rafmacalaba/data-use-mentions-extended huggingface dataset:
This individual-level evidence is therefore fully consistent with the mechanisms emphasized in the main analysis of the paper.<sup>25</sup>

# **7 Alternative explanations**

Though our results point to the importance of the interaction between the dismantling of coercive institutions and rent-sharing in the mining industry, the heterogeneity in spatial income across former homeland communities could in part be reflecting alternative ex-

> 24Note that income is available in categories. We imputed the mid-point of each category and truncated the highest category at its initial value (that is, 30,001+ rand was assigned value of 30,001).

> 25We would have liked to use similar data from the 2011 census in order to examine how the wage premium for black mining workers evolved over time. Unfortunately, the 2011 individual-data released by Statistics South Africa do not contain information on industry of employment or occupation. While this information was part of the census questionnaire, it has been excluded by Stats SA from the released files due to incomplete coding.

17
increase, but at a decreasing rate, with education. Further, earnings increase with potential experience (age) up until the age of 44 after which they decrease again. Males appear to earn 31 percent more than women, conditional on the other covariates.<sup>18</sup>

> 16In each round of the rotating panel, the questionnaire records absent members, adds any new members who have joined the household, and records any changes in schooling that have taken place. If none of the original group of household members is found to be living in the dwelling unit in the follow-up survey, the household is recorded as a new household. The interviewers do not track households that move, so they leave the panel. Rates of attrition are comparable to other developing countries (See Antman and McKenzie, 2007).

> 17Since 2004, the ENEU has been replaced by the Encuesta Nacional de Ocupacion y Empleo (ENOE, Survey of Occupation and Employment) in 2005. Unfortunately, however, the ENOE instrument differs from ENEU in important ways that make it impossible to match the surveys with confidence.

> 18For robustness we have also run alternate earnings specifications, allowing for both more and less

10
>_ <mark>, Edward Elgar, Cheltenham, UK and Northampton, MA, USA.</mark>

- <mark>UNCTAD (United Nations Conference on Trade and Development). 2013.</mark> _<mark>World Investment Report 2013—Global Value Chains: Investment and Trade for Development.</mark>_ <mark>Geneva: UNCTAD.</mark>

- <mark>Venables, A. J. 2005. “New Economic Geography,” The New Palgrave Dictionary of Economics. Steven N. Durlauf and Lawrence E. Blume (eds.),</mark> _<mark>The New Palgrave Dictionary of Economics Online</mark>_ <mark>, Palgrave Macmillan.</mark>

- <mark>Wagner, J. 2007. “Exports and Productivity: A Survey of the Evidence from Firm-level Data,”</mark> _<mark>World Economy</mark>_ <mark>, 30(1), 60-82.</mark>

- <mark>WEF (World Economic Forum). 2013. “Enabling Trade: Valuing Growth Opportunities,” WEF, Geneva.</mark>

- <mark>Winkler, D. 2010. “Services Offshoring and Its Impact on Productivity and Employment: Evidence from Germany, 1995-2006,”</mark> _<mark>World Economy</mark>_ <mark>, 33(12), 1672-1701.</mark>

- <mark>World Bank. 2012.</mark> _<mark>Lao PDR - Labor Standards and Productivity in the Garments Export Sector: A Survey of Managers and Workers</mark>_ <mark>, Washington, DC: The World Bank.</mark>

- <mark>World Bank. 2015</mark> _<mark>. Interwoven: How the Better Work Program Improves Job and Life Quality in the Apparel Sector,<
br>identified)|591||
||d2) Individual's father cannot be<br>identified (dropped)|1,124||
|Total number of men (20-65<br>age group) whose father is<br>identified: a) + b) + c)||56,154|52,010|
|Percentage of men (20-65 age<br>group, panel A) whose fathers<br>are identified||96.494%|91.433%|

