rafmacalaba/datause-encoder-data huggingface dataset:
## _Inherent disaster resilience_

Some sectors are more vulnerable to climate change than others. For instance, evidence on the impact of the 1959 Ise Bay Typhoon in Nagoya City, Japan suggests that firms in retail and wholesale have a lower probability of surviving typhoon-induced disruptions, compared to those involved in manufacturing and construction (Okubo and Strobl, 2021). This sectoral variation can stem from differences in disaster resilience. Retailers suffer due to broken production links and damaged storage facilities, while construction sector generally benefits from increased demand and new opportunities associated with the disaster recovery efforts. Uneven access to financial aid and assistance (e.g., manufacturing may receive more aid) and other government programs (including insurance payments) also explains why some firms replace old capital and invest in newer more productive capital (Noth and Rehbein, 2019) while others do not.

## _Reallocation across sectors_

The varying impact of climate-related disaster across industries and sectors yield an empirical prediction that labor and capital will flow to those sectors with a higher rate of return. In India, evidence from storms during the period 1995-2006 suggests that the impact on the formal and listed firms can be transmitted through reallocation within- and between-industries (Pelli et al., 2020) because capital destruction leads to a reallocation of sales towards better performing firms and industries. That is, shocks trigger reallocation _within sector, structural shifts or spatial_ adjustments. An optimistic twist on the creative destruction hypothesis is that firms that survive extreme events will ”build back better” (Leiter et al., 2009; Coelli and Manasse, 2014; Noth and Rehbein, 2019; Pelli et al., 2020).

# **2.2 Locational Choice as an Adaptation Response**

In the aftermath of major natural disasters, people move to places offering greater economic opportunities. Some examples include Hurricane Katrina’s impact on New Orleans and the 1930s Dust Bowl’s impact on Kansas where longitudinal research has documented that many people who were forced to move away from these shocked places prospered later on as they moved to more vibrant local labor markets (Deryugina et al., 2018; Hornbeck, 2012).

The economic geography literature emphasizes a type of ”chicken and egg” dynamic concerning whether people move to places where the jobs are available or do firms locate in places where people cluster. In choosing their location, firms tradeoff balancing the agglomeration benefits derived from sharing, matching and learning on the one hand, versus 7
**Table 8. Distribution of Bilateral ODA by Sector for Selected Countries, 1999 (Percentage of total bilateral commitments)**

||**Social & Adm.**<br>**Infrastructur**|<br>**Economic**<br>**e Infrastructur**|**Produ**<br>**e Agriculture**|**ction**<br>**Industry**|**Multi-**<br>**sector**|**Programme**<br>**Assistance**|
|---|---|---|---|---|---|---|
|**Japan**|18.9|31.5|7.6|5.7|4.8|11.2|
|**United States**|31.3|12.9|2.3|0.1|9.9|9.9|
|**European Union**|36.8|6.4|4.7|0.9|7.2|2.4|
|**France**|39.2|9.0|5.7|0.9|7.6|2.2|
|**Germany**|34.7|25.8|3.9|0.7|9.9|2.4|
|<br>**United Kingdom**|29.2|12.2|9.4|4.1|2.9|8.7|
|<br>**DAC Total**|29.9|17.2|5.5|2.2|7.4|6.9|

Source: _OECD, DAC_ Correspondingly, the share of social development, such as the environment, poverty reduction, education, health and nutrition, has been limited in Japanese bilateral ODA. In 1999, the share of social infrastructure was 19 percent, compared with 31 percent for the United States and 37 percent for the European Union. Given the increasingly important role private capital is expected to play in financing hard infrastructure, particularly in middle-income developing countries, this feature of Japanese ODA will not be sustainable. Japan will then face the challenge of having to shift its emphasis from hard infrastructure towards soft infrastructure (such as education, health and basic medical care) and policy reform-related assistance.

# **6. Dominance of Government-Led ODA**

In terms of both design and execution, Japanese ODA is weak in the participatory approach, being typically administered by governments and other official agencies. The involvement of non-governmental organizations (NGOs) and of civil society in general is limited. To be sure, grass-root participation by NGOs has begun and the Japanese government has announced a more accommodating policy towards the involvement of NGOs (see Tominaga and Sera 2000 for details),'<sup>8</sup> but the size remains small in comparison with the situation in other major donor countries.

According to DAC data for 1997, the share of NGO-participated grants was only 2 percent of total ODA in Japan, while the share was much higher in other major DAC countries, such as the United States (37 percent), Germany (16 percent) and the United Kingdom (9 percent). Reflecting the large absolute size of Japanese ODA, however, the value of NGO-participated grants in Japan ($223 million) was the fourth largest.

