rafmacalaba/data-use-mentions-extended huggingface dataset:
||Servi<br>|ces<br>|||||
|Family Run|0.533|0.150|0.326|0.448|0.550|0.649|0.708|
|ProfessionallyRun||0.518|0.147|0.308|0.438|0.532|0.633|0.696|

Notes: ‘Management Score’ is calculated following MOPS (See Bloom et al., 2018). ‘Family-Owned’ is a firm, where the majority of the shares are held by the founder and/or family member of the founder. ‘Non Famlily-Owned’ is a firm where it is owned by anybody else (where the majority of the shares are held by private individuals, managers, dispersed shareholders, private capital or risk capital, etc. and run by managers). ‘Family Run’ is a firm, where it is run by the founder or a family member of the founder. ‘Professionally Run’ is a firm where it is run by other than the founder and/or family member of the founder.
|69|65|81|69|76|88|
|Male|99|96|106|105|93|96|90|87|84|73|68|85|72|80|92|
|Female|84|84|92|94|74|77|72|71|68|61|59|71|62|67|78|

_Source:_ Calculations based on Cambodia Socio-Economic Surveys, the Sri Lankan 2006 Household Income and Expenditure Survey, and the 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. Note: T&G = textiles and garments. The local currency was transformed into the U.S. dollars using the Consumer Price Index (CPI) and the exchange rate from the World Development Indicators Database.

# **4.2 Decomposing the Gender Wage Differential**

The Oaxaca-Blinder decomposition is a very well-known technique that is generally applied in studies of gender wage differentials. Men and women may have different wages for
gender ratios in entrepreneurship (e.g., Minniti 2010, Minniti and Naudé 2010), and we analyze very local determinants. Second, our paper contributes to a growing set of spatial studies on entrepreneurship in India (e.g., Khanna 2008, Ghani et al. 2011a,c, Mukim 2011) and a broader set of work on India’s industrial organization and economic performance.<sup>1</sup> These contributions constitute an important input to the growing body of work on entrepreneurship and economic advancement in developing countries (e.g., Ardagna and Lusardi 2008, Schoar 2009, Klapper et al. 2009).

Beyond the few papers on the spatial determinants of entrepreneurship in India, the closest paper to our work is Rosenthal and Strange (2011). Rosenthal and Strange (2011) document within-metropolitan area sorting for female entrepreneurs in the United States. They show that a spatial mismatch exists for female entrepreneurs similar to that found earlier on racial lines. Our work has several key differences. First, Rosenthal and Strange (2011) focus on spatial differences within metropolitan areas (e.g., using one-mile rings around a business). Our Indian data only identify districts for firms, which are often larger than U.S.metropolitan areas. Thus, we do not study sorting at the same spatial level. Second, and more generally, the contextual differences of the two countries are very stark. Rosenthal and Strange (2011), for example, consider automobile commuting patterns for U.S. entrepreneurs, whereas our focus will be on whether basic infrastructure like paved roads and electricity exist. Nonetheless, the two studies are connected in that they describe a link of past female business ownership in a spatial area with subsequent female entry. We hope that further research continues to refine our understanding of this interface between incumbent conditions and gender ratios of new entrants.

The plan of this paper is as follows. Section 2 discusses our entrepreneurship data and spatial differences in
# **References:**

Ahmad, Q. K., Chowdhury, A. K. A., Imam, S. H., and Sarker, M. 2001. _Perspectives on flood 1998_ . Dhaka: The University Press Ltd.

Ahmed, A. U. and Mirza, M. M. Q. 2000. ‘Review of causes and dimensions of floods with particular reference to flood ’98: national perspectives’ in Ahmad, Q. K., Chowdhury, A. K. A., Imam, S. H. and Sarker, M. (eds) Perspectives on Flood 1998, University Press Ltd, Dhaka, pp67–84 Auffhammer M., S.M. Hsiang, W. Schlenker, and A. Sobel. 2011 “Global Climate Models and Climate Data: a User Guide for Economists,” _Working Paper_ Banerjee, Lopamudra 2007. “Effects of Flood on Agricultural Wages in Bangladesh: An Empirical Analysis.” _World development,_ 35, no. 11, pp1989-2009 Blankespoor, B. and Yoshida, N. 2010. _Market Accessibility in Bangladesh_ . 16pp. mimeo, Washington DC, World Bank Deaton, Angus 1992. _Understanding Consumption_ Carendon Lectures in Economics, Oxford University press.

