rafmacalaba/datause-encoder-data huggingface dataset:
Unemployment among youth is high (9.6% for women, 8.5% for men) (International Labour Organization 2011). Levels of formal education are low, as about 58% of students drop out after primary school (Aggarwal, Hofmann, and Phiri 2010). Most youth rely on low-productivity subsistence agriculture or self-employment to sustain themselves and their families. Youth are particularly vulnerable as they often possess fewer productive alternatives.

To address the employability issues, promote productive self-employment and reduce vulnerability to risky sexual behavior, in 2009 the Government of Malawi decided to pilot a new apprenticeship program aimed at vulnerable youth. The program was implemented by the Technical Education and Vocational Education and Training Authority (TEVETA) across all 28 districts of Malawi. The program was targeted to vulnerable youth, mainly defined as orphans or school dropouts. The geographic scope of the program made data collection more expensive, but it allowed us to evaluate the program based on a nationally representative sample of vulnerable youth.

Baseline results (see Table 1) show that the selection process was successful in choosing participants who were vulnerable and poor. More than a third are orphans of both parents, over 60 percent live in a dwelling that has a grass roof (a proxy measure for poverty), and over 80 percent report skipping a meal “often” or “sometimes” due to lack of money. Around two-thirds of the participants were male. They were 21 years old on average, and 10% were still in school. When compared to a nationally representative sample of Malawian youth aged 15-24 from the Malawi Third Integrated Household Survey (National Statistical Office 2011), youth in our sample are more likely to live in a house with a grass roof, more than three times as likely to be an orphan, and less likely to still be in school.

TEVETA then identified a pool of potential trainers in each district. The MCs (master craftspeople) were selected from this pool based on their expertise and business performance in the neighborhood. MCs were compensated for their work, and benefited from the free labor that the apprenticeship program brought. In the 23 districts where our survey took place, there were 164 MCs that offered 17 different trades. Each had an average of 14 years of practical experience in their specific field. TEVETA created a set of training modules customized for each of the principal trades, and provided a one-day training to the MCs on how to use these modules.

During the apprenticeship, each MC trained between 1 and 8 trainees at their workshops. MCs’ workshops tend to be located in urban areas, while many of the many of the trainees lived 6
**Table A7: Identifying the $1.90 poor based on covariates of poverty** _Mali 2009_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Asset ownership|-0.042***|-0.174***|0|-0.391***|-0.523***|0|
|Literacy|-0.008*|-0.060***|0.0001|-0.205***|-0.257***|0.0001|
|Access to electricity|-0.020***|-0.144***|0|-0.395***|-0.520***|0|
|Piped drinking water|-0.002|-0.021*|0.1702|-0.221***|-0.240***|0.1702|
|Improved sanitation|-0.015***|-0.063***|0.0008|-0.239***|-0.287***|0.0008|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

**Table A8: Identifying the $3.20 poor based on covariates of poverty** _Mali 2009_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Asset ownership|0.006|0.031|0.3504|-0.612***|-0.587***|0.3504|
|Literacy|0.007*|0.072***|0|-0.254***|-0.189***|0|
|Access to electricity|0.032***|0.122***|0|-0.514***|-0.424***|0|
|Piped drinking water|0.018***|0.090***|0|-0.221***|-0.149***|0|
|Improved sanitation|0.024***|0.051***|0.1046|-0.226***|-0.199***|0.1046|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> 51
The second factor that determines whether a firm will export is its cost efficiency. Clerides et al. (1998) derive a model where exporting is associated with a fixed cost, incurred in every period of exporting. The authors show that, in this model, firms with marginal costs below some threshold choose to export, while firms with marginal costs above the threshold remain focused on the domestic market. Thus, this model predicts that relatively efficient firms will self-select into the export market. A number of studies have tested for self-selection based on data from both developed and developing countries. On balance, these papers present quite strong evidence that efficient firms do indeed self-select into the export market.

The paper by Bigsten et al. (2004) analyzes whether there is any evidence of self-selection into exports markets. They find this effect to be relatively weak. Van Biesebroeck (2005a) finds a somewhat stronger self-selection effect on a larger sample. Both studies report results suggesting that causality runs in the other direction, i.e. from exporting to efficiency. We discuss this next.