Notes: Column 1-3 are directly obtained from Table 8 in Azam (2015) using IHDS 2005 while column 4 is based on authors’ own calculation using IHDS 2012.
# **Table S21. Relationship between the World Press Freedom Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.308***<br>(0.06)|-0.030<br>(0.03)|-0.027<br>(0.03)|||||
|Open Data Barometer<br>||||0.336***|0.000|0.000||
|Score||||||||
|||||(0.05)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.393|
||||||||(0.07)|
|Trade (% of GDP)|||0.041*|||0.000||
||||(0.02)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||-0.108|||0.000||
||||(0.15)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||-0.185||
, social security information from the Ministry of Family, Labor and Social Services, and vehicle ownership information from Ministry of Finance, among others. ISAS has been touted as international best practice, significantly reducing delays and errors in processing applications and social assistance.<sup>14</sup> In order to be eligible, applicant households need to meet at least one of the following six demographic criteria: a) dependency ratio greater than or equal to 1.5 (essentially, at least three dependents for every two able-bodied adults), b) families with four or more children, c) single females, d) elderly headed households, e) single parent households (male or female), and f) households with one member at least 40% disabled. These criteria were chosen as proxies of household vulnerability, using correlation analysis with per capita expenditure of refugees in Turkey, vulnerability definitions of Turkish Social Assistance System, and international evidence from refugees in Lebanon and Jordan.<sup>15</sup> Upon the receipt of the application, the information declared by the household is cross-checked and verified digitally through the databases integrated in the ISAS system. The beneficiary selection process is strictly based

> 13 UNHCR Turkey, Key Information for Syrians.

> 14 Republic of Turkey Ministry of Family and Social Policy and The World Bank (2017).

> 15 Altındağ et al. (2020) develop a targeting model for refugees in Lebanon using administrative data that are routinely collected by humanitarian agencies, which compares favorably to the “scorecard” approach requiring household surveys.

7
The second step was a four month _implementation_ phase given only to the treatment plants. In this phase, the consulting firm followed up on the diagnostic report to help introduce as many of the 38 key management practices as the firms could be persuaded to adopt. The consultant assigned to each plant worked with the plant management to put the procedures into place, fine-tune them, and stabilize them so that they could readily be carried out by employees. For example, one of the practices was daily meetings for management to review production and quality data. The consultant attended these meetings for the first few weeks to help the managers run them, provided feedback on how to run future meetings, and adjusted their design. This phase also involved about 15 days a month of consulting time per plant.

The third phase was a _measurement_ phase which lasted until August 2010. This phase involved only three consultants (and a part-time manager) who collected performance and management data from all treatment and control plants. In return for the firms’ continuing to provide this data, the consultants provided some light consulting advice to both the treatment and control plants. This phase involved about 1.5 days a month of consulting time per plant.

So, in summary, the control plants were provided with the diagnostic phase and then the measurement phase (totaling 225 consultant hours on average), while the treatment plants were provided with the diagnostic, implementation and then measurement phases (totaling 733 consultant hours on average).

# **_III.C. The experimental design_**

We wanted to work with large firms because their complexity means management practices are likely to be important. However, providing consulting to large firms is expensive, which necessitated a number of trade-offs detailed below.

<u>Cross-sectional sample size: We worked with 17 firms. We considered hiring cheaper</u> local consultants and providing more limited consulting to a sample of several hundred plants in more locations. But two factors pushed against this. First, many large firms in
|||||-0.137<br>(0.175)|<br>||
|Bandits/criminals||||||-0.378|||||-0.471||
|||||||(0.508)|<br>||||(0.327)|<br>|
|Pastoralists/nomads|||||||0.215**|||||-0.126*|
||||||||(0.092)|||||(0.073)|
|Observations|2,075|2,075|2,075|2,075|2,075|2,075|2,075|2,075|2,075|2,075|2,075|2,075|
|R-squared|0.053|0.115|0.053|0.053|0.053|0.053|0.054|0.118|0.113|0.113|0.113|0.113|
|Number of households|717|717|717|717|717|717|717|717|717|717|717|717|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|

