rafmacalaba/data-use-mentions-extended huggingface dataset:
Tanzania_2014 17.3%<br>LOW QFD AND STEADY LOW QFD AND STEADY<br>Benin_2011 0.2% Benin_2011 6.7%<br>Benin_2012 0.3% Benin_2012 10.7%<br>Benin_2013 0.3% Benin_2013 12.1%<br>Cameroon_2013Cameroon_2014 0.6%0.7% H Cameroon_2013Cameroon_2014 36.2%41.4%<br>Central African Republic_2013 0.4% Central African Republic_2013 70.9%<br>Central African Republic_2014 0.4% H Central African Republic_2014 68.1%<br>Gabon_2012 0.3% Gabon_2012 17.7%<br>Gabon_2013 0.3% Gabon_2013 18.2%<br>Gabon_2014 0.4% Gabon_2014 19.2%<br>Niger_2012 0.6% Niger_2012 39.2%<br>Niger_2013 0.6% Niger_2013 40.9%<br>Niger_2014 0.5% Niger_2014 31.0%<br>Mozambique_2011 1.0% Mozambique_2011 50.4%<br>Mozambique_2012Mozambique_2013 0.8%0.9% H Mozambique_2012Mozambique_2013 39.5%44.2%<br>Mozambique_2014 0.9% Mozambique_2014 45.3%<br>LOW QFD AND MIXED LOW QFD AND MIXED<br>Liberia_2012 0.5% Liberia_2012 40.2%<br>Liberia_2013 0.2% Liberia_2013 20.9%<br>Liberia_2014 0.4% Liberia_2014 33.4%<br>Kenya_2012 0.8% Kenya_2012 31.1%<br>Kenya_2013 0.7% Kenya_2013 33.6%<br>Kenya_2014 0.6% Kenya_2014 28.6%<br>Kenya_2015 0.8% Kenya_2015 40.4%<br><!-- End of picture text --> _Source_ : World Bank staff calculations using utility data.

96
Policy Research Working Paper 8770

# **Abstract**

Using panel data on input-output intensities and expenditure prices from 28 countries, this paper finds the elasticity of substitution across sectoral inputs to be less than one in each of the three broad sectors of the economy. Intermediates are most complementary in the production of services while it is easiest to substitute across intermediates in the production of agricultural goods. Differences in relative prices alone account for a non-trivial fraction of the cross-country variation in sectoral linkages. Abstracting from the price channel that allows for substitution across inputs in response to changes in relative prices delivers biased aggregate implications of changes in productivity and distortions.

This paper is a product of the Development Research Group, Development Economics. 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/research. The author may be contacted at rishabhsinha@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
relative importance of the services sector to total productivity growth, along with a minor contribution from reallocation. Keep in mind that the contribution of each sector to the total is a function of the growth in sectoral productivity weighted by that sector’s share in output. Perhaps most importantly, the third column of Figure 5 clearly shows the stronger role of manufacturing in the 2000s, with a reallocation effect that actually turns negative. The negative reallocation effect can be explained by a reduced labor productivity growth in services during that period, combined with a services employment share that increased from 49 percent of the total in 2000 to nearly 54 percent in 2007. Thus, in this case, labor was moving out of the relatively more productive industry sector into a less productive services sector during the more recent period.

# **International Comparisons**

We now place the experience of Malaysia in a broader international context through comparing the sector-level output and productivity growth of similar countries. In addition to examining the relative performance of the industry/manufacturing and service sectors in these countries, we can also assess the _levels_ of productivity among countries and sectors. We take country-level data from a variety of sources, including Bosworth and Collins (2003), World Bank (2009), and various national accounts published by country statistical offices.

We compare the level of output per worker in 2005 by major sector for several countries in Table 3, using international purchasing power parity dollars to adjust for differences in price levels among the countries. The total-economy productivity levels broadly align with 14
MFIs is attributable to informal insurance and risk-sharing mechanisms, as 16 percent cite commercial banks as their potential go-to source for business funds.

