rafmacalaba/data-use-mentions-extended huggingface dataset:
informal earnings from self-employment. Regarding the relative riskiness of migration loans versus business startup loans, we compare the fraction of migrants who were expelled from their overseas employment before they generated enough income to cover migration costs to the fraction of microenterprises that exit their markets before generating enough profits to cover initial investment costs. According to the BRMS data, the share of temporary migrants whose total earnings overseas are lower than the total migration costs they incur is 11.9%. In contrast, the estimated share of Bangladeshi microentrepreneurs who do not generate sufficient profits over the lifespan of their enterprise to cover initial investment costs is 60.2%.<sup>11</sup> Based on this

> 11We use the average annual survival rate of microenterprises from McKenzie and Paffhausen (2019), and average net incomes generated by household enterprises in Bangladesh from the HIES 2016/2017 to compute

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institution, or are caring for incapacitated persons. The details of the allocation of these deductions are reported in Annex B.

## **_4.3.2. Property tax_**

**The PECS 2016/17 includes self-reported annual rental property value used to estimate property tax, with simulation including the following steps:**

- (i) Used administrative data to identify the cities from which property tax was collected.

- (ii) Used a city identifier from the location module to identify the core set of municipalities in which property taxes are collected in the West Bank and Gaza.

- (iii) Identified owners within the dataset using self-reported status (given that property tax is paid by owners only).

- (iv) Used a self-reported estimated annual rental value variable to estimate the tax on buildings.

- (v) For tax collection on land, we used administrative data on the valuation of land by applying an average per governorate.<sup>42</sup>

- (vi) Estimated property tax on land is by multiplying the tax rate by the area of land and average land valuation (bifurcated by cities).

- (vii) Scaled-down the survey-estimated property tax by applying a scaling factor such that the effective property tax rate in the survey (that is, the size of the property tax relative to households’ consumption) matches administrative data.

# **4.4. Indirect taxes**

**Indirect taxes in the model include customs duties, VAT and excises, which together represented about 92 percent of total tax collections in the West Bank and Gaza in 2017.** These taxes were simulated by applying statutory tax rates to self-reported household expenditure on each product in the PECS 2016/2017.

## **4.4.1. Customs**

**Simulation of customs duties followed these steps:**

- (i) Mapped customs duty rates to PECS 2016/17 consumption items (average duty for those goods mapped to
then equation (8) implies that the cereal share must change by _β_ 1 _gH_ + _β_ 2 _gH_ ( _gH_ + 2 log( _c_ )) by year _H._ With _β_ 2 = 0, the quadratic Engel curve collapses to the Working-Leser model, and the change in the cereal share is a constant, _β_ 1 _gH_ , across households. In this case, economic growth does not affect the distribution of welfare costs of higher cereal prices; every household experiences the same decline in its cereal share and hence the same fall in welfare costs.

Estimating equation (8) on the NSS61 sample of around 117 thousand households, we obtain _β_<sup>�</sup> 1 = _−_ 0 _._ 351 (0 _._ 012) and _β_<sup>�</sup> 2 = 0 _._ 020 (0 _._ 0009),<sup>33</sup> which allows us to reject WorkingLeser in these data. Figure 12 displays the quadratic Engel curve along with a semiparametric counterpart estimated by partially linear regression. The parametric model does a good job of capturing the flattening of the Engel curve at the upper tail of the per-capita expenditure distribution but overpredicts the extent to which cereal shares fall with income growth for the poorest households. Even so, adjusting the net consumption ratio to reflect the cereal shares that will prevail in 2040 has no appreciable distributional implications. Figure 13 shows the incidence of price effects by percentile in rural and urban areas assuming that _H_ = 35, _gurban_ = 1 _._ 49 _,_ and _grural_ = 1 _._ 17 (see next subsection). In both sectors, the revision of _px/c_ leads to a small upward shift in the incidence curve. The ”true” distribution of welfare costs of the 10 percent rise in cereal prices is likely to lie somewhere in between these pairs of incidence curves. Repeating this exercise using the better-fitting cubic Engel curve (see Figure 12)
70 years in 2009, compared to 73.3 years in the other Central American nations. The under-5 mortality rate is significantly higher than in the rest of Latin America and the Caribbean (39.8 and 22.5 deaths per 1,000 live births, respectively).

