rafmacalaba/data-use-mentions-extended huggingface dataset:
Since the micro data from POEA does not include skill levels, we calculate average education levels by occupation using the 1992-2003 Survey of Overseas Filipinos (SOF),<sup>7</sup> and assign each occupation the average education level. We use this to then construct skill quartiles of aggregated occupational cells in our data. The average years of education for occupations in the first quartile is 11.6 years, 12.8 years for the second quartile, 13.8 years for the third quartile, and 15.1 years of education for the fourth quartile. One sees notable differences in the wages that a worker of a given skill level can earn across destination countries. For instance, OFWs in the first skill quartile in Saudi Arabia receive an average wage of $336 per month, whereas OFWs of the same skill level in Japan earn an average monthly wage of $1,505. This large variation across destination countries holds for the more skilled quartiles as well. The highest skilled workers in Saudi Arabia earn $553 per month, whereas in Japan these OFWs earn $1,661 on average each month.

# **3.2 Macro Data**

Data on annual real GDP (constant 2000 US$) over the sample period were obtained from the World Development Indicators database and the CIA World Factbook. These data are then matched to the POEA data based on destination country and year of departure. Over the sample period, destination countries in our sample experience vastly different rates of GDP growth as well as varied fluctuations in growth. For instance, during the Asian Financial Crisis, Asian countries such as Japan or South Korea faced dramatic reductions in GDP growth, whereas Middle Eastern destinations such as Bahrain or Kuwait maintained fairly stable growth. Figure 3 plots real GDP growth in the top 10 destinations for OFWs. In addition to the differences in growth rates in 1997 during the Asian Financial Crisis, another period of high volatility was during the Global Financial Crisis, which by 2009 had affected some
price averaged ₦216.25 and ₦229.75, respectively; similarly, the price of rice averaged at ₦612.25 and ₦615.88, respectively. Focusing on yellow maize, high variability was observed in the daily mean of crowdsourced data (₦156 to ₦296), as compared to enumerator range (₦120 to ₦374). However, the observed variability attenuated as the submissions were averaged into weekly and monthly datapoints (Figure 3b-c). By averaging both datasets into coarser temporal granularity (i.e. daily =>weekly =>monthly), the cohesion and similarity of price signals became more evident, and correlation coefficient improved (36% increase) from 0.69 at daily timestep to 0.94 at monthly timestep. Further, at monthly timestep (Figure 3c), 86% of the variability inherent in ground truth prices is represented in the crowdsourced data (p<0.001, α=0.05). In the corollary, the co-variability explained at weekly and daily timesteps are lower, likely owing to data noisiness at daily and weekly time intervals.

**Table 1:** Relationship between Ground truth reference prices from enumerators (Gr), Crowdsourced (Cr), and Artificial Intelligence (AI) estimated prices for maize and rice. The GrCr price data comparison was based on intraday data submissions over 8-months period (Mar - Oct 2021), while the Cr-AI data comparison was based on 3-years data (2019-2021) monthly average Cr prices juxtaposed with monthly AI-estimated closing prices. The values in the table represent the overall mean prices for each period, the corresponding correlation coefficient (R), and coefficient of determination (r<sup>2</sup> ), indicating the direction and strength of the relationship between the paired price datasets.

|||**8-month**<br>**Gr (**₦**/kg)**|**s Validatio**<br
University. The CHIPS is drawn from the same sample frame as the HBS, and an analysis of an earlier round of the survey, collected in 2007, yielded similar poverty rates as the official HBS-based estimates (Zhang et al, 2014). The poverty rate for urban and rural China, derived from the 2013 HBS, is applied to the CHIPS data to generate profiles of the extreme and moderate poor in China.

