rafmacalaba/data-use-mentions-extended huggingface dataset:
Romania<br>Slovakia<br>Oman<br>Poland<br>Hungary<br>Portugal<br>Estonia<br>Lithuania<br>Czechia<br>Israel<br>Spain<br>Republic of Korea<br>Japan<br>Italy<br>The United Kingdom<br>Canada j e9<br>Australia} 2<br>FinlandFrance} 4 § 4910254 a 425<br>Belgium<br>Kuwait 086 084<br>Sweden<br>Netherlands<br>Austria<br>Germany<br>USA<br>Switzerland<br>Note: Columns 1-4 are regression coefficients based on the country-specific Poisson model described in the text applied to the WHO Global Excess<br>Deaths dataset. Column1 is the predicted female mortality per 100,000 at age 65. Column2 is the predicted ratio of male-to-female mortality at age<br>65. Columns 3 & 4 is the increase in mortality risk for women and men respectively associated with an additional 10 years in age. In 2020, for all age<br>groups at or above 55, excess death estimates for males in Thailand are negative, since these values were bottom coded to 0 the sex-ratio at age 65 is<br>0 and the association of excess deaths with age for males is also 0. Countries are sorted by GNI per capita (PPP)<br><!-- End of picture text -->
collected well-managed school database before SY 2002/03, which makes it infeasible to examine the parallel trend assumption using the pre-intervention period.<sup>13</sup> We obtain income data on municipalities (or school district) from the 2000 Census. Local income level is an important factor that determines school and family environments. Controlling local income levels is crucial because competition between public and private schools matters in the selection of students in the Philippine context. In high-income municipalities (school districts), students from well-off families and with high test scores are likely to be accepted into private schools. Therefore, we expect differences in the ability distribution in public schools between high- and low-income municipalities. If school quality and student ability are complementary, the effect of TEEP on NAT change is expected to be different between high- and low-income districts.

We assigned an income category to each school district based on the 2000 Census. The census defined income category (ranking from 1, highest, to 6, lowest) for each municipality.<sup>14</sup> Note that some municipalities are split into a few school districts. In cities, we ranked school districts as 1 based on the income threshold used for municipalities.

> 13 We have also detected discontinuity of summary statistics of some key variables between before and after SY 1999/2001.

> 14 The income classification of municipalities (municipality income) used in this paper is based on Republic of the Philippines, Department of Finance (2001), Department Order No. 32-01 (effective November 20, 2001) and Census 2000. The income categories for 1,435 municipalities are defined as follows: 1: Philippine peso (PHP) 35 million (M) or more (number of municipalities: 130); 2: PHP 27M or more but less than PHP 35M (140); 3: PHP 21M or more but less than PHP 27M (204); 4: PHP 13M or more but less than PHP 21M (543); 5
Policy Research Working Paper 9362

# **Abstract**

This paper evaluates the impact of two export finance support schemes, the Export Finance Scheme and the Long-Term Finance Facility for Plant & Machinery on firm-level export performance in Pakistan. These policies offer loans to exporters at concessionary interest rates to finance short-term working capital and long-term investment in machinery and equipment, respectively. The paper combines customs data with information on firms that participated in each scheme and the value of the loans they obtained between 2015 and 2017. Using matching estimators to control for the nonrandom selection of firms into the schemes, the analysis finds that the Export Finance Scheme and Long-Term Finance Facility for Plant & Machinery increased the growth rate of export sales by 7 and 8‒11 percentage points, but they do not have a significant impact on the number of products that a firm exports or the number of foreign countries to which it sells to. A cost-benefit analysis shows that although both schemes deliver net benefits, they entail a substantial financial cost to Pakistan’s central bank.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gvarela@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
br>Level Dependent Variables|Richard D. McKelvey|<br>Positive Changes in Political Science: The<br>Legacy of Richard D. McKelvey's Most<br>Influential Writings|1088|18|
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, whereas the panel data is available from 1998 onwards.

> 16 Our final sample includes Andhra Pradesh, Assam, Bihar, Chhattisgarh, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, Uttarakhand, West Bengal.

> 17 To define expenditure on materials consumed, we use the Tabulation Programme provided by the MoSPI. Materials consumed include expenditure on total basic items, non-basic chemicals, packing items, consumable stores, and total imports consumed.