Illiteracy is high: 23.7 percent of individuals aged 18 to 49 have no formal education (Living Standards Measurement Survey, 2006). Primary school coverage is virtually universal, though the quality of education is low. Alvarez and Schieflbein (2007) find that teachers use inadequate instruction methods given the cultural barriers and sociodemographic context. Primary school repetition rates are high, especially during the initial grades (for example, they reach 30% in first grade). As a result, Guatemala has one of the 4
the capital city Asunción with population sizes from as low as 717 inhabitants in San Carlos, Concepción, to approximately 243,800 inhabitants in Ciudad del Este, Alto Paraná and 530,5000 in Asunción in 2023<sup>3</sup> (de Catastro, 2022). However, not all 263 districts are sampled for the EPH in every year. Based on all available districts in a given year, we match household survey data from the EPH with weather data. We use data on average and maximum daily temperature from the ERA5 Land Aggregates provided by the European Centre for MediumRange Weather Forecasts (ECMWF) and Copernicus Climate Change Service (Muñoz Sabater, 2019; Service, 2017). ERA5 data are available from 1950 to three months from real-time, and have a spatial resolution of 0.1° (approximately 9km _×_ 9km grid spacing). For precipitation data, we use daily rainfall data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) global rainfall data set, which are available from 1981 and have a spatial

> 2Total income is composed of labor income, rental income, income from interest and dividends, income from divorce benefits or child care, income from family assistance (remittances) from both within Paraguay and abroad, income from pension, income from social assistance programs (e.g., Tekoporã), social pension (the program Adulto Mayor), and other miscellaneous incomes. In 2019, labor incomes made up 85.6% of total incomes, on average (INE, 2020).

> 3The average population size per district in 2003 was 22,334 including Asunción, and 20,293 excluding Asunción.

8
424|1.9|Jakarta-Bandung (22)|
|80th pct|8 (7)|41|5.1|64.42|25.6|55.39|25,382|1.3|Jakarta (15)|
|90th pct|5 (4)|21|4.2|38.16|15.1|36.24|8,110|0.4|Jakarta (11)|
|95thpct|3(3)|13|4.3|26.18|10.4|25.99|2,633|0.1|Jakarta(9)|

**_Notes:_** (Urban) Population data based on the 2014 Indonesian household survey (Survei Sosial Ekonomi, SUSENAS).

# **_Duranton Algorithm_**

As expected, the number of metro areas, the total number of districts that compose those metro areas, the total (urban) population living in metros, as well as the total land area covered by them, all steadily increase as the commuting flow threshold is lowered from 27 percent to 7 percent. At a threshold of 27 percent, we find a single metro area (Bandung) comprised of two districts that has an entirely urban population of just over 3 million and an area of 191 km<sup>2</sup> . A second metro area (Jakarta Selatan), also comprising two districts, then appears at a threshold of 23 percent, more than doubling the overall urban population that lives in metro areas to 6.7 million and the total land area covered by metro areas to 513 km<sup>2</sup> . By the time the threshold reaches 10 percent, which is Duranton’s preferred threshold in his application to Colombia, the number of metro areas has increased to 24, 17 of which are located on Java – Bali. The aggregate population of these metro areas is 64.5 million (
instrument is based on an argument that past exposure to climate-related disasters is correlated with beliefs about climate action by affecting people’s perceptions of the risks of experiencing similar disasters in the future. Having been affected by disasters in the past may make an individual more likely to support policies aimed at limiting the impacts of such disasters, but this increased willingness to pay operates through changed perceived risks of such events happening in the future.

