rafmacalaba/datause-encoder-data huggingface dataset:
only relies on employment status and occupation. Moreover, the sample does not represent the entire country as there is inadequate coverage of the Eastern provinces of Indonesia. Yet, these areas are important from a developmental perspective given historically low GDP per capita levels and high poverty rates. It is also worth highlighting the 7-year gap between the last three waves of the IFLS. While this feature is useful for the study of longer-term trends, it makes it difficult – if not impossible – to study short-term phenomena. Lastly, because the original IFLS sample has not been refreshed or boosted since the pilot wave in 1993, the IFLS may not represent the contemporary labor force, including informal workers. The aging of the sample also means that future waves of the IFLS – if any – will likely suffer from higher levels of attrition, which has remained minimal (though not ignorable) thus far.

As a final point, both SAKERNAS and IFLS researchers need to be aware of changes in key definitions. In 2001, Statistics Indonesia disaggregated employees into two groups – i.e., regular employees and casual workers in the agricultural and non-agricultural sectors. This change has ramifications for the identification and subsequent measurement of informal employment. Because casual workers (informal) were previously lumped together with employees (formal), estimates of informal employment prior to 2001 are likely to be under-estimated.

# **THEORETICAL PERSPECTIVES ON INFORMALITY**

Having reviewed the sources of data on informality in Indonesia, we now turn our attention to the empirical literature. To ground our review, we first briefly summarize the overarching theories of informality.

Two broad schools of thought have dominated the literature on informality. The theory of _segmented labor markets_ views informality as a form of exclusion or employer of last resort (Harris and Todaro, 1962; Tokman, 1992) due to factors such as dualism (Lewis, 1954) as well as high entry barriers into the formal sector (e.g., de Soto, 1989; 2000). In contrast, the theory of _rational exit_ views informality as the result of a private cost-benefit calculation (Magnac, 1991; Maloney, 1999).

In this view, enterprises and workers choose to be informal when the perceived costs of formality 11
|||Nigerian 2006’s SAM|GTA|P database version 8.1|Aggrega|ted 21 GTAP sectors|
|---|---|---|---|---|---|---|
|No.|Code|<br>Description|No.<br>Code|Description|<br>No.<br>Code|<br>Description|
|19|ccoff|Coffee|||||
|23|ctoba|Unprocessed tobacco|||||
|26|crube|Rubber|||||
|27|cocrp|Other crops not specified|||||
|33|cfore|Forestry|13<br>frs|Forestry|||
|28|ccatl|Cattle|9<br>ctl|Cattle, sheep, goats, horses|9<br>Livestock|Cattle, sheep, goats and horses|
|29|cgshp|Live goats and sheep|||||
|30|cpoul|Live poultry|10<br>oap|Animal products nec|10<br>OAnimalPrd|Animal products including<br>dairy and milk|
|37|ceggs|Eggs|||||
|31|coliv|Other live animals|||||
|38|cmilk|Milk and dairy products|11<br>rmk|Rawmilk|||
||||22<br>mil|Dairy products|||
|Zer|o sector||12<br>wol|Wool, silk-worm cocoons|||
|Zer|o sector||15<br>coa|Coal|11<br>OMining|Coal and other minerals|
|49|comin|Other mining|18<br>omn|Mineralsnec|||
|47|ccoil|Crude petroleum and natural gas|16<br>oil|Oil|12<br>CrOilGas|Crude Oil and Gas|
||||17<br>gas|Gas|||
|32|cfish|Fish and fish meat|14<br>fsh|Fishing|13<br>MeatFisch|Meat products including<br>Fishing|
|34|cbeef|Beef|19<br>cmt|Meat: cattle, sheep, goats,|||
|35|cgsmt|Goat and sheep meat||horse|||
|39|comet|Other livestock meat|20<br>omt|Meat products nec|||
|36|cpmet|Poultry meat|||||
|42|ctext|Textiles and leather products|27<br>tex|Textiles|14<br>TxWaLeWd|Textile, wearing apparel,<br>leather and wood|
||||28<br>wap|Wearing apparel|||
||||29<br>lea|Leatherproducts|||
|43|cwood|Wood, wood products, furniture|30<br>lum|Wood products|||
|46|cfert|Fertilizer|33<br>crp|Chemical, rubber, plastic|15<br>OManufacrd|Other manufactured goods|
|45|comfc|Other manufactured products||products|||
||||31<br>ppp|Paperproducts, publishing|||