**Figure 1: Sources of Start-up Financing, by Country (average 2009-10)**

<u>Source: Gallup World Poll 2009 and 2010.</u> It would be quite natural to wonder at this point as to why this distinction is important. After all, shouldn‟t the source of a loan be immaterial as long as the poor have someone to borrow from when the need arises? However, the literature draws a clear advantage of access to formal lending institutions for a number of reasons. For instance, Collins et al. (2009) observed that microcredit is far more reliable, in terms of both availability and price, than one‟s informal 10
Facts on SOEs Based on the BOS Database

In this section, we present descriptive statistics of SOEs for a subsample of countries in the database. In particular, we characterize SOE patterns for employment, revenues, and state participation by region, type of contestability, type of ownership, age, and size. In the BOS database, employment is measured as the number of workers that are employed by the SOE. Operating revenues is measured as the income that is generated by the SOE from business operations. As explained in section 2.2.2, the BOS database provides information on the shareholding percentages between the government and their direct subsidiaries and between directly owned SOEs and their indirect subsidiaries (and between subsidiaries of those subsidiaries). Using this information, we classify firms into three groups of state participation: majority owned SOEs (shareholding percentages higher than 50%), SEOs with blocking minority participation (shareholding percentages between 25% and 50%) and SOEs with minority participation (shareholding percentages between 10% and 25%). A firm is considered an SOE with majority government participation if the shareholding percentage is above 50% for all ownership layers that link it to the government. If at any ownership layer, the shareholding percentage between two firms is less than 50%, then all the firms beyond that layer are not considered as SOEs that have a majority government participation.<sup>48</sup> In the same manner, a firm is an SOE with blocking minority participation if the shareholding percentage is above 25% for all ownership layers that link it to the government, but less than 50% for at least one of the ownership layers that link it to the government. Last, a firm is an SOE with minority state participation if the shareholding percentage is above 10% for all ownership layers that link

> 47 For instance, in Angola, the Banco de Poupanca e Credito is owned 75% directly by the Government of Angola, and 15% is owned indirectly by the national social security
<br>implies that the antidumping duty imposed was less than the full margin.<br>_Source:_Nogués and Baracat (2005)|
|**AE.**|**METHOD**|Methodology used to determine the dumping margin (DM- Domestic<br>market, TC- Third country, CB- Cost based)<br>_Source:_Nogués and Baracat (2005)|
|**AF.**|**TYPE**|Either D-Dumping Case or RD -Review of Dumping Case<br>_Source:_Nogués and Baracat (2005)|

In terms of incomplete or missing data, the Argentine data set does not have complete information on the revocation or continuation of measures, as this information is only available for cases that have undergone a review. Furthermore, the database contains only partial information on foreign exporters as only some were named in the official documents.

# **3.2 Australia (AUS)**

The Australian Customs Service (or Customs) is solely responsible for anti-dumping investigations and determines if dumping and/or injury has occurred for each case. The decisions of the Australian Customs Service’s Dumping Unit are sometimes reviewed by a Trade Measures Review Officer from the Trade Measures Review Secretariat. The initiation and outcomes of each investigation are published in the Australian Commonwealth Gazette and in Australian Customs Dumping Notices (ACDNs), which are available online at the Customs website. The sources for data included in the database is online 16
that includes every case in a specific court, division (for example, civil or criminal cases), and country. These databases tend to include at least the following information: type of case and tribunal, the stage of the case, the dates of filing and resolution, reason for claim or accusation, resolution of the case, information about judges and parties involved, and the history of the case. Oftentimes, these cases can be merged to the judgment text. In an ideal scenario, this data will also be complemented by other sources of data to create a larger ecosystem. Case data can be linked to users or firms through tax or national IDs to evaluate the potential effect of policies or reforms on the affected parties. Case data can also be linked to other records, such as police and prison records, or to geographic areas, to better understand regional access. Finally, the data should be complemented by legal needs or court-user surveys to evaluate satisfaction with the functioning of courts, and to consider both (1) the needs of those who have access to courts and (2) of those who have a legal need but resort to other methods to resolve it.

