rafmacalaba/datause-encoder-data huggingface dataset:
percent decline. The number of extreme poor people also declined from 45 million in 2000 to 26 million in 2010 – a massive 41 percent decrease. Large numbers of people however still remain poor, and an even larger number are vulnerable to falling into poverty.<sup>4</sup> Rural households that are engaged in casual farm labor are particularly vulnerable, and their situation can get aggravated by recurring natural disasters and strong seasonal variations in food production. Seasonal poverty, induced by agricultural cropping patterns, is often a characteristic feature of rural poverty in Bangladesh. Agricultural casual laborers typically face two lean seasons – one before harvest and another before planting – that render them temporarily unemployed. During these two lean seasons many male casual farm laborers migrate to cities looking for work leaving their families behind. This brings about an added vulnerability for these temporary femaleheaded households in rural areas. Female employment is traditionally low, and in rural areas it is primarily confined to unpaid home-based work. While seasonality is fairly predictable, a household’s decision regarding which coping mechanism to choose depends on a combination of household-level factors, leading to the use of both informal mechanisms as well as formal options offered by government policies and programs. Bandyopadhyay and Skoufias (2012) provide empirical evidence that intra-household employment diversification prevails in rural Bangladesh as a coping mechanism. Additional coping options include migrating to cities for temporary employment, using credit to smooth consumption, and using transfers from social safety nets (World Bank, 2013).

Bangladesh has a long history of implementing a wide spectrum of safety net programs that include both cash and in-kind programs. Early efforts in social protection were rolled out as emergency relief measures in response to a cyclone or a famine crisis. The frequency and scale of efforts instilled a culture of experimentation and innovation in these responses: the nature of the crisis determined the nature of instruments. The structure of Bangladesh’s social protection landscape, which consists of a large number of fragmented cash and in-kind transfer programs, was largely determined by these initial conditions. PWPs

> 4 The consumption distribution based on the nationally representative Household Income and Expenditure Survey 2010 is such that there is a wide band of households living around the poverty line making those living above the poverty line vulnerable to falling into poverty in face of shocks. In 2010 52 million non-poor people consumed less than 1.5 times the value of the poverty line (World Bank, 2013).

6
wage regressions, also explain wage changes (as well as, in part, an increase in schooling). The program also tends to keep female students in school, thus increasing their educational attainment. Though we found that improved school quality significantly increased female wages, female labor supply was reduced too. This result shows that an increase in opportunity cost did not offset a reinforced incentive to advance schooling among females.

# **7. Conclusion**

This paper examined the long-term impacts of improved school quality at the elementary school stage on subsequent schooling investments and labor market outcomes using unique data from a recent survey that tracked students from the areas that experienced (and did not experience) a large-scale school intervention in the Philippines. We find that improved school quality enhanced female advantage (or decreased female disadvantage) in subsequent schooling investments, migration, and labor market earnings. That is, females study more (relative to males) and tend to migrate and earn more if they receive high-quality educational investments at an early stage.

The above specific results are consistent with the observation that returns to schooling are significantly higher among females than males, especially after high school completion. A labor market imbalance, represented by a significant gender gap in the returns to schooling, seems to create a unique situation in which the impact of improved school quality is biased in favor of females. This result is also supported by the behavior of parents, who were found to prioritize investments in the schooling of their daughters in the face of financial constraints. Our example demonstrates the importance of linking education and labor markets when predicting the impact of any intervention in schooling.

# **Appendix**

The first-step probit regression results are reported in Table A2. The results suggest that the probability of the wage being observed is positively correlated with age (in a decreasing manner), being female in the TEEP cohort, and height (which captures physical endowment). It is intuitive that older and/or taller individuals were more likely to participate in the labor force in this relatively young sample. The positive coefficient of Female × TEEP cohort is consistent with the observation 25
most successful countries expansions were minor or non-existent. We should note that the data available in the DHS surveys on water sources are hardly satisfactory and the choice of tap water as a proxy for safe drinking water is far from a perfect choice. While most DHS surveys offer a variety of possible water sources, the way the question is phrased make it very difficult or in most cases impossible to distinguish between safe and unsafe sources. This holds for comparisons between countries as well as over time.

