rafmacalaba/datause-encoder-data huggingface dataset:
work on trade in value added makes clear that a more substantial proportion is traded indirectly, i.e. is embodied in traded goods. From a development policy point of view, there is considerable scope for low- and middle-income countries to expand their exports and imports of services with corresponding benefits for consumers and producers. However, there is as yet no cross-country quantitative tool for making an assessment of the sectors in which increased trade is likely to take place, with which partners, and the position of one country relative to others from a competitiveness standpoint.

The present paper makes a first attempt at filling that gap in the literature. Due to data constraints, it focuses exclusively on pure cross-border trade (GATS Mode 1), and to some extent movement of the consumer (Mode 2) through travel services. The approach could in principle be extended to other modes of supply as better data become available for a wider range of countries. The core idea of the paper is that tradability in the modern sense—the likelihood (probability) of trade taking place—is determined by the interplay of three factors—trade costs, technology, and comparative advantage (including in institutions)—as well as exporter-, importer-, and sector-specific factors. Van der Marel (2011) shows that the theoretical model due to Chor (2010), which includes these sets of variables, can be expanded to deal with the case of cross-border trade in services. Specifically, that model makes it possible to derive a gravity-like equation in which trade flows between country pairs are determined by trade costs, as in a standard gravity model, along with technology (Ricardian productivity differences), and factor endowments including institutions. The other specific factors are included as fixed effects. Following the exploratory analysis of Chor (2010) for goods trade, we convert services trade data to a binary dependent variable and then estimate via Logit. The estimated probabilities provide us with four outputs. The first of them is an estimated probability of observing trade for each exporter-importersector triad. That number is an index bounded by zero and unity, which indicates how likely it is that a given country exports to a given partner country in a particular sector—the tradability of that service for those countries. In addition, the model produces three other outputs: average tradability indices based on the simple average estimated probabilities for each exporter, each importer, and each sector. The average indices provide a starting point for interpreting our results across countries and sectors, and the 3
before planting and after harvesting seasons. The seasonality of nonfarm activities shows the close linkages to agriculture: households can substitute part of their income during the low season, when alternative job opportunities outside agriculture are limited.<sup>11</sup>

# **4. Small enterprise dynamics**

# **Entry into the nonfarm sector**

The nonfarm enterprise sector in rural Tanzania appears to be less dynamic than in comparable countries. The annual rate of new start-ups was about 11% in 2005,<sup>12</sup> a rate higher than the 6 to 7% rate often reported for industrialized countries, but substantially lower than the approximate 20% reported for urban and rural enterprises in other Sub-Saharan African countries (Liedholm and Mead, 1999).<sup>13</sup> Tanzanian start-up rates are even lower than in Ethiopia (with an estimated 17%) where rural enterprise activity is low and highly depend on agriculture (Loening et al., 2008). The comparatively low rate could be a result of high investment constraints, or possibly due to weaker entrepreneurship in Tanzania than in other countries. The majority of new enterprises are small firms - more than 60% are created as one-person establishments, mostly in the informal sector (Table 2). Formal enterprises are more likely to start as relatively big enterprise. A sectoral breakdown reveals that in the construction, manufacturing, and agro-processing sectors comparatively more enterprises are created in the category of having five or more workers.

> 11 Seasonality can affect enterprise performance in following ways: First, with worker participation in both agriculture and the nonfarm economy, firms experience an ebb and flow of workers that hampers continuity and ability to upgrading skills. Second, seasonal demand fluctuations can also drive entrepreneurs into informality. Third, seasonality often implies an additional need for short-term capital (demand peaks) which cannot be met.

> 12 This number is likely to provide a lower bound estimate because the estimate does not include firms that opened and closed during the survey period. The calculations are based on cross-sectional data and follow the methodology advocated by Liedholm and Mead (1999).

> 13 Surveys were undertaken for Botswana, Kenya, Malawi Swaziland, Zimbabwe, Lesotho, Niger, Nigeria, and South Africa, covering more than 50,000 rural enterprises (Liedholm and Mead, 1999).

