rafmacalaba/datause-encoder-data huggingface dataset:
occupation. The model incorporates the drivers of employment choices that have been emphasized in the macro-labor and structural transformation literature, which include heterogeneity in workers’ ability and hence their comparative advantage, gender-specific barriers, differential returns to ability across occupation-sectors (Hsieh et al., 2019; Cassan et al., 2024), as well as non-homothetic demand, sector-specific technological change, and changing human capital supply (Ngai and Pissarides, 2007; Herrendorf et al., 2014; Boppart, 2014; Ngai and Petrongolo, 2017; Comin et al., 2021; Porzio et al., 2022; Feng et al., 2023).

The paper is organized as follows: Section 2 describes the data and provides new evidence on the link between economic development and gendered labor market outcomes. Section 3 presents the theoretical model and Section 4 describes the identification and model quantification. Sections 5 and 6 present the estimation results and the counterfactual simulations. Section 7 examines mechanisms and tests the robustness of our analysis. Section 8 concludes.

# **2 Empirical Facts**

## **2.1 Data**

**Data Sources and Sample Description.** We use data from the Integrated Public Use Microdata Series (IPUMS International, 2020), which provides harmonized individuallevel data on demographic and employment variables from nationally representative censuses, and household and labor force surveys for many countries and years. We extract employment information by occupation, sector, and gender for 91 countries and 305 country-years. The time coverage ranges from 1960 to 2020 and includes, on average, 3-4 rounds of data for each country. Online Appendix C shows that the data has good coverage over the time period and the entire development spectrum. We complement the IPUMS data with labor force surveys from the World Bank Global Labor Database (GLD) and the World Bank i2d2 database.<sup>6</sup>

– For our quantitative exercise, we use a core sample of six large economies India, – Indonesia, Mexico, Brazil, Canada, and the United States which additionally offer high-quality micro data on hours worked and compensation, enabling us to calculate workers’ hourly wages. In addition, these countries provide data for more than four

> 6A description of the database can be found at https://github.com/worldbank/gld.

7
of registration as other forms may vary considerably across countries, which will make the analysis less easy to interpret.<sup>17</sup> Second, the coverage of proprietorships and other unincorporated firms in Amadeus is poor and uneven: in most European countries only limited liability companies are required to file statements. However, most European countries require all corporations to file financial statements, therefore, the coverage for corporations is extensive and the best available. We use the information on legal form in Amadeus – which is country-specific – to identify public and private limited companies (see Annex 4 for legal definitions, by country).

We exclude from our sample several European countries where the coverage is incomplete or the data quality is poor. In Annex 2, we summarize the cross-country differences in the collection of company accounts in Amadeus. First, we exclude Switzerland, since small firms are not required to file. Second, we exclude the countries of the former Republic of Yugoslavia (Bosnia-Herzegovina, Croatia, Former Yugoslav Republic of Macedonia, and Federal Republic of Yugoslavia), which were at war during our sample period and where data coverage is limited. Third, we exclude Slovakia, Slovenia, Russia and the Ukraine, which have only a very small number of total filings (i.e. less than 1,000 firms annually).

As shown in Table 1, Column 2, after applying these exclusion criteria, we have a smaller, comprehensive sample of incorporated firms in a large number of European countries, which enhances comparability across countries.<sup>18</sup> Our sample now has over 3 million annual firms and 57 million employees.

We are not done yet. We have national statistics from Eurostat (2003) on numbers of, and employment in, firms of different sizes. In Table 2, we compare the ratio of firms and employment in Amadeus and in published national statistics in Eurostat (2003).<sup>19</sup> Columns 1 and 2 show the coverage in Amadeus of large firms (the ratio of firms and employment at firms with

> 17 For example, Fan and White (2003) use data on small businesses in the United States to show that personal liability negatively affects the level of entrepreneurial activity and the decision to incorporate. 18 These restrictions exclude 342,216 firms over 2 years (9.8% of total firms).

> 19 Data, by firm size, is unavailable for non-EU countries. This data will be updated to compare EU corporations when the data becomes available in 2004.

11
45 3. Mismanagement of communal land In the ejldos depresses the average productivity of this sector In relation to private farms.

4. Ejidatarlos face greater difficulties in access to credit than private famers: first, because they cannot use their land as collateral, cutting them off from commercial bank loans; second, because lending by Banrural has to be arranged thorough Ejldo leadership. This Introduces a possibly non-economic element in the docision rogarding credit alocation.

Hypotheses (1)-(4) each suggest that land productivity will be lower on average In the ejido sector than In the private farm sector. There Is one countervailing hypothesis:

- S. Stipulations in the land reform law concerning holding-size limits and land use restrictions are complex and contradictory: this makes it difficult for private farmers to Judge when they are contravening the law and when they are likely to face expropriation; this will reduce the Incentive for large private farmers to Invest In their land, pulling down the average productivity of the private farm sector In relation to the ejido sector.