two main limitations impeding utilization of this data. The first is difficulties in merging different databases with different definitions, IDs, or relationships for each case (Schauffler, 2014). Nonetheless, in contexts where data cannot be merged, valuable insights will still arise from comparing the results of different sources. For example, learning about access to justice in remote regions can be achieved by comparing the outcomes from legal need surveys in a region to case-level data from courts. The second limitation is related to human capital. Judicial officers are rarely experts on data analysis, and engineers generally lack the domain expertise necessary to understand the functioning of the law. In courts without sufficient human capital to take advantage of available data, training bureaucrats to learn the necessary skills may be a valuable long-term investment for improving the functioning of courts. Alternatively, there
1997. Table 1 illustrates the acceleration of manufacturing growth and GDP growth in general in India since the early 1980s.

**Table 1: Historical Sectoral Growth Rates in India**

|PLAN|Agriculture|Manufacturing|GNP|
|---|---|---|---|
|FIRST(1951-56)|2.5|5.0|3.7|
|SECOND(1956-61)|2.2|4.5|4.2|
|THIRD(1961-66)|-.30|5.3|2.8|
|ThreeAnnual Plans (1966-69)|4.5|2.7|3.9|
|FOURTH(1969-74)|1.3|1.8|3.4|
|FIFTH(1974-79)|3.6|5.9|5.0|
|SIXTH(1980-85)|3.1|4.9|5.4|
|SEVENTH(1985-90)|3.1|6.2|5.9|
|EIGHTH(1992-97)|3.4|6.9|6.8|

Source: Government of India, _Economic Survey_ , 2000-2001, Tables S-4 and S-5. Notes: GDP is estimated to be 27% industry, 25% agriculture for 2000. (World Bank India Data Profile: www.worldbank.org/data) In 1991, a new industrial policy ushered in a period of truly dramatic reforms. The number of industries reserved for the public sector was halved.<sup>6</sup> Licensing was abolished in all but a small group of industries. Perhaps more importantly from our perspective, there were complementary changes in policies relating to trade and financial markets. Foreign investment was encouraged, quantitative import restrictions were largely abandoned and tariffs were significantly reduced. A phased reduction in import tariffs has been undertaken since. The peak tariff rate was reduced from over 300 to 110 percent in 1992, 85
71 percent own the school building. Of those which do not own the school building, only 56 percent pay a rent. Only 11 schools reported currently having outstanding debt, credit or loans, and an equal number of private schools reported plans to borrow money in the next year. Based on the 2015 Morogoro data, on average, in both types of schools, outgoing expenses (including registration and approval fees, taxes, recurring expenses) are less than the tuition and non-tuition fees collected.<sup>4</sup> FBEP may change the financial sustainability of public school systems and sharpen the divide between public and private schools, as families of students in private schools pay significantly higher tuition and non-tuition fees.

# 6.4 Student Performance

According to 2015 Morogoro data, there is some evidence that private schools fared better on some national examinations. While schools’ self-reported percentages of students sitting the 2014 PSLE and CSEE exams were higher among public schools, the difference was not statistically significant ( **Table 4** ). Passing rates of the PSLE and CSEE exams, however, were higher among private schools and significantly higher for the PSLE.

Administrative data provided by the National Examination Council of Tanzania (NECTA) on average PSLE marks and CSEE grade point averages (GPA) from 2013 to 2016 were merged with our 2015 Morogoro data. This merge shows that the average PSLE marks were significantly higher among private schools over all four years. One anomaly is that while overall secondary pass rates are higher for private schools compared to public schools, CSEE GPAs, a measure of secondary school success, were lower among private schools in all years except 2014 (see **Table 3** and **Figure 11** ).

> 4 More details on schooling costs in private and public schools can be found in Baum and Cilliers (2018).