The alleged lack of visibility of Japanese ODA may stem from the fact that ODA activities are carried out mainly by governments, and contracted to large consulting firms, with little grassroots participation by Japanese NGOs and individuals, or by members of local civil society in the recipient countries.

'8 The Cabinet decision of August 1999, "The Medium-Term Policy on Official Development Assistance," recognized a "growing demand for finely tuned assistance that directly reaches local populations" and a "growing need for collaboration with NGOs in the implementati **o** n f ODA projects" (as quoted in Tominaga and Sera 2000).

13
the analysis controls for male homicides, male hospitalizations for assault, and reports by health care professionals of violent incidents against men per 100,000 men ages 15–49.<sup>10</sup> It also controls for three factors that are correlated with the bargaining power of women within the household. First, data of the Brazilian electoral court are used to construct a gender ratio of educational achievement—specifically, high school completion—for each municipality by year. Given that elections are held only every other year, the analysis imputes, by municipality, the average education ratio of surrounding years for each year between election years. For example, the high school completion ratio of women relative to men in 2013 is computed as the average of the ratios in 2012 and 2014. Second, the analysis takes advantage of the fact that conditional cash transfers have been shown to influence women’s bargaining power in Brazil (Litwin, Perova, and Reynolds 2019). So, data of the Ministry of Social Development (Ministério do Desenvolvimento Social) on the number of households receiving the social program Bolsa Família family allowance and the value disbursed by the program in each municipality by year are combined with population data to control for the share of women receiving the Bolsa Família benefit as well as the per capita benefit in each municipality-year. Third, the analysis controls for the share of the population represented by women ages 15–49 because the “scarcity” of women may influence how women are treated (Grosjean and Khattar 2019).

The analysis also controls for the log population of women ages 15–49, the per capita number of health clinics and hospitals, and the per capita number of hospital beds. **Sample Selection** Initially, 409 municipalities that showed all zeros in all outcomes in all years were excluded. RAIS data were missing for one additional municipality. Brasília, the capital city, was also excluded because wages for government positions are occasionally coded to Brasília, and this anomaly might generate a small amount of noise in a typical municipality, but would certainly generate a great deal of mismeasurement in Brasília. Municipalities were also removed that had any missing data to establish a balanced panel. Of Brazil’s 5,570 municipalities, 4,205 remained that had viable observations. However, 75 percent of the homicide observations, 76 percent of the overnight hospitalization observations, and 57 percent of the reports were zeros. This suggests that changes in levels of violence in the smallest municipalities result in a large amount of noise because violent events are rare occurrences. The observation of a violent event in a small municipality

> 10 Unlike the  violence registry for women, not all incidents of violence against men are included in the SINAN registry.  Reports of men experiencing non-family, interpersonal violence or violence within the community are excluded.

14
of private market) could better help fishermen to plan their career and cope with aid quality problem.

Empirical section considers the potential beneficiary selection bias from the non-random quality assignment between treated (the recipients of bad quality aid) and untreated (good aid recipients) groups similar to Chen, Mu, and Ravallion (2009). I offer a balancing test in the later section which supports the exogeneity of the aid quality variation conditional on observable characteristics. Using the survey data on the quantity and quality of housing and fishing reliefs, it also deals with the confounding effect from the multiple treatment problem.

This paper found that the aggregate quality shock (adverse labor demand shock common within a village borne by a supply of faulty aid boats) created substantial income losses, driving many fishermen to choose non-fishing jobs. Moreover, the aggregate shock created a _negative selection effect_ , leading to a small-scale and less productive fishing sector. A subset of aid recipients further faced the idiosyncratic shock (loss of production capital due to the imperfect durability) in later years. Households needed to take costly coping actions if they experienced the shocks, creating a new form of income inequality. As private market gradually developed over time, better access to private market helped fishermen to undo the idiosyncratic shock. <mark>These results imply that the quality monitoring of in-kind aid and the soft aid for developing private market would be key policy agenda for donors in the post-disaster context.</mark> The paper is structured as follows. Section 2 explains the context and survey. Section 3 provides theoretical predictions. Section 4 and 5 carry out empirical analyses on job choices, while Section 6 studies the impact of aid quality on inequality. Section 7 concludes.

# **2.     Background and survey data**

# _2.1   Labor market developments after the tsunami_

Creating permanent jobs is an important challenge in the aftermath of the natural disaster. Figure 1 shows the change in the fraction of workers in each of the four sectors and the underemployment rate.

3
Another instrument (in addition to the policy reform based instrument) is included in the specification presented in Table A3, namely if the pupil’s father has at least upper-secondary education. Here the objective is twofold: first, test the over-identifying restrictions and, second, test the hypothesis that if the treated group is an even more privileged one (have taken advantage of the capitation grant system _and_ have educated fathers), its cognitive skills will be better and more homogeneous compared to the treated group in Table 1 (which in turn had better and more homogeneous cognitive skills compared to the average individual). Therefore, the contribution of cognitive skills to earnings should be small, while returns to schooling should increase.