99. There are no recent studies of the relative productivity of the two tenure catgories: the avallable sources are based either on agricultural census data up to 1970 or pro-1970 case studies. (Appendix A explains why data from the 1981 Agricuitural Census does not lend Itself to productivity comparisons). To assess the findings of the avallablo studles, It Is necessary to understand the policy onvironment that prevalled before 1970.

100. In the immediate aftermath of the dynamic phase of the Mexican land reform (1934-40), Mexico experienced impressive agricultural growth. Many authors
Importantly, we can rule out some potential mechanisms. On the supply side, we can exclude any mechanism that operates through the credit registry, as our findings are based on loan approvals rather than loan issuances, and loan approvals are not recorded in the Peruvian credit registry. On the demand side, we can rule out complementarities in borrowing whereby an initial loan from our partner bank increases demand for credit from other lenders; few of the borrowers in our sample who received loans from competing lenders first borrowed from our partner bank.

Though we cannot pin down a single explanation, we argue that the precise mechanism by which this free riding occurs does not influence our broad conclusions; so long as a loan approval by our partner bank _causes_ (directly or indirectly) other lenders to approve the same borrowers, the private returns of identifying credit worthy borrowers and expanding the financial supply will diverge from the corresponding social returns.

Our paper relates to several literatures. First, we complement the literature examining how variation in the credit supply decisions of one lender influences the credit supply of competitors (Agarwal et al. 2018, Karlan and Zinman 2018, Azevedo et al. 2019, Burke et al. 2019). Through variation in loan terms or access to credit, each of these papers finds evidence that an initial loan from one lender causes other lenders to provide access to credit as well. Our paper is distinct in this literature in that we identify information spillovers that occur even before the initial loan is issued, and are therefore more deleterious to private incentives to expand the credit supply. Also of note, much of this focuses on consumer lending (Burke et al. 2019), or microfinance (Agarwal et al. 2018, Karlan and Zinman, Azevedo et al. 2019), while our focus is on SME lending.

Second, our paper relates to the literature on the consequences of information sharing through credit registries on bank competition and credit supply (eg. Jappelli and Pagano 2002, Djankov et al. 2007, Hertzberg et al. 2011, Liberman 2016, Dobbie et al. 2016, Foley et al. 2018, Sutherland 2018). As noted above, our principal contribution to this literature is to document that information spillovers between lenders can also occur through loan approvals rather than loan issuance. Because loan approvals are not encoded in the Peruvian credit registry, our results imply that information spillovers can occur even before information is encoded in the credit registry, and before the initial lender has derived any benefit from screening new borrowers. And our results imply that information 4
Estimates from CIS low income countries are different from the rest for some variables. For instance, life satisfaction is higher for those who are members of the communist party. A difficulty in answering questions during the interview is associated with lower life satisfaction. Also the coefficients of relative income are higher and significant for CIS low income countries. This indicates that the negative correlation between reference group’s income and the reported life satisfaction is more significant in CIS low income countries compared to other regions. Surprisingly, labor shocks in the most recent period are associated with higher reported life satisfaction.

# **5. Sensitivity analysis**

In this section we reexamine our empirical findings by addressing issues related to specification of the model, and measurement and identification of certain variables. Some studies claim that the effect of relative income differs between rich and poor (Veenhoven, 1991). Empirical outcomes shown in table 4.1 and 4.2 assume that relative income has equal effect on life satisfaction at different income level. We estimate life satisfaction separately for three income groups: rich, middle and poor. We perform Wald statistics to check if there exists any structural change between different income groups. The significant Wald statistic indicates there is structural change between these groups. In table 5.1 we compare coefficients of relative income between income groups. For the average person, income relative to median in the country produces stronger dissatisfaction than income relative to one’s locality (PSU level), excluding the comparison at the supracountry level<sup>10</sup> .

# **[Table 5.1 is about here]**

> 10 Supracountry reference group might have some bias from its definition since inequality across countries in any supracountry group is presumably lower than the inequality within any particular country in that group