Elbers, C., J.W. Gunning, and B. Kinsey 2007. “Growth and Risk: Methodology and Micro Evidence.” _The World Bank Economic Review,_ 21, 1–20.

Ellis, Frank. 2004. “Occupational Diversification in Developing Countries and the Implications for Agricultural Policy” Programme of Advisory and Support Services to DFID (PASS).

Hossain, M. 2004. Rural Non-Farm Economy in Bangladesh: A View from Household Surveys. Center for Policy Dialogue (CPD) Occasional Paper Series no 40, Dhaka: Center for Policy Dialogue.

Islam, M. J., Parul, S. S., Pathan, A. B. M. B. U., Quasem, M. A., &
income levels. This is a very different approach from those discussed in the preceding paragraphs. The household survey data are used in the estimation process, but they are not used in the simulation model itself.

This has several advantages. Firstly, since all households in a given country have the same preferences, there is also a well-defined poverty level of welfare, and the analysis of consumption impacts is clean and straightforward. Secondly, to the extent there are measurement errors in the household survey, these will likely be eliminated in the process of estimation of the demand system. Finally, it permits the authors to adjust predicted consumption levels in cases where the household survey data and CGE model refer to different base periods. On the other hand, one important drawback of this econometric approach to assessing the consumption impacts of trade reform is that one can no longer refer to the impacts on particular households in the survey.<sup>12</sup>

**_Transactions Costs:_** In their survey of the empirical evidence on trade liberalization, Winters, McCulloch, and McKay (2004) highlight the important role of transactions costs between the border and the consumer (for imports), and between the border and the producer (for exports).  Assuming these marketing costs are specific (as opposed to _ad valorem_ ), they tend to dampen the impact of world commodity price changes on domestic consumers, while exaggerating the impact of such price changes on producers of export products. As a result, the presence of such marketing costs can sharply alter the predicted impact of trade policy changes on remote rural households.

Recent work by Arndt et al. (2000) in Mozambique underscores the empirical significance of marketing margins in low-income countries. The authors report producer-consumer margins as high as 300% (cassava). The biggest margins reported in their study appear to be for food products, which tend to dominate both the consumption and production bundles of the poor in that country. So the existence and behavior of these margins is critically
<br>Mean|<br> <br>SD|Arithm.<br>Mean|Geom.<br>Mean|<br> <br>SD|<br>gov.<br>Bonds|<br> <br> <br>ao|
|**Full sample**, 1815-2016, yearly|6.85|5.78|15.03|<br>7.99|7.13|13.69|<br>4.29|0.32|
|... without world wars|6.99|5.99|14.50|7.69|6.87|13.21|<br>3.94|0.30|
|**By era**, yearly|||||||||
|1815-1869|7.10|5.66|17.28|<br>6.90|5.87|14.84|<br>2.96|0.23|
|1870-1914|6.28|5.97|8.01|6.18|5.91|7.45|<br>3.70|0.48|
|1915-1945|6.01|4.29|19.91|<br>7.12|6.00|16.19|<br>2.33|0.16|
|1946-1973|5.85|4.91|14.52|<br>10.15|9.19|15.12|<br>8.07|0.52|
|1995-2016|9.89|8.96|14.29|<br>12.40|11.41|14.97|<br>6.26|0.33|
|Monthly returns, full sample|0.54|0.47|
# Universal Health Care and Informal Labor Markets: The Case of Thailand

## by

## Adam Wagstaff<sup>a</sup> and Wanwiphang Manachotphong<sup>b</sup>

a Development Research Group, The World Bank, Washington DC, USA b Faculty of Economics, Thammasat University, Bangkok, Thailand

**Corresponding author and contact details:** Adam Wagstaff, World Bank, 1818 H Street NW, Washington, D.C. 20433, USA. Tel. (202) 473-0566. Fax (202)-522 1153. Email: awagstaff@worldbank.org.