_Note:_ Dependent variables are household (log) health and education expenditures. Data used are from the three waves of the GHS and telephone survey for conflict. All regressions are conducted using weights. Controls include all household
- De Neve, J.-E., Imbert, C., Spinnewijn, J., Tsankova, T., & Luts, M. (2021). How to Improve Tax Compliance? Evidence from Population-Wide Experiments in Belgium. _Journal of Political Economy_ , _129_ (5), 1425–1463. Publisher: The University of Chicago Press. 2, 5, 18

- DellaVigna, S., Otis, N., & Vivalt, E. (2020). Forecasting the Results of Experiments: Piloting an Elicitation Strategy. _AEA Papers and Proceedings_ , _110_ , 75–79. 10

- DellaVigna, S., & Pope, D. (2018). Predicting Experimental Results: Who Knows What? _Journal of Political Economy_ , _126_ (6), 2410–2456. Publisher: The University of Chicago Press. 10

- Gangl, K., Kirchler, E., Lorenz, C., & Torgler, B. (2015). Wealthy Tax Non-Filers in a Developing Country: Taxpayer Knowledge, Perceived Corruption and Service Orientation in Pakistan. SSRN Scholarly Paper ID 2643456, Social Science Research Network, Rochester, NY.

- URL `https://papers.ssrn.com/abstract=2643456` 3

- Gordon, R., & Li, W. (2009). Tax structures in developing countries: Many puzzles and a possible explanation. _Journal of Public Economics_ , _93_ (7-8), 855–866. Publisher: Elsevier. 2

- Hallsworth, M. (2014). The use of field experiments to increase tax compliance. _Oxford Review of Economic Policy_ , _30_ (4), 658–679. Publisher: Oxford Academic. 4

- Hoy, C., McKenzie, L., & Sinning, M. G. (2021). Improving Tax Compliance without Increasing Revenue: Evidence from Population-Wide Randomized Controlled Trials in Papua New Guinea. _Policy Research working paper_ , _Washington, D.C. : World Bank Group._
capital depreciates. Population and productivity growth are exogenous drivers of the model‟s dynamics. The former is taken from the projections of the United Nations Population Division, where labor force growth corresponds to growth of the population aged 15-64 years. Productivity growth is modeled as exogenous and factor neutral for agricultural sectors and labor augmenting for industrial and service sectors. Productivity of energy follows an autonomous energy efficiency improvement (AEEI) path so that there is no endogenous technological change in the model.

# **2.2 Data**

Like in any CGE model, the main data needed are in two folds: (i) social accounting matrix (SAM) and (ii) elasticity parameters. In this section, we briefly introduce the data used for the study. For more detailed information, please refer to Timilsina et al. (2010).

# **2.2.1 The Social Accounting Matrix**

For SAM, the model uses the GTAP database (Narayanan and Walmsley, 2008). However, the database has been substantially updated for the purpose of this study. First, we have introduced corn as a separate sector/commodity, whereas it was included in “other cereal grains” in the GTAP database. The splitting process was implemented through a program called Splitcom (Horridge, 2008). A significant amount of data was required to perform the split, including information on production, consumption and trade flows for corn and other cereal grains. Detailed documentation on the splitting process and the data required to execute the sectoral split is available upon request to the authors. Second, the GTAP database does not have biofuel sectors, so we specified sectors for ethanol and biodiesel. Moreover, we introduced three subsectors for ethanol: corn-based ethanol; sugar-based ethanol (i.e., ethanol produced from sugar cane and sugar beet) and other grains-based ethanol (i.e., ethanol produced from wheat and other cereal grains). We also added three biodiesel sub-sectors for biodiesel produced from oilseeds, soybeans
even though several parks have enjoyed rapid and sustained growth (annual growth in sales of 50%). 10 Electronics and telecommunications industries, despite considerable efforts toward their development, do not generate many new jobs. 11 Altogether these industries’ share reached 8.7% of the manufacturing total labor force in 2000, up from 7.3% in 1994 (table 2). On the other hand, the labor-intensive light industries employed more than 30% of the labor force in 2000. Growing steadily, the traditional basic industries—such as metal products, chemicals, and machinery, which have not received priority attention from the authorities—had about 57% of the labor force in 2000.

|**Table 2. Number and Share of Workers in Manufacturing**|**Industries (1994-2000)**<br>**a**|
|---|---|
|1994<br>1996|1998<br>2000|

> 8 Calculated based on data from _China Statistical Yearbook 1999_ , and _China Labor Statistical Yearbook 2002_ , China Statistics Press, Beijing.