Based on the value of Hansen’s J-statistic (see Hansen 1982), the null hypothesis (that the instruments are appropriately uncorrelated with the disturbance process) is not rejected (in all cases, that is, columns (1), (2) and (3), p-values are 0.15 or higher). As hypothesized, the contribution of cognitive skills to earnings is nearly zero (column 2). At the same time, the estimate of the return to schooling increases by between 7-19 percent compared to Table 1.

In Table 2 the binary instrument is modified and takes the value of 1 for those who at the time of the reform where 6 years or older (22 years or older in 1997 – the year of the survey) and up to 18 years of age included (34 years old in 1997) and 0 otherwise; that is the group includes all those who could have been affected by the reform, either by switching to private subsidized schools because of their perceived higher quality compared to municipal schools, or by choosing to continue schooling at the secondary level as a result of the increase in the supply of private schools associated with the education reform, which led to a large increase in secondary school participation. Estimates of the return to schooling are now lower by 37 percent in the Mincerian 16
# Contents

|**1 Methodological framework**|**1**|
|---|---|
|**2 The impact of environmental factors on infrastructure endowments**|**5**|
|Variations across regions|5|
|Variations across types of countries|10|
|**3 The impact of historical trends on infrastructure endowments**|**17**|
|Variations over time|17|
|Panel data models|23|
|Benchmarking|26|
|**4 Conclusions**|**28**|
|**References**|**29**|
|**Annex A Alternative regression models**|**31**|
|**Annex B Country-specific deviations from regression models**|**32**|
|**Annex C Regional infrastructure endowment**|**36**|
|**About AICD**|**38**|

## **The authors**

Tito Yepes is an economist in the World Bank’s Latin America and Caribbean Region. Justin Pierce (justinrpierce@gmail.com) is a doctoral student in economics at Georgetown University. Vivien Foster (vfoster@worldbank.org) is a lead economist in the World Bank’s Africa Region and the staff director of the Africa Infrastructure Country Diagnostic.

iii
Our study period was interrupted by the onset of the COVID-19 pandemic. The childcare centers began operating in November 2019 and were intended to run continuously for a year, but instead closed in March 2020 due to COVID-19. They reopened in October 2020, with the support of the Burkina Faso Ministry of Women and Ministry of Education, the _mairies_ (municipal governments), and the World Bank, which each provided funding, in-kind resources, or technical assistance to operate the childcare centers after the closing of the PEJDC project in February 2020. Participating households therefore had a maximum of eight months of potential exposure to the childcare centers (four months before the COVID-19 closure and then four months following the reopening and before our follow-up phone survey). Importantly, the second phase of operation occurred after program participants had completed their public works assignment, which ended in February 2020. This timing allows us to observe usage of the childcare centers and impacts on economic outcomes in a period during which program participants were no longer guaranteed employment through the public works program. Additionally, the childcare centers operated mainly from 8am to 2pm to coincide with the public works schedule during the first phase of operation, but they were optionally open to parents for a full workday of care when they reopened in October 2020.

# **3.4 Empirical Strategy**

We use the following analysis of covariance (ANCOVA) specification to estimate Intent to Treat (ITT) effects: where _Yist_ is the outcome for individual _i_ from public works site _s_ at time _t_ . _Treatments_ is an indicator for whether site _s_ was one in which a childcare center was established under the project. _Yist−_ 1 is the baseline value of the outcome measure for individual _i_ . **X**<sup>_′_</sup> _is_ is a vector of control variables. Household-level controls include household size, number of children aged 6 or under, number of children aged 7 to 15, number of economically active household members, head is female, head is ethnically Mossi, head age, head is in a monogamous marriage (to capture differences in 12
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**Table 35:  Rank of prospects to be an innovative city (Percentage of respondents)**

|The chance of city becoming a center for<br>knowledge-based firms and industries within|High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|this decade|||||
|_Beijing_|||||
|Very high|40|30|20|30|
|High|46|45|80|56|
|No opinion|7|10|-|6|
|Low|7|15|-|8|
|Very low|-|-|-|-|
|Total|100|100|100|100|
|_Shanghai_|||||
|Very high|62|22|45|48|
|High|24|67|25|32|
|No opinion|14|11|5|10|
|Low|-|-|20|8|
|Very low|-|-|-|-|
|Total|100|100|100|100|