The fact that exporters tend to be more productive than non-exporters is a well-known result that seems to hold both in rich and poor countries. Mengistae and Pattillo (2004) report a positive correlation between productivity and exporting among firms in Ethiopia, Ghana and Kenya. The question of whether this is because exporting actually _causes_ efficiency gains has received a lot of attention in the recent literature. From a policy perspective, whether or not firms in developing countries can “learn” from exporting is an important issue. As we have seen, the domestic markets for manufactures are typically very small in Africa, so industrialization will partly have to be through exports. Under learning-by-exporting the competitiveness gap can be reduced endogenously through increased international trade. As discussed in the previous paragraph, one methodological challenge in testing for learning-by-exporting effects is that causality may run in the other direction, i.e. efficient firms may selfselect into the export market. Therefore, efficiency and exports may be correlated even in the absence of learning effects. Clerides et al. (1998) propose an econometric framework that can be used to tease out the relative importance of learning effects and self-selection effects. Using this framework, Bigsten et al. (2004) find relatively strong evidence of learning effects, in the sense that participating in the exports market has positive effects on total factor productivity in subsequent periods. Van Biesebroeck (2005a) uses similar methods and obtains results that confirm evidence of learning effects.

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# **Models of Public Service Reform: A Problem-Solving Approach**

Willy McCourt<sup>1</sup> <mark>Poverty Reduction and Economic Management Network Public Sector Board</mark>

## **JEL Codes: H11, H83, I18, I28**

_Acknowledgements_ : This paper has benefitted greatly from comments by Naaz Barma, Nick Manning and Gary Reid.

> 1 Senior public sector specialist (East Asia and Pacific), World Bank
24 effect on current use in urban areas, but in rural areas those clinics decrease use of IUDs22. Pharmacies in urban areas increase use of the pill and other supply methods in both structural and reduced form models. Health access also seems to increase the use of traditional methods. In particular, access to hospitals in urban areas seem to increase their use in both structural and reduced form equations. This is difficult to explain. In general the effects of hospitals are not what is expected and further in country analysis of htospitals, particularly in the central part of the country seems necessary.

Access to family planning methods are the most consistent policy variables affecting use in both structural and reduced form equations on contraceptive use. This is revealed by the importance of the number of methods available in a community in the current use of contraception and of access to specific methods in the choice of specific methods as revealed by the choice based coefficients. The only anomaly in the availability of methods is reflected by whether supplies of contraception are said to be available in the community and the frequency with which supplies are brought. Frequency reduces overall use in the reduced form, but not in the structural equations. In the method specific equations, frequency increases use of the IUD in the structural and reduces it in the reduced form while the presence of supplies reduces IUD use in the structural equation. It has been found elsewhere that access to contraceptive supplies cap reduce the use of IUDs because it might be chosen instead of supply methods when supplies of other contraceptives are not available. (See Cochrane (1973)) and more recently ... ) More information on the process of supplying contraceptives in rural areas is needed to evaluate these ambiguous results. Nevertheless, the persistence of the other measures of access to family planning are indicators of their important role in a policy to increase contraceptive usage. Finally, another measure of accessibility of family planning in its broadest sense is the knowledge that people have about it. Women who have heard a message on the media about family planning in the last month were consistently more likely to use contraception and to use every method of contraception except traditional methods.

Finally access to good water and sanitation have significant effects in several equations, either in reducing deaths or increasing usage in general or in the choice of modern reversible methods. Their effects on usage probably operates through mortality which in turn affects motivation to restrict fertility. The effect of motivation on use of contraception and the determinants of that motivation are important for the appropriate design of policy.

2 This may bc due to the fact that when clinic access is limited, women tend to select a method that docs not require resupply and thus will choose the IUD. Whcn clinics are available they will select othcr resupply methods.
Szekely (2000:38) note in a cross- country study which includes Latin American countries that “a common feature across countries is that we do not find strong evidence of negative saving or even declining saving in the last part of the life cycle in any country”.