**Keywords:** Universal health coverage; informal labor markets; informality.

JEL codes: I1, I38, J08, J3.

**Acknowledgements:** We are grateful to Pummarin Hmokthong at the Thai National Village and Urban Community Fund Office for providing us with the village fund data, to Pornpan Kaewsringam at the Thai National Statistics Office for helping us with the Labor Force Survey data, and Sutayut Osornprasop for helpful comments. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.
81%|8.8%|53.7%|16.4%|27,348|34,045|77%|
|Finland|5,960|96%|19.0%|50.5%|37.7%|29,904|35,426|61%|
|Denmark|9,210|72%|23.6%|53.1%|44.5%|38,433|37,304|86%|
|Memo Items:|||||||||
|Memo Item: India Rs.<br>Avge 1999-2004|678|5%|0.8%|33.8%|2.3%||2,972|29%|
|Rs. Converted to|||||||||
|Euros<br>Sources: Dexia:2008 a|13<br>nd Mohanty et alia:|2007, World Deve|lopment Indicators.||||||

Comparing all these countries to India shows it to be an outlier in most respects, even when taking into account the low level of income in India. The country in the EU whose finances appear to most resemble India’s is Malta, a tiny country with a population of only 400,000, a mere neighborhood in one of India’s major cities. Bulgaria’s per capita income is six times that of India, but spending at the municipal level is 18 times that of a sample of 35 of the larger municipal corporations in India. India’s municipal spending as a share of sub-national spending, at only about 5%, is a small fraction of even the most federalized country in this group, Spain. This is particularly noteworthy given India’s size and complex city system.

8
.7|23.1|61.2|+265|+10.2|
|Total|4,533.8|13,836.7|+305|+11.8|52.0|130.7|+251|+9.7|

_Source:_ Ocean Shipping Consultants Limited 2010. _Note:_ TEU = twenty-foot equivalent unit.

Although southern Africa is endowed with several port options, Durban and Dar es Salaam are by far the most intensively utilized. Overall, due to rapid expansion of traffic, a few of the region’s ports are beginning to experience capacity constraints (figure 2.12). This is most notable in the case of Luanda (Angola) and Dar es Salam, where the general-cargo traffic handled significantly exceeds design capacity. Luanda, which is at almost 80 percent capacity, is on the threshold of facing a gridlock that would significantly undermine the efficiency of the port. Container traffic in Durban and Dar es Salaam exceeds 100 percent of their capacity. Cape Town also experiences challenges due to excess handling relative to design capacity, albeit on a smaller scale.

Durban struggles with handling South Africa’s own traffic and has experienced recurring berth congestion during the peak season. Shipping lines are threatening to reintroduce a surcharge for berthing delays that existed between 2002 and 2005. Although there are plans to bring significant additional capacity on stream, such as the new Pier One scheme, demand is strong and in the short and medium term could outstrip any additional planned capacity. Carriers are seeking additional new locations in the Indian Ocean (such as Mauritius), compounding Durban’s challenges.

**Figure 2.12 Ratio of current demand to reported capacity**

_Source:_ AICD Ports Database,2008 28
on average over all 3 rounds, reduced housing expenditures at endline, had no impact on food consumption over endline and followup, had no impact on depression over all 3 rounds, and reduced child socio-emotional wellbeing over rounds 2 and 3. Overall Housing Quality is defined as a normalized housing quality index that includes indicators for quality floors, roofs, and walls, indicators for access to grid electricity and piped water, and the number of people per room. Total monthly housing expenditures is only reported for endline in the pooled table due to variations in measurement of housing expenditures across rounds and due to when treatment ended. Housing expenditure was measured including assistance payments at midline, and excluding assistance payments at endline since most recipients had stopped receiving payments but were still subject to rent freezes at part of the program. Results on housing expenditures in each round are reported in the appendix. Total consumption, including food and non-food expenses, was only measured at endline due to the length of the module. Reported in this table is food consumption, which was measured at endline and followup. Total food consumption is the log of the sum of food consumed in last seven days (Cereals and cereal products, Live animals, meat, and other parts of slaughtered land animals; Fish and other seafood; Milk, other dairy products, and eggs; Oils and fats; Fruits and nuts; Vegetables, tubers, pulses; Sugar and desserts; Ready-made food and other food products (baby food, spices)), plus home-produced foods produced in the last 7 days, plus assistance received in the last 30 days divided by 4.33. CES-D was measured in all rounds with exactly the same questionnaire. It includes 10 questions on depressive symptoms. Individuals who skipped more than 2 questions were marked as missing. The continuous scores were standardized with respect to the control group. The Child Strengths and Difficulties questionnaire (SDQ) was administered regarding a randomly selected child aged 3-8 at endline. At follow-up, the SDQ was administered regarding the same child
question would stick out as very different in sensitivity; iii) could be seen by respondents as not easily inferable by the researchers (since if we knew the true answers for the household for each of 1 through 3, then we would be able to infer individual responses for question 4 in group A); and iv) that we thought that people would think it was plausible that many people would answer that at least one question was true, but that few people would be likely to regard all three statements as true.

