rafmacalaba/data-use-mentions-extended huggingface dataset:
, I.T. & Black, A.M.S., 2001. A systematic review of comparisons of effect sizes derived from randomised and non-randomised studies. _Health Technology Assessment_ , 4(34): 1-153.

- Mannheimer, S.B., Mukherjee, R., Hirschhorn, L.R., Dougherty, J., Celano, S.A., Ciccarone, D., Graham, K.K., Mantell, J.E., Mundy, L.M., Eldred, L., Botsko, M. & Finkelstein, R., 2006. The CASE adherence index: A novel method for measuring adherence to antiretroviral therapy. _AIDS Care_ , 18:7, 853-861.

- Motsoaledi, A., 2011. _Health Budget Vote Policy speech by Minister of Health_ , National Assembly, 31 May, Cape Town.

- Nachega, J.B., Mills, E.J. & Schechter, M., 2011. Antiretroviral therapy adherence and retention in care in middle-income and low-income countries: current status of knowledge and research priorities. _Current Opinion in HIV & AIDS_ , 5(1): 70-77.

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# _Approach in this study_

None of the depreciation rates in the above studies have firm empirical basis. As a result, the depreciation rates used here have been taken from figures published by the Bureau of Economic Affairs in the United States (Table 5), with the exception of housing where official Chinese depreciation rates are available. The US data distinguish three types of assets at the aggregate level:

- Structures

- Equipment

- Intellectual property

In addition, it distinguishes between the capital stock of general government and the rest of the economy.

**Table 5 Non-residential depreciation rates in the United States** Average 1990 to 2015, % per year

||Private sector|Government|
|---|---|---|
|Non-residential|8.7|4.6|
|Equipment|13.9|13.2|
|Structures|3.0|1.9|
|Intellectual property|25.1|16.7|
|Equipment and structures|6.8|3.2|

_Source_ : Bureau of Economic Affairs, https://www.bea.gov//national/FA2004/DownSS2.asp In this study it has been assumed that the depreciation rates for the Chinese business sector are the same as those for the private sector in the United States. The rates for the US government sector have been used for the government and infrastructure sectors in China, with the same depreciation rate being used for both sectors as the split of investment between structures and equipment is very similar in the two sectors. The US data, however, show intellectual property assets as a separate category, while for China such assets are included in equipment.

# _Accounting for software and intellectual property in the capital stock_

The official Chinese investment data split investment into two assets: structures and equipment. A growing share of investment in most countries has been in items such as computer software and other items of intellectual property. This type of investment has a very high deprecation
few oil enclaves the productive structure of South Sudan is one of a rural pastoralist society where more than 4 in 5 people practice subsistence agriculture (World Bank, 2018, 2016).

Despite initial intentions to expand the HFS across the entire country, continued insecurity made it impossible to reach the former states of Jonglei, Unity, and Upper Nile. To account for this gap in coverage and obtain countrywide poverty rates, a statistical model imputes poverty in inaccessible areas. The resulting poverty predictions are intended as supplemental to the survey estimates and serve as a proof‐of‐concept for using geo‐spatial information alongside on‐the‐ground data collection. A growing body of research has emerged leveraging the increasing availability of alternative data sources such as satellite imagery and other geo‐spatial characteristics. The estimates are derived by exploring the potential correlations between existing spatial data sets as well as custom‐derived spatial data with geo‐referenced poverty estimates obtained in the HFS. Once a set of spatial correlates were

> 7 The translated testimonials are available at: http://www.thepulseofsouthsudan.com.

3
# **Conclusion**

In this paper, we have presented what is to our knowledge the first attempt to model empirically the correlates of IBRD graduation. Under Bank policy, graduation from the IBRD is not an automatic consequence of reaching a particular income level, and the IBRD per-capita income threshold is used only as a trigger to initiate discussions about the country’s readiness. Graduation is instead supposed to be based on a determination of whether the country has reached a level of institutional development and capital-market access that enables it to sustain its own development process without recourse to Bank funding.

We assess how this policy is implemented in practice through a panel analysis of 55 countries, including 34 countries that were IBRD borrowers at some time since 1970. We find that the observed correlates of Bank graduation are generally consistent with the stated policy. Among the countries that have crossed the IBRD income threshold (currently $6725), those that are wealthier, more institutionally developed, and are less vulnerable to trade, financial, and other shocks are more likely to have graduated.

With a relatively few variables, our graduation model is able to predict correctly the IBRD graduation status of a country in a high percentage of cases. Predicted probabilities generated by the model conform closely to the actual graduation and de-graduation experiences of Trinidad and Tobago and Korea, and suggest that Hungary and Latvia graduated prematurely – a prediction subsequently borne out by their return to borrowing from the IBRD in the wake of the global financial crisis.