**Figure 7: Access to tap water and U5M rates**

<!-- Start of picture text --> 260 2<br>Gui<br>240 Ni<br>0<br>-1220 0 1 Mal 2 3 4 5 6<br>200 -2 Mal<br>Ke Moz<br>180<br>Ke Ug<br>-4<br>160<br>Mad Sen<br>Moz<br>140 -6 Sen<br>120 Mad Ug Gui<br>-8<br>100<br>Ni<br>80<br>-10<br>60<br>0 -12 0.1 0.2 0.3 0.4 0.5 0.6<br>Share of children with access toAnnual GDP p c growthublic tap or private water source<br>U5M<br>Annual % point change in U5M<br><!-- End of picture text --> Source: Authors’ calculation from DHS surveys. Note: The arrows point from the first survey year to the second for each country.

Data constraints also complicate the analysis of supply side factors. As mentioned above, only very few DHS surveys contain information on the availability and quality of health facilities. This is why we did not use these datasets in our comparative analysis. Beyond the DHS surveys, it is extremely difficult to find general information on the health system or to construct variables. There is some information on the use of services, but for many of the cases studied this information is incomplete. Because of these constraints, this paper focuses on the role of vaccination as the best available proxy. Of course, vaccination rates only partially reflect general health service coverage and quality, as vaccination campaign may be implemented as part of international health campaigns. Yet, the effectiveness of such campaigns may depend to some extend on the health infrastructure in the respective country. As Figure 8 shows there is a fairly strong positive relationship 23
**Table 14. Heterogeneous effects of preschool availability on female’s work participation by educational attainment**

|||D<br>|DD<br>|**Work par**<br>|**ticipation**<br>|DD<br>|D-FE<br>||
|---|---|---|---|---|---|---|---|---|
|Education:|< Primary|Primary|Lower<br>Secondary|> Upper<br>Secondary|< Primary|Primary|Lower<br>Secondary|> Upper<br>Secondary|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Panel A: Public**|||||||||
|Num. kindergartens|0.059**|0.063**|0.018|0.054*|0.053**|0.083***|0.027|0.094***|
|* has kid 3-6|(0.029)|(0.025)|(0.034)|(0.029)|(0.027)|(0.028)|(0.035)|(0.024)|
|Public Preschools|-0.035*|-0.027|0.031|0.007|-0.036**|-0.016|0.030|0.002|
||(0.018)|(0.021)|(0.032)|(0.018)|(0.017)|(0.019)|(0.034)|(0.016)|
|Has kid age 3-6|-0.030***|-0.044***|-0.029**|-0.069***|-0.002|-0.025***|-0.018*|-0.053***|
||(0.009)|(0.010)|(0.013)|(0.009)|(0.007)|(0.008)|(0.010)|(0.008)|
|**Panel B: Private**|||||||||
|Num. kindergartens|-0.006*|-0.003|0.000|0.007***|-0.002|-0.000|0.002|0.005***|
|* has kid 3-6|(0.004)|(0.002)|(0.003)|(0.002)|(0.003)|(0.002)|(0.003)|(0.002)|
|Private Preschools|-0.002|-0.004|-0.002|0.000|-0.001|-0.002|-0.000|0.001|
||(0.004)|(0.003)|(0.003)|(0.002)|(0.004)|(0.002)|(0.003)|(0.002)|
|Has kid age 3-6|-0.001|-0.018|-0.026|-0.095***|0.012|-0.010|-0.022|-0.066***|
||(0.014)|(0.016)|(0.019)|(0.014)|(0.012)|(0.013)|(0.015)|(0.012)|
|Observations|58,976|61,541|35,362|67,034|58,976|61,541|35,362|67,034|
|Mean|0.613|0.514|0.441|0.494|0.613|0.514|0.441|0.494|

Note: Sample includes all mothers with and without any preschool-aged eligible child (age 3-6). Comparison group includes non-mothers and mothers of children with the wrong ages. Each column is restricted to individuals with lifetime educational attainment as indicated in the column heading. All columns regress work participation on preschool density, preschool eligibility dummy, and their interaction. All regressions include district, year, mother’s age fixed effects and an urban residence dummy. DDD excludes while DDD-FE includes individual fixed effects. Standard errors clustered at the district-level is shown in parentheses; *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. Number of observations and work participation means for each column are indicated at the last two rows.