The data from India also deserve special mention. In general, the results presented below are based on schedule one of the 2011 National Sample Survey (NSS), which is the primary source underlying both the estimates of poverty reported by the Indian government and the international poverty rate reported by the World Bank. The schedule one survey, however, does not collect information on labor market outcomes. Therefore, all information on sector of work is taken from schedule ten of the NSS, which collects both labor market information and sufficient information on household expenditure to construct an unofficial consumption aggregate. To calculate the poverty status of Indian workers by sector, the World Bank’s urban and rural headcount poverty rates, which are derived from the schedule one survey, are applied to the corresponding percentiles of the urban and rural distribution of schedule ten’s per capita consumption measure. Thus, the shares of agricultural workers that are below the $1.90 and $3.10 thresholds in India are estimated using the unofficial welfare aggregate collected in schedule ten.

> 7 Due to the nature of the license agreements between the World Bank and National Statistical Offices, data for most countries cannot be made publicly available.

> 8 Only one survey is not nationally representative: Argentina’s household consumption survey, the Encuesta Permanente de Hogares, which is not nationally representative and covers only about two-thirds of the country’s urban population instead. Given that the urban population accounted for about 90 percent of Argentina’s total population in 2013, the survey effectively only represents 61 percent of the national population.

> 9 For
Finally, we exploit the public dataset to examine whether the correlation between aid and haven deposits has diminished in the most recent years (not covered by the restricted dataset). Since around 2009, all havens have enhanced financial transparency in response to pressure by international organizations like the OECD (Johannesen and Zucman, 2014) and individual countries like the United States (Johannesen et al., 2018). In the same period, a number of data leaks by whistleblowers in the wealth management industry, e.g. _Swiss Leaks_ and _Panama Papers_ , have increased the risk of exposure for public figures with undeclared money on foreign accounts (Johannesen and Stolper, 2017). As shown in Column (11), there are no clear signs that aid disbursements are associated with smaller increases in haven deposits in the period with more financial transparency: the coefficient on the aid variable is almost identical in the periods 1990-2008 and 2009-2018. However, the standard errors are large and we also cannot reject the null hypothesis of a zero correlation in the post-2009 period. In principle, it is possible to conduct more high-powered tests that exploit country-level variation in transparency and information exchange, but we leave that for future research.

# **5 Concluding remarks**

We document that aid disbursements to the most aid-dependent countries coincide with significant increases in deposits held in offshore financial centers known for bank secrecy and private wealth management. Aid capture by ruling politicians, bureaucrats and their cronies is consistent with the totality of observed patterns: it can explain why aid does not trigger flows to non-havens, why the capital outflows occur precisely in the same quarter as the aid inflows and why the estimated effects are larger for more corrupt countries. Other explanations are possible but we find them harder to reconcile with all the patterns in the data. We cannot exclude that firms benefiting from aid-sponsored spending receive payments in quarters with aid disbursements and deposit the funds with foreign banks; however, this mechanism cannot explain why the money only flows to havens. It seems even less
levels, is significantly associated with test performance. However, measures of nutritional status account for only a small fraction of the association between wealth, parental education and TVIP scores.

An innovative feature of the data collected in Ecuador is that it includes information on parenting quality, including the Home Observation for Measurement of the Environment (HOME) scale, which has been widely used in research in the United States (Bradley 1993; Caldwell and Bradley 1984; Bradley et al. 2001). This is constructed from 11 items that are assessed by enumerators at the close of the interview, and measures punitiveness (for example, whether parents yelled at or hit children during the interview), and responsivity to children (for example, whether they responded to and encouraged children in a positive way during the interview). Each item is scored as a dichotomous variable, and the final scale ranges from 0 to 11, with higher values corresponding to less responsive and harsher behavior. Paxson and Schady (2005) show that children whose parents have lower (better) HOME scores, reflecting warmer and less punitive behavior, have significantly higher TVIP test scores. Interestingly, the parenting measures account for a substantial fraction of the associations between socioeconomic status and cognitive development that are observed in the data.

