These proxies are counts of (i) _n_<sup>_c_</sup> _pdt_<sup>,thenumber</sup> of firms from origin country _c_ exporting product _p_ to destination _d_ ; (ii) _nfpt_ , the number of destinations to which firm _f_ exports product _p_ ; (iii) _nfdt_ , the number of products that firm _f_ exports to destination _d_ ; (iv) _n_<sup>_c_</sup> _dt_<sup>,thenum-</sup> ber of (product _×_ firm) combinations active in the bilateral trade between origin _c_ and destination _d_ ; they also include (v) _vfpdt_ , the initial value of firm _f_ ’s export spell (product _p_ to destination _d_ ); and (vi) _zfp_ , the share of product _p_ in firm _f_ ’s overall export sales. That is, the notation convention is to omit the index of the dimension over which the count is summed. All counts are put in logs, and we use robust standard errors clustered at the product-destination level throughout.

In customs data, E.U. countries are entered as separate destinations rather than as a whole. We have kept this convention, so a destination should be taken, as far as the E.U. is concerned, as a member state. This creates an asymmetry in the treatment of destinations between the U.S., which is taken as a whole, and the E.U., which is broken down. However, as African exports tend to be heavily concentrated on E.U. markets, the alternative assumption (bundling all E.U. destinations together) would have drastically reduced the number of destinations and potentially obfuscated some geographical diversification issues, as marketing channels are, in spite of the Single Market, still somewhat separate across E.U. member states.

We estimate equation 2 by probit, reporting marginal effects. Typically, marginal effects of a probit estimation can be interpreted like the coefficient in a linear probability model, and also in the present case a robustness check reveals that quantitatively the difference between the results from a linear probability model and the probit’s marginal effects at the mean are very small.<sup>8</sup>

> 8Results of a comparison of the linear probability model and Probit estimates are available upon request.

16
mortality. There is little research that addresses this relationship as we have formulated it, so we have drawn on work for South Africa (Bell et al., 2006), which turns out to be transferable to Kenya. On this basis, we find that separate programmes to subsidise – post-primary education and combat the epidemic are both socially profitable the – latter strikingly so, due to its indirect effects on the expected returns to education and that a combination of the two profits from a modest synergy effect.

No single paper can hope to deal with all aspects of such a topic, so it will be helpful to say at the outset what will be left out. The intimate connection between risk-taking and demography in the context of AIDS is recognised, but not analysed: infection and demography are treated here as exogenous processes, in contrast to Young’s (2005) study of the South African epidemic. Nor do we go into the bargaining that takes place between the young and their parents. Since some rule for determining outcomes is unavoidable, we adopt a simple one. We also steer clear of learning on the job as a contributing factor to the accumulation of human capital, and of youth unemployment as a barrier thereto. For contributions to the macroeconomic literature on AIDS that use an OLG framework in which agents can also invest in physical capital, the reader is referred to Corrigan, Glomm and M ́endez (2004, 2005).

# **2 The model**

The model is a substantial extension of Bell, Devarajan and Gersbach (2006): there are now essentially three overlapping generations within an extended family, in which all surviving adults care for all related children. This arrangement reflects Kenya’s social structure reasonably well. There are two levels of the educational system, with a corresponding elaboration of the process through which human capital is formed. In applying the model, moreover, we choose a still finer unit time period than a generation, namely, the inter-censal interval of a decade. This permits a relatively detailed 5