12
As to the main part of paid-in capital, we take the firms with the main part of paid-in capital from Hong Kong SAR, China; Macao SAR, China; and Taiwan, China as the reference group, because their share in the total remained relatively stable in 2001-2005. The results show that firms with state capital, collective capital or corporate capital are more volatile, and their positive effect on productivity volatility is enhanced from 2001 to 2005, especially for corporate capital (Table 4 and Figure 4). On the contrary, foreign capital plays a role in reducing productivity volatility.

**Figure 4 – Effect of main part of paid-in capital on productivity volatility**

<!-- Start of picture text --> 2.789<br>2.574<br>2.352<br>1.879<br>1.631<br>1.159<br>0.167<br>-0.323<br>-2.060<br>-3.555<br>2001 2005<br>State capital Collective capital Corporate capital<br>Private capital Foreign capital<br>Référence : Hong Kong, Macao & Taiwan capital<br>4<br>2<br>0<br>Effect<br>-2<br>-4<br><!-- End of picture text --> The coefficients of long-term investment and export are significant in a positive way for the 2001 sample, but not significant for the 2005 sample. The ratio of new product to output value has a negative effect on productivity volatility, and this effect became much stronger from 2001 to 2005. The ratio of labor unemployment insurance to total output value is used as a proxy of the insurance that workers could have during job transitions. Its coefficient is significantly negative 16
# **1. Introduction**

The ready‐made garment in sector in Bangladesh showcases how trade has increased female labor participation over the past 30 years. The sector has expanded on average by 17 percent per year since 1980 and represents over three‐quarters of Bangladesh's export earnings. The sector also offered formal job opportunities to women and now employs 4 million workers, an estimated 90 percent of them women, representing a 100‐fold increase in workers within just three decades (Heath and Mobarak 2015). Similarly, recent evidence from Indonesia suggests that women benefit from trade liberalization. Input tariff reduction has been linked to higher female labor participation through a relative expansion of female‐intensive sectors in a region (Kis‐Katos et al. 2018).

Can the positive link between trade and female labor participation in Bangladesh and Indonesia be confirmed for other developing and emerging countries in general? Using a cross‐section of more than 29,000 manufacturing firms in 64 countries from the World Bank’s Enterprise Surveys, this paper assesses whether trading firms have a female labor share premium relative to non‐trading firms. It focuses on four types of trading firms: exporters, importers, global value chain (GVC) participants and foreign firms. We find a female labor share premium for all four types of trading firms, controlling for firm output, capital intensity, total factor productivity and fixed effects. The results also hold controlling for differences in relative wages between men and women, and excluding traditional export sectors (apparel and electronics) from the sample.

Interestingly, the female labor share premium is much higher for production workers compared to non‐production workers (i.e. managers, sales, administration), implying that women in our country sample specialize in low‐skill production. In line with these findings, the female labor share premium for exporters and GVC participants is highest in low‐tech sectors, suggesting the largest benefits for women in low‐skill intensive sectors. The paper also analyzes the role of female firm ownership and female top management for the relationship between trading and the female labor share and finds that both expand the female labor share premium of trading firms. Finally, additional analysis explores the link between a firm’s female labor share and average wage rates and in particular the mediating role of the firm’s trading status. The results suggest that while average wage rates are lower for firms with higher female labor shares, this negative correlation is smaller for trading firms.

1
# (7.1) The Pattern of Crowding Out: Gender Bias in the Labor Market or Social Norm?

The evidence that the gender-based crowding out happened across the board suggests that it is primarily due to factors that are gender specic, and unrelated to the socioeconomic background of a child. There are two potential explanations. The rst hypothesis is that in the 1970s and early 1980s, most of the parents considered primary completion as a social norm for girls, and paid little attention to their progression beyond this level. The second explanation is based on possible gender dierences in the labor market. If there were limited labor market opportunities and the returns to education were low for girls with secondary or more schooling in the 1980s, then we would expect the advent of Inpres schools to increase primary completion rate for girls, but still many of them would not go on to nish higher secondary schooling. There is substantial evidence that rejects this labor market based explanation. There was a considerable expansion of jobs for educated girls, especially in civil service; the proportion of women civil servants increased from 18% in 1974 to 27% in 1984 (see Calkins and Sengupta (1992)). More importantly, returns to education estimates for the relevant cohorts suggest higher returns for women at post primary levels in Indonesia (Deolalikar (1993), Behrman and Deolalikar (1995)).