9
|
|N|180,243|180,243|180,243|180,243|

_Source_ : Author’s analysis based on data from the Ethiopian census of 2007. _Note_ : The dependent variable is either years of schooling in column 2 or number of births in the other three columns. � Years of Schooling _izy_ is the reported years of schooling from the data; Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure _Izy_<sup>FPE</sup> , two mother tongue instruction (MTI) intensity measures _Izy_<sup>MTI-T</sup> and _Izy_<sup>MTI</sup> , which denote the measures for MTI regions without and with script change, respectively, and the interactions for regions in which two interventions occurred. All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects and zone-specific linear trends. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.24
- Jolliffe, Dean and Espen Beer Prydz. 2016. Estimating international poverty lines from comparable national thresholds. _Journal of Economic Inequality_ 14(2), 185–198. <u>https://doi.org/10.1007/s10888-016-9327-5</u>

- ———. 2021. “Societal Poverty: A Relative and Relevant Measure.” _The World Bank Economic Review_ 35(1): 180–206. https://doi.org/10.1093/wber/lhz018.

- Kramer, Steven. 2020. “With Billions Confined to Their Homes Worldwide, Which Living Arrangements Are Most Common?” _Pew Research Center, FACTANK_ (blog). March 31, 2020. https://www.pewresearch.org/fact-tank/2020/03/31/with-billions-confined-to-their- <u>homes-worldwide-which-living-arrangements-are-most-common/.</u>

- Lahoti, Rahul, Arjun Jayadev, and Sanjay G. Reddy. 2016. “The Global Consumption and Income Project (GCIP): An Overview.” SSRN Scholarly Paper ID 2480636. Rochester, NY: Social Science Research Network. https://doi.org/10.2139/ssrn.2480636.

- Lanjouw, Peter and Martin Ravallion. 1995. “Poverty and Household Size.” _The Economic Journal_ 105(433): 1415–1434. https://doi.org/10.2307/2235108

- Muhammad, Andrew, Seale Jr, James, Meade, Birgit and Anita Regmi. 2011. _International Evidence on Food Consumption Patterns: An Update Using 2005 International Comparison Program Data._ USDA-ERS Technical Bulletin, no. 1929. Washington, DC: U.S. Department of Agriculture, Economic Research Service. <u>https://doi.org/10.2139/ssrn.2114337</u>

- OECD. 2015. “In It Together: Why Less Inequality Benefits All.” Paris: OECD Publishing. https://doi.org
the fact that the average share of government budget allocated for education has decreased during the pandemic and remained below 2019 levels in 2022,<sup>5</sup> reinforces the salience of accurate understanding by policy makers.

_Figure 2: Policy makers underestimate learning poverty by large margins in EAP_ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Estimated by officials Actual estimates<br><!-- End of picture text --> Source: CGD–World Bank surveys, 2020 and 2022 and estimating interim learning poverty for Pacific Island Countries (2023). Only countries for which both data were available were included in the average (“Non-EAP”). When countries were surveyed in both waves, we use the most recent data. Solomon Islands and Vanuatu were surveyed in 2021.

Why is the disconnect between official estimates and estimated learning poverty levels by international organizations such as the World Bank so high in EAP? Some uncertainty in the officials' estimates of learning poverty is understandable for the following reasons: (i) the concept of a globally defined and agreed-upon “minimum proficiency level” against which students can be judged to be proficient is recent and still a work-in-progress; (ii) learning poverty levels are themselves uncertain for several reasons. Computations of learning poverty are based on sampled-based standardized tests which may not be perfectly comparable despite efforts to equate them, while sampling may lead to biases, especially for large and diverse countries such as Indonesia and the Philippines. For all participating countries in EAP, the SEA-PLM assessment appears more difficult at specific levels of competency than some of the other assessments used in the learning poverty estimates, even though in theory this is accounted for in the equating exercise; (iii)
ALLOCATION AND MANAGEMENT OF PUBLIC SPENDINO

# Monltoring **and Managing Plans and Investment Programmes**

34. Monitoring of development projects **Is** rightly a matter of concern. Thls **is** appropriate both to keep track of projects' implementation and to discover whether they have the effects intended. But the factors that are most Important In the monitoring of a plan/investment programme as a whole are not necessarily the same as for the individual projects of which it **is** comprised. Different types of information may be required and by different sets of people, and for the programme as a whole there **Is** a premium on standardised project information that can be aggregated. Very often elementary Information that might be regarded almost as trivial by someone responsible for the M&E of a particular project would actually be extremely useful **if** consistently available for all projects. When it comes to the aggregate monitoring of **a** PIP, there **is** a clear order **of** priority for the Information that **Is** needed, and sophisticated but partial information is much less useful than basic but comprehensive data.

