rafmacalaba/datause-encoder-data huggingface dataset:
always persist over time, however, and digital adoption tends to be concentrated among relatively younger and wealthier people (Saka, et al., 2021).

# **_2.3 Women and financial inclusion_**

Financial inclusion is especially important for women, given research showing that accounts can enable financial independence and strengthen economic empowerment. In the Philippines, for example, women with a type of personal commitment savings account that encouraged regular deposits increased their household decision-making power (Ashraf, et al., 2010). In Kenya, geographic areas with high levels of mobile money access had female poverty rates 9 percentage points lower, and female consumption levels 18.5 percentage points higher, than areas with low mobile money access (Jack and Suri, 2014). In another study, women-headed households spent 15 percent more on nutritious foods after receiving free savings accounts (Prina, 2015). While women have often been underserved by the formal financial sector, since the Global Findex began collecting data in 2011, women’s account ownership in developing economies has risen dramatically from 37 percent of women in 2011 to 68 percent in 2021. Women still own accounts at lower rates than men (74 percent of men in developing economies own an account), though there has been progress, evidenced by the fact that the gender gap in account ownership has shrunk from 10 percentage points to 6 percentage points between 2011 and 2021.

The continued gender gap notwithstanding, financial inclusion advocates are wary of focusing too much on narrowing gaps, because doing so does not necessarily drive equitable inclusion. For example, the gender gap could narrow as a function of men becoming unbanked (after rising unemployment, for example) while women’s access remains unchanged. The goal is instead to promote approaches that enable equitable access to financial services for everyone who can benefit from them.

Moreover, gender is not the sole determinant for equity in account ownership. For example, Table 2 shows regression results of more than 120,000 adults in 139 economies, including country fixed-effects and standard errors clustered at the country level.<sup>2</sup> This analysis finds that being male, wealthier (living in the wealthiest 40 percent of households), older (age above 35), in the workforce, and having obtained an education at the secondary level or above are all

> 2 Summary statistics of all variables used in the regression are shown in Table 1.

6
issue of transfer efficiency is also important, especially in light of possible political economy resistance to direct financial transfers to developing countries.<sup>22</sup> Johnson (1967) was among the first to discuss some of these issues. An early formal analysis is McCulloch and Pinera (1977). Assuming a political equilibrium in a preference granting country that has determined the external level of protection, they assume there is a shift in social preferences that gives higher weight to development abroad. There is then a choice to be made between trade (preferences) and aid. The former will upset the initial trade policy equilibrium. Preferences have a number of effects in their model—they may enhance consumer welfare both because they care about development and because the preferences entail partial liberalization and may lower import costs. But they come at a cost to the initial social objective that underpinned protection in the first place, which helps explain why preferences are in practice often “modulated”. While the political economy issues are exogenous in their model, this paper formalizes some of the issues also discussed by Johnson [1967] on the tradeoffs and linkages between preferential trade and aid.<sup>23</sup> Ray [1987] provides an empirical assessment of the hypothesis that political economy forces play an important role in shaping the extent of effective market access granted under GSP schemes. He shows that special interests were able in the US to limit the ability of “preferred” suppliers to contest the most protected market segments—which are protected by other instruments in addition to tariffs, including NTBs. The Caribbean Basin Economic Recovery Act (CBERA) is a good example of the prevalence and impact of such political constraints. When enacted, only about one-fourth of the exports of CBERA countries were subject to duty;<sup>24</sup> Ten years later, just 19 percent of the region’s exports to the United States entered duty-free under the CBERA, and only 10.1 percent would not otherwise have been eligible for duty-free treatment under the GSP or the US off-shore assembly program (see above). If account is taken of product coverage restrictions, CBERA benefits implied a net reduction in applicable tariffs of some 40

> 22 For example, Borrell (2004) argues that for every dollar of benefit that the EU banana policy brought to producers in ACP countries, the regime harmed non-ACP developing country producers by almost exactly one dollar—and in the process harmed EU consumers by more than US$13. Aksoy and Beghin (2003) report that every dollar equivalent transferred to recipients under US preferences costs US$5.

> 23 See Adam and O’Connell (2004) for a more recent analysis of this question.

> 24 What follows draws on OECD (1997).