We supplement the graduation analysis with an analysis of the level of borrowing from the Bank, which is a continuous variable. Interestingly, the findings are largely consistent with the analysis of the (discrete) graduation variable: as countries become wealthier, develop higher-quality institutions, and become more creditworthy, they rely less on IBRD financing for their development needs. This finding is consistent with the expectation under the policy that as the country develops, it will have less need for IBRD assistance and thus will be ready for graduation.

A concern raised
Sources: Authors' calculations based on Ministry of Finance data Regarding in-kind transfers, social spending on health amounted to 5.9 percent of GDP in 2016. Health care in Serbia is provided on a near universal basis through a compulsory health insurance for all employed and self-employed individuals and their families. The state also provides insurance for pensioners, unemployed <mark>and their family members, workers from lossmaking state-owned enterprises,</mark> refugees, and certain vulnerable groups (e.g., single parents). According to data from the Health Insurance Fund, only around 3 percent of the total population is not covered by health insurance. Mandatory health insurance covers the whole basic range of health services. <mark>The central government (Ministry of Health) or local governments (for primary protection) pay for investments in facilities and equipment from general tax revenues. The Health I</mark> nsurance <mark>Fund covers current expenditures (salaries, utility expenses, medical supplies, etc.) and is mainly financed through health contributions.</mark> Spending on education amounts to 3 percent of GDP. Preschool (for 5 to 6-year-olds) and primary school (up to age 15) are mandatory. Most students are in free public schools. The public education system has the largest enrollment with 91 percent of the children enrolled in pre-school programs, 99 percent in primary schools, 98 percent in secondary schools, and 87 percent at higher education institutions. Preschools are financed by the local self-governments, so the parent fee depends on the extent to which daycare is subsidized by the local government. In the capital, Belgrade, 80 percent of the full price is subsidized by the city government that, since 2015, started to subsidize daycare in private pre-school institutions as well.

# III. **Data and methodology**

## **Data sources**

This analysis combines household survey data with data from the national accounts, Ministry of Finance public finance data, and reports of the Statistical
where _yit_ is the outcome of interest for country _i_ in year _t_ . _αi_ and _γt_ control for time-invariant, crosscountry differences in outcomes and year-to-year common changes to outcomes, respectively. The indicator variables 1 [ _RTAs,it_ ] measure time-since-enactment _s_ of any RTA<sup>12</sup> while 1 [ _enviros,it_ ] measure time-since-enactment _s_ of an RTA that includes environmental provisions under consideration— hence, _δs_ will measure the dynamic effects of the entry into force of RTAs without environmental provisions, _δs_ + _ξs_ will measure the dynamic effects of RTAs with environmental provisions, and _ξs_ will capture the marginal impacts of provision inclusion. RTAs with and without provisions enter into force at different times for different countries allowing us to separately identify the _δs_ and _ξs_ coefficients from the year fixed effects, as well as from each other. Countries may enact more than one RTA in our sample period which differentiates our event study framework from the RTA-level triple difference model above as well as other common applied microeconomic settings in which an event only occurs once per individual within the sample window. We allow for overlapping events in our framework and normalize all estimates to the period before enactment by omitting the corresponding indicator variable.<sup>13</sup> We also include long-run RTA indicators that correspond to four or more years before/after entry into force of an RTA and allow these long-run indicators to vary by the presence of relevant environmental provisions.

We briefly contrast this country-level approach to our agreement-level approach outlined in Section 3. Because countries enact more than one RTA in the sample, the triple difference approach is not viable in this setting. In the RTA-level approach, individual countries may enter into multiple RTAs with different trading partners at different times, but the entire RTA group enacts an RTA only once in the study window. Because of this, our country-level approach estimates the dynamics around RTA enactments—a so-called “overlapping” or “multiple” event study because treatment events can overlap in timing—and allows them to differ based
Particularly salient from a transparency perspective are mechanisms through which foreign firms can provide feedback to authorities about whether and how easy it is to get accurate information on what is needed to participate in a market. The WTO does not have a comparative advantage in this regard, as the approach to transparency is centered around a “notification mindset” – reflected in numerous provisions in WTO agreements requiring governments to notify policies and changes in policies to the WTO secretariat. This is important but can be slow and may only imperfectly reflect conditions that prevail “on the ground.” On this front it is firms that have up-to-date information on what in practice is needed to satisfy regulatory requirements and whether governments live up to transparency commitments. Options to mobilize such granular, real-time information have been developed by many countries and have become simpler to implement as a result of new technologies. In the goods trade facilitation context systems have been put in place to allow traders to transmit information to the relevant authorities, e.g., through smartphone apps or “voting” type systems where satisfaction can be expressed regarding the quality of entry and clearance processes. Regular surveys of firms are another means of collecting data and assessing facilitation performance. International agencies that work with both governments and the private sector such as the World Bank Group and the International Trade Centre (Geneva) can assist countries to put in place such systems for services providers and in the process help stakeholders to provide information needed to establish baseline trade facilitation performance metrics and monitor progress over time in facilitating trade in services.

