rafmacalaba/datause-encoder-data huggingface dataset:
these dynamics, understanding the extent and drivers of the parenthood penalty in Indonesia is essential for informing policy interventions aimed at promoting gender equality in the labor market.

While much of the existing literature on the parenthood penalty focuses on high-income countries with extensive social safety nets and generous parental leave policies, research on this topic in Southeast Asia remains limited. Studies in this region have typically focused on fertility trends or general labor market dynamics, with little attention given to the specific impact of parenthood on employment outcomes (Halim, 2022). Moreover, the few existing studies on Indonesia have largely overlooked the role of extended family support, education level, and urban versus rural differences in moderating the parenthood penalty. This paper aims to fill this gap by providing a detailed analysis of the parenthood penalty in Indonesia, with a focus on the heterogeneity of its effects across different population subgroups.

Our study makes several contributions to the literature. First, we provide estimates of the persistence of the parenthood penalty in Indonesia using data from the Indonesia Family Life Survey (IFLS), a longitudinal dataset that spans over two decades (1993–2014) and covers both urban and rural areas. This rich dataset allows us to track the labor market trajectories of individuals before and after the birth of their first child, providing a comprehensive picture of the long-term effects of parenthood on employment outcomes. Second, we employ a Difference-in-Differences (DID) methodology with staggered adoption, as proposed by Callaway and Sant’Anna (2021), to account for the timing of parenthood and to estimate the causal impact of becoming a parent on labor market participation. This approach allows us to control for potential biases arising from differences in pre-parenthood labor market trajectories between treated (parents) and control (non-parents) groups.

Our methodological contribution lies in the application of a staggered Difference-in-Differences (DID) framework, particularly suited for the context of a developing country with limited administrative data. This approach allows us to disentangle the dynamic effects of parenthood on employment outcomes over time, while accounting for cohort-specific heterogeneity. By leveraging this methodological innovation, we provide more nuanced estimates of the parenthood penalty in Indonesia, improving upon previous analyses in this area. However, an important 3
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consultancy with some key experts ( _e.g._ , the World Bank team and country experts). We model the disruption duration as a linear interpolation between the three values, as is shown in Figure E2. We can thereby link the amount of damage an asset endured (Section 3.2) to the time needed to restore infrastructure services. We ignore that in some cases, temporary or emergency measures are taken to provide essential services while assets are being repaired or reconstructed ( _e.g._ , emergency health, education or latrines provision).

Taken together, we estimate the _disruption risk_ per asset, expressed in the terms of average expected number of days per year an asset cannot provide services. By multiplying this disruption risk output by the number of dependent households, we provide the _service disruption risk_ , expressed in units of Household Disruption Days (HDD) per year.

Apart from the aggregate service disruption risk, we can also model the service disruption risk to every individual household. That is, we can approximate for every individual household whether it loses access to water, sanitation, electricity (if connected in the first place), schools, health facilities and hospitals. We will utilize this in Section 3.5 to model who loses access to (some of these) essential services, which will ultimately affect their level of MDP poverty during the time it takes to restore infrastructure services.

# **3.4 Impacts on multidimensional poverty**

To estimate the welfare impacts to disaster-affected households, we look at the relationship between disaster impact and MDP in two ways.

- 1) Exposure of multidimensionally poor households: by overlaying the flood maps with the location of households that are considered multidimensionally poor, we can estimate the exposure of multidimensionally poor households. We define exposed households as those experiencing at least 0.5 meter of flood depth at their house. In general, households that are multidimensionally poor are considered to have a higher socioeconomic vulnerability, and hence may suffer disproportionally.

- 2) Temporary incidence of MDP: by modeling the disruptions to infrastructure services and the households that depend upon them, we can evaluate which households temporarily face additional deprivations, and whether this pushes them into moderate or extreme MDP.

These two metrics are complementary since some regions might have a low baseline incidence of MDP but have a high potential for seeing an increase in MDP after a shock. Similarly, some areas have a high baseline of MDP but a relatively small number of households that are prone to falling into MDP. Moreover, it is necessary to consider both aspects, since those already facing MDP cannot experience further deprivations beyond the extreme MDP threshold.

Note that we only look at the impacts caused by flooding, since several of the infrastructure assets are considered unaffected by extreme wind. In theory, one would need to evaluate the 19
## 4.1.6. Step 5: Project Selection

The IPF methodology is intended to inform project selection and foster more transparent, evidence‐based infrastructure policy. The IPF does not, however, propose the issuance of a definitive list of investments. As such, the process of project selection and the discussions and decision processes surrounding it remain a crucial part of infrastructure policy‐making. The IPF is intended to frame government decision‐making in a way that ensures the consideration of key policy goals and fosters accountability in decision‐making.

