rafmacalaba/datause-encoder-data huggingface dataset:
OZIER - REPLICATION REDUX – p. 7 of 41 A crucial step for any systematic review is that of defining a category of study well enough that there are both enough studies to meaningfully aggregate, and yet that the topics of the studies remain sufficiently similar: _“whether experiments can be pooled to provide cumulative evidence depends further on which features of a study or results are considered scientifically equivalent enough to pool,”_ as Goodman, Fanelli, and Ioannidis (2016) wrote. To provide some perspective on the challenges inherent in systematic reviews, consider the pattern, depicted in Figure 2, based on Evans and Popova (2016). They examined six reviews of education interventions in developing countries to understand why the reviews had come to differing conclusions. The set of studies ultimately included in a review is, of course, central to the review’s conclusions. The pattern that Evans and Popova (2016) uncover is stark:

# **Figure 2**

<!-- Start of picture text --> Across six reviews: percent of studies appearing in...<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Only one review 2‐5 reviews All six reviews<br>Adapted from Evans and Popova (2016)<br><!-- End of picture text --> Despite the similarity of their goals at the outset, the six reviews defined their meta‐analytic inclusion criteria slightly differently, so that of 229 studies included in _any_ of the reviews, most underlying studies were included in just _one_ of the six reviews. Of the 229 underlying studies, only three of them (roughly
# **Table S9. Relationship between the SDG Index Overall Score from the 2023 Sustainable Development Report and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -<br>Model 1|ODIN -<br>Model 2|ODIN -<br>Model 3|ODB -<br>Model 1|ODB -<br>Model 2|ODB -<br>Model 3|GDB -<br>Model 1|
|---|---|---|---|---|---|---|---|
|ODIN Score|0.424***<br>(0.03)|0.005<br>(0.01)|0.008<br>(0.01)|||||
|Open Data Barometer Score||||0.328***<br>(0.03)|0.011**<br>(0.00)|0.011**<br>(0.00)||
|Global Data Barometer Score|||||||0.375***|
||||||||(0.04)|
|Trade (% of GDP)|||-0.009*|||-0.018*||
||||(0.00)|||(0.01)||
|Agriculture, forestry, fishing value added<br>(% of GDP)|||<br>-0.006|||<br>0.012||
||||(0.05)|||(0.06)||
|Manufacturing value added (% of GDP)|||<br>-0.029|||<br>-0.060||
||||(0.03)|||(0.04)||
|School Enrollment, Primary (% gross)|||<br>0.026**|||<br>0.000||
||||(0.01)|||(0.03)||
|Year 2014|||||0.483***|<br>0.409***||
||||||(0.07)|(0.07)||
|Year 2015|||||<br>0.903***|<br>0.758***||
||||||(0.09)|(0.10)||
|Year 2016|||||<br>1.148***|<br>0.907***||
||||||(0.11)|(0.13)||
|Year 2017||0.649***|0.605***||<br>1.577***|<br>1.316***||
|||(0.04)|(0.05)||(0.17)|(0.18)||
|Year 2018||<br>0.968***|<br>0.878***|||||
|||(0.07)|(0.08)|||||
|Year 2019||<br>1.383***|<br>1.239***|||||
|||(009)|(011)|||||
|Year 2020||.<br>1.569***|.<br>1.529***|||||
|||(0.10)|(0.10)|||||
|Year 2021||<br>1.919***|<br>1.817***|||||
|||(0.11)|(0.12)|||||
|Year 2022||<br>2.016***|<br>1.904***|||||
|||(0.13)|(0.16)|||||
|Constant|47.217***|||57.217***|||57.382**<br>*|
||(1.86)|||(1.24)|||<br>(1.63)|
|Sigmau||6.53|5.24||5.89|4.81||
|_<br>Sigmae||0.84|0.77||0.71|0.57||
|_<br>R2|0503|0997|0997|0566|0998|0998|0523|
|<br>Country FE|.<br>No|.<br>Yes|.<br>Yes|.<br>No|.<br>Yes|.<br>Yes|.<br>No|
|<br>No of countries|144|144|144|100|100|100|93|
|No of observations<br>Note: * p<0.10, ** p<0.05, *** p<0.01. Sta<br>Development Indicators (WDI), Open Data|992<br>ndard errors a<br>Watch (ODIN|992<br>re clustered<br>), Global D|992<br>at the cou<br>ata Barome|361<br>ntry level. D<br>ter (GDB),|361<br>ata from the<br>and Open D|361<br>World Ba<br>ata Baromet|93<br>nk's World<br>er (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