**_Quality of regulation and regulatory heterogeneity._** A major element of facilitating trade in services is to enhance the quality of regulation and related institutions, and to reduce the costs for firms that are created by differences in regulatory requirements for a given service across jurisdictions.  Transparency will help do so to some extent by clarifying what is required in each jurisdiction but will not reduce market-specific compliance and certification-related costs. Such costs may additionally be influenced by regulations affecting access and use
to a charity supporting syrian refugees. The other two options were allocating to a charity supporting low-income Jordanians or keeping for self. **Panel B** reports the two significant subcomponents of the social attitudes and perceptions index. **Panel C** reports other statistically significant treatment effects on neighbors. Subjective health is the response to “Would you describe your general health as good, fair, poor, or very poor? ” Life satisfaction is the response to “All things considered, how satisfied are you with your life as a whole these days on a scale of 1 to 10? ” Days of media consumption is the response to “In the past 7 days, how many days did you read or listen to the news from any source, including newspapers, online, WhatsApp, etc.?” Neighborhood perceptions of refugee aid reciept is the response to a question asking “Of the refugee households in your neighborhood who receive assistance, what do you think is the average value in Dinar of the assistance (in cash or in kind) that they receive from organizations in a typical month?” The sample size for this question is smaller due to the large number of “don’t know” responses. There is no treatment effect on the probability of reporting “don’t know” to this question.

In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values in panel A and C are calculated per Anderson (2008) using the outcome families in the pre-analysis plan. Panel B was not prespecified and is reported to aid in interpretation of the negative treatment effect on social attitudes and perceptions. The q-values are estimated using using an outcome family comprised of all the components of the social attitudes and perceptions index.

47
and 2020Q4. Column (1) replicates column (2) in table 4 as our baseline estimate of elasticity. Columns (2)-(9) consider grouping countries based on their GDP per capita and sector share relative to the median of the sample. For each variable, below (above) indicates below (above) the median of that variable in the sample. For example, column (2) includes countries whose GDP per capita is below the median. Each time period used to estimate the variances and covariances covers two years, 2013-14, 2015-16, 2017-18, and 2019-20. Throughout, standard errors are in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

# **6 Combining Nighttime Lights and National Accounts**

GDP growth as reported in national accounts may not always be an accurate measure of economic activity, in particular if statistical capacity is weak or if there is political pressure on statistical authorities. These concerns may be aggravated during periods 18
# **1. Introduction**

Having achieved remarkable economic growth over the last four decades (1960-2000), the citystate of Singapore entered the 21<sup>st</sup> century with a relatively high level of income/capita, but growing recognition of the need to sustain future growth through innovation. As highlighted in Wong (2004, 2003), for much of its history of rapid economic growth, Singapore had relied on a strategy of attracting direct foreign investment (DFI) from global multinational corporations (MNCs) and leveraging this to exploit technologies and know-how developed elsewhere. However, as the city-state’s costs increasingly approach those of leading cities in the advanced economies, and as global competition for DFI continues to intensify particularly with the opening up of major economies in Asia like China and India with large domestic markets and abundant supply of skills, Singapore’s only recourse to stay competitive is to become more innovative, i.e. not just by being efficient in “using” technologies and knowledge produced elsewhere, but by “creating” (commercializing) its own intellectual capital as well.

This paper examines the challenges and concerns that Singapore faces in becoming more innovative through the lenses of the industry players currently involved in the three leading business sectors where innovation is thought to be critical for competitive survival: high-tech manufacturing industries, knowledge-intensive business services (KIBS), and creative content industries. In particular, the paper highlights the key findings from an explorative, semi-structured survey with the senior management of 40 representative firms from these three innovative sectors.

# **2. Brief Profile of the Leading Innovative Sectors in Singapore**

As widely discussed in the literature (see e.g. Yusuf and Evenett 2002, Yusuf et. al. 2003, OECD 2003, Muller and Zenker 2001, Florida 2002 and Howkins 2003), the New Economy has been identified with the following three key business sectors deemed to have a high innovative propensity: high-tech manufacturing, particularly information and communications technologies (ICT
budget to accommodate increased childcare burdens.