32
**Figure 1: Employment status among those aged 20-59, by sex**

<!-- Start of picture text --> Men Women<br>3.83<br>9.18<br>20.64<br>6.50<br>72.87<br>86.99<br>Employed Unemployed Out of Labor Force Employed Unemployed Out of Labor Force<br>Men Women<br>3.97 12.63<br>20.03<br>4.91<br>75.06<br>83.40<br>Employed Unemployed Out of Labor Force Employed Unemployed Out of Labor Force<br>2012<br>2018<br><!-- End of picture text --> _Notes_ . The analysis relies on cross-sectional data from the Egypt Labor Market Panel Survey (ELMPS) in 2012 and 2018. This figure presents the employment status of individuals aged between 20 and 59 years old in each survey round. We rely on the market definition of work status, search required (reference 1 week). Expansion weights are used.

We now turn to the sectoral make-up of the Egyptian labor market. We adopt a tripartite classification of private informal, private formal, and public formal sectors. Egypt’s large informal sector is similar in size to those of Mexico and Colombia, each having around 60% of workers employed informally.<sup>5</sup> However, Egypt had a substantially larger public sector, which accounted for over one-quarter of employment. Instead, in 2020, the public sector accounted for 13% of Mexico’s employment<sup>6</sup> and just over 4%<sup>7</sup> of Colombia’s. Other countries at similar level of GDP

> 5 Lanau, S, Rodríguez-Delgado, D., and Toscani, F. 2018. ‘Colombia Selected Issues’. IMF. p 17. Gurría, Á. 2019. Presentation of the 2019 Economic Survey of Mexico. OECD.

> 6 Source: ILO and Encuesta Nacional de Ocupación y Empleo. Data on the year of 2020. Retrieved Jan 2022.

> 7 Source: ILO and Gran Encuesta Integrada de Hogares. Data on the year of 2020. Retrieved Jan 2022.

6
**The P2P platform (LocalBitcoins in this application) used only for one of the two legs**

**The amount in Bitcoin of the two legs might differ.**

**The two trades lie farther apart than our time window (five hours in the baseline case) allows** .

**The payment technology in (at least) one country involved is slow.**

**The trade is matched but wrongfully disregarded, because of the probability of it being matched by chance**

**The parties involved in a planned trade cancel the transaction to complete it in cash and avoid the escrow fees**

Imagine a remittance sending agent, working and living in a country with centralized exchanges and sending his remittances to a developing country, where the only platform available for Bitcoin trades is the P2P exchange. It might be cost efficient to purchase the Bitcoin on the lower cost centralized exchange of the sending country, transfer it to the e-wallet of the P2P platform, and then sell it on the P2P platform in the destination country. Our algorithm would not detect this vehicle trade, as only one of two trades is recorded in our P2P data.

The algorithm only identifies a vehicle trade where the amount of Bitcoin for both two legs is identical. Yet, some cases might exist, where an agent uses his wallet both for vehicle trades and speculative purposes, so the two amounts might differ. The same can happen when the best P2P price offer of the second leg is limited to a trade-size Z < X, when the agent to might split the second trade into two.

Our algorithm does not match any two trades when the time difference between them exceeds 300 minutes (for a five-hour window). If the trader has access to a perpetual futures contract, that allows her to wait for better terms of trade on the destination’s country P2P market, this can allow her to accept much longer periods of exposure to Bitcoin price volatility.

Because the clearing time of the two trades is highly dependent on transfer technologies in the countries involved, our methodology might not capture trades from countries where the prevalent money transaction technology has a clearance time that exceeds the chosen window.

The algorithm applies a fairly conservative methodology in identifying trades, skewed towards reducing Type 1 errors (FalseIdentification as vehicle trades) at the cost of larger Type 2 Errors (disregarding a trade that indeed was a vehicle trade).

Whereas a 1% fee for the transaction amount of 100 USD hardly creates an incentive to face the potential risk and nuisance of a arranging an in person transaction, anecdotal evidence suggests that agents seeking to make larger trades often seek to circumvent the fees by cancelling trades on the LocalBitcoins.com and instead use the messenger function on LocalBitcoins.com to organize an 29
quality assurance exercises performed. For example, the ethnic-name database can be applied to foreign patents registered with the USPTO. The ethnic-name database assigns ethnicities to 98% of foreign records. Moreover, estimated inventor compositions are quite reasonable– for example, approximately 90% of inventors filing from Chinese countries and regions are classified as ethnically Chinese.