59
Those who consume grains such as rice, wheat, or maize as their staple foods are willing to give up other expenditures (including health and education) to continue to eat their grain, so the consumption demand is very steep and unresponsive to price (“inelastic”); large changes in price are needed if consumption must adjust to the full impact of a supply shock unmoderated by adjustment in stocks. In 1972/73, for example, a reduction in world wheat production of less than 2% at a time when stocks were almost negligible caused the annual price to more than double, as indicated in Figure 3. Figure 12 also shows how, when stocks are clearly above minimum working stocks, storage demand, added horizontally to consumption demand, makes market demand much more elastic (less steeply sloped) at a given price.

The responsiveness of this aggregate consumption demand to price is difficult to estimate, for several reasons. One is that, in empirical demand studies at the level of the individual consumer, it is difficult to distinguish consumption from storage (including stocks held by consumers) as prices fluctuate, and when the two get confounded the estimated response overstates the consumption response. Secondly, at the aggregate level, years with high prices and negligible stocks above working levels are too rare to establish, by themselves, the steepness of the consumption demand. Estimation of the dynamic storage model enables us to use data from all available years in determining consumption demand. However, the storage model has been difficult to implement empirically. One major hurdle is, again, the lack of reliable stock (or consumption) data for global markets. (In recognition of this, grain statistics refer to “disappearance” rather than consumption.) Work that pioneered the econometric estimation of this model in the 1990s, assuming no supply response, finessed the data problem by estimating the model on prices alone.<sup>14</sup> Recent application of a model in this tradition to prices of a set of commodities suggests that aggregate food-consumption demand responds very little to changes in the price of major commodities; the slope of the consumption demand curve for major grains may be even steeper than previously believed.<sup>15</sup> To compensate for the low price response of consumption, more of

> 14 Deaton and Laroque (1992, 1995, 1996).

> 15 Cafiero, Bobenrieth, Bobenrieth, and Wright (2009). For major grains, confirmation of this result is the subject of ongoing empirical research.

– 22 –
Table 8: Heterogeneity by Availability of Free Daycare (Ethiopia DOT)

||Nu|mber of chil|dren|
|---|---|---|---|
||Free daycare<br>(1)||No free daycare<br>(2)|
|Treatment follow-up 1|0.229**||-0.047|
||(0.101)||(0.080)|
|Treatment follow-up 2|0.199*||-0.054|
||(0.112)||(0.090)|
|Observations|415||686|
|Control group mean|3.082||2.724|
|p-val—follow-up 1||0.029||
|p-val—follow-up 2||0.074||

Notes: This table displays the results of an OLS regression of the number of children at each follow-up on the treatment status in the subgroup of women who have access to free daycare or not. We proxy free daycare by a variable indicating who is taking care of the women’s children when she is at work and they are not at school. We assume that women who declare that their children are being taken care of by their relatives/friends or elder siblings and not by a babysitter or childcare center have access to ‘free’ daycare. The controls include: number of children at baseline, marital status, highest level of education completed, age at baseline, age square, a dummy equal to one if the woman is household head, and household size. We include stratification fixed effects when required. We control for time when there are two periods. *, **, *** denote significance at the 10, 5 and 1 percent levels respectively.

Togo, half of the program recipients were male business owners. They received the same PI training as women and experienced an increase in profit of the same magnitude. In Table 9, we examine the impact of PI training on men who are married or in a relationship with a woman of reproductive age.