58
# **1 Introduction**

The linkages between the financial sector and the export sector have attracted growing attention in recent years. Firms’ export can be particularly influenced by financial development. To become exporters, firms must devote resources to identify export markets, tailor their products to match foreign tastes and regulations, and set up distribution networks (Baldwin and Krugman 1989; Dixit 1989). Most of these export costs have to be paid up front so that firms that aim at starting an export activity need enough liquidity at hand (Manova, 2013). Firms must also sustain relevant expenses after starting an export activity. Expanding sales from a single foreign market to multiple markets or even scaling up the volume of exports within a foreign market may require substantial liquidity (Eaton et al., 2008; Chaney, 2016).

Thus far, most studies have treated the financial sector and the export sector as “monolithic entities”, focusing on the impact of financial development on the overall size of the export sector. A broad literature, however, documents significant cross-country heterogeneity in the structure of financial systems: countries differ in the importance of concentrated financial institutions (e.g., banks) relative to decentralized financial markets (e.g., stock markets) and in the composition of the banking sector, such as the relative importance of domestic and foreign banks (see Allen and Gale, 2001; Levine, 1990, and references therein). These differences in financial structures may not necessarily reverberate in the total volume of liquidity flowing to the business sector, but primarily in the allocation of liquidity across businesses, including the incentives of financiers to support entrant or incumbent firms and to shut down funding to incumbent enterprises (Allen and Gale, 2001; Beck, Levine and Loayza, 2000).

While these observations are compelling, we know little about the impact of financial structures on export activities, and even less so about their impact on the dynamics of the export sector (entry into, and exit from, export). The goal of this paper is to help fill this gap. To this end, we exploit rich data on the structure of financial systems from the Global Financial Development Database (GFDD) as well as detailed, disaggregated information on the dynamics of the export sector from the World Bank Exporter Dynamics Database (EDD). Our data refer to a panel of 62 countries, across all geographic regions and income levels, for the 1997-2014 period. The EDD database contains detailed information at the level of 2-digit ISIC industries on the number of exporting firms and on exporters’ entry, exit and turnover rates drawn from exporter-level customs data which cover the universe of annual exporter transactions. Further, we are able to construct various indicators of the concentration of the export sector. On the financial side, we capture financial structures through widely used indicators of the importance of banks relative to financial (stock and 2
tariffs or take other trade enhancing actions in Individual Action Plans (LAPs). Members then review other countries' IAPs and "watch" for implementation. In 1996, the APEC process developed one step further by asking countries to submit their Individual Action Plans for public viewing. Countries submitted their first IAPs in 1997, allowing leaders and the general public to better evaluate the APEC process. However, since IAPs are non-binding commitments to liberalization by individual economies, a rigorous review is necessary to see if countries actually implement their plans. The IAP mechanism for implementing liberalization makes analyses of country commitments a difficult task.

One recent study'<sup>96</sup> has developed an in-depth scorecard for the LAP process. It scored 18 out of the 21 APEC countries in 13 areas including tariff and non-tariff measures, services, investment standards and conformance, customs procedures, intellectual property rights, competition policy, government procurement, deregulation, rules of origin, dispute mediation and business mobility. It did not produce rankings for the three newest members (Peru, Russia and Vietnam) because of insufficient data.

The results are mixed. Countries committed themselves to liberalization in concrete terms only until 2000. The countries' commitments are characterized as Uruguay Round-plus-alpha ('a'). "However, 'a' turned out to be small in many economies and areas."1 The Individual Action Plan annual revisions in 1997 and 1998 were minimal, reflecting countries' reluctance to commit themselves to more trade liberalization measures before what was supposed to be the beginning of the next World Trade Organization (WTO) round in Seattle in 2000. The findings of this study, as well those of a study by the PECC Trade Policy Forum, are shown in Table 3.5.