The developed approach, in combination with the interactions embedded in model Mit<sup>kout-</sup> lined in the main text, has the subtler advantage of allowing the construction of instruments for many more countries and their ancestry groups than what one can directly identify through inventor names, either due to lack of names for some countries (e.g., Ethiopia, Oman) or due to extensive name overlap among countries within an ethnic group (e.g., most of Latin America sharing Hispanic names). The instrument also has only non-zero predicted changes for country-industries due to its summation over cities and the connection to Anglo-Saxon technology development in cities.

The Costinot et al. (2012) model dictates the inclusion of importer-industry-year fixed effects δ<sup>k</sup> jt<sup>inthefirst-differencedspecificationforstructuralreasonssuchascountrypreferences.Two</sup> other rationales exist for having at least industry-year fixed effects due to the data development depicted. First, the US technology frontier is taken as exogenous in the IV model, and the identification of the θ parameter should thus be independent of the pace of US technology expansion in different industries. A second methodological rationale stems from the US patent process. US patent grants have increased dramatically since the early 1980s. While several factors lie behind this increase, it is clear that USPTO grant rates grew faster than the underlying growth of US scientific personnel and innovation can explain. Moreover, differences in grant rates exist across industries. The fixed effects account for these secular changes in the underlying patenting productivity.<sup>1</sup>

# IV Extended OLS Empirical Results

Table S3 considers several extensions of Table 1 to characterize heterogeneity in the sample. Column 2 interacts the regressor with the GDP/capita level of the exporter, broken down into quintiles. Interestingly, the OLS link between base productivity and exports is mainly coming off of the lower-income countries, suggestive of higher trade due to varieties among developed economies. This may be due to the greater potential for within-country productivity dispersion 1For example, Griliches (1990), Kortum and Lerner (2000), Kim and Marschke (2004), Hall (2005), Branstetter and Ogura (2005), Jaffe and Lerner (2005), and Lemley and Sampat (2008).

7
# **1.  Introduction**

Access to external finance and the ability to undertake profitable investment opportunities is an important ingredient for success of any new business and ultimately for economic development and growth (see Levine, 2005). However, liquidity constraints hinder potential entrepreneurs from starting businesses (see, for example, Evans and Jovanovic, 1989) and reduce growth rates, especially in small businesses (Beck, Demirguc-Kunt, and Maksimovic, 2004). Relaxing these constraints can promote new firm entry and success. For example, a cross-country study of 35 European countries finds that entry is higher in more financially dependent industries in countries that have greater financial development (Klapper, Laeven, and Rajan, 2006).

Financing options of mature firms could be explained by the pecking order theory of financing (Myers and Majluf, 1984). These firms generally have more internal funds (retained earnings) due to higher profitability and lower growth opportunities and, therefore, might prefer to use internal funds first (Bulan and Yan, 2007, Brealey and Myers, 2002). Furthermore, a good reputation, such as a long credit history, mitigates the adverse selection problem between borrowers and lenders. Mature firms, therefore, are able to obtain loans on better financial terms compared to their younger firm counterparts (Bulan and Yan, 2007; Carpenter and Rondi, 2000) and generally use debt before equity for their financing needs (Bulan and Yan, 2007).

As a result, around the world, new firms without a proven track record experience more severe financing constraints. For instance, studies conducted in China, Italy and the U.S. found that information asymmetry significantly limits the debt capacity of young firms (Carpenter and Rondi, 2000; Shirai; Bulan and Yan, 2007). In addition, higher 2
-9

<!-- Start of picture text -->
-<br><!-- End of picture text -->

Table 1: Distribution of Bank-Supported Education Sector Investments, by Education Category (2)

||1963-76|1977-86|
|---|---|---|
|General Education|31.4|46.1|
|Primary Education|(5.9)|(21.9)|
|Total VET, Broad Definition|61.8|50.8|
|Secondary|(10.8)|(5.2)|
|Post-secondary|(8.7)|(6.9)|
|Non-formal|(10.9)|(13.5)|
|Teacher Training (VET)|(1.0)|(1.0)|
|Subtotal, Narrow Definition|(31.4)|(26.6)|
|Diversified Secondary|(11.7)|(0.8)|
|University|(7.6)|(17.0)|
|Teacher Training (general)|(11.1)|(6.3)|
|Unallocated|6.7|3.1|
|Total Project Base Costs|100.0|100.0|
|Total Bank Lending (SUS million)|1,580.1|5,868.5|