As shown in Table 9, if men’s income increases, women do not have a fertility response: the point estimate is close to zero and not statistically significant. Only if the woman’s own income increases does she have more children. This constitutes a rejection of the incomepooling cooperative model of the household, in line with a body of recent work suggesting that women bear a greater financial responsibility for children than men do in Sub-Saharan Africa’s households (Dunbar et al., 2013; Heath, 2017; Finlay, 2021). The mechanism behind the positive fertility effect we observe is not reducible to a simple revenue effect at the household level. This further suggests that the observed fertility response results from women’s strategic decisions to invest in their pension when they can afford to.

# **5.1.3 Sons as old age support for the poor**

The stronger fertility response observed from the poorest women aligns with the old-age security motive hypothesis. Firstly, because the poorest women are likely to be the most dependent on their son to provide them with financial support during old-age, due to fewer alternative resources. Secondly, because the demand of poor women for a son to provide for them in old age is most likely to have been budget constrained prior to the treatment. When this constraint is released and they feel that they can now afford to have a son, they 23
# **<mark>2. Sector context</mark>**

While infrastructure investment in the region overall has been strong (Table 1), there is wide variation across countries in the quality and quantity of infrastructure (Table 2). The high-income Gulf Cooperation Council (GCC) group has the best infrastructure endowments and services in the region, reflecting advanced stage of development and commitment to infrastructure investments financed by oil revenues. However, infrastructure deficiencies in developing MENA remain a concern.

**Table 1: Infrastructure endowments in the developing world (averages 2005-2008)**

|**Sector**|**EAP**|**ECA **|**LAC**|**MENA **|**SA **|**SSA **|
|---|---|---|---|---|---|---|
|**Density of paved road network**<br>**km / 1,000 km2 of arable land**|1,128|1,051|2,965|**2,179**|467|1,095|
|**Telephone Density**|||||||
|**Fixed and mobile subscribers per**<br>**1,000 people**|400|929|839|**537**|353|273|
|**Electricity generating capacity**<br>**Million KWh per million people**|0.30|0.92|0.44|**0.30**|0.31|0.11|
|**Access to electricity**<br>**% of population with access**|62|NA|86|**91**|48|31|
|**Improved Water**<br>**% of population with access**|81|94|91|**88**|82|67|
|**Improved Sanitation**<br>**% of population with access**|62|90|78|**83**|55|33|

_Source:_ World Bank Development Indicators

**Table 2: Infrastructure endowments in MENA by country grouping (averages 2005-2008)**

|**Sector**|**OIC**|**OEC**|**GCC**|
|---|---|---|---|
|**Density of paved road network**<br>**Km / 1,000 km2 of arable land**|3,220|618|16,907|
|**Telephone Density**||||
|**Fixed and mobile subscribers per**|535|538|1,351|
|**1,000 people**||||
|**Electricity generating capacity**<br>**Million KWh per million people**|0.3|0.4|2.9|
|**Access to electricity**<br>**% of population with access**|98|85|98|
|**Improved Water**<br>**% of population with access**|93|79|97|
|**Improved Sanitation **<br>**% of population with access**|84|81|99|

_Source:_ World Bank Development Indicators The infrastructure gap challenge is particularly important for oil importing countries (OIC). In these countries, public investment spending has been following a downward trend and continues to be weak simply because they have much more limited fiscal space than the oil exporters (Figure 1). Growth in public-private partnerships had recently been helping to close the gap in some oil importing countries, but the economic consequences of the Arab uprisings, combined with economic difficulties in Europe, have reduced private investment, with possible negative consequences for infrastructure spending.

3
31 using earlier data, for example the 1976-77 Malaysian Family Life Survey. I leave this question as a possible avenue for further research.