Table 3.5

|STUDY|FINDINGS|
|---|---|
|Yamazawa/Urata<br>(2000)|AverageSingleTariffs:<br>*<br>Reduced tobelow5percentin three economies.<br>*<br>Reducedto5-10percentinnineeconomies.<br>*Willbe reducedto10percentinthe nearfuture in three<br>economies.<br>*Tariffsofmorethan20percent remainonsensitive<br>productsin eighteconomieswithnocommitmenttotheir<br>gradualreduction.|
||Non-TariffMeasures:<br>*Industrialized membersstillimpose NTMsofmorethan<br>20percentfrequency' <sup>98 </sup>onseveralsectors.<br>*<br>SomeAsian developingmembers imposeNTMsofhigh<br>frequency onmanysectors.<br>*Papua NewGuinearemovedallNTMsin1997.<br>*ThePhilippines andThailand currentlyhaveimposedhigh<br>frequencyNTMsbutreportedintheirIAPs future plans<br>thattheywillremovethem.|

60
Effects of Fiscal Consolidation in the OECD <!-- Start of picture text --> ; 2<br>Fiscal Deficits in EC Countries 1992<br>16<br>Grc<br>14.<br>12 t<br>-- ~~- Ita<br>10 4<br>8Re<br>8- ~UKBe:Pt<br>6<br>Fra Neth<br>4 Ger Ire<br>MaiDt dcht<br>Source: JMFj19-933J<br><!-- End of picture text --> In Europe the amnount of possible reduction varies across countries and is much less likely. Nonetheless, the extent of reduction conditional on reaching the Maastricht targets by. 1997 would be: France (1% of GDP); Italy (7% of GDP), United Kingdom (3% of GDP); and other EMS countries (2 % of GDP). In the following sections we examine the global impact of fiscal reduction of these magnitudes. A key aspect of both the U.S. and European polices is that they are to be phased in gradually over tiIne. This is the focus of the next section.

# _3. The Global Impact of OECD Deficit Reduction_

Understanding the major channels through which deficit reduction in the OECD impacts on countries within the OECD and throughout the world, requires a global model. We use the MSG2 model for severl reasons. First it has a clear theoretical fiamework which makes disentangling the key channels of transmission relatively straightforward. Secondly it is empirically based, thus in addition to the theoretical insights we can also uncover the magnitude of certain effects. Thirdly it has been shown to be able to track importat events in the global economy since the 1980s and is contin-ally being used and evaluated by a wide range of users inside governments and at academic institutions. We first describe the model briefly. A number of deficit reduction scenarios are then 4
studies of the effect of private ownership on the rate of change of scale of operations (from CEE, CIS and China) suggest that this effect is not statistically significant except in some well defined categories of ownership.

Estimates of the effect of privatization on labor productivity (not controlling for the use of others inputs) are similar to the TFP results -- the effect of privatization is primarily positive or insignificant. As in the case of TFP, foreign ownership and concentrated ownership are found to have a positive or insignificant effect, while the effects of employee and management ownership are estimated to be mostly statistically insignificant. The corresponding studies of firms in China yield mostly insignificant estimates of the effects of private/non-state ownership on labor productivity.

In terms of the effect of privatization on employment, the estimates indicate that there is a tendency for privatized firms, especially those with foreign owners, to increase or not to reduce employment relative to firms with state ownership. In general, employee ownership and control do not have a significant effect on employment, providing parallel evidence to the TFP studies that this form of ownership does not result in excess employment.

Studies of the effects of ownership on wages find that state ownership is associated with lower wages in some countries, such as Russia and former Czechoslovakia, but not in others, such as Poland. In Russia, where in the 1990s firms tended to owe wages to their workers, SOEs were more likely to exhibit wage arrears than firms with domestic and foreign private ownership, firms with mixed ownership and _de novo_ firms.

Studies that have analyzed the effect of privatization on other dependent variables show that (a) privatization results in higher exports and greater efficiency, as measured by the cost of inputs relative to sales, Tobin’s Q, and soft budget constraints, and (b) privatization to foreign firms leads to more restructuring and sale of assets, greater likelihood of payment of dividends, and smaller likelihood of default on debt. These results exhibit a pattern that is in line with the above measures of performance.

The structure of the paper is as follows. In Section 2 we discuss the theoretical and institutional issues raised by privatization in transition economies. In section 3 we briefly examine the macroeconomic evidence about the impact of privatization before turning in 5
on the existing datasets.<sup>15</sup> We pursue this service ladder approach and apply our ordinal poverty measures to study the improvement in sanitation deprivation in Bangladesh. We classify households’ _access to sanitation_ in the five ordered categories presented in Table 2. The five categories are ordered as ‘open defecation’ _≻D_ ‘unimproved’ _≻D_ ‘limited’ _≻D_ ‘basic unsafe’ _≻D_ ‘improved’. We consider all persons living in a household deprived in access to sanitation if the household experiences any category other than ‘improved’.