**Figure 1a. Fewer People  age 15-65, and More Age 65+ in the Future Brazilian Population**

**Figure 1b. Average Saving Rates Virtually Unchanged after Age 40 — Except if Public** **<u>Pensions are Excluded</u>**

<!-- Start of picture text --> 60%<br>23% 71%<br>40%<br>21% 70%<br>20%<br>69%<br>19%<br>68% 0%<br>17%<br>0 10 20 30 40 50 60 70 80 90<br>67% -20%<br>15%<br>66% -40%<br>13%<br>65% -60%<br>11%<br>64% -80%<br>9% 63% -100%<br>7% 62%<br>-120%<br>2010 2015 2020 2025 2030 2035 2040 2045 2050<br>-140% Saving rate, excl. public pensions<br>Share of retirees in the popualtion Saving rate, incl. public pensions<br>Share of workers in the population (right axis)<br><!-- End of picture text --> Source: UN Population Division. Source: Author’s estimations based on POF 2008.<sup>5</sup> There are several possible explanations for why people save in old age. First, elderly may desire to leave bequests for their working aged children or their grandchildren. The literature on this subject is well explored theoretically by, for example, Barro (1971) and Stokey (1979, 1980). In the case of Brazil, however, there is lacking analytical work on this issue, but there is evidence suggesting that intra-household transfers are from retired elderly to their adult children with whom they cohabitate. Therefore, public pension benefits indirectly end up being a transfer that covers the entire household. The economic implications of such two-sided intra-household transfers are also explored theoretically by, for example, Nerlove and Raut (1997) but, again, not in the case of Brazil. This remains as an issue for future research.

# **_Implications of Income Inequality_**

Is there more to the story about age-specific saving rates? On average, they remain unchanged after the age of 40, but this aggregate view on age-specific saving rates may hide some important heterogeneity among households in Brazil. In particular, the high income inequality in Brazil may lead to differential saving behavior by income group.

The difference between age-specific saving rates among the non-poor above the poverty line and the poor below the poverty line are illustrated by Figures 2a and 2b. Clearly, there is a crucial difference between the saving behavior of the poor and the non-poor: Despite the fact that the overall pattern is

> 5 After age 63 the saving rates for both the rich and the poor become extremely low, varying between -500 to - 23,000 percent. These saving are represented by very few households and are therefore not shown here, but are included in subsequent simulations with the partial equilibrium model.

9
within the household achieved through various legal and policy changes that grant women more control over resources. This policy implication is consistent with a number of studies that provide evidence that more control of resources by women leads to an improvement in child health, nutrition, education, and on women’s own well-being. Moreover, women’s participation in the labor market, a key policy lever in the unitary framework itself is endogenous, and is an outcome of intrahousehold bargaining.

However, the change in a woman’s bargaining power may affect marriages (and perhaps divorces). The measure of bargaining power used in this paper (assets at marriage) is largely determined by parental wealth, and in this society the norm is that parents arrange marriages for their offspring. Thus, future work perhaps could focus on how the policy change leading to increased female controls of resources could potentially affect household formation and dissolution and not just intrahousehold allocation in a traditional poor rural society.

# **Acknowledgements:**

I thank Richard Blundell, Andrew Chesher, Imran Rasul, Sonia Bhalotra, Giacomo de Giorgi for their valuable comments and suggestions. I also thank Howarth Bouis, Wahid Quabili, and Agnes Quisumbing in helping me accessing the IFPRI data and with numerous queries. All the remaining errors are mine.

# **References**

- Adato, M., de la Bri` _e_ re, B., Mindek, D., Quisumbing, A. R., 2003. The Impact of PROGRESA on Women’s Status and Intrahousehold Relations. In: Quisumbing, A. R. (Ed.), Household Decisions, Gender, and Development. International Food Policy Research Institute, Washington, D.C.

- Barcellos, S. H., Carvalho, L., Lleras-Muney, A., 2010. Child Gender and Parental Investment in India: Are Boys and Girls Treated Differently, mimeo, Rand and UCLA.

- Bardhan, P. K., 1974. On Life and Death Questions. Economic and Political Weekly (Special Issue).