A number of broad conclusions can be drawn from the papers on Mexico, Brazil, and Ecuador. First, when comparisons are made of the performance of children in Latin America with the reference populations that were used to norm the tests, Latin American children show large developmental deficits. Second, there appear to be sharp gradients by socioeconomic status. Children from poorer households, and children with parents with lower education levels perform significantly worse than other children. Third, the gradient between socioeconomic status and child development appears to become stronger with age. This may be because the protective effect of socioeconomic status is cumulative, as Paxson and Schady (2005) speculate, which would be consistent with the model in Cunha et al. (2005). It is
, depending on the items included and the scale of the economic entity considered (Barthel and Neumayer 2010). For example, from Munich Re records (Kron 2011) the difference between actual and insured losses is clear. Insured losses represent between less than 1% to over 70% of actual losses (see Table 1) depending largely on country and insurance coverage rates which also vary greatly (Gaschen et al. 1998). For example, in 2010 the Pakistan flood with an estimated $9.5bn in economic loss resulted in very low insurance claims of $100m as it occurred in very low income areas of a low income country where insurance coverage is almost negligible (Munich Re 2011). In contrast, the recent Queensland floods were among the most expensive

10
Table 15 – Educational Attainment of Migrants and Non-migrants

|Education level|Non-Migrants|Migrants|Total|
|---|---|---|---|
|Less than 6 years|22.8|2.7|21.6|
|Illiterate|19.6|26.2|20.0|
|Read & Write|12.6|11.4|12.6|
|Less than Intermediate|16.0|13.9|15.9|
|General Secondary|3.6|4.1|3.7|
|Tech. Secondary|15.9|21.9|16.3|
|Above Intermediate|2.0|3.8|2.1|
|University|7.3|15.5|7.8|
|Above University|0.2|0.6|0.2|
|Total|100|100|100|

Source: Authors’ calculations using Labor Force Survey

# **Agricultural Sector Involvement**

The second stylized fact that can explain Egypt’s low internal migration rate is involvement in agricultural activities that ties people to the land, often as unpaid family workers. Agricultural workers have lower migration rates than workers with other occupations (see Table 16).

Agricultural activities are typically characterized by low productivity (output per input) and lower wages (see Figure 4) than in other sectors. Using the share of agriculture in total employment as a proxy for productivity, as in Gollin, Parente and Rogerson (2008), it can be seen that governorates with high shares of agriculture, and hence lower productivity, are associated with lower wages.

Figure 4- Wages and Share of Agricultural Sector Employment by Governorate <!-- Start of picture text --> suez<br>cairo 6-Oct<br>red sea<br>helwan<br>north sinai<br>south sinaiport said<br>
Several data sources based on representative surveys of rural households offer the most straightforward descriptive statistics on the effects of employment shocks on migrant workers.<sup>6</sup> Indeed, rural-to-urban migrants in China rarely move to urban areas with their entire families, and thus members of the family (older parents, children, and sometimes spouses) are left behind in home villages. Since 2003, the two survey institutes conducting national rural household surveys—the National Bureau of Statistics (NBS) and the Ministry of Agriculture’s Research Center for the Rural Economy (RCRE)—have been fielding household surveys that included modules with detailed questions on the activities of migrant family members. Much of the empirically based Chinese-language literature detailing the effects of the crisis on migrants is based on these data sources. Unfortunately, these data are not readily available for public use.

Several papers written using the NBS or RCRE household surveys document the gross effect of the crisis on unemployment. Early in 2009, analysts using the NBS survey network estimated that 20 million migrant workers were laid off as a result of the crisis (Chen 2010) and, in March 2009, the NBS released a report estimating that 23 million migrant workers were out of work (NBS 2009).That number amounts to 16 percent of the long-term migrant workforce.<sup>7</sup> Much analysis to date on the effects of the crisis on migrant workers focuses on job loss and does not examine the employment impact based on a reasonable counterfactual assumption of what employment would have been in the absence of the financial crisis. In addition, some of these studies focus on ―gross impacts‖ and thus miss the reallocation of labor across sectors.<sup>8</sup> A study by Huang et al. (2011) uses a panel survey, known as the China National Rural Survey (CNRS), collected by the authors to establish a counterfactual ―business-as-usual‖ level of off-farm employment and then analyze the effect of the crisis on
Isik, I., Gunduz, L., and Omran, M., 2004. “Managerial and Scale Efficiency in the MENA Banking: A Panel Study of the Jordanian Banking Sector”.