The above discussion leaves us with social norm as a plausible explanation for the genderbased crowding out irrespective of family background. A credible way to test this explanation is to check if the gender-based crowding out is dierent between patrilineal and matrilineal tribes. If patrilineal social norm against women's higher education and participation in the formal labor market are driving the results we found in Table 2 earlier, then we should not observe any gender penalty against girls at the secondary level in a matrilineal society; if anything, we expect crowding out of sons in this case.<sup>45</sup> Indonesia is an excellent case study to test this hypothesis, as its West Sumatra province is the Matrilineal island" where the largest matrilineal tribe in the world Minangkabau resides.<sup>46</sup> We report the estimated impacts

> 45As noted in the introduction, we are concerned with gender norms for the 1950s- 1960s birth cohorts in Indonesia. A substantial anthropological literature suggests that there was signicant gender bias against girls in education in older cohorts (see the survey by Dube (1997)). However, by the 1990s birth cohort, gender dierences were no longer signicant (Levine and Kevane (2003), Afkar et al. (2020)).

> 46A large literature suggests that, in a patrilineal and patrilocal kinship system, a major reason for underinvestment in girls' education is that they leave the natal family to join their husbands family after marriage (Dube (1997), Dreze et al. (1999), Behrman et al. (1999)). Minangkabau has a matrilocal kinship norm where

26
- FSD-Kenya (2009). _Definition of a Standard Measure for Consumer Interest Rates in Kenya: A Scoping Study_ , Nairobi: Kenya. [http://www.fsdkenya.org]

- Gokhale, K. (2009). “A Global Surge in Tiny Loans Spurs Credit Bubble in a Slum.” _The Wall Street Journal_ , August 13 _._ [http://online.wsj.com/article/SB125012112518027581.html]

- Kendall, J., N. Mylenko, and A. Ponce (2010). “Measuring Financial Access Around the World,” World Bank Policy Research Working Paper No. 5253.

- Lusardi, A., and P. Tufano (2009). “Debt Literacy, Financial Experiences and Overindebtedness,” NBER Working Paper No. 14808.

- Miller, M., N. Godfrey, B. Levesque, and E. Stark (2009). “The Case for Financial Literacy in Developing Countries: Promoting Access to Finance by Empowering Consumers,” World Bank, DFID, OECD, and CGAP joint note, Washington, DC: World Bank (accessible at http://www.oecd.org/dataoecd/35/32/43245359.pdf).

- Office of Fair Trading (2009). “Consumer Credit Interventions Evaluation,” OFT 1091, Office of Fair Trading, London.

- Peterson, C. L. (2003). “Truth, Understanding, and High-Cost Consumer Credit: The Historical Context of the Truth in Lending Act,” _Florida Law Review_ , 55: 807–903.

- Porteous, D. (2009) “Consumer Protection in Credit Markets,” Policy Focus Note: 1, Financial Access Initiative. [http://www.financialaccess.org]

- Porteous, D. and B. Helms (2005). “Protecting Microfinance Borrowers,” Focus Note No. 27, CGAP.

- Reille, X. (2009). “The Rise, Fall, and the Recovery of the Microfinance Sector in Morocco,” Brief, Washington, DC: CGAP. [http://www.cgap.org/p/site/c/ template.rc/1.1.41164/].

- Reuters (2009). “Balkan Loan Guarantors Struggle to Pay Others’ Debt,” _Reuters_ , August 16. [http://www.reuters.com/article/ idUSTRE57G0H720090817]

- Rutledge, S.L. (2010). “Consumer Protection and Financial Literacy: Lessons from Nine Country Studies,” Working Paper, Europe and Central Asia Region, Financial and Private Sector Department, the World Bank.

- U.S. G.A.O. (2005). “Credit Reporting Literacy: Consumers Understood the Basics but Could Benefit from Targeted Educational Efforts,” United States Government Accountability Office, Report to Congressional Committees, GAO-05-223, Washington, DC.