18
_Annex 5. Descriptive Statistics of the BOS database Table A.5.1. Aggregate Statistics of Sample by Region_

|**Region**|**No.**<br>**countries**<br>**(70%+**<br>**coverage)**|**Total firms**|**Total**<br>**revenues**<br>**(thousand**<br>**USD)**|**Total**<br>**employment**|**Firms**<br>**directly**<br>**owned**|**Firms with**<br>**minority**<br>**participation**<br>**[10-24%)**|**Firms with**<br>**blocking**<br>**minority**<br>**participation**<br>**[25-50%)**|**Firms with**<br>**majority**<br>**participation**<br>**[50%-100%]**|
|---|---|---|---|---|---|---|---|---|
|East Asia & Pacific|2|2,160|144,800,000|901,251|847|308|530|1,311|
|Europe & Central Asia|15|37,228|925,500,000|6,755,836|25,732|3,191|4,669|26,779|
|Latin America & Caribbean|4|189|33,070,622|162,704|143|4|14|171|
|South Asia|5|504|70,867,819|959,967|303|64|54|384|
|Sub-Saharan Africa|10|515|21,091,076|233,462|347|45|59|400|
|**Total**|**36**|**40,596**|**1,195,329,517**|**9,013,220**|**27,372**|**3,612**|**5,326**|**29,045**|

# _Table A.5.2. Descriptive Statistics of Sample by Region_

|||**Emp**|**loyment**||**Op**|**erating Reve**|**nues (thous**|**and USD)**||**Level of stat**|**e participation**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Region**|**Min**|**Median**|**Std. Dev**|**Max**|**Min**|**Median**|**Std. Dev**|**Max**|**Min**|**Median**|**Std. Dev**|**Max**|
|East Asia & Pacific|1|120|1,969|46,508|0|7,194|369,706|5,618,062|10|50|24|100|
|Europe & Central Asia|1|28|4,516|729,281|0|530|399,939|29,853,835|10|100|31|100|
|Latin America & Caribbean|3|301|1,778|13,037|157|17,546|533,514|4,585,247|12|100|23|100|
|South Asia|3|281|6,438|75,000|0|7,066|545,451|7,098,000|10|99|33|100|
|Sub-Saharan Africa|1|168|1,324|18,031|0|6,492|125,463|923,094|10|100|30|100|

_Table A.5.3. Aggregate Statistics of Sample by Contestability of Sector_

|**Sector type**|**Number**<br>**of firms**|**Operating**<br>**revenues**|**Employment**|**Firms**<br>**directly**<br>**owned by**<br>**the state**|**Firms with**<br>**minority**<br>**participation**<br>**[10-24%)**|**Firms with**<br>**blocking**<br>**minority**<br>**participation**<br>**[25-50%)**|**Firms with**<br>**majority**<br>**participation**<br>**[50%-100%]**|**Subnational**<br>**firms**|
|---|---|---|---|---|---|---|---|---|
|Competitive|27,212|517,500,000|3,856,672|18,068|2,662|3,563|18,796|13,467|
|Natural Monopoly|6,201|146,600,000|1,748,488|5,062|185|437|5,412|4,773|
|Partially Contestable|6,295|527,900,000|3,377,853|3,886|653|1,094|4,395|2,235|

# _Table A.5.4. Descriptive Statistics of Sample by Contestability of Sector_

|||**Em**|**ployment**||**Op**|**erating rev**|**enues (thou**|**sand USD)**||**State par**|**ticipation**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Sector type**|||||||||||||
||**Min**|**Median**|**Std. Dev**|**Max**|**Min**|**Median**|**Std. Dev**|**Max**|**Min**|**Median**|**Std. Dev**|**Max**|
|Competitive|1|28|1,832|238,997|0|534|285,833|22,558,914|10|100|32|100|
|Natural Monopoly|1|35|1,734|78,484|0|816|185,092|5,958,143|10|100|24|100|
|Partially Contestable|1|45|10,454|729,281|0|1,308|802,163|29,853,835|10|100|33|100|

43
percentage points (23.7% and 7.5%) more likely to work in low-skilled occupation and the agricultural sector (Columns (3)–(4)). Finally, worse mental health reduces the number of work hours by 10.1 (30.9%) and life satisfaction by 0.6 points (7.9%) on a 0-to-10 scale (Columns (5)–(6)). This is perhaps unsurprising given that mental health and life satisfaction are closely related (Danzer and Danzer, 2016). Our estimation results are thus consistent with the negative impacts of worse mental health on the probability of being employed and labor income discussed earlier (Table 1).

Once we establish strong evidence of the impacts of mental illness on refugees’ labor market outcomes, we shift our attention to its spill-over effects on their family members. Table 3 shows that higher mental health scores lead to a lower probability of refugee partners being employed, although the coefficient is statistically insignificant (Column (1)). Mental illness also reduces life satisfaction of the partners by 1.0 point (13.4%), although the impact is statistically significant at only 10% level (Column (2)).