Khwaja, A., and Mian A., 2005. “Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market”. _The Quarterly Journal of Economics_ , 120(4), 1371-1411.

Kobeissi, N., and Sun, X., 2010. “Ownership Structure and Bank Performance: Evidence from the Middle East and North Africa Region” _Comparative Economic Studies_ 52 (3), 287-323.

Körner, T., and Schnabel, I., 2010. “Public Ownership of Banks and Economic Growth - <u>The</u> Role of Heterogeneity” Working Paper Series of the Max Planck Institute for Research on Collective Goods 2010_41.

La Porta, R., López-de-Silanes, F., Shleifer, A., 2002. “Government Ownership of Banks” _Journal of Finance_ 57 (2), 265-301.

Levy-Yeiati, E., Micco, A., and Panizza, U., 2007. “A Reappraisal of State-Owned Banks” _Economica_ 7(2), 209-247.

Madeddu, O., 2010. “The Status of Information Sharing and Credit Reporting Infrastructure in the Middle East and North Africa Region” unpublished manuscript (available at MENA Finance Flagship website: http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/MENAEXT/EXTMNAREGTOPPOVRED/0,, contentMDK:22734614~pagePK:34004173~piPK:34003707~theSitePK:497110,00.html) Micco, A., and Panizza, U., 2007. “Bank Ownership and Lending Behavior” _Economics Letters_ 93, 248-254.

Micco, A., Panizza, U., and Yañez, M., 2007. “Bank Ownership and Performance: Does Politics Matter?” _Journal of Banking and Finance_ 31 (1), 219–41.
food for my guardians who were not working anymore and the situation had gotten difficult.” Hence, there is a sense in which cash transfers may have increased aspirations, but ended up becoming a source of frustration to the adolescent beneficiaries when their families became reliant on them for basic necessities during (at least the early stages of) the pandemic. This is similar to the finding in Baird et al. (2013), where being the target beneficiary of a cash transfer program that is providing a non-negligible sum of money for the household can become a burden for adolescents and increase their psychological distress. This finding is also consistent with a recent evaluation of a cash transfer program trialed in Oakland, California, which finds overwhelmingly zero or negative impacts on outcomes and suggests that “...the windfall made participants’ (unmet) needs more salient, which caused distress” (Jaroszewicz et al., 2022).

Quantitative survey data also support this interpretation when we examine the adolescent’s own response to COVID-19, as well as their perception of their household’s response to it. Table 11 shows the impacts on a set of outcomes regarding how the respondent and their

> 23Impact estimates with covariate adjustments for Table 8, Table 9, and Table 10 can be seen in Appendix Tables A7, A8, and A9. Impact findings remain qualitatively the same.

17
the maximum amount that the average survey respondent would be willing to pay for the proposed improvement in WSS in the context of the existing institutional regime within which households are free to allocate their financial resources. In the context of the case study presented here, WTP is the amount of monthly income that the average household could give up after obtaining the improved and or new WSS and be just as well off as in the situation without an improvement in water supply. By this token WTP is a measure of the average households’ economic value and the basis for its preference for the proposed improvements in WSS.

Maximum WTP for the average household is related to but not equivalent to the future economic demand or to the monthly bill paid by households to the utility for new or improved WSS services. Although maximum WTP is related to both these concepts because it contains similar behavioral information about household preferences, it is different primarily because it is an _ex ante_ measure of consumer surplus. From the perspective of estimating future demand, it does not tell us what level of the service (e.g., how much water) will be consumed. Instead, as a measure of consumer benefits, maximum WTP measures are best used in Kaldor‐Hicks type social cost‐benefit analysis – do the benefits of expanding piped water services exceed the costs of the infrastructure and operational investments?