# **VI. Success Factors**

The micro-level data on the composition of households with respect to age, type of employment and earnings, together with evidence of differential compensation for citizens vs. non-citizens suggest that immigration policy, possibly in conjunction with complementary measures, can control the quantity and quality of foreign labor. It is likely that additional factors played a role in policy effectiveness in Malaysia and Singapore, in view of their particular country circumstances. In the first place, both countries exhibit tremendous institutional capacity to implement and enforce regulations. Singapore's priority of the state over individual preferences facilitates policy effectiveness, albeit at the expense of individual rights. The permit system, involving issuance of new permits to large numbers of applicants as well as the renewal of expired permits subject to eligibility, requires substantial monitoring, enforcement and effective administration. Singapore's Work Permit and Employment Department of the Ministry of Labor is efficient in processing applications, especially in view of the volume of business; this is reflected by an average processing period of 3 working days for electronic submissions and 7 working days for all others (Singapore Ministry of Labor, 1995). Malaysia is less successful at enforcing and regulating permits, due to greater challenges with respect to the size of its foreign labor force and its vast territory and borders. Nevertheless, Malaysia exhibits significant institutional capacity that has improved during the recent wave of development.

Additional explanatory factors of Singapore's and Malaysia's effective foreign labor management include the structure of their economies and the associated skills mix which accommodates foreign workers. For example, Singapore's production base is highly diversified and labor-intensive in fairly high value-added industries. Moreover, government policy promotes private sector development and encourages investment and foreign participation with a view to competing on international markets. Both countries are outward-oriented and export-driven, in addition to importing intermediate goods, labor and technology. The Malaysian economy still depends to a large extent on commodity exports that demand unskilled labor (such as rubber, timber and palm oil) and a manufacturing sector that accounts for almost 25 percent of total employment and more than 30 percent of GDP (World Bank, 1995; International Monetary Fund, 1997).
41 Chi Minh City Statistical Office provides information on population in each district in the city and for the city as a whole. The most recent data we were able to obtain on poverty rates is from 2003 from the Inter-Ministerial Poverty Mapping Task Force work, as cited by the Asian Development Bank (2010).<sup>25</sup> We use this data, coupled with the Steering Center for Flood Control’s GIS map of buildings in districts and catchments to determine the number of poor and non-poor in each subcatchment. District population and poverty rates are listed in Table A.1.

The Ho Chi Minh City Statistical Office also provides information on tax revenues in each district in the city and for the city as a whole. Vietnam’s GDP in 2011 was estimated by the World Bank at US$106.4 billion and the ADB estimates that Ho Chi Minh City contributes 23% of this. The GDP for Ho Chi Minh City is then US$24.5 billion. We use this data, coupled with the Steering Center for Flood Control’s GIS map of buildings in districts and catchments to determine the economic value in each subcatchment. District revenue and GDP data are provided in Table A.2.

**Table A.1. 2010 population and 2003 poverty rates in Ho Chi Minh City and in districts in the study area.**<sup>**26**</sup>

||2010<br>Population<br>(millions)|Percent of<br>Total Ho Chi<br>Minh City<br>Population|2003<br>Poverty<br>Rates|
|---|---|---|---|
|_Ho Chi Minh_<br>_City Total_|_7.4_||_5.4%_|
|Binh Thanh|0.50|6.4%|5.0%|
|District 1|0.78|2.5%|2.4%|
|District 10|0.32|3.1%|3.0%|
|District 3|0.30|2.6%|2.8%|
|Go Vap|0.61|7.4%|6.9%|
|Phu Nhua|0.26|2.4%|3.7%|
|Tan Binh|0.66|5.8%|5.5%|

_________

> 25 There are other measures of income disparity such as the GINI Coefficient (UNDP, 2011). We used poverty rates in our case study because it is an intuitive measure and appropriate for demonstration.

> 26 Population data are from the Ho Chi Minh City Statistics Book, Table 2.01. Poverty rates are from the Inter ‐ ministerial Poverty Mapping Task Force, 2003 but accessed from Asian Development Bank (ADB). 2010. Ho Chi Minh City. Adaptation to Climate Change. In collaboration with the Ho Chi Minh City People’s Committee and DONRE. Prepared by ICEM - International Center for Environmental Management. Pg. 54.