Figure 2 Panel A presents how hours of childcare from different providers (excluding schools) vary with the number of household children, using data from after schools fully reopened.

Non-household members provide very little childcare on average—86% of households with children report 0 hours of care from non-household members in the last 7 days. While childcare availability has been increasing in Kenya (particularly in urban areas), affordability remains a challenge for most households (Clark et al. 2021; Murungi 2013).

Other adults besides the parents are present in 37% of households with children, and on average provide around 10 hours per week of childcare. Siblings also play an important role in childcare (Jakiela et al. 2020). In households with at least 2 children 55% of children provided childcare to siblings in the last 7 days, for 15-20 hours on average in total, demonstrating how childcare burdens can also fall on household children. Sibling childcare hours may have been higher during school closures as school-age children were at home, but we only measure sibling childcare after the full

> 8. This may be overstated but is consistent with data from a survey of 3,000 grade 8 students in Busia County, Kenya, which shows that 97% of these students reported back to school after the partial reopening in October (Bonds 2021).

> 9. The survey asks about time spent on childcare in the last 7 days, but does not distinguish between time actively spent caring for a child and time spent on other activities while responsible for a child. We topcode reported childcare hours at 140, or 20 hours a day.

7
district, rather than village level, as this is the lowest level geographical identifier available in all datasets that we use. Since decentralization reform in 1999, districts have often split over time. In 1993, there were 290 districts; by 2014, there were 511 districts. To ensure equal comparisons across time, we harmonize district boundaries as they existed in 1993. As PODES is not available annually, we follow Halim et al. (2022) and infer preschool data from in-between PODES years using the closest upper year available. For instance, year 2007 is sandwiched between PODES 2005 and 2008, so we infer preschool data from the

> 3 Specifically, we are looking at _Taman Kanak-kanak_ (TK) and _Raudhatul Afthal_ (RA).

7
Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

Export values are not just skewed towards particular firms and countries; they are also skewed in terms of the range of participation across products and countries.

Table 7 cross tabulates two aspects of firms’ exporting behavior – by number of products and countries. The top half of the table provides the percentage counts of firms – for instance, firms that sell one product to one country account for 32.38% of the total number of firms in the census in 2002, while firms that export more than 11 products to more than 11 countries account for just 1.86% of the census of firms. What is striking is the lower half of the table, which summarizes the percentage of export values accounted for by the given classification of firms. For instance, in line with the example above, firms that export one product to a single country account for 1.15% of total export value in 2002, while firms that export more than 11 products to over 11 countries account for more than 50% of the total export value. This is an additional strong indication that only a few firms are active in several markets at the same time, but that these few firms account for the lion’s share of exports. Indeed, compared to peer countries, Pakistan appears to have a low average number of products per exporter but a high average number of destinations per exporter (Figure 2 0 ). Taken together, these results suggest that like many other countries Pakistan posts a high concentration of exports in the hands of a limited number of large exporters. The dominance of few exporters may however have drawbacks in terms of product innovation and experimentation. The fact that firms tend to export only few products each may be a symptom of market distortions due to the dominant position of few firms. We will investigate this issue further in the following paragraphs.

**Table 7**

**Export behaviour: Countries & Products
export (Rocha and Piermartini, 2023).<sup>5</sup> This shift in demand may create new job opportunities that were previously scarce for individuals who faced difficulties in finding employment. Furthermore, this shift in demand towards green goods may require more specific skills and tasks, which can open up new opportunities for individuals who may have struggled to find jobs in the past. The expanding green goods market not only affects specific industries, but also has broader implications for the overall labor market.

# **4.1 Methods**

In this study, we investigate the correlation between the share of green goods trade, the share of female workers, the share of high skilled workers, and annual earnings by linking customs-level trade data with the Philippines Labor Force Survey at the 2-digit industry level.

The first analysis focuses on understanding the relationship between green goods trade and the share of female workers and high-skilled workers across different industries as well as within industries (see Equation (5)).

_EmploymentSharejt_ = _μ_ + _σGGExpjt_ + _βGGImpjt_ + _γADjt_ + _δj_ + _εjt_ (5) The second set of regressions focuses on the annual earnings of individual _i_ working in industry _j_ at time _t_ (see Equation (6)).

_AnnualEarningsijt_ = _μ_ + _σGGExpjt_ + _βGGImpjt_ + _ψZit_ + _γADjt_ + _δj_ + _εjt_ (6) _EmploymentSharejt_ measures the proportion of female workers and high-skilled workers

> 5https://www.imf.org/en/Publications/fandd/issues/2023/06/trade-drives-gender-equality-anddevelopment-rocha-piermartini

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