Source: Schwartz (1988, forthcoming) Patterns for lending in support of VET, in the narrow definition are shown in Table 2. A number of significant shifts may be observed. On a sectoral basis, emphasis has shifted away from education and training for agriculture (from 25.1% to 12.7%) towards industry (from 67.9% to 80.4%). Investments in VET for commerce and services has remained constant at about seven percent. Within the industry sector, moreover, there is a notable shift from secondary institutions towards non-formal, with marginal changes in the proportions of investment targeted towards post-secondary and teacher training.
Policy Research Working Paper 5996

# **Abstract**

International river basins will likely face higher hydrologic variability due to climate change. Increased floods and droughts would have economic and political consequences. Riparians of transboundary basins governed by water treaties could experience noncompliance and inter-state tensions if flow falls below levels presumed in a treaty. Flow information is essential to cope with these challenges through water storage, allocation, and use.

This paper demonstrates a simple yet robust method, which measures gauge station runoff with wetness values derived from satellite data (1988-2010), for expanding sub-basin stream flow information to the entire river basin where natural flow information is limited. It demonstrates the approach with flow level data that provide estimates of monthly runoff in near real time in two international river basins: Zambezi and Mekong. The paper includes an economic framework incorporating information on existing institutions to assess potential economic and political impacts and to inform policy on conflict and cooperation between riparians. The authors conclude that satellite data modeled with gauge station runoff reduce the uncertainty inherent in negotiating an international water agreement under increased hydrological variability, and thus can assist policy makers to devise more efficient institutional apparatus.

This paper is a product of the Computational Tools and Environment and Energy Teams, Development Research Group. 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://econ.worldbank. org. The author may be contacted atbblankespoor@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
Table 5: Marriage penalty by education

|Dependent variable||||Working|||
|---|---|---|---|---|---|---|
|Sample||SAR|BGD|IND|MDV|NPL|
|||(1)|(2)|(3)|(4)|(5)|
||_Panel _|_A: No child _|_sample_||||
|Post-marriage||-0.146***|-0.153***|-0.178***|-0.073|-0.058**|
|||(0.013)|(0.023)|(0.012)|(0.061)|(0.022)|
|Post-marriage _×_ Higher education||0.111***|0.089*|0.086***|-0.104|0.08|
|||(0.029)|(0.047)|(0.026)|(0.100)|(0.055)|
|Post-marriage _×_ Husband higher e|ducation|-0.052|-0.038|0.016|0.15|-0.024|
|||(0.042)|(0.053)|(0.036)|(0.130)|(0.062)|
|No. of obs.||7848|1712|3779|434|1916|
|R-squared||0.552|0.156|0.168|0.178|0.135|
|Birth Cohort FE||Yes|Yes|Yes|Yes|Yes|
|Country X Year FE||Yes|Yes|Yes|Yes|Yes|
||_Panel B: _|_Ignore chil_|_d sample_||||
|Post-marriage||-0.186***|-0.165***|-0.214***|-0.262***|-0.108***|
|||(0.01)|(0.02)|(0.01)|(0.05)|(0.02)|
|Post-marriage _×_ Higher education||0.075***|0.083**|0.071***|0.065|0.124***|
|||(0.02)|(0.04)|(0.02)|(0.09)|(0.05)|
|Post-marriage _×_ Husband higher e|ducation|-0.025|-0.008|0.004|0.134|-0.068|
|||(0.02)|(0.04)|(0.03)|(0.11)|(0.05)|
|No. of obs.||9583|2163|4413|570|2434|
|R-squared||0.608|0.152|0.215|0.2|0.106|
|Birth Cohort FE||Yes|Yes|Yes|Yes|Yes|
|Country X Year FE||Yes|Yes|Yes|Yes|Yes|

Note: Standard errors in parentheses clustered at the cohort level. All estimates weighted by cohort size. SAR - South Asia Region, BGD - Bangladesh, IND - India, MDV - Maldives, NPL - Nepal. No child sample refers matching post-marriage observations in the sample without children. Ignore child sample refers matching post-marriage observations in the full sample. Cohort FE refer to birth cohort. Husband education is the share of women in the pseudo-cohort whose husband has any post-secondary education. Women’s education is an indicator variable for any post-secondary education. *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

27
financial development from WDI. Finally, we use a measure of Rule of Law from The World Bank‘s World Governance Indicators.