> 9 The lower estimate reflects published data of Ministry of Public Security, the higher estimate is by the World Bank based on the 2000 census.

> 10 Dahlman, C. and J. Aubert, _China & Knowledge Economy: Seizing the 21st Century_ , 2001, the World Bank, Washington, DC.

> 11 Ibid.

5
collect price data on the firm's main products, inputs, and investment goods. In addition, the survey becomes more valuable when it contains enough information to estimate production functions, including value added, number of employees, capital stock, and material inputs. This information would help to isolate the contribution to productivity changes of various production, technological, and institutional factors.<sup>5</sup> Only a few surveys, such as RPED and ICS, include consistent productivity measures.

5Consider the Bank's general interest in Small and Medium-sized Enterprises (SMEs). To understand if SMEs are as efficient as other firms, we must hold constant capital and labor and then compare productivity to that of different types of firms.

5
Policy Research Working Paper 9954

# **Abstract**

Using geo-coded, firm-level data on more than 51,000 establishments in 649 metropolitan areas in 98 developing economies, from the World Bank’s Enterprise Surveys and a new global database on city-level mobility and congestion, this paper estimates the “pure” firm productivity gains of urban density, net of negative externalities associated with limited mobility, crime, and pollution. The results suggest that the average size of agglomeration economies in the developing world is comparable to the one observed in advanced countries, but the magnitude of the benefits of density on firm productivity substantially varies across firms. Returns to urban density are higher for firms operating in the tradables sector, exporters, foreign-owned firms, larger firms, and more experienced firms. Agglomeration economies are lost through both limited uncongested mobility and congestion, but the latter has a stronger negative effect on agglomeration economies and reduces relatively more the agglomeration benefits of firms in the non-tradables sector than those producing tradables.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 mburger@ese.eur.nl, eianchovichina@worldbank.org, or prottoy.akbar@aalto.fi.

_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
Figure B14: Heterogeneity in poverty gradient across countries, DB <!-- Start of picture text --> MWI<br>UGA<br>NER<br>LSO<br>AGO<br>BFA<br>GNB<br>TCD<br>SEN<br>GIN<br>CIV<br>GHA<br>TZA<br>MRT Core<br>ETH Suburb<br>Towns<br>GAB<br>−.6 −.5 −.4 −.3 −.2 −.1 0 .1<br>Regression coefficients and confidence intervals<br><!-- End of picture text --> Source: International Urban Poverty Database. Each dot represents the regression coefficients _DBj_ ( _i_ ) of regression 6 for each country _c_ , excluding control variables. Confidence intervals are represented with a line. The dependent variable _POVi_ is a dummy variable taking the value 1 if a household’s per capita expenditure expressed in PPP and spatially deflated falls below the poverty line. _DOUj_ ( _i_ ) is a vector of dummy variables: "Core", "Suburb", "Town" respectively take the value 1 if household _i_ lives in an "Core", "Suburb" or "Town" as defined by the DB method. The baseline category is "Other rural". GHSPOP 1km is used.

55
family members. Total assets is the number of assets owned by the households which can take a maximum number of 15, and own dwelling is a dichotomous variable [=1] if the household owns the place where they live.

We also explore the distribution of wealth for the Venezuelan and Colombian households of our sample. For this purpose, we constructed an index measure of the household’s cumulative living standard constructed following he Demographic and Health Surveys (DHS) methodology. The index combines information on adequate dwelling materials (for floors and walls), asset ownership for 15 items (including telephones, cellphones, TVs, computers, tablets, microwave, washing machine, fridge, blenders, water heater, radio/recorder, cars/taxis, motorcycles, bikes and trailers), and access to 10 services(encompassing energy, gas, garbage, aqueduct, sewage, sewerage, sanitary, kitchen, internet, TV, telephone), using a principal component analysis.