22
Policy Research Working Paper 4975

# **Abstract**

This paper examines the phenomenon of the oversupply of teachers but shortage of qualified teachers in Indonesia. Using a theoretical framework of governmentdominated market with government-set wage rate and demand for teachers, the analysis explores how teacher supply, particularly the composition of the teaching force with low or high qualification, would be determined by current and future public policies. Using 2001 to 2008 Indonesian Labor Force Survey data, the paper further estimates the potential effect of the most recent teacher law, which could give college educated teachers a significant pay increase, on the composition of the Indonesian teaching force with differentiated education backgrounds. Using a sample of workers _with college education,_ the author finds that the relative wage rate of teachers and that of alternative occupations significantly influence the decision of college educated workers to become teachers. It is also found that the wage rate set by the most recent teacher law would increase the share of teachers approximately from 16 to 30 percent of the college-educated labor force. This increase that is due to the new government-set wage rate, would result in a pupil-teacher ratio of 24 to 25 pupils per teacher with college education, but will require a more than 31 percent increase in the wage bill for teacher salaries. The empirical approach of this paper is derived from a structural model that takes into account the endogeneity of the wage rate and corrects for sample-selection bias due to occupational choice.

This paper—a product of the Human Development Sector Department, East Asia and Pacific Region—is part of a larger effort in the department to strengthen evidence-based human development policy advisory and dialogue. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dchen1@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The _Dibao_ program is China’s main social policy response. However, not unlike most past policy debates on antipoverty policy in England, Europe and North America (as discussed in the Introduction), there is little or no evidence on the actual incentive effects in practice of this major new social program. The main problem identified in past research on the program is seemingly weak coverage of the eligible participants rather than leakage to ineligible participants (Chen et al., 2008; Gao et al., 2009). However, this assessment ignores incentive effects and measurement errors. Coverage may be weak but expanding coverage would be ill-advised if in fact the program is creating a poverty trap.

The data structure we devised for studying this program is somewhat unusual. Similarly to other researchers, we could not obtain access to the complete micro data for China collected by the National Bureau of Statistics (NBS) Under Chinese law NBS cannot provide those data. So we could not estimate a censored regression model (such as a Tobit model) of DB receipts on a sample of the entire urban population (or even selected provinces). However, with NBS’s cooperation, we could obtain a sub-sample of actual and potential DB participants for selected cities and follow up this sample for re-interviewing.

As our sample for estimating the BWR for the DB program we use actual participants or likely participants for seven cities, as given in Table 1, which gives sample sizes and participation rates by city. The seven cities were chosen to span China’s main geographic areas as well as representing a range of city sizes. In terms of growth rates, they also span a wide range. In identifying actual or likely DB participants we are explicitly excluding the vast majority of urban Chinese for whom participation in the DB program is unimaginable as they have incomes well beyond the DB poverty lines, and are very unlikely to ever need the program. On _a priori_ grounds, DB receipts and (of course) the BWR can be set to zero for them.

We drew our sample from the 2007 Urban Household Short Survey (UHSS) by China’s NBS. The UHSS is an unusual survey. It is the first step in constructing the sample for the regular Urban Household Survey (UHS), which has a much longer questionnaire, but much smaller sample. The big advantage of the UHSS here is that its sample size allows us to capture a significant number of DB participants, to be interviewed further. Also, while the UHSS is a relatively short survey, it allows us to measure a fairly wide range of household characteristics including income by source. The UHSS is unlikely to give as accurate a measure of total income as obtained from surveys that use more detailed questions on income by source, such as NBS’s 12
|(B9, alt 5)|0.73|0.45|
|---|---|---|
|Read JEEM regularly (dummy, 1if read and 0 if not)|||
|(B9, alt. 1)|0.20|0.41|
|Read AJAE regularly (dummy, 1if read and 0 if not)|||
|(B7)|3.7|5.5|
|Number of benefit transfer exercises conducted|||
|(B 12)|1.9|2.2|
|Number of multi-country(i.e. 2 or more countries) CV or CE<br>studies conducted in Europe|||

20
# **Table A4**

## **Information criteria for selecting the number of factors**

|Global|Factors by||CIF|||COD||
|---|---|---|---|---|---|---|---|
|factors|Group|ICP2|BIC|HQ|ICP2|BIC|HQ|
|1|1|3.8|7.0|7.1|3.7|7.0<br>|7.1<br>|
|1|2|6.2<br>|7.5<br>|7.6|6.1|7.5<br>|7.6|
|1|3|8.6|7.9|8.0<br>|8.5<br>|7.8|7.9|
|2|1|5.4<br>|7.4<br>|7.5<br>|5.3<br>|7.4|7.5|
|2|2|7.8<br>|7.8|7.9|7.8|7.7<br>|7.8<br>|
|2|3|10.2|8.1|8.2<br>|10.2<br>|8.0|8.1|
|3|1|7.1<br>|7.7|7.8|7.0|7.6<br>|7.7<br>|
|3|2|9.5<br>|8.0|8.1|9.4<br>|7.9|8.0|
|3|3|11.9<br>|8.2<br>|8.3|11.9|8.2|8.3<br>|

Note: CIF = gross inflows; COD = gross outflows.