Table 3: Change in population distribution across sanitation categories in Bangladesh

||B|anglade|sh||Dhaka||R|ajshah|i||Sylhet||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||2007|2011|2014|2007|2011|2014|2007|2011|2014|2007|2011|2014|
|Open defecation|7.5|4.2|3.3|7.5|4.0|2.2|13.8|3.9|3.2|2.1|12.5|9.4|
||(0.8)|(0.3)|(0.5)|(1.4)|(0.7)|(0.8)|(2.3)|(0.8)|(0.9)|(0.4)|(1.5)|(1.3)|
|Unimproved|47.1|38.3|25.7|44.3|35.9|22.6|45.3|36.4|28.3|57.2|34.3|23.0|
||(1.1)|(0.9)|(1.3)|(1.9)|(1.7)|(2.8)|(2.6)|(3.1)|(2.7)|(3.3)|(2.0)|(2.6)|
|Limited|13.4|16.7|20.9|14.4|18.0|26.1|14.7|20.7|20.2|10.1|17.7|22.3|
||(0.5)|(0.6)|(0.8)|(1.1)|(1.5)|(2.0)|(1.2)|(1.4)|(1.3)|(1.9)|(0.9)|(1.8)|
|Basic|3.5|4.3|2.3|8.6|10.5|5.4|0.2|0.2|0.3|0.6|0.1|0.3|
||(0.4)|(0.5)|(0.4)|(1.0)|(1.6)|(1.0)|(0.1)|(0.1)|(0.1)|(0.2)|(0.1)|(0.2)|
|Improved|28.5|36.6|47.8|25.2|31.6|43.7|26.0|38.8|48.0|30.1|35.4|45.0|
||(1.0)|(0.9)|(1.1)|(1.9)|(1.9)|(2.5)|(1.9)|(2.3)|(2.3)|(2.3)|(1.9)|(1.7)|

Sources: Authors’ own computations. Standard errors are reported in parentheses.

Table 3 shows how the estimated population shares in different deprivation categories have evolved over time in Bangladesh. Clearly, the estimated percentage in the ‘improved’ category has gradually increased (statistically significantly) from 28.5% in 2007 to 36.6% in 2011 to 47.8% in 2014. Thus, the proportion of the population in deprived categories has gone down over the same period. Changes within the deprived categories are however mixed.

> 15The JMP document titled _WASH Post-2015: Proposed indicators for drinking water, sanitation and hygiene_ was accessed in November 2019 at https://www.who.int/water ~~s~~ anitation ~~h~~ ealth/monitoring/ coverage/wash-post-2015-rev.pdf.

28
<!-- Start of picture text --> gs Ln[Women-owned establishments per population, 1994]<br>=<br>oo<br>Le<br>co<br>8 t<br>a2§ (~) MN<br>5O ——<br>A<br>ESTz (©)<br>Bq<br>Be af 63)<br>2 Sy, CoA(eu)<br><eap<br>= 1992 1996 2000 2004<br>§ Year of First Election Under 73rd Amendment<br>Bubble sizes represent state population.<br><!-- End of picture text --> <!-- Start of picture text --> Women-owned share of establishments, 1994<br>x<br>Ow<br>Oy :<br>#<br>c<br>2s d<br>(€) :<br>—<br>wo<br>So<br>$ N (#)<br>s =<br>xe]<br>o ~ D — Ai<br>é a BK) CH<br>2 @) \K<br>1992 1996 2000 2004<br>Year of First Election Under 73rd Amendment<br>Bubble sizes represent state population.<br><!-- End of picture text --> <!-- Start of picture text --> @ Ln[{new women-owned establishments per population, 1994]<br>=<br>c<br>Bo<br>on (m) MN<br>2<br>3a2<br>25 a<br>=<br>cL28<br>| @<br>BB<br>o-= eyaa fa<br>i= Aah vie BH<br>% 1992 1996 2000 2004<br>= Year of First Election Under 73rd Amendment<br>= Bubble sizes represent state population.<br><!-- End of picture text -->

<!-- Start of picture text --> Women-owned share of new establishments, 1994<br>tT<br>S<br>-Oo<br>o °<br>5=<br>5 te<br>| OM —<br>oO<br>c<br>re}<br>Oo<br>aN<br>s DNH = D<br>3 ish , 7<br>é Yen<br>5 -<br>= 1992 1996 2000 2004<br>Year of First Election Under 73rd Amendment<br>Bubble sizes represent state population.<br><!-- End of picture text -->
-41

# 6. Individual projects are executed in five steps:

Mgbiliza£5gn. At the outset, the program manager arrives on site, assembles a project team (consisting of staff of the local valuer and temporary hires), organizes office space, and procures materials and equipment.