In <u>tables 6</u> and <u>7 we report marginal effects of age in regressions without the perceived risks</u> variable (Column 1), with the “perceived risks for self” variable added (Column 2) and “perceived risks for children” added in column 4, and 2SLS estimates in which the perceived risks for self are instrumented with past exposure to climate disasters (Column 3) and with perceived risks for children instrumented with past exposure in Column 5. In all specifications, the tests reject the null of exogeneity and pass the weak instrument tests.<sup>14</sup> Subject to the validity of the assumptions underlying the instrumentation strategy, the age gradient in beliefs that climate change action should be a government priority remains positive when the model includes perceived future risks to one’s self. The gradient becomes smaller but remains statistically significant when the model includes perceived risks for children. IV estimates do not change the significance of the age gradient in willingness to pay additional taxes or give a part of one’s income to combat climate change when risks to one’s self are instrumented with past exposure to climate disasters. However, the correlation between age and willingness to pay becomes negative when perceived risks to children are instrumented with past exposure.

# _Alternative framing of climate action questions_

So far, the willingness to pay for climate action results were based on a question that implies that the money would go to “combating climate change” without specifying what combating climate change means. The LiTS survey includes three questions on
43

## FOOTNOTES

<!-- Start of picture text --> Jj<br><!-- End of picture text --> <!-- Start of picture text --> y<br><!-- End of picture text --> Applications to international trade and taxation issues are surveyed in Shoven and Whalley (1984). For models with a focus on energy policies see Hudson and Joergenson (1978). An exception is Dixon et al (1982) where a CGE model for Australia is used for analyzing a wide range of macroeconomic and microeconomic issues.

Bobinson (1986) surveys CGE applications for developing countries. In that survey, the emphasis is on macroclosure and applications rather than on international trade. The two surveys are therefore coaplementary.

Devarajan, Lewis and Robinson (1986) and Decaluwe and Martens (1986) provide bibliographic surveys.

...

<!-- Start of picture text --> 3/<br>-<br><!-- End of picture text --> <!-- Start of picture text --> 4/<br>-<br><!-- End of picture text --> A recent example of pass-through, though for a developed economy, is the dollar appreciation during 1981-85. Dornbusch (1987) observes that a 20 percent appreciation in the dollar only resulted in a 5 percent increase in the index for manufactured imports. He goes on to show that this relative price behavior among traded goods is consistent with a number of competing hypotheses about firm behavior and product characteristics including the one presented here. Krugman (1986a) also develops other pricing models that are consistent with this observation.

The presentation here follows de Melo and Robinson (1986).

The CET and CBS formulations in this context were first introduced by Powell and Gruen (1967) and Armington (1969) respectively.

Edwards and Ng (1985) discusses alternative definitions of the real exchange rate and of
Duty drawbacks boosted trade growth in China as they lowered the cost of imported inputs thus increasing export competitiveness. Right after their introduction in 1979, processing trade comprised only 5 percent of Chinese trade flows. By 1988 processing trade more than doubled. Between 1988 and 1991 processed exports produced with inputs purchased from abroad almost tripled, increasing from US$140 million in 1988 to US$324 million in 1991 (Table 1), while total exports rose by 50 percent over the same period (World Bank, 1994). In 2000, processing trade accounted for 50 percent of total trade, with export processing accounting for 55 percent of total exports.<sup>38</sup> Duty drawbacks facilitated China’s integration into the world production sharing system, which in turn speeded up the diversification of Chinese manufactured exports (Lemoine and Ünal-Kesenci, 2004). Textile and garment producers pioneered export processing trade (Naughton, 2000), and despite the restrictive effect of the textile and apparel quotas in North America and Western Europe, made China the largest exporter of textiles and clothing in the world accounting for 8.8 percent of world textile exports and 16.2 percent of world clothing exports in 1999. China later emerged as an important exporter of toys, sports goods, and more recently electronics, telecommunications and electrical equipment (Lardy, 2002).

The boom in the export-oriented sector created millions of jobs.<sup>39</sup> It speeded up the transition of the Chinese economy from an agrarian to an industrial society and shaped China’s industrial composition to reflect the country’s comparative advantage in products that are intensive in its most abundant resource – low skilled labor.