# **3.2 Surveys**

In order to trial this methodology, we took advantage of surveys in four countries that were being taken for other purposes and added this list randomization question. The surveys in Ethiopia and Morocco were surveys on Migration and Development conducted by Maastricht Graduate School of Governance, and oversampled households with migrants. The Philippines survey was conducted by the World Bank, Innovations for Poverty Action and the University of Michigan as a baseline survey for an experiment intended to help lower barriers to international migration. The Mexican survey was conducted by the World Bank and Innovations for Poverty Action as a follow-up survey for a financial literacy experiment. We describe the population of interest in each study.

The Ethiopian sample was a study explicitly designed to study migration and so over-sampled areas with high concentrations of migrants. Sampling was based on a two stage purposive sampling technique. The five regions of Amhara, Oromia, SNNP, Tigray, and Addis Ababa were chosen, and within each region, three Woredas were selected for enumeration: one urban Woreda and two rural Woredas. The Woredas were selected to represent a city within the region (urban area), a rural area Woreda close to an international border area, and a rural area farther from the border. The final criteria for selection were Woredas that were known to have migration. Within each Woreda, the Kebeles selected for the initial household listing were selected based on accessibility. After listing, the survey randomly chose households to survey, oversampling households
growth, along with caveats and remaining issues. Lastly, a conclusion is presented in Section 5.

# **2. Structural Shifts in Korean Manufacturing Industries**

## **2.1 Shifts in Industries within Manufacturing: Changes in “flagship” industries**

One interesting pattern observed during the expansion of Korea’s manufacturing sector is that there was a dramatic shift between industries _within_ the manufacturing sector. Export-oriented industrialization and growth is an important characteristic of the Korean economy. While the Korean manufacturing sector has concentrated on a few key products, the composition of coreexport products has changed over time. For example, fabric clothes, apparel, and shoes were the major export products in the 1970s, but the share of these products in total exports has steadily declined. Ships, cars, and semiconductors have been the most important three exports since the early 1990s.<sup>5</sup> We used the data from national accounts that were constructed by Bank of Korea for twodigit industries (i.e., in terms of GDP and GNI according to Economic Activities) to examine the extent to which the value-added shares changed between 1970 and 2017. Figure 2 shows the changes in value-added shares for selected manufacturing industries that experienced substantial changes during the sample period (see Appendix Table A1 for all 13 two-digit KSIC industries).<sup>6</sup> We focus on the following four industries. We consider textiles and leather products as a declining industry. Transportation equipment and electronic and electrical equipment represent two growing industries. The chemical products sector, which has maintained a steady value-added share, shows

> 5 See Figure A1 in the Appendix for changes in export shares of major products.

> 6  I acknowledge Frederic Warzinski for pointing out the dynamics in the value-added share changes in the selected industries.

6
# **3.2 Exposure to disasters**

The EM-DAT data provide insight into the Caribbean’s exposure to major natural disasters. In the countries included in this study, the disaster categories captured in the EM-DAT database combined to a total of 188 events that were large enough to overwhelm local capacity since 1950 (Figure 1a). Meteorological events and hydrological events occurred most frequently, while geophysical events and climatological events were less common. Haiti stands out as the most frequently affected of the five countries. It experienced a total of 126 events, 44 of which were meteorological and 68 of which were hydrological in nature. It is also the only country to experience geophysical and climatological events on a somewhat regular basis. Meteorological events are also common in the other countries in the Caribbean hurricane belt; Belize, Dominica, and Saint Lucia respectively experienced 17, 13, and 15 meteorological events. Belize and Saint Lucia respectively also experienced five and four hydrological events. Suriname, which is not located in the Caribbean hurricane belt, recorded the lowest number of disasters with five hydrological events.