Projects categorized as 'High Priority' via IPF are natural targets for implementation and are recommended for selection, as they represent the set of projects that score highly with respect to both social‐environmental and economic‐financial considerations. It must be noted, however, that a "good" project in terms of financial and economic performance, may nevertheless be undesirable from a social and environmental perspective, and vice versa. For instance, Project 23 (Greater Mannar Water Supply Project) has a high SEI but a relatively low FEI score. The identification of medium‐priority projects leaves space for expert review, flexibility, and informed political debate. The framework informs decisions regarding projects in this category but leaves room for structured professional and political judgment.

In contrast, a project that achieves very low SEI and FEI scores is an indication that it has serious shortcomings in terms of its expected impacts as compared with other project alternatives. Decision‐makers should have strong, evidence‐ or policy‐based justification for the selection of a project that lies near the origin of the Infrastructure Prioritization Matrix.

# 5.Way Forward

The IPF is a dynamic exercise, in that it can be refined with access to more informative and accurate data. It is also a preliminary exercise that provides a quick overview of how proposed projects compare according to the two parameters of interest. Therefore, this final section looks at what subsequent steps will likely be required to make best use of the IPF in project prioritization in Sri Lanka. Four general tactics for moving forward are described in the following section. These include supplementing the analysis with actual budget data; integrating IPF into the current planning process; building capacity for project prioritization; and identifying the most effective procurement options for proposed projects.

## a) Supplementing analysis with actual budget data

For the purposes of this report, the analysis was based on budget estimates as indicated in 4.1.4 above. As described above, the classification of projects into priority categories (high, medium, lower) requires identifying or estimating the actual budget constraint for the sector.

Naturally, budget increases will result in more resources available to implement additional projects. An expansion of the budget limit would shift the imposed budget lines on the matrix 30
tend to consume fewer but better calories—calories from a greater variety of sources, some of which are more nutritious—after controlling for household expenditures.

# **4.4 The impact of women’s decision-making power on children’s school enrollment**

In this section, I examine the relationship between women’s bargaining power and children’s school enrollment. It is commonly believed that school enrollment increases if women become more powerful in the household decision-making process. However, some recent literature has challenged this view. Specifically, Basu’s (2006) theoretical model argues that ―if the woman has more decision-making power than the man, the woman will garner a greater share of the income produced by child labor and actually benefit from child labor.‖ That non-linear prediction was further supported by some empirical evidence, including Felkey (2005) in Bulgaria, Lancaster et al. (2005) in three states of India, and Gitter and Barban (2008) in Nicaragua. Figure 5 clearly shows an upward linear relationship between women’s decisionmaking power and child school enrollment. Such relationship by the non-parametric analysis is confirmed by the parametric analysis for which results are presented in Table 5. The finding is consistent with the classic views and the results of many empirical studies, including Schultz (1990) in Thailand, Thomas (1990) in Brazil, Binder (1999), and Adato et al. (2003) in Mexico. Table 5 also shows that when women have greater bargaining power, the girls are more likely than boys to be enrolled in school. Such gender-differentiated impacts are also consistent with the literature. For example, Thomas (1994) shows that Brazilian mothers’ non-wage income positively affected their daughters’ health but not their sons’. Duflo (2003) shows that in South Africa the impacts of exogenous income transfers in the form of old-age pensions were more likely to increase health outcomes of granddaughters of grandmothers than any other 14
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40
above 20% - implying that our instrument barely passes the weak instrument test (Stock and Yogo <u>2005). Therefore, the second stage results of the IV analysis need to be treated with caution.</u> Indeed, there are limits to how far cross-country comparisons, with limited degrees of freedom, can be considered conclusive about the effects of social protection policies on economic outcomes in a causal inference setting.

# **7. Conclusions**

In response to the COVID-19 pandemic, countries across the world implemented an array of social protection measures, some of them aiming at protecting the income and livelihoods of families, others aiming at protecting employment through job retention schemes. To assess the short-term impacts of these policies on post-COVID-19 economic recovery, we put together a database on the social protection response expenditure during the pandemic. It reveals that, on average, countries spent about 2 percent of GDP on social protection measures, although high-income countries spent almost five times more than low-income countries. Expenditure on income protection measures was about 1.2 percent of GDP, with less variation across country income groups than expenditure on job protection. That spending averaged 0.8 percent of GDP but ranged from almost zero in low-income countries to 1.9 percent of GDP in high-income countries.

Our analysis shows that countries that allocated larger shares of their stimulus budget to job protection measures seem to experience more robust GDP growth, higher employment, and lower inactivity and poverty rates than countries that allocated a larger share of the stimulus package to income protection measures. These results could be partly explained by the pre-pandemic coverage of social insurance programs. In countries with broader coverage, the income and job protection programs implemented during the pandemic had a smaller economic effect. The social protection response had a significant effect in countries with limited pre-pandemic social insurance coverage. In countries with advanced social protection systems, some social protection programs implemented during the pandemic may have been redundant as automatic stabilizers were already in place. In addition, job protection programs may have limited labor reallocation from low productivity to high productivity firms during the post-pandemic recovery.

