rafmacalaba/datause-encoder-data huggingface dataset:
Detailed information on the different types of fishing gear used by households was specifically collected in order to better understand the patterns of usage of different fishing gear and whether MPAs have an impact on these decisions. Tanzania has committed itself to increasing the percentage of its coastal and marine areas under MPAs to 10 percent by 2012 and 20 percent by 2025 (Ruitenbeek and others, 2005). Knowledge of whether MPAs can effectively reduce fishing pressure by affecting the use of destructive fishing gear and how MPA activities impact the poor is essential to inform policy decisions to ensure coastal and marine resources are managed in a sustainable, effective, and equitable manner. We find that MPAs reduce the likelihood of households targeting near shore fish species and using destructive fishing gear. However, the latter impact is indirect and limited to influencing the choice of fishing gear of households participating in AIGA established by the MPA. We also find the use of destructive fishing gear is associated with higher consumption levels, whereas participation in AIGA does not significantly affect household consumption levels.

The paper is structured as follows. Section 2 provides an overview of fishing in coastal communities in Tanzania and the policies pursued in Tanzania to address problems in this sector. Section 3 describes the dataset used for the analysis, focusing on the characteristics of coastal fishing communities. Section 4 introduces the empirical strategy employed for the analysis, including the underlying theoretical framework and econometric specification adopted. Section 5 presents the results of the econometric analysis and section 6 concludes with a discussion of the policy implications.

# **2. Fishing and the role of MPAs in Tanzania**

Tanzania’s coastline runs approximately north-south and is dominated by three large offshore island—Pemba, Zanzibar, and Mafia Island. There are also other small islands, such as Misali Island, west of Pemba, and Mnemba and Chumbe islands, off Zanzibar. There are fringing and patch reefs along much of the mainland coast and these islands (Leon and others, 2004). Tanzania’s continental shelf covers an estimated 17,900 km<sup>2</sup> , with waters up to 200 meters in depth. It is generally narrow—typically extending about 4 km offshore—except at this survey with demographic data and detailed fishing gear usage data provides a unique opportunity for a detailed analysis.

5
## **Table 5. Summary of the robustness tests results: Regression estimates of the relationship between the availability of childcare and time allocation on primary activities**

||(1)|(2)|(3)|
|---|---|---|---|
||Market work<sup>d</sup>|Unpaid work<sup>d</sup>|Other<br>|
||||activities<sup>d</sup>|
|**1.**<br>**Limit the age of women to prime age: 25–49 years**<sup>a</sup>||||
|_Ref: A. No child or youngest child is 6 years and older_||||
|B. Youngest child is 0–5 years and childcare available|-2.197***|1.732***|0.465|
||(0.576)|(0.383)|(0.433)|
|C. Youngest child is 0–5 years and no childcare available|-3.238***|1.887***|1.351***|
||(0.520)|(0.347)|(0.428)|
|Observations|829|829|829|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|
||Yes|Yes|Yes|
|**2.**<br>**Limit the age of child to 0–12 years**<sup>b</sup>||||
|_Ref: A. Youngest child is 6–12 years_||||
|B. Youngest child is 0–5 years and childcare available|-2.395***|1.945***|0.451|
||(0.538)|(0.343)|(0.432)|
|C. Youngest child is 0–5 years and no childcare available|-3.606***|2.109***|1.500***|
||(0.510)|(0.337)|(0.421)|
|Observations|758|758|758|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|
|**3.**<br>**Limit the age of child to 0–8 years**<sup>**c**</sup>||||
|_Ref: A. Youngest child is 6–8 years_||||
|B. Youngest child is 0–5 years and childcare available|-2.366***|1.601***|0.764|
||(0.605)|(0.383)|(0.478)|
|C. Youngest child is 0–5 years and no childcare available|-3.635***|1.759***|1.875***|
||(0.588)|(0.372)|(0.473)|
|Observations|599|599|599|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|

Note:<sup>a</sup> Full table provided in Appendix C, Table C5.<sup>b</sup> Full table is in Appendix C, Table C6.<sup>c</sup> Full table is in Appendix C, Table C7.<sup>d.</sup> For the definitions of labor market work, unpaid work and other activities see footnote of Figure 3.<sup>e.</sup> *** 1%, ** 5% and * 10% level of significance.<sup>f.</sup> Standard errors are in parentheses.<sup>g.</sup> Survey weight applied.