Next, we employ the unique data on refugee children in wave 3 and examine two child outcomes, mental health status and school performance. The medical literature suggests that the mental health of refugee parents has an important role in child refugee mental health (Fazel et al., 2012). For example, lack of parental support is shown to be a predictor of PTSD in children. Our study reaffirms this finding by showing that a one standard deviation increase in refugee mental health scores causes a 3.2 unit increase (35.4%) in the mental health score of their children (Column (3)). A one standard deviation increase in parental mental health scores also leads to a 1.6 percentage point (26.2%) increase in children's probability of having school performance below the average (Column (4)). This is consistent with previous findings for the general population showing that mothers’ mental health is a strong predictor of their children's human capital accumulation (Johnston et al., 2013). Overall, these findings suggest that 15
- 28 -

- (vii) Income or expenditure based poverty assessments typically ignore publicly provided goods, as these cannot be easily valued in terms of money. Though there is little hard evidence, there have been numerous casual observations that the mid and late 19808 witnessed a deterioration in the availability and quality of some publicly provided goods for which the poor are amongst the beneficiaries, such as piped water supply, health services, nutrition programs anid schooling. Public services to the poor are clearly relevant to poverty assessments, but they are unlikely to be reflected in the consumption expenditure distributions used in the present study.

- (viii) The SUSENAS estimates household caloric intake by applying fixed caloric food values to observed consumptions of about 170 catagories of food and beverage for each household, based on 7 day recall by the respondent. Caloric intakes are almost certainly underestimated, mainly because consumption of food in street-side stalls and restaurants is underestimated (such expenditures being classified elsewhere in the survey where physical consumptions are not asked; see van de Walle, 1988). This is more likely to bias the urban caloric distributions than those for rural areas. However, there is no obvious reason why the problem should bias our comparisons over time.

# The Price Deflator

An important problem in comparing consumption levels across space and time is that prices are not constant. For Indonesia the interregional
# **V.4 Law, institutions and business environment**

The picture that emerges of the SME sector from our surveys clearly indicates that the sector deals with widespread corruption and has little recourse to the legal system. Informal mechanisms based on trust, reciprocity and reputation play a much more important role than legal remedies in settling disputes and enforcing contracts. As for their dealings with the regulatory authorities, corruption is taken for granted as part of doing business.

Over 80% of the firms we surveyed needed a license to start a business, and for about 47% obtaining the license was a difficult process. In about two-thirds (63%) of the latter cases, the difficulty was caused by government officials. Payment of bribes was the most frequent complaint. When asked how they thought other firms dealt with such problems, 87% of the respondents who answered said bribes were regularly paid. The second most common response (about 23% of the respondents who answered this question) was using friends of government officials to negotiate for them. Clearly, networks and connections are of crucial importance in negotiating the government bureaucracy.

As for conducting day-to-day business, while the firms claimed not to function outside the scope of law, legal concerns are far less important to them than the demands and responsibilities of the informal networks within which they exist and function. Panels A and B of Figure 4 show the mean score (on a scale of 1-3) of the importance attributed to the various consequences of nonpayment of dues and breach of contract respectively. In the first type of violation involving default (Panel A), the primary concern is loss of reputation, followed closely by loss of property. In the second type of violation (breach of contract; Panel B), loss of future business opportunities ranks the highest, followed by loss of reputation. Significantly, in both types of violation, the fear of legal consequences (adverse court sentence or jail term) is the least important concern. Even threat to personal safety ranks higher than legal consequences. Clearly, violation of the “unwritten rules” of 33
containerized freight rates to the increasing input costs, such as fuel, ship prices, and port costs. From the market structure perspective, the industry has significant entry lags due to the time it takes to build a new ship. Bridgman (2021) attributes the lack of a dramatic fall in freight rates at least in part to the strong labor unions in ports. Due to their strong bargaining power, port-related labor costs did not fall much even though containerization resulted in enormous labor productivity gains. Finally, the demand for international trade and transportation services is constantly increasing (see Fig. 1), which may allow maintaining high freight rates in the highly concentrated industry with the legalized collusion.

**Stylized Fact 10.** _Between 1970 and 2004, containerized freight rates did not exhibit a decreasing trend. The non-decreasing trend, despite some technological advances, can be blamed on the increasing input costs, market structure, and constantly increasing demand._

# **3 Transport Costs Measures and Data Sources**

over We consider three measures of transportation costs: (i) approximation by distance, (ii) the c.i.f. f.o.b. ratio, and (iii) the direct (either specific or ad-valorem) measures. This section discusses the advantages and disadvantages of each measure in the context. We also describe available datasets 1. and provide their summary in Table

## **3.1 Transportation Costs Approximated by Distance**

Understanding the size and nature of transport costs requires detailed data on freight rates. These data are sparse. Researchers have widely used physical distance as a proxy for transport cost to overcome the paucity of transport cost data.<sup>14</sup> This tendency in empirical literature has been in part motivated by a robust to many specifications and periods ln( _Distance_ ) coefficient of ( _−_ 1) in the _a_ ggregate trade gravity regressions initially introduced by Tinbergen (1962). In these regressions, 14An example of the widely-used bilateral distances is CEPII trade barriers and geography dataset.

14