- Becker, G. S., 1973. A Theory of Marriage: Part I. Journal of Political Economy 81 (4), 813–846. Behrman, J., 1990. Intrahousehold Allocation of Nutrients and Gender Effects: A Survey of

27
data sources include the Bangladesh Demographic and Health Surveys (DHS) conducted from 2000 to 2014. Households were sampled within primary sampling units (also called clusters) which are geocoded. The GPS information from the Bangladesh DHS allows us to construct the salinity level in drinking water for the households located within the clusters.

Our results indicate that there is a strong U-shaped relationship between salinity and infant mortality. With fresh drinking water, the marginal effect of salinity measured by groundwater electricity conductivity (GWEC) on infant death is always negative. With brackish drinking water and slightly saline water, the negative effect is small. As drinking water becomes moderately saline, the predicted probability of infant death starts to increase, and the marginal effect becomes positive. The marginal impact of salinity is always positive when above 6ds/m. The relationship between drinking water salinity and neonatal death shows a similar pattern.

This paper proceeds as follows. In the next section we review the literature that motivates the study. In Section III we discuss the data used in this study while Section IV discusses methodology. In Section V we discuss the results and Section VI concludes.

# **II. Literature Review**

Copious literature exists on the determinants of Maternal and Child Health (MCH) as well as infant and neonatal mortality in Bangladesh. Rubayet et al. (2012) found that declines in neonatal mortality between 2000 and 2010 could be attributed to increases in coverage of key interventions, such as skilled attendance at birth and post-natal care, reductions in total fertility and changes in gross national income. However, other factors such as socio-economic and geographic inequality as well as frequent changes in government remain major challenges to future progress. It was also found that <mark>for participatory women's groups to have a significant effect on neonatal mortality in rural Bangladesh, detailed attention to program design and contextual factors, enhanced population coverage, and increased enrollment of newly pregnant women might be needed (Azad et al., 2010). Implementation of a sector-wide approach improved delivery of health services which in turn improved infant and neonatal mortality (Ahsan et al., 2016). Another study (Baqui et al., 2008) found that a home-care strategy to promote a package of preventive and curative newborn care is effective in reducing neonatal mortality in communities with a weak health system, low health care use, and high neonatal mortality. Regarding causes of neonatal death, birth asphyxia (52.8%) was the single largest cause in the early period, while meningitis/sepsis (48.3%) was the single largest category in the later period (Chowdhury et al., 2010). Similar results were found by Chowdhury et al. (2009; 2005). Finally, Dancer et al. (2018) showed that male children have a lower chance of survival relative to female children in Bangladesh during the first year of life. However, none of these studies focused on the impact of high levels of water salinity on either infant or neonatal mortality.</mark> 4
# How Foreign Investment Affects Host Countries

_Magnus Blomstr6m_ _Ari Kokko_ Ma,gnus Blomstr6m Stockholm School of Economics P. 0. Box 6501 113 83 Stockholm, Sweden E-mail: gmb@hhs.se and NBIER and CEPR Ari Kokko Stockholm School of Economics P. 0. Box 6501 113 83 Stockholm, Sweden E-mail: gak@hhs.se
designation affects carbon storage. CF designation is in reality, however, a subset of broader forest collective action and there is evidence in the data that some NCFs engage in significant collective action. For example, even though they have no legal status, 37 of 65 NCF leaders are able to identify the year their forest user group was formed. The first group started in 1991 and the most recent NCF “group” was established in 2012. Whether forest user groups can identify the year they were formed is therefore a potentially important alternative and more inclusive measure of collective action<sup>9</sup> .

Many NCFs not only identify their formation year, but also claim collective action behaviors. For example, in our community survey 74% of NCF leaders agreed or strongly agreed with the statement “ _the community forest has clear boundaries between legitimate users and nonusers and nonusers are effectively excluded.”_ Furthermore, 68% of NCF leaders report that they have “... _formal, informal or customary rules and regulations that govern the access, use (harvesting) and maintenance (management) of the forest”_ and 22 say these rules are in writing. Appendix 2 provides descriptive statistics at the household level from a 1300 household survey that indicate NCF households also perceive significant forest collective action in their user groups.