35. The first requirement **Is** Information that should be, but often **is** not, available from government accounting records concerning actual expenditures by Individual projects and the programme as a whole. Simple breakdowns by implementing agency, sector, source of finance etc. are crucial, as is their prompt availability. Of course financial information of this sort may conceal as much as It reveals. Physical progress may not be commensurate with expenditure, a project whose financial implementation **Is** going according to plan may nevertheless be totally misconceived and so forth. Financial monitoring Is not enough, but It Is a vital starting point and first priority.

36. As far as Investment programmes are concerned, a second elementary requirement is that it must be possible to relate expenditure information
marginal benefits of higher (kharif) rainfall are lower in more intensively irrigated districts. Unlike the cross-sectional results, however, there is a significant and negative interaction between temperature and irrigation, at least in rabi season. This has a straightforward interpretation: Since irrigation is essential for most rabi cultivation, any impact of rabi season temperatures must be concentrated in heavily irrigated (i.e., rabi-cultivated) districts. Based on this specification, a 1<sup>_◦_</sup> C increase in both kharif and rabi temperature would reduce output per hectare by around 29 percent for the average district. Damages would vary, however, from -21 percent in nonirrigated districts to -35 percent in fully irrigated districts. Lastly, we reconsider temperature-precipitation interactions in column 6. Though not as overwhelming as in the cross-section, the panel evidence suggests that the negative impact of higher temperatures in kharif season is, once again, attenuated in wetter districts.

Guiteras (2009) also estimates the impact of weather shocks on gross agricultural productivity in India, using district-level panel data for 1960-99. Besides the less recent production data, Guiteras’ productivity index consists of just six major crops, compared to our twenty, and his sample drops most of eastern India as well. On the other hand, Guiteras uses daily gridded temperature data (not available to us) from which he constructs growing season degree-days, an arguably more relevant measure of weather than average annual or seasonal temperature. In any event, Guiteras’ sophisticated econometric analysis uncovers a much smaller response to warmer temperatures than we do, even for the latter half of his sample period.<sup>25</sup> If our cross-sectional estimates based on land values are to be believed and the long-term decline of agricultural productivity due to a 1<sup>_◦_</sup> C increase in temperature is indeed near 13 percent, then Guiteras’ damage estimate of less than 11 percent is implausibly small, implying virtually no scope for adaptation. By
not have state participation or is below 10%, those firms and their subsidiaries are excluded from the. This extra step helped to update/remove linkages that were no longer valid as of 2019.

- 4.2.2 Cleaning Rules: Ensuring alignment with conceptual framework and SOE harmonized definition

- _Drop sectors not included in the database:_ As per the global SOE definition described earlier, we exclude public administration, defense, social security (including pension funds), education, human health, social work activities, and activities of extraterritorial organizations sectors, among other activities.<sup>43</sup>

- _Drop entities that are not aligned with the SOE definition:_ ORBIS includes ministries, public authorities, regulators, and Central Banks as entities. We carefully reviewed that those are not counted as SOEs.<sup>44</sup>

- _Keep domestic firms only:_ Although the database allows to trace both domestic and foreign-based firms linked to a specific country-government, for the purpose of the subsequent data validation and for the creation of the country-level dashboards (e.g., revenues as % GDP), only domestic firms are kept in the database. Domestic firms are defined as those with the same country ISO code as the government.<sup>45</sup>

- _Drop entities that refer to branches as opposed to companies_ : Using the ORBIS interface branch variable, we delete all branches that are not SOEs because they are not separate legal entities.

- 4.2.3 Country Team Validation: Data Validation and Integration

Even after combining the information from ORBIS and the SOE Supplementary databases, important data gaps remain. To address this issue, we engaged with country teams and country experts to help review the information collected and to retrieve as much of the missing information as possible. The steps carried out through the Country Team Validation are the following:

- 1) _Validate the list of SOEs identified by the methodology._ If there are firms that are not SOEs,
' factor, i.e. private farming is only an escape from unemployment and poverty. To test these two hypotheses we use the Moldova HBS panel 1998-2002 and split the sample of workers into wage and non-wage workers and into 'stayers' and 'movers' between the two categories defining four groups: Stayers into wage, movers from wage to non wage, stayers into non-wage and movers from non wage to wage. We then run a probit regression between stayers and movers in the two wage and non wage groups. We use a