> 38 These statistics are based on data for 2000 from Customs General Administration, People’s Republic of China.

> 39 Source: Ministry of Foreign Trade and Economic Cooperation (MOFTEC), China.

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The effect of these modifications is readily apparent in the results for 2007, but as the emphasis on measuring female labor force participation waned, the measured rate declined in 2008, specifically in rural areas. Nevertheless, the measured FLFP rate remained stable at a relatively elevated level for the next nine years (just under 20%), compared to the period before the methodological innovations were introduced. The overall rate measured by the ELMPS in 2012 was very close to the overall rate measured by the LFS, but, as shown in Figure 1, the ELMPS reported a larger gap between the rates in urban and rural areas than the LFS. The decline in the FLFP rate continued through 2020 in the LFS but was sharper in rural areas. Some recovery in the rate was observed in 2021.<sup>5</sup> There is no clear measurement-related explanation for the sharp decline in FLFP rates in 2018 and 2019, although the decline in 2020 can be clearly attributed to the pandemic (See ILO and ERF 2022).

# **3 Data and methods**

# **_3.1 Surveys_**

This research uses the latest waves of the Labor Market Panel Surveys for Egypt (ELMPS 2018) and Tunisia (TLMPS 2014). These surveys were carried out by the Economic Research Forum (ERF) in collaboration with the respective national statistical offices. Publicly accessible microdata are available through ERF’s Open Access Microdata Initiative (Assaad et al. 2016; Krafft, Assaad, and Rahman 2021; OAMDI 2016; 2019). The surveys are nationally representative after the application of sample weights, which are used throughout. The TLMPS 2014 sampled 16,430 individuals in 4,521 households and the ELMPS 2018 sampled 61,231 individuals in 15,746 households. Our analyses focus on working age individuals aged 15-64.

# **_3.2 Detection of employment and work_**

# _3.2.1 Individual questions_

The data collection instrument for the LMPSs is made up of household and
Niinimaki, P. (2007). Evergreening in banking. Journal of Financial Stability 3 (4), pp. 368–393.

Nunn, N. (2007). Relationship-Specificity, Incomplete contracts, and the pattern of trade. Quarterly Journal of Economics 122(2), 569–600.

Paravisini, D., V. Rappoport, P. Schnabl, and D. Wolfenzon (2015). Dissecting the effect of credit supply on trade: Evidence from matched credit-export data. Review of Economic Studies 82(1), 333–359.

Rajan, R. G. and L. Zingales (1998). Financial dependence and growth. American Economic Review 88(3), 559–586.

Rauch, J. E. (1999). Networks versus markets in international trade. Journal of International Economics 48(1), 7–35.

Verhoogen, E. A. (2008). Trade, quality upgrading, and wage inequality in the Mexican manufacturing sector. Quarterly Journal of Economics 123(2), 489-530.

Volpe Martincus, C. and J. Carballo (2009). Survival of new exporters in developing countries: Does it matter how they diversify? IBD-Working Paper Series, WP-140.

Yibin M., P. Phelp, and J. G. Stotsky (2013). Bond Markets in Africa. IMF Working Paper WP/13/12.

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5. Identify the legal forms of non-corporatized SOEs in the country. To do this, we use the variable “National Legal Form” in the ORBIS interface and identify all entities (and their BvD IDs) that report the legal forms of non-corporatized SOEs for the variable “National Legal Form” and with the support of the country teams we verify whether those are fully aligned to our definition.<sup>36</sup> 6. Use the ORBIS interface to eliminate inactive entities or branches identified in step 5. We refer to the remaining active set of SOEs as Non-corporatized SOEs.

7. Compare the sets of Corporatized SOEs and Non-corporatized SOEs, to see if there is any overlap.

8. For each entity in the Non-corporatized SOE set and not in the Corporatized SOE set, construct the ownership tree, only if this tree exists.<sup>37</sup> Any extra entities (and their BvD IDs) from these additional trees are added to the set of Corporatized SOEs.