While the analysis in this paper focuses mainly on the “instrumental” value of social protection, that is, on the role of social protection for economic recovery and growth, we recognize the 20
**Figure 7: Returns Effects for Urban-Rural Gaps in Less Integrated Region, 2000 & 2005**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0.05 0. 1 5 0. 25 0.35 0.45 0.55 0.65 0.75 0.85 0.95<br>-0.05<br>-0.1<br>Percentile<br>2000 2005<br>Figure 8: Returns Effects for Urban I - Rural LI Gaps, 2000 & 2005<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95<br>Percentile<br>2000 2005<br>Difference in LRPCE<br>D ifferen ce in L R PC E<br><!-- End of picture text -->
Second, we conducted yearly surveys for all schools. These surveys contained a number of modules, including a facility survey, roster data on around 4900 teachers and detailed surveys for head teachers and Grade 3 teachers. Third, for all tested grades, we administered a short child questionnaire to 10 randomly selected children in order to collect household-level information (6,000 children).

Finally, we conducted surveys with parents separately from the schools. This household questionnaire, with an extended focus on educational investments, was fielded for 1,800 households in the sample villages and stratified to oversample students eligible by age for Grade 3 (the tested grade). These three data sources allow us to triangulate self-reported data from multiple sources and investigate the role of school and household inputs. Here, we use data from the first two rounds of the LEAPS surveys, augmented to check for longer-run effects with data from the third round. Online Appendix I.B provides further details on the content of and respondents for the different school- and household-based surveys as well as their timing across rounds.

On average, there are 631 households in a village with an adult literacy rate of 37.3 percent (Table 1). Among children between the ages of five and fifteen, baseline enrollment rates (public and private) were 76% for boys and 65% for girls in 2004. Schools in our sample enroll an average of 166 children, with 18 children enrolled in the tested Grade 3. Teacher quality is relatively low: just over half the teachers in these schools report more than a secondary education. The enrolled children in Grade 3 are on average 9.7 years old and 55.7% are male.

# **B. Patterns in the Baseline Data**

Households spend 3-5 percent of their monthly budget on each child’s schooling, with private school fees averaging about Rs. 1,200 per year. Analysis years using item response scaled scores, where identification is based on a set of rotating questions that were repeated across years (see Das and Zajonc, 2010).

9
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

**Cocoa exports now dominate Cote d’Ivoire’s trade and economy.** The importance of cocoa exports in Cote d’Ivoire’s trade and economy increased significantly (Figure 9). By 2000, raw cocoa represented 80 percent of the country’s commodity exports, over 50 percent of all exported goods and services, and 21 percent of GDP. Considering that several other sectors are also cocoa-related, it is clear that the country has become very dependent upon one raw commodity, cocoa.

**Figure 9: Cocoa Exports as a Percentage of Commodity Exports, All Exports (Including Services), and Gross Domestic Product**

<!-- Start of picture text --> Cote d'Ivoire: Cocoa Exports as a Percent of Basic Com m odity Exports, of<br>Exports of All Goods & Services, and as a Percent of Gross Dom estic<br>Product<br>90%<br>% C ommodities % All Exports Percent GD P<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Years: 1984 to 2002<br>Percent (Based on 1987 US$)<br><!-- End of picture text -->

**Commodity price declines since 1977 have undermined the benefits of growth in Cote d’Ivoire’s physical cocoa output.** The two periods in Cote d’Ivoire’s growth history exactly coincide with the price profiles on world commodity markets.  (Table 2). Real prices for raw cocoa, cotton and coffee increased on world commodity markets from 1960 until they hit all-time highs in 1977. After 1977 real prices crashed, and on average have declined steadily ever since. Since 1977, cocoa price declines have greatly exceeded production increases. The results for cocoa are reinforced by developments in coffee and cotton during 1977-2003.  Coffee production has not increased, while real prices declined. Cotton output increased, but real prices fell. Simply put, raw commodity production in general and cocoa production in particular have been very low rate-ofreturn investments for Cote d’Ivoire.  Consequently, the strategy of specialization in cocoa has not been successful.

**Since Cote d’Ivoire as the world’s leading cocoa producer has certain market power, the world price trend is not entirely exogenous.**<sup>**10**</sup> Regression estimates provide evidence that Cote d’Ivoire’s cocoa production (quantity) impacts world prices, and that a

> 10 To test the hypothesis that Cote d’Ivoire is indeed a price-maker in the world market for cocoa, we estimated the world cocoa price elasticity with respect to Cote d’Ivoire production quantity (in metric tons) an efficient fully modified least squares estimator (FM-OLS) (Phillips and Hansen (1990), Hansen (1996) and Gregory and Hansen (1996)). For details, see Bogetic, Espina, and Noer (2004).

11