PreRaration of Reference MaR. The project team then assembles an initial reference map on the basis of existing parcel maps, subdivision plans, and road and utility maps; it then makes an initial reconciliation of existing property tax records with parcel information.

Field Work. ProJect teams then take to the field to verify and supplement data on the physical characteristics of the land and structures--to be subsequently used in preparing parcel maps calculating individual property valuations--and information on claimants to ownership--to be subsequently used in billing and collection.V Calculation of Unit Costs. While the field work is ongoing, the project manager oversees preparation of new unit cost tables.

Preparation of Assessment Roll. Post-field, the project team then converts parcel sketches into base maps, calculates individual property valuations by applying the unit cost tables to the characteristics of each property, and--after the appeal opportunity--prepares individual property records for delivery to the treasurer.

7. In Brazil, the sequence is normally implemented in three to five months; in the Philippines, one year.

# Technical ImRact

8. Within the confines of their specific objectives, the programs appear to be an effective means of delivering technical assistance on a mass scale. The average RPTA project is estimated to increase valuations by about 40%: 20% of this increase is due to the discovery of missing land parcels; the remainder is due to the application of more accurate property information to the valuation of properties already on the rolls. In Brazil, CIATA's impact is more difficult to document, but it appears to be substantial. The average project increases the number of discovered properties by about 50%. The application of more accurate property data to existing properties also, presumably, increases _2/_ In the Philippines, this work is carried out by teams of four, consisting of a supervisor, a tax mapping aide (responsible for sketching parcel boundaries on the base map), a data gatherer (who acts as custodian of tax declarations and is responsible for verifying and updating the data), and the local ward captain. Production averages twenty parcels per day. In Brazil, field work is carried out by teams of two, with average of fifteen completions per day.
3 primary education system, including program design and implementation, monitoring and evaluation system.

The PARE states comprised 13.2% of the total Mexican population but a much higher proportion of those living in poverty. Viewed together, these states had an average primary school completion rate of 39%, with dropout averaging 9.9%. Reported repetition rates averaged 14.7%, which has been shown to be an underestimate of the real rates.

In all four states, the educational system as in other states is predominantly public. The control of public schools, however, differed slightly. When PARE was implemented, ownership of Federal schools have not yet been turned over to the states. In Oaxaca and Hidalgo, all public primary schools were managed and operated by federal authorities through SEP’s delegations, while in Chiapas and Guerrero, both federal and state systems coexisted (the latter being small and relatively concentrated around urban areas). For the whole project area, the federal system was thus predominant (92% of public schools) and even more so in rural areas. Indigenous schools and CONAFE community courses, in particular, were all federal schools, which were run by various entities. The Federal Government has the prerogative to provide direct support to state schools and to coordinate actions taken under both educational systems.

The project was directed towards all public (federal and state) primary schools students, teachers and supervisors in the four states. Several of PARE’s interventions, however, were specifically targeted towards rural areas because in those areas the degree of poverty and the lack of family financial support and school resources are greatest.

The project had two main components: The Educational Services Improvements Component (72% of total cost including contingencies) to improve the availability and quality of educational services for the primary school students (i.e., books, didactic materials, training of teachers, school infrastructure and distance education technologies and the Institutional Strengthening Component (28% of total cost including contingencies) to further the efficiency of the of the primary education sector.

SEP was responsible for overall coordination and attainment of project objectives. Implementation responsibilities were shared by SEP at the central level and its state delegations. The State delegations were also responsible for supervising and monitoring the support provided under the project to state schools. There was a survey of school needs to refine the precise contents of the educational materials packages and other school needs.

In each of the states, a small Program Coordinator Unit was established and its head directly reported to the director of each state delegations. It was responsible for coordinating activities of all participants agencies and monitoring project implementation at the state level. Some of the problems faced by the compensatory programs were: (a) annual delays in budget approval for project expenses; (b)