9. Search for the SOEs of the SOE Supplementary Database within the Corporatized and Noncorporatized sets of SOEs. We identify those entities that are in the SOE Supplementary Database and not in the Corporatized or Non-corporatized sets of SOEs and classify them into either the Corporatized set or the Non-corporatized set based on the legal form information within the SOE Supplementary Database.

10. Search for each entity identified in step 9 within the ORBIS interface to determine if this entity is present in ORBIS with its ownership information and whether it might have missing or incorrect ownership links that may have resulted in its omission from the Corporatized or Non-corporatized sets.

11. For those entities (and their BvD IDs) found in the ORBIS interface in step 10), we proceed to construct the ownership tree in the same way as in step 2, only if this tree exits. Any extra entities (and their BvD IDs) from these additional trees are added into the set of Corporatized SOEs.

12. Use the ORBIS interface to identify all the firms
survey-year and region fixed effects. Columns 1-3 and 4-6 have different subsamples. The sample considered in columns 1-3 includes all panel individuals (interviewed in one survey and 12 months later) who worked in both periods, were 20-64 years old and were informal-employee at the initial period. The sample considered in columns 4-6, includes all panel individuals (interviewed in one survey and 12 months later) that worked in both periods, were ages 20-64 years, and were self-employee in the initial period. All totals from the household surveys are computed applying sampling weights-expansion factors provided by the institutes of statistics. Robust standard errors are in parentheses: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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|Hu and Schlosser<br>(2015)|Higher and<br>increasing SRB<br>(indicator of<br>higher and<br>increasing<br>prenatal sex<br>selection)|Girls’<br>mortality<br>and<br>nutritional<br>outcomes|Indian census and<br>pooled individual<br>data from NFHS<br>(1992-93, 1998-<br>99, 2005-06)|Quasi-experimental study:<br>Triple difference-in-difference<br>that exploits geographic<br>variation in the prevalence of<br>prenatal sex selection over<br>time, and changes in outcomes<br>for boys v/s girls across states<br>over time|No reduction in girls’<br>mortality or reduction in<br>son preference in states<br>with increasing prenatal<br>sex selection|Reduction in<br>surviving girls’<br>malnutrition<br>(underweight and<br>wasting). Prenatal<br>sex selection does<br>not lead to a<br>selection of girls<br>into households<br>with more<br>resources, but<br>girls are more<br>likely to be born<br>into families with<br>weaker son<br>preference. Also,<br>rising SRB leads<br>to larger reduction<br>in family size and<br>increase in<br>breastfeeding<br>duration for girls|
|---|---|---|---|---|---|---|
|Kalsi (2015)|Access to sex<br>selective<br>abortion in 1985|Girls’<br>university<br>attendance|Individual level<br>data from 1991-<br>2010 Taiwan<br>Family<
new listings) after which is started to decline reflecting overall declines in the market valuations. The value traded also reached a peak in 1999 and declined sharply in 2000 and 2001 (declined by about 50 percent in each year).<sup>11</sup> The

> 9 The indicator takes value of one if one of the following rules are satisfied: (a) the price is the same for at least two years in a row, (b) the volume traded is the same for at least two years in a row, (c) the price or volume is a round number such as 100 or 1000, (d) the firm is not traded in the previous year and in the following year (this rule isolates a few companies with occasional trades that occur once every several years and are most likely done for “cosmetic” purposes). Of course, this indicator is not perfect and some measurement error is expected as these rules cannot pick all the “cosmetic” trades or, alternatively, could misclassify some “real” trades as “cosmetic” trades but this error is thought to be small.

10 We only perform this exercise for the firms for which we have both financial and market data to make sure we are not misclassifying any firms that were not listed as “not-traded.”

> 11 Note that these numbers do not match numbers reported by CMA—the order of magnitude is right but there is about a 40 percent difference for 2000 (the CMA numbers are lower) and about a 40 percent difference for (continued)

12