# **Table A5 Panel unit root and stationarity tests**

|Test|Null Hypothesis|CIF<br>P-Val|COD<br>P-Val|
|---|---|---|---|
|Im-Pesaran-Shin|Ho: All panels contain unit roots|0.00|0.00|
|Hadri LM|Ho: All panels are stationary|0.35|0.61|

Notes: the specification includes two lags and no time trend. CIF = gross inflows; COD = gross outflows.

42
production and exports, this study also presents evidence suggesting that coal production did not diminish and that coal exports may have accelerated due to the combined policies.

The paper is organized as follows. Section 2 presents a review of literature, followed by the description of data and methodology in Section 3. Results and sensitivity analyses are presented in Section 4. The mechanisms of the policies’ impacts are explored in Section 5. Section 6 investigates if the carbon tax policy caused changes in coal production and exports. Section 7 concludes.

# **2. Literature Review**

A number of studies investigate the effectiveness of carbon pricing and other pricing instruments to reduce GHG emissions. Timilsina (2022b) synthesizes results from existing empirical studies on the impacts of various pricing instruments on GHG emissions. Examples of such studies include Green (2021), Best et al. (2020), Dechezlepretre, et al. (2020), Andersson (2019), Jaraite ̇ and Di Maria (2016), Murray and Maniloff (2015), Aydin and O<sup> ̈</sup> mer (2018) and Martin et al. (2014). With only the exceptions of Green (2021) and Best et al. (2020), these studies are either for a single country or a relatively small group of countries, mainly focusing on the European Union.

Green (2021) reviews empirical studies published since 1990 on the impacts of carbon pricing on CO 2 emissions. It concludes that carbon pricing policies have caused limited reduction in CO 2 emissions, between 0% and 2% per year on average. One difficulty is that the review compares many studies with heterogeneous identification strategies estimating the effect of many heterogeneous policies (including sectoral and fuel-level policies).

Best et al. (2020) estimate the effectiveness of carbon pricing at reducing national CO 2 emissions using panel data from 142 countries from 1997-2017. They find that the average annual growth rate of CO 2 emissions has been around two percentage points lower in countries with a carbon price system compared to countries without. They estimate that an additional euro per ton of CO 2 carbon price is associated with a reduction in the subsequent annual emissions growth rate of approximately 0.3 percentage points, all else equal. On methodology, Best et al. face two main identification challenges. First, a country panel with country and time fixed effects can only control for time-invariant country-specific characteristics and a global time trend that does not vary by country. Failure to capture unobserved time-varying country heterogeneity may lead to omitted 5
safeguard measures and the question of which of those exporters will challenge those measures. Would any of the measures in Table 4 that indicate evidence of differences between policy types under dispute turn out to be useful in explaining the separate (and arguably more important) selection question? Are these potential determinants of which WTO-inconsistent applications of trade policy, out of the universe of all applied policy actions, within a class of policies actually face a formal dispute?

# **4.2 Comparing respondent disputed product imports from complainants versus non-complainants**

Our final exercise involves an expansion of the set of exporters of the disputed product to the respondent’s market beyond the complainant. We use the disaggregated HS-06 level trade data to reveal all of the countries that export the disputed product to the respondent market. Given that an increasing body of research examines the role of non-complainant exporters in WTO disputes, we argue that an important starting point involves stepping back and understanding the levels of and changes to the trade in the disputed products that non-complainant exporters may also have at stake under WTO litigation.<sup>28</sup> Consider Table 5 which breaks out non-complainant exporters into two further categories – those exporters that, in the process of the dispute, formally identify themselves as being an interested third party, versus all other ‘non-participant’ exporters.

# **_Relative to non-complainants, complainants have larger exports and market share and on average suffer larger market access reductions according to a number of different measures._**

First, we use the data on market access levels in Table 5 to compare complainant exports to the respondent in the disputed products to non-complainant exports to the respondent. The first column reveals that, across the entire sample of disputes, complainant exports are larger ($741.4 million), on average, than exports from formal third parties ($463.6 million) and nonparticipants ($43.4 million). Furthermore, the complainant’s share of the respondent’s import market in the disputed products is much larger (22.7 percent), on average, than the third party exporters’ share (6.2 percent), which itself is larger than the non-participants’ share (0.9 percent). We note in both instances that the middle and right-most columns of Table 5 indicate that the ordering of these relationships is present across both the global policy and the partial policy subsamples of disputes.