# _3.3. Childcare as a Secondary Activity_

As mentioned earlier, a unique feature of the dataset is the inclusion of information on childcare performed as a secondary activity, along with other paid and unpaid work. Childcare is considered a primary activity if the caregiver is actively involved in children’s activities for a large part of the day. It is considered a secondary or supervisory activity if the caregiver engages in another primary activity and keeps an eye on children at the same time. This information makes it possible to explore the relationship 23
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Policy Research Working Paper 9863

# **Abstract**

In recent decades, hurricane frequency and intensity have increased in the Caribbean Basin. From 2000 to 2012, more than 100 hurricanes impacted lives, infrastructure, and economic activity along the region’s shorelines. Studies suggest that mangrove forests’ dense root systems might mitigate the impact of hurricanes, which would help stabilize the coastline and prevent erosion from waves and storms. Although many tropical mangroves are found on Caribbean coasts, climatic and anthropogenic events have been clearing these wetland ecosystems at an annual rate of 1 percent since the 1990s. This study quantifies the effects of hurricane windstorms on economic activity using nightlight as a proxy at the highest spatial resolution data available (1 square kilometer). Using different widths of the mangrove belt, it measures levels of mangrove natural protection against the impact of hurricanes and studies the broader socioeconomic and environmental effects of this protection. The results suggest that while major hurricanes reduce nightlight by approximately 2 percent and up to 16 percent in storm surge prone areas, the presence of mangroves on the coast mitigates the impact of hurricanes, reducing nightlight by 1–6 percent.

This paper is a product of the Environment, Natural Resources and Blue Economy 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 jjmiranda@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
**_Figure 4. Annual TFP growth rate for developing countries, average weighted by real GDP by region and decade_**

<!-- Start of picture text --> 3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04<br>-94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14<br>East Asia Europe Latin America Middle East South Sub-Saharan<br>and and and and Asia Africa<br>Pacific Central Asia Caribbean North Africa<br>Source: Authors' calculation, using PWT 9.0 data complemented, in a few cases, with GTAP data.<br>Note: The former Soviet Union countries are excluded in the period 1985-94 considering their independence in the early 1990s.<br>4.2 Main determinant indexes<br>Figure 5 shows the median of the subcomponent indexes representing the main categories of TFP determinants, as<br>well as the median of the overall index, for all, 21 OECD, and 115 developing countries by decade. All the<br>median indexes are lower for the developing countries as compared to the OECD countries. A noticeable<br>difference is that the innovation index stays at the lowest level for the developing countries, whereas it increases<br>in the OECD group over time. For both groups, the subcomponent indexes of education, market efficiency, and<br>infrastructure increase over decades, whereas that of institutions stays at the same level.<br>Figure 5. Median of subcomponent and overall determinant indexes for all, OECD, and developing countries by decade<br>World<br>85 95 05 85 95 05 85 95 05 85 95 05 85 95 05 85 95 05<br>-94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14<br>Innovation Education Market Efficiency Infrastructure Institutions Overall<br>Annual TFP growth rate,<br>average weighted by GDP<br>100<br>80<br>60<br>40<br>20<br>1<br><!-- End of picture text --> Figure 5 shows the median of the subcomponent indexes representing the main categories of TFP determinants, as well as the median of the overall index, for all, 21 OECD, and 115 developing countries by decade. All the median indexes are lower for the developing countries as compared to the OECD countries. A noticeable difference is that the innovation index stays at the lowest level for the developing countries, whereas it increases in the OECD group over time. For both groups, the subcomponent indexes of education, market efficiency, and infrastructure increase over decades, whereas that of institutions stays at the same level.

**_Figure 5. Median of subcomponent and overall determinant indexes for all, OECD, and developing countries by decade_**

21
_Data_ The analysis relies on three main sources of data. I use the Young Lives International Study of Childhood Poverty, which tracks the lives of 2,052 Peruvian children born in the years 2000–1 for 15 years. The first round of data was collected in 2002, and the second round was collected between late 2006 and early 2007. The rate of attrition in the study was small (4.39%), and, as Table S3.1 in the supplemental appendix confirms, the only significant differences at a 5% level of means are for mother’s height and the probability of having an immunization program in the child’s community. I also test and discard the hypothesis that attrition can be explained by the spending patterns within regions. This survey includes information about each child as well as the household and the social, economic, and environmental context of the community in which the child resides. These data allow me to control for different aspects in the child’s life that may affect his or her nutrition, such as age and household attributes. A description of the design of this study can be found in section S3.1 in the supplemental appendix.