In the remainder of this short paper, we show that in addition to generating a general measure of economic benefits, WTP surveys often produce two important pieces of operational information. First, because we usually implement a WTP study by using a split‐sample survey experiment (different households are asked to consider different charges say for improved water services), we can trace out the demand in the service area that maps the relationship between charge and the number of people who will take up the service at that particular charge. Such an estimated relationship can depict the ‘uptake rate’ for the service improvements. This interpretation of WTP survey data is common in the health
government is not operative, the district commissioner becomes the sole discharged of legislative, judicial and executive functions at the district level. No wonder, one sees that local governments are not allowed to succeed in Pakistan (see Shah, 1996)4.

# _Institutional Factors_

Institutional factors also impede effective decentralization. Traditional institutions and mechanism of governance and accountability over time have withered away but these have not been replaced by newer institutions. Instead, all pervasive role of the state have retarded critical look at public policies and institutions. There is almost complete monopoly of the government on institutions of critical thought and media in many countries. Any critical review of government policies and programs invites a government backlash. In Pakistan, rural self government worked well in earlier days of its independence. This system was abandoned in favor of a more centralized system which has resulted in denial of access to basic services by rural population. While lack of institutional capacity was cited as a reason for disbandment of the participatory system, the newer system left a majority of citizens with no voice and participation and no access to basic public services. Indonesia, on the other hand, is now nurturing self-government in rural areas through its village development and poor villages grant programs (see Shah, 1996).

Another important aspect of institutional factors is the citizens' philosophy towards government. Do people generally (politicians, bureaucrats, public employees generally and the public/voter) view the public sector as one where one does service for fair compensation or a position to exploit for personal gain. Various opinion surveys suggest that the prevailing public perception about the public sector especially in South Asia and Africa tend to support the latter view and citizens tend to associate public sector with a predatory role.

Contrary to common misconceptions (see for example, Tendler's characterization of Decentralization and Participation paradigm (D&P)), the success of decentralization policies critically require a strong responsive and accountable government at the nationial level just as the success of centralized governance critically depends upon responsive, accountable and
characteristics, shipping technology, fuel costs, and economies of scale at the vessel level. We discuss these components in Sections 4.1–4.3. The fixed cost component is a function of the vessel prices and port infrastructure. We discuss the port infrastructure in Section 4.4.<sup>24</sup>

# **4.1 Market Structure: Network Organization, Industry Concentration, AnnualContract versus Spot Markets**

Containership transportation is organized similar to bus services, with the routes labeled as rotations and ports serving as bus stops. Major rotations include multiple ports, with the total duration of a rotation often exceeding 60 days. Both ports and ships vary in their capacity and cost of transportation. As discussed above, the average containership has become much larger over the last decades. These larger ships require automatic and modern equipment such as engines and cranes to load and unload them. The developments in shipping technology have created the need for ports to develop their infrastructure to handle these larger ships, such as special berths, bigger cranes, more storage space for the increased cargo size, and more developed and efficient port logistics. Since the investment in port infrastructure requires a large amount of capital and human resources, the largest container ports handled a disproportionate volume and value of world trade. Notteboom (2018) calculated that in 2015, the 20 largest container ports handled 312.5 million TEU, which is 45% of world trade. The network structure of the containerized shipping was explored in detail by Heiland et al. (2019) and Ganapati et al. (2020). The latter also showed that a handful of ports play a disproportionately significant role:<sup>25</sup>

**Stylized Fact 18.** _Larger ports serve as hubs accommodating larger ships, making hub-to-hub transportation significantly cheaper and hubs essential for maritime trade._

Most networks in liner focus on cross-sectional data The papers analyzing shipping analysis. literature is scant when it comes to documenting the network