13
# **1. Introduction**

What, if any, are the gender-specific consequences of the current global financial crisis for women and their children in poor countries? To help answer this question, this paper reviews recent empirical evidence in developing countries on the ways women have been affected by and responded to aggregate shocks differently from men. Because women’s time allocation is intimately linked with decisions about children, the paper also reviews data from past crises to understand how fertility and children’s health and schooling are affected, disaggregating these effects by the sex of the child.

We hypothesize that the first-round impacts of the current global financial crisis will include (a) a reduction in women’s income and an increase in household poverty risk, as a result of losses in employment in export-oriented industries; (b) a tightening of microfinance lending; and/or (c) a fall-off in remittances (Figure 1). These first-round impacts should be particularly salient in the current crisis compared to past crises, when export and credit markets were much smaller and women were less integrated into them, and when remittances were a much smaller part of household incomes.

The crisis will also have second-round impacts, as vulnerable households respond to the decline in household income with coping strategies that can have genderdifferentiated effects. Women can respond to the drop in household income by increasing their hours of work (if they are already in the labor force), entering the labor force, or adjusting their time and effort in the home. Women can further cope by altering their fertility, and households can curtail (or increase) investments in children’s health and/or education.

2
informal business owners. Section 4 lays out the identification strategy and summarizes transitions into different occupations in the pre-reform period. Section 5 presents the impact estimates of the reform on firm formalization and transitions on wage work. Section 6 concludes.

# **2. Mexican Employment Survey Data and Species Classification**

The main data source used in this paper is the Mexican National Employment Survey (ENE), the survey that the Mexican government relies on for calculating unemployment statistics and the size of the informal sector. The ENE was conducted quarterly starting in 2000-II and covers a 3
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Calculating Profits<br>(Standardized)|S3|Core<br>Respondent|No|4|Standardized index of ability to calculate<br>profits from hypothetical scenarios.|
|Total Time Spent on<br>Competencies<br>(Standardized)|S3|Core<br>Respondent|No|4|Standardized index of average time taken<br>across the five competencies|
||||||=1 if core respondent ever married at the time|
|Ever Married|II|Core<br>Respondent|Yes|2,3,4|of the household survey. Variable is corrected<br>for inconsistences across rounds.|
|Age at First Marriage|II|Core<br>Respondent|Yes|4|Age of core respondent at first marriage.|
||||||=1 if core respondent ever pregnant at time of|
|Ever Pregnant|II|Core<br>Respondent|Yes|2,3,4|the household survey. Variable is corrected for<br>inconsistences across rounds.|
|Number of Live Births|II|Core<br>Respondent|Yes|2,3,4|Number of live births reported by core<br>respondent at time of the household survey.|
|Age of First Birth|II|Core<br>Respondent|Yes|4|Age of core respondent at first live birth.|
|Desired Fertility|II|Core<br>Respondent|No|4|Reported number of total children desired by<br>the core respondent (including any they<br>already have).<br>=1 if core respondent is HIV positive.|
|HIV Positive<br>Anemic|III<
- Dunleavy, P., Helen Margetts, Simon Bastow, and Jane Tinkler. 2006. “New Public Management Is Dead--Long Live Digital-Era Governance.” _Journal of Public Administration Research and Theory_ 16(3): 467–94.

- Elhorst, J. Paul. 2014. _Spatial Econometrics_ . Berlin, Heidelberg: Springer Berlin Heidelberg. http://link.springer.com/10.1007/978-3-642-40340-8 (May 20, 2020).

- Enikolopov, R. et al. 2013. “Field Experiment Estimate of Electoral Fraud in Russian Parliamentary Elections.” _Proceedings of the National Academy of Sciences_ 110(2): 448–52.

- Ermoshina, Ksenia. 2014. “Democracy as Pothole Repair: Civic Applications and CyberEmpowerment in Russia.” _Cyberpsychology: Journal of Psychosocial Research on Cyberspace_ 8(3). https://cyberpsychology.eu/article/view/4315/3365 (31 May, 2020).