A clear increase in the number of major events can be observed over time. Figure 1b shows the number of events experienced per country in 10-year intervals (11-year period from 1950 to 1960 and 13-year period from 2021 to 2023). Especially the number of meteorological and hydrological events amplified visibly over the period from 1950 to 2023. For instance, before the 2000s the number of events experienced by Haiti in each category tended to be in the low single digits. By the 2000s, the number of meteorological and hydrological events was in the double digits. Likewise, before the 2000s, Suriname experienced only one hydrological event. In the 2000s, it experienced four. Moreover, a comparison with other regions (not displayed) makes clear that the prominence of meteorological events sets the Caribbean apart. In contrast, in each of the main regions in the EM-DAT database (Oceania, Europe, Africa, Americas, Asia), the data show a
# **Figure 18. Growth, sovereign ratings and policy space in EMDEs**

# **A. Growth by group**

# **B. Growth Differential between EMDEs and**

# **advanced economies**

<!-- Start of picture text --> Percent 1990-2008 Average<br>10 2003-2008 Average<br>8<br>6<br>4<br>2<br>0<br>EMDEs EMDE EMDE Advanced<br>commodity commodity economies<br>importers exporters<br>C. Sovereign rating and commodity prices<br>Average sovereign rating Index<br>Commodity exporters<br>11 1000<br>Commodity price index (RHS)<br>BB+<br>11.5<br>BB12 500<br>12.5<br>BB-<br>13 0<br>E. 2015 fiscal balances<br>Oct. 2014 forecast<br>Percent of GDP<br>April 2016 estimate<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>-6<br>Commodity exporters Commodity importers<br>2011 2012 2013 2014 2015 2011 2012 2013 2014 2015 2011 2012 2013 2014 2015 2011 2012 2013 2014 2015<br>2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>6<br>4<br>2<br>0<br>-2<br>-4<br>1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->

# **D. Stock of EMDE bonds maturing**

<!-- Start of picture text --> Percent of gross bond issuance in 2015<br>80
sector and demonstrate that ignoring informality can lead to overestimating spatial inequality in firm-to-firm trade and the extent of urban primacy. This oversight may cause researchers to underestimate the economic connectedness and vulnerability of smaller regions. Our finding that formal sector activity is disproportionately concentrated in urban hubs is also consistent with spatial patterns in other contexts that exhibit a similar formal-core, informal-periphery structure (Z ́arate, 2022). In complementary work, Bacilieri et al. (2023) examine how varying reporting thresholds for firm-to-firm transactions affect the comparability of aggregate network statistics. While they demonstrate that missing data can bias network statistics, our focus differs in two key ways. First, transaction reporting thresholds are not a concern in our setting; we instead address other sources of network incompleteness in tax data. Second, while they analyze aggregate statistics, we examine distributional implications at the regional level – a margin where we find informality plays a substantial role.

Finally, we contribute to the growing literature on estimating network statistics and reconstructing networks with missing data (e.g., Chandrasekhar (2016); Mungo et al. (2023)).<sup>9</sup> We contribute to this literature by proposing the use of a structural approach. The structural route seeks to tackle two challenges: first, nodes are missing in a non-random manner. Second, we do not observe network characteristics for missing nodes, but linking preferences of missing firms can systematically differ from those of observed firms. To address these challenges, we combine multiple data sources and estimate a network formation model that recovers both the prevalence of missing firms and their linking behavior, accounting for sectoral and geographic preferences. In comparison, Mungo et al. (2023), for example, use a link prediction algorithm to predict the existence of links among missing nodes using data on the characteristics and linking behavior of non-missing nodes. This approach is challenging to apply in the context of informality, as informal firms are missing in a non-random manner and information on their linkages are not