We use dynamic panel regressions based on three-year panel data for each country. The basic regression specification is the following: where is the natural logarithm of GDP per capita, is the level of GDP per capita at the beginning of each three year period, is the dynamic service export sophistication measure, is the set of relevant control variables for growth determinants, represents time dummies, stands for country fixed effects and is the error term.

We present the growth regression results in Table 5. In column 5, we add three European groups that we interact with _Service EXPY_ , to see if there is a significantly different impact of service sophistication in EU-15, NMS or ACC compared to the rest of the world. The result is show that in the NMS case, there is a positive significant additional impact on growth. This means that service sophistication has a positive association with growth in all countries but the association – given a certain level of sophistication – is stronger in NMS than in other countries.

**Table 5: Regression results with EU-15, NMS and ACC dummy, Fixed effect 3-year panel of growth in** **<u>per capita GDP, 1990-2007.</u>**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|log lag initial GDP per capita|-0.481***|-0.644***|-0.650***|-0.690***|-0.680***|
|log lag initial Service EXPY|(0.0780)<br>0.315***|(0.0895)<br>0.221***|(0.0995)<br>0.223***|(0.106)<br>0.199**|(0.112)<br>0.207**|
||(0.0553)|(0.0733)|(0.0737)|(0.0793)|(0.0803)|
|years of schooling||0.108***|0.110***|0.0968***|0.112***|
|||(0.0306)|(0.0309)|(0.0344)|(0.0355)|
|log financial develoment|||0.00466|-0.00529|-0.00488|
||||(0.0314)|(0.0333)|(0.0342)|
|external liberlization||||0.00377**<br>(0.00158)|0.00183<br>(0.00170)|
|EU15*Service EXPY|||||-0.318**<br>(0.160)|
|NMS*Service EXPY|||||0.379*<br>(0.212)|
|ACC*Service EXPY|||||-0.0278<br>(0.474)|
|Constant|1.504**|3.008***|3.007***|3.491***|3.374***|
||(0.594)|(0.710)|(0.771)|(0.819)|(0.830)|
|Observations|702|582|579|533|524|
|R-squared|0.079|0.114|0.115|0.130|0.129|
|_Note:_* Significant at 10% leve<br>Number of countries|l ** Significan<br>157|t at 5% level *<br>128|** Significant<br>128|at 1% level. t-st<br>115|atistics are in<br>113|

_Note:_ * Significant at 10% level ** Significant at 5% level *** Significant at 1% level. t-statistics are in parentheses.

17
# **Annex. The Progression of Severity: Full Sample**

To focus on just a few countries, this paper restricted the sample from a total of 160 countries to just 48, which will be discussed below. As shown in Table A1, our dataset includes 160 countries (including 15 that report zero COVID-19 deaths). We focus on countries with over 10 million inhabitants and at least 100 COVID-19 fatalities. The population restriction removes a large number of small HICs, whereas the fatality restriction removes many poorer countries.

Table A1. Sample Selection by Population Size and Minimum COVID-19 Fatalities

|||Number of countries||
|---|---|---|---|
||Total|Population ><br>10 million|+ COVID-19<br>deaths >100|
|Total|160|82|48|
|High income|52|19|19|
|Upper-middle-income|48|48|16|
|Lower-middle-income|36|36|12|
|Low income|24|24|1|

Source: Johns Hopkins University; World Health Organization; World Population Prospects.

Having obtained 48 countries, we then split the sample up into four groups that proxy the level of severity: high, medium, lower and low. The summary metric used to divide the sample is the peak daily severity ratio observed over the course of the pandemic thus far. The peak daily severity ratio not only dimensions the extent of the health emergency of each country; it also helps us organize the sample in buckets with similar ranges for the daily severity ratios. The high group comprises 8 countries with peak severity above 50 percent; medium refers to 11 countries with peak severity between 50 and 10 percent; lower refers to 13 countries with the ratio between 10 and 2.5 percent; and low refers to 16 countries with peak severity below 2.5 percent.

In what follows, we illustrate the progression of the pandemic for the full set of 48 countries. We organize the discussion according to the four groups defined by the peak severity ratio (the high, medium, lower and low cases). This complements the main text, which organizes the illustrations of the 30-country subset by the criterion of the change in the cumulative severity ratio (indicating intensifying or weakening severity).

29