> 21 http://www.statistica.md/

30
physical and soft components and institutional reform. Besides investing in physical buildings and textbooks, TEEP provided training to teachers and principals and introduced school-based management by partnering school with community. Our study estimates the total impacts of these investments and reforms on students’ learning performance, measured by a change in student test scores during the elementary school cycle, though we expect that such an intervention has longer term effects beyond this stage, changing their activities in labor markets.<sup>3</sup> Methodologically, we combine double differences with propensity score matching. We compare the change in test scores before and after the intervention in TEEP-treated schools with the change in nontreated schools. Propensity score matching is used to reduce the pre-intervention differences between the treated and nontreated schools. We find that a two-year exposure to the TEEP intervention significantly increased test scores in grade 4. Our estimates show that test scores increased by 4 to 5 score points (out of 100) from grades 4 to 6, which amounts to an increase of about 12–15 score points if students are exposed to the intervention for six years of elementary school education (grades 1 to 6). We also examine the effects of individual components of TEEP and find that school building constructions and renovations, instructional training of teachers, and additional textbook provision significantly increased student test scores. Interestingly, investments in textbooks for earlier grades have large positive effects on student performance at higher grades.

> 3 We collect individual and household data from 3,500 students in four TEEP and four non-TEEP divisions to study long-term impacts of TEEP. This component includes tracking the sample students who migrated out of their original communities.

4
# **Introduction**

**Skills development is a key component of Vietnam’s future growth, but key elements of the skills development system need to be strengthened.** Vietnam’s labor market faces challenges as the continued growth of knowledge-based industries, digitization and automation, an urgent need to facilitate climate-friendly development, and population aging alter the types of skills that are demanded, the speed at which skills demands change, and the workers available to meet these demands. Recent research has highlighted the important role of skills development in Vietnam’s future growth (Cunningham and Pimhidzai 2018). But key elements of Vietnam’s skills development system must be strengthened. One foundational area that recent diagnostic work has revealed as a weakness is Vietnam’s labor market information (Mazza et al. 2019; Moroz, Nguyen, and Chu 2019b; Cunningham and Pimhidzai 2018). Vocational education and training institutions and providers of employment services lack the detailed information on in-demand skills that they need to develop curricula, advise students, and put jobseekers on the path to the right jobs.

**The Survey of Detailed Skills (SDS) is a first step in filling the gap in information on detailed skills in Vietnam.** Efforts in developing countries to profile occupational skills and tasks often draw on data from other countries, most frequently the United States’ Occupational Information Network (O*NET). However, using the O*NET database outside the United States requires the assumption that the occupational characteristics described in O*NET are consistent across countries. Research has shown that this assumption does not hold due to differences in tasks, skills, and production technologies, particularly in low- and middle-income economies (Caunedo, Keller, and Shin 2021; Lewandowski et al. 2021; Lo Bello, Sanchez-Puerta, and Winkler 2019; Alatas, Granata, and Posadas 2020). To avoid this, the SDS draws on O*NET and other proven methodologies and takes a countryspecific approach. The SDS asks workers in
# **Minimum wages are relatively high in CEE and low in CIS**

Virtually all transition countries have in place minimum wages, although their “bite” measured by the ration of the minimum wage to the average wage differs considerably across country groups. In he European transition economies minimum wages are pitched at a relatively high level, on average around 40 percent of the average wage (Figure 13). The minimum ranges from about one-third of the average wage in Bulgaria and Romania, to over 50 percent of the average wage in Slovakia to two-thirds of the average wage in Slovenia. These minimum wage/average wage ratios are well within the OECD range, although in Europe the ratio is usually higher than in non-EU OECD members.

# **Figure 13  The “bite” of the minimum wage varies across ECA countries**

**Minimum Wage as Percentage of the Average Wage 2002**

<!-- Start of picture text --> 70.00<br>60.00<br>50.00<br>40.00<br>30.00<br>20.00<br>10.00<br>0.00<br>AzerbaijanBelarus Russia CIS-7MoldovaArmenia BRUEstoniaBulgariaRomania Poland Czech RepublicLatvia Slovakia CEEHungaryUkraineLithuaniaSlovenia<br>percent<br><!-- End of picture text --> Source: UNICEF TransMONEE database; Bank staff calculations.

In SEE in most cases there is no national minimum wage. However, minimum wages are fixed at the industry level by collective agreements between trade unions and employer representatives. In addition, these countries apply the minimum social security contribution threshold, which tends to be substituted for the minimum wage.<sup>14</sup> In the CIS national minimum wages are pitched at a much lower proportion of the average wage, with the notable exception of Ukraine, where currently (2002) the minimum wage accounts for 44 percent of the average wage, which is in the upper end of the CEE range.