**Table 36: Aspects of efficiency**

||**High-tech **|**KIBS**|**Media/Content**|**Overall**|
|---|---|---|---|---|
|**Beijing**|||||
|Public services|2.8|2.4|2.6|2.6|
|Communication facilities|3.3|3.1|3.1|3.1|
|Transportation system|1.8|1.6|1.9|1.7|
|Law and order|2.7|2.7|2.7|2.7|
|Public health|2.7|2.4|2.5|2.5|
|Crisis|2.2|2.0|2.0|2.1|
|Disaster management|1.9|2.1|2.1|2.0|
|Regulatory framework|2.6|2.9|2.4|2.7|
|Zoning|2.5|2.4|2.5|2.4|
|E-governance|2.7|2.5|2.3|2.5|
|**Shanghai**|||||
|Public services|3.2|2.8|3.1|3.1|
|Communication facilities|3.4|3.1|3.3|3.3|
|Transportation system|2.6|2.1|2.3|2.3|
|Law and order|2.7|2.8|2.5|2.7|
|Public health|3.0|2.9|3.3|3.1|
|Crisis|3.1|2.8|2.5|2.8|
|Disaster management|3.2|2.8|2.7|2.8|
|Regulatory framework|3.2|2.9|2.7|2.9|
|Zoning|3.6|2.8|2.9|3.1|
|E-governance|3.4|2.7|2.7|2.9|

_Note_ : outstanding=4, adequate=3, inadequate=2, seriously inadequate=1 Table 36 shows the ranking of efficiency from a business point of view by firms in Beijing and Shanghai. Overall, firms in Shanghai ranked almost all items higher than those in Beijing, except law and order. Transportation was most unsatisfying in both cities because the 45
# _Discussion_

The school readiness intervention was found to have a positive effect on overall school readiness measured by the TEHCI for girls of mothers with no high-school education. It also affected the school readiness literacy domains of girls and boys in this group. Children of mothers without high-school education had lower levels of school readiness compared to children of higher educated mothers in the baseline TEHCI; hence, the school readiness intervention seems to have benefited the more vulnerable children.

Table 5. Effect size per 100 USD

||all|mothers w<br>school e|ithout high<br>ducation|mothers<br>school e|with high<br>ducation|
|---|---|---|---|---|---|
|||girls|boys|girls|boys|
|average TEHCI score|0.16|0.79**|0.32|‐0.08|‐0.28|
||(0.48)|(0.79)|(0.63)|(0.55)|(0.4)|
|average TEHCI literacy domains|0.28|0.75**|0.67**|‐0.08|‐0.12|
||(0.4)|(0.63)|(0.55)|(0.48)|(0.4)|

Standard errors included in parentheses. Statistical significance at the 1, 5 and 10 percent levels are denoted by ***, **, and *, respectively. Estimates are calculated by dividing the effects presented in Table 4 by the per child cost of the intervention in 100s.

No effects were found for the index of home activities, though positive effects were found for specific activities. Of course, the data do not provide a comprehensive picture of the quality of the activities or caregiver-child interaction more generally. Effects on school readiness likely reflect changes in the home environment that are not specifically captured in the TEHCI data. The lack of effect on school readiness for mothers with high-school education may be because the home environment is better for this sub-population, mitigating the effect of CPBA participation on the home environment. The six home activities are more prevalent among mothers with high-school education in the baseline, 2014 TEHCI.

While we cannot attribute these effects exclusively to CPBA participation, it is likely that CPBA is the main source of effect. Given the low participation in CPBAs, the effect of participating in a CPBA is likely quite high. For example, the intervention increased overall school readiness of 20
11 and others have shown,<sup>20</sup> these two measures of income typically do not agree. Income (expenditure) as measured by household surveys is calculated from the responses of individual households. However, income as measured by GDP data comes from the national accounts, which measure household income as a residual item, so that errors and omission elsewhere in the accounts automatically affect the calculation of household income (expenditure). Since the national accounts data also include many items (such as the expenditures of nonprofit organizations and the imputed rent of owner-occupied dwellings) which are not included in the household surveys, it is little wonder that the two measures of income do not correspond.

For the purposes of this study, we will use estimate equation (1) using both measures of income. This will allow us to test the robustness of our findings to different definitions of income.

In the literature equation (1) is often measured in first differences, in order to deal with possible correlation problems between the variables, since the dependent and independent variables are drawn from the same single source of data (household budget surveys). In this study, however, we will estimate equation (1) as a level equation since the dependent and independent variables come from different sources of data: the dependent variable being drawn from household budget surveys and the independent variables (for migration and remittances) from various other sources.<sup>21</sup> Using the migration data, OLS estimates of equation (1) are presented in Table 1. Since all of the variables are estimated in log terms, the results can be interpreted as elasticities of poverty with respect to the relevant variable.