> 28 Examples of research examining various aspects of non-complainant activity in formal WTO dispute settlement include Bown (2004c, 2005a), Busch and Reinhardt (2006), and Johns and Pelc (2013).

25
7 The main categories and areas of collection of personal data have been identified as (World Economic Forum 2014):

- data volunteered or created by individuals online such as through online registration, ecommerce transactions, social networks, emails, tweets;

- data observed from internet browsing, and location data from smart phones; and

- data used to infer a personal profile from a range of data, some of which is personal data and some of which is non‐personal data, but when synthesized and analyzed can produce a personal profile.

As this taxonomy reveals, distinguishing between personal data and non‐personal data is not straightforward. For instance, collecting data on habits, locations and physical conditions may, over time create a personal profile of a person, even if each individual bit of data collected is not personal (USFTC 2015).

# _The Controller and the Processor of Personal Data_

The approach in the Data Directive and the GPDR to protecting privacy is based on a framework that breaks down data and data processing (online and offline) into data subjects, controllers, and regulators. The GDPR defines a controller as “...the natural or legal person, public authority, agency or other body which alone or jointly with others determines the purposes and means of the processing of personal data”. A processor is defined as “...a natural or legal person, public authority, agency or any other body which processes personal data on behalf of the controller” (GDPR Article 4.8). Whereas a controller is able to control the collection and processing of data, a processor processes data under instructions from the controller.

# _Key Privacy Commitments_

The GDPR lists five principles that govern the processing of personal data. These are essentially good practices for handling personal information. These principles require that personal data are (Directive Article 5):

- processed fairly and lawfully;

- collected for specific, explicit and legitimate purpose and not further processed in a way incompatible with those purposes;

- adequate, relevant and not excessive in relation to the purposes for which they are collected

- accurate and where necessary, kept up to date; and

- kept in a form that permits identification of the data subject for no longer than is necessary for the purpose for which the data were collected.

The GDPR also gives a data subject a range of rights, the main ones being a right to information on the personal data being held by a controller, including how it is stored, the right to rectification (the so‐ called right to be forgotten) and a right to the erasure of personal data (GDPR Articles 15‐20).

The GDPR allows for processing personal data only under specific circumstances. The main ones are where: the data subject has unambiguously given consent; processing is necessary for the performance of a contract to which the data subject is a party, for compliance with a legal obligation to which the controller is subject, to protect the vital interests of the data subject, or it is in the public interest.
2004 (over 25 days as compared to 20 )<sup>12</sup> . This is also true for transit trade through Mombasa, Kenya.<sup>13</sup> 2. The _border(s)_ mean waiting time for further document reviews. On the Northern Corridor to Rwanda, it takes on average more than 24 hours to cross the KenyaUganda border.<sup>14</sup> 3. The _final goods clearance_ is completed in the capital city.<sup>15</sup> Ultimately, transit goods will have gone through three to four clearance processes.

Since most developing countries rely heavily on tariff duties, they tend to develop redundant procedures to avoid fiscal loss. The main bottleneck is the inadequacy of the applied transit regime, as it is conceived as a chain of control rather than the freedom of transit given to compliant operators in exchange of guarantees.

The reasons for the supply chain fragmentation are:

- The initiation of transit, often as cumbersome a process as final clearance in the gateway country.

- Inadequate carnets and guarantee systems or bad implementation of good transit systems (TIR) (transit from Douala to Bangui needs seven documents, none of them being a proper transit manifest).

- Uniform implementation of transit controls, irrespective of the principal’s reliability and competence.

- Convoy or escort systems not only on risky cargo or insecure vessels (open trucks), but also on containers.

- Excessive controls en route, paving the way for additional illegal controls.

- Obsolete freight regulations (particularly in Africa).

- Regulatory barriers that impact the market structure and the quality of key support services (brokers, finance, insurance...) (Appendix 1 for more details).

> 12 TPA, _brief on the Dar Es Salaam Port_ , June 2004. 13

> _Kenya Diagnostic Trade Integration Study_ , 2005.

> 14

> _NCTTCA Observatory survey_ , 2005, based on a sample of 200 trips by well known transporters. 15

> In Rwanda, 3-5 days ( DTIS Rwanda, report, 2005) and in Uganda, 3-4 days (authors interviews, 2004).

14