Uniquely, by collecting both current and retrospective information from Venezuelan households about their circumstances while they were in Venezuela, we can examine the wealth distribution before and after displacement episode for this group. This comparative analysis offers valuable insights into the wealth dynamics affecting the displaced population, 24
D intensive products is about eight times more valuable for developing country TFP increases. On the other hand, for sectors that are low in R&D intensity, their results suggest that for technology diffusion trade with developing countries can be as important as trade with industrialized countries.

Based on these considerations, we first classify the increasing returns to scale sectors of our model into low, medium and high technology sectors. Due to lack of data for Armenia, the classification is defined by the share of R&D expenditures in total sales, based on U.S. data. For low R&D intensive sectors, we assume that the elasticity of firms with respect to price is the same for the CIS region as for the EU, but the elasticity is only one-third of Rest of the World 20
**<u>Table 6.2.  Estimated Annual Cost of Crop Losses Due to  Land Degradation</u>**

||Total<br>Low|Loss(billion Rs<br>Mean|)<br>High|% of GDP<br>2010|% GDP of<br>thepoor|
|---|---|---|---|---|---|
|Salinitylosses|63|110|148|0.2%|1.1%|
|Waterlogged land losses|103|133|163|0.2%|1.2%|
|Erosion losses|320|460|600|0.7%|4.1%|
|Total CropLand Degradation Losses|480|703|910|1.1%|6.4%|
|<br>_Source:_Staff estimates.||||||

# **_6.3 Rangeland Degradation_**

Land use reported in India suggests that the main causes of rangeland degradation in India are irrational land use management practices leading to denudation of vegetation from rangelands which, exacerbated by intermittent droughts, has resulted in many pockets of desertification. According to land use data from Indiastat.com about 10 million hectares are classified as permanent pastures. At the same time, about 1.5 times more land, including that under miscellaneous tree crops and groves and cultivable waste land, is also used as pastures. There is a substantial share of degraded lands within all these land categories. Forest lands that are used as pastures are estimated in the next section to avoid double-counting. An estimated 60 percent of livestock grazes in the forest area (Kapur et al., 2010).

The loss in yield is valued in two ways. In the first method the reduction in fodder production is valued at the price of fodder. In the second method the loss of fodder is converted into a loss of livestock
2012, Eckernkemper and Gribisch 2021, Jorda and Niño-Zarazúa 2019; Hajargasht et al. 2012). Wealth indices have been used to predict full distributions for individual countries that lack consumption or income data but have a Demographic and Health Survey (Filmer and Prichett 2001, Dang et al. 2019). However, all these methods require at least one survey-based welfare vector, thus making them inapplicable for countries that do not have any survey data.

Remote sensing data and mobile phone data have been used to predict mean welfare, the poverty rate, or another distributional statistic in a country in the absence of survey data (Pinkovskiy and Sala-i-Martin 2016; Blumenstock et al. 2015, Steele et al. 2017; Pokhriyala and Jacques 2017; Lee & Braithwaite 2022; Engstrom et al. 2022). However, these approaches do not predict full distributions. Given the multiple poverty lines and welfare measures used in practice (see for example Jorda et al. 2023, Decerf and Ferrando 2022, Kanbur et al. 2022, Jolliffe & Prydz 2021), using such a model for each relevant welfare metric would jeopardize the simplicity of our approach. Moreover, the remote sensing data needed for these approaches do not stretch back far in time for long-term time trends and are not always publicly available, making these methods difficult to implement for practitioners.

The remainder of the paper is structured as follows. The data and method are described in sections 2 and 3. Sections 4 and 5 present results and robustness checks. Section 6 applies the models to global poverty measurement, and section 7 concludes.

3