In our sample a total of 23 NCFs claim to be proposed CFs. We do not know the quality of those proposals, but many are likely to have been waiting for many years and may never be approved by DFOs. Of these 23, a total of 18 proposed CFs have identifiable start dates and 5 do not.

Our collective action variables therefore run from narrow to broad and include the following:

- Narrow definition: Forest and community are registered CFs (CF)

> 9 All CFs can, of course, identify such years, because it is legally recognized.

**16 |** P a g e
**<u>Table 1: Descriptive statistics based on administrative data</u>**

||<br>**Total**|<br>**1981-2009**|**2010-2013**|**2014-2019**|
|---|---|---|---|---|
|**First time registration**|||||
|<br>No. of leases issued|90,251|16,308|49,803|24,140|
|per year|2,314|562|12,451|4,023|
|Plot area (m<sup>2</sup>)|1,211|1,452|1,062|1,393|
|Sporadic|37,548|16,308|6,437|14,803|
|Systematic|52,703|-|43,366|9,337|
|Sporadic/year|963|562|1,609|2,467|
|Systematic/year|1,351|-|10,842|1,556|
|_Approach and land use type_|||||
|<br>Sporadic registration|0.416|1.000|0.129|0.613|
|Systematic registration|0.584|0.000|0.871|0.387|
|Residential land|0.834|0.818|0.863|0.786|
|Commercial land|0.062|0.141|0.038|0.057|
|Agricultural land|0.004|0.004|0.001|0.012|
|<br>Other Use|0.099|0.038|0.098|0.145|
|_Subject of registration_|||||
|Ownership male/female joint|0.402|0.023|0.512|0.431|
|of which systematic reg.|0.756|0.000|0.886|0.463|
|Ownership by females only|0.295|0.230|0.326|0.276|
|<br>of which systematic reg.|0.651|0.000|0.888|0.436|
|Ownership by males only|0.247|0.676|0.150|0.158|
|<br>of which systematic reg.|0.339|0.000|0.817|0.377|
|<br>Ownership by companies or others|0.056|0.072|0.012|0.136|
|<br>of which systematic reg.|0.081|0.000|0.390|0.054|
|<br>**Transfers**|||||
|No. of transfers|10,848|2,578|1,576|6,694|
|per year|278|89|394|1,116|
|Transferred by male owners|0.387|0.619|0.413|0.291|
|Transferred by female owners|0.304|0.230|0.298|0.333|
|<br>Transferred by males/female jointly|0.263|0.028|0.260|0.354|
|Transferred by companies|0.047|0.123|0.030|0.022|
|<br>Days to get consent|100|340|111|32|
|Days to complete registration|124|162|127|108|
|<br>Days since approval of consent|80|80|78|80|
|Purchase price (US$)<sup>a</sup>|11,088|13,785|14,688|9,273|
|Plot area (m<sup>2</sup>)|<br>1,166|<br>1,502|<br>1,146|<br>1,046|
|Price (US$/ m<sup>2</sup>)|13|15|17|12|
|**Mortgages**|||||
|No. of registered mortgages|8,101|3,981|1,240|2,880|
|per year|208|137|310|480|
|Mortgage value (US $)|51,628|43,514|76,954|51,938|
|Plots reg. to male owners|0.538|0.726|0.521|0.292|
|mortgage value (US $)|43,834|35,137|69,274|53,753|
|<br>Plots reg. to female owners|0.198|0.176|0.203|0.225|
|mortgage value (US $)|36,311|26,874|48,508|41,617|
|<br>Plots reg. by males & female jointly|<br>0.185|<br>0.016|<br>0.192|<br>0.411|
|<br>mortgage value (US $)|41,104|32,748|44,619|40,838|
|Plots reg. by companies|0.079|0.083|0.084|0.073|
|mortgage value (US $)|169,153|158,281|263,473|139,489|
|Days for registration process|78|137|29|19|
|<br>Days to get ministerial consent|98|98|||

_Source:_ LAA, land administration database and digitized records, 2020. aNote that the number of observations for purchase price and perice per unit of land slightly different due to missing plot area, and hence unit price cannot be computed from the mean values.

21