Second, I match these data with statistics at the regional level using the 2002 and 2006 National Household Surveys (ENAHO). These are yearly surveys conducted throughout the entire country to measure living conditions, poverty levels, and the impact of social programs. The ENAHO surveys are representative at both the regional level and the urban–rural level. Finally, I collect information about public expenditure, and I construct the value of unanticipated mining revenues using information from the Ministry of Economics and Finance statistics database.

_Descriptive Statistics_ . The differences between urban and rural areas for both rounds of the survey are illustrated in Table 2. The most remarkable divergences are related to the possession of private assets, such as maternal education and wealth. In addition, community 13
inauspicious. Given that the horoscope for boys differs than the one for girls, this leaves a large number of potential explanatory variables to look at. Our fi eld work nevertheless suggests that in the case of Vietnam, parents care more about whether the year is auspicious or not, while they pay less attention to years considered as inauspicious.<sup>5</sup> The results of the estimation of (1) are shown in Table 2 panel A. The fi rst two columns show signi fi cantly larger cohort sizes in years that are auspicious for either boys or girls. The difference is estimated to be around 7 percent. This fi gure is of the same order of magnitude than earlier fi ndings from Goodkind (1996) who fi nds between 12 and 22 percent increase in fertility during the years of the Dragon (1976, 1988) among Chinese households in Singapore. It moreover seems that the differences are mainly driven by years that are auspicious for boys. First, columns (1) and (2) give coefficients of similar magnitude, and second, columns (4) and (5) do not show any signi fi cant correlation between cohort size and horoscope for girls. Column (5) restricts the sample to years that are inauspicious for boys exclusively. Although the coefficient on the variable “auspicious for girls” is positive, it is not signi fi cant. We also tested for fi rst and second order serial correlation, and could not reject the null hypothesis of no serial correlation of the error terms.

Graphically, the effect of astrology can be captured in Figure 1. The horizontal axis represents time. The smooth line predicts birth rates at the national level (predicted value of the regression of the logarithm of cohort size against a quadratic time trend), while the connected line shows actual country-level cohort sizes. We fi rst notice a sharp fertility transition since the end of the 1980s, coinciding with the beginning of the “Doi Moi”, or transition towards a market economy. The results found above indicate that most auspicious years (for boys, for girls, or both) are above the predicted line. Finally, if we add an extra observation corresponding to 1997 (column (6)), the results are similar to column (1), once we have readjusted cohort size and taken into account that 2 to 3 months have been truncated due to the timing of the survey.

Although Vietnamese, Chinese and Korean horoscope are similar, we can see graphically that Vietnamese data exhibit neither a “dragon-year baby boom” in 1976 or 1988, nor a “horse-year baby bust” in 1978 or 1990 contrary to the fi ndings of Goodkind (1996) or Lee and Paik (2006). One reason for this, is that according to Vietnamese astrology, the year of the dragon is not systematically an

> 5 Lee and Paik (2006) on the contrary fi nd evidence that parents in South Korea tend to avoid having children in the inauspicious year of the horse.

7
3. Eligibility is determined by primary school starting age in the country. Those aged 6 years or older than the starting age are eligible. For starting age in each country, see <u>http://uis.unesco.org/.</u> 4. A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘feeling safe from crime and violence when at home’ was not available, and the indicator only considers answers to the questions on safety when walking alone.

5. Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe waters sources according to international guidelines. See <u>https://washdata.org/monitoring/drinking-water.</u> 6. Pit latrine without slab, bucket, hanging toilet, no facility (open defecation) facilities are considered as unimproved sanitation facilities according to international guidelines. See <u>https://washdata.org/monitoring/sanitation.</u> 7. According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to), are actively looking for work and are available to start, or those currently waiting to start work are classed as unemployed. See <u>https://www.ilo.org/ilostat-files/Documents/description_UR_EN.pdf.</u> 8. For more information on survey coverage, design and the data for each survey, see <u>https://microdata.worldbank.org/index.php/catalog.</u> 9. The chi<sup>2</sup> test for school attendance features a null hypothesis that no relationship exists between displacement status and achievement for those who are not deprived in attendance yet living in multidimensionally poor households with another child who is deprived. The following chi<sup>2</sup> tests feature as well a null hypothesis that no relationship exists between an interaction of gender by displacement status and achievement for those who are not deprived in attendance yet living in multidimensionally poor households with another child who is deprived, as well as one simply for gender. Likewise, the chi<sup>2</sup> tests for years of schooling replace school attendance with years of schooling as the variable of interest, and so on and so forth for the additional indicators.