Falco, Enzo, and Reinout Kleinhans. 2018. “Beyond Technology: Identifying Local Government Challenges for Using Digital Platforms for Citizen Engagement.” _International Journal of Information Management_ 40: 17–20.

- Fountain, Jane, and Carlos Osorio-Urzua. 2001. “Public Sector: Early Stage of a Deep Transformation.” In _The Economic Payoff from the Internet Revolution_ , eds. Robert Litan and Alice Rivlin. Wahsington, D.C.: Brookings Institution Press.

- Geddes, Barbara, Joseph Wright, and Erica Frantz. 2012. “New Data on Autocratic Regimes.” http://www.personal.psu.edu/jgw12/blogs/josephwright/GWFAutocraticRegimes1.pdf (30 September, 2020).

- Gershkovich, Evan. 2018. “Sergei Sobyanin, Moscow’s High Priest of Urban Renewal, Is Biding His Time.” _The Moscow Times_ . https://www.themoscowtimes.com/2018/09/06/moscow-highpriest-of-urban-renewal-sergei-sobyanin-is-biding-his-time-a62785 (30 May, 2020).

Gilmore, Dw et al.
into the CPHS (denoted by “CPHS-NSS”<sup>30</sup> ). Mean consumption per capita from the PLFS 2017 is estimated at Rs. 2385, which is approximately 7 percent higher than the NSS-2011 on an annualized basis. Note that these predictions rely only on changes in non-expenditure variables -- meaning that the growth of non-monetary predictors of consumption, as captured by the nationally representative official survey, must have been positive since 2011. This is further evidence that poverty in 2017 is lower than in 2011.

||20|17|20|18|20|19|
|---|---|---|---|---|---|---|
||**PLFS**|**CPHS**|**PLFS**|**CPHS**|**PLFS**|**CPHS**|
|**PLFS-NSS**|2385|-|2525|-|2712|-|
|**CPHS-NSS**|-|2557|-|2843|-|3139|
|**CPHS-NSS-PLFS**|2404|2443|2548|2539|2758|2803|

Table 3: Mean consumption per capita based on different imputation models and surveys.

Notes: Mean consumption values are deflated using CPI-AL and CPI-IW in rural and urban areas. The PLFS and NSS-2011 samples excludes states which are not included in CPHS. “PLFS-NSS” denotes consumption per capita based on an imputation model that uses variables that are common to PLFS and NSS-2011 (see footnote 28); “CPHS-NSS” denotes a model using a set of variables that are common between CPHS and NSS-2011 (see footnote 29); and, “CPHS-NSS-PLFS” denotes the model using variables common across all three surveys (see footnote 30).

Nevertheless, the first two rows in Table 3 underscore potential differences between the imputed consumptions into the CPHS and PLFS: Consumption imputed into the
interior solutions for _s_ = 1 or _s_<sup>_f_</sup> = 0. The presence of the business-stealing effect (that disincentivizes research in crowded sectors) suggests that an interior solution could exist for some initial conditions, at least in the short run. Exploring such cases would shed more light on the role of lock-in in South and the transition dynamics of the system after North introduces a subsidy for clean R&D. Consideration of interior solutions is also necessary to confront the predictions of the model with the pattern observed in data on research expenditure in clean and dirty industries (Fried 2018).

Future research could also explore optimal policy mixes. The only instrument of climate policy we considered in the paper was a clean R&D subsidy. In the future one could consider 40
papers.

Studies on informal housing have primarily focused on two issues. The first is the estimation of price or rent differentials between squatting and non-squatting areas (Jimenez, 1984, Jimenez, Friedman and Mayo, 1988). In these two papers, the focus is on the demand side of the housing market. Differentials in rents (or prices) between squatter and non-squatter units are attributed to eviction risk in the informal sector. The second ap-

> 4For example,in Morocco, the 1994 Census identified about 230,000 households living in slums (shacks), and more than 700,000 living in concrete informal dwellings.

> 5General equilibrium effects are mentioned in introduction by Lanjouw and Levy (2002), p 988, but not considered afterwards.

2