# References

- Admasu, Y., Alkire, S., Ekhator-Mobayode, U.E., Kovesdi, F., Mitchell, C., Santamaria, J., and Scharlin-Pettee, S. (2021). ‘A Multi-Country Analysis of Multidimensional Poverty in Contexts of Forced Displacement.’ _Forthcoming._

28
increase of the subsidy on labor can be analyzed considering the Slutsky decomposition: Under the concavity assumption of _U_ , the first and third term are strictly negative. On the other hand, the second term might be either positive or negative. Therefore, the effect of a subsidy that increases schooling has an ambiguous effect on child labor. More specifically, if leisure and schooling are (utilitycompensating) substitutes, a school incentive may have either no or positive impact on child labor. On the other hand, the effect on child labor is negative if schooling and leisure are complements. Thus, in the former scenario child labor increases or stays the same as schooling increases, whereas in the latter child labor decreases.

According to the previous setup, by identifying the determinants of school attendance, enrollment and child labor in Bolivia, the empirical analysis as described in the following section allows inferring how Bolivian households allocate time and thus whether these goods are complements or substitutes. More specifically, the evaluation of the determinants across time (1999-2007) helps shading light on the dynamics of the joint schooling/child labor decisions made by different groups such as (not) indigenous and (not) extremely poor households.

# **5. Empirical Strategy**

This section describes the chosen empirical strategy, while the model specification is illustrated in the following subsection.

The enrollment, attendance and working decisions are modeled assuming that these are made by a representative agent within the household wishing to maximize his or her family’s welfare.

Therefore, a linear random utility function is employed, where the utility associated with both the decision to enroll or not to enroll the child in primary school is assumed to be a linear function of a set of household’s socio-economic characteristics ( _X i_ ) , and of a stochastic term, which represents unobservable and measurement errors (<sup></sup> i ) . Hence, the indirect utility of household _i_ associated with the enrollment decision ( _U i_ , _E_ ) and not enrollment ( _U i_ , _N_ ) can be expressed as: Thus, the representative agent of the household _i_ will choose to enroll the child if the utility associated with the decision is higher than the utility associated with the alternative decision: ( _U i_ , _E_ )  ( _U i_ , _N_ ) . If a variable _Y_ is defined such that _th_<sup>_Y_</sup> _i_ , _E_  1 if the _i_ household enrolls the child and<sup>_Y_</sup> _i_ , _E_  0<sup>if it does not, the</sup> probability that the _i th_ household enrolls the child is 8
measurement. As explained above, GPS-based area measures are obtained within a specific radius that is defined ex-ante by the implementing agency, traditionally in terms of subjective assessments of distance, travel time, and plot location as it relates to enumeration area/village boundaries. Hence, to the extent that a smallholder production system presents a sufficient degree of similarity to the Malawian or Ethiopian contexts and a given implementing agency is fielding a survey that mirrors the fieldwork set up in these countries, following the post-planting visit, the survey management team could consider reviewing the set of unmeasured, distant plots, and select a random subset of those in an attempt to achieve one of eight tolerable rates of missingness, as reported in Table 7, and as a function of the distance threshold-outcome variable combination. This random subset of distant plots could then be prioritized for GPS-based area measurement during the subsequent visit(s) to the sampled households, and the resulting, “more complete” dataset could be subject to MI to predict the remaining missing GPS-based plot areas.

However, since the tolerable missingness rates may vary by country, distance threshold and outcome variable, similar analyses could be replicated (i) based on the IHS3 and ESS datasets that inform our analysis but by using alternative distance thresholds and outcome variables, and (ii) using other survey data that exhibit low rates of missingness in GPS-based plot areas. The findings would in turn catalyze the convergence onto comprehensive operational guidelines for survey practitioners.<sup>14</sup> Finally, although dealing with missingness empirically in the post-fieldwork period is always an option, there is no substitute for good fieldwork to prevent unwarranted missing measurements as much as possible. Thus, survey practitioners, including agricultural and development economists involved in primary data collection, should follow a combination of (i) well-supervised field practices to reduce missingness, as exemplified in Section 2, and (ii) sound MI applications to fill the data gaps that will still be unavoidable to a degree.

> 14 Along with the paper, we make available the Stata formatted datasets and syntax, along with a readme file, that could be used not only to replicate our analyses, but to select different distance thresholds and outcome variables based on which tolerable rates of missingness can be derived.

29