- U.S. G.A.O. (2006). “Credit Cards: Increased Complexity in Rates and Fees Heightens Need for More Effective Disclosures to Consumers,” United States Government Accountability Office, Report to the Ranking Minority Member, Permanent Subcommittee on

19
We applied the two methods(covariance<sup>andlinearsegments)onthreedatasets.</sup> The first is the Guinea 1994<sup>IntegratedHousehold Surveycovering4,416 representative</sup> households. As expected,<sup>theclaim thatboth methodswouldproducealmostthesame</sup> results in this case is confirmed<sup>by theresults presentedin table 2.2.Exceptfor the case</sup> when the aversion parameter<sup>isequalto1,theestimatespresentedintable2.2are</sup> identical up to the third decimal.

Table 2.2. Alternative Estimates of<sup>the ExtendedGini Coefficient</sup>

|Aversion Parameter <br>|Covariance<sup>Method </sup><br>|<sup>LinearSegments</sup><br>|
|---|---|---|
|1.0<br>|-4.58E-29<br>|6.47E-06<br>|
|1.5<br>|0.283868<br>|0.283881<br>|
|2.0<br>|0.403372<br>|0.403385<br>|
|2.5<br>|0.472096<br>|0.472112<br>|
|3.0<br>|0.517874<br>|0.517894<br>|
|3.5<br>|0.551041<br>|0.551064<br>|
|4.0<br>|0.576415<br>|0.576442<br>|
|4.5<br>|0.596588<br>|0.596618<br>|
|5.0<br>|0.613091<br>|0.613124<br>|
|_5.5_<br>|0.626898<br>|0.626934<br>|
|6.0|0.638656|0.638696|

Data Source: Guinea 1994 Integrated<sup>Household Survey</sup> In order to test the performance<sup>ofthetwomethodswhenthenumberof</sup> observations is less that 20,<sup>we chosetheextremecaseof a populationof twoindividuals</sup> with the following levels of<sup>living(25,75).The resultsarepresentedin table2.3.Both</sup> methods produced very different<sup>results. However,contrary to the conclusion reached by</sup> Chotikapanich and Giffiths<sup>(2001),thecovariancemethod outperformedthelinear</sup> segment approach. We base<sup>thisobservationonthefollowing considerations. Inthis</sup> simple case, it can be shown<sup>eitherbygeometryorbyintegration(Essama-Nssah</sup> 2000:54) that the ordinary<sup>Gini coefficientisequalto onehalf minus theproportionof</sup> the welfare enjoyed by the<sup>worseoff of both individuals.For the distributionat hand,we</sup> therefore know that the Gini<sup>coefficientis equalto0.25.</sup> The covariance method produces exactly this result<sup>whilethelinearsegment approachyieldsa much highervalue</sup> of Gini: 0.62. We further<sup>testedboth methodson the followingtextbook8exampleof a</sup> distribution of income in a population<sup>of four individuals(10,20, 30,40), again thelinear</sup> segrnent method produced<sup>muchhighervalues comparedto thecovariancemethod.The</sup> covariance method produced<sup>aGiniof 0.25whilethelinear segmentmethodgives an</sup> estimate of 0.425.

Finally, it is important to note<sup>that, when theaversionparameterisequalto one,</sup> we expect the Gini coefficient<sup>tobezero,thecovariancemethodproduces thisresult,</sup> while the linear segment method<sup>tends toyield values thataresignificantly differentfrom</sup> zero. These results cast<sup>somedoubtontheconclusionbyChotikapanich andGiffiths</sup> 8 Lambert 1993b:260
<!-- Start of picture text --> Polbvy, Re"veh, anid Extert Maim,<br><!-- End of picture text -->

# WPS 599

- This paper a product of the Population, IJcalth, and Nutrition Division, Population and Human Resources Departmentis part of a larger effort in PRE to update denaographic estimates on an annual basis. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Otilia Nadora, room S6-065, extension 31091 (202 pages with graphs and tables).

Almost half the world's population lives in Asia. This proportion is projected to decline to 40 percent by the end of the next century, mainly because of slowing growth in China. Other countries will continue to grow rapidly, and India, which adds more people every year than any other country, is projected to surpass China in total population.

Recent contraceptive prevalence surveys in several countries in the region show increasing proportions of couples using birth control.

Fertility in these countries, mostly in Southeast Asia, has consequently declined rapidly.

Population growth rates started to drop in many countries in the region in the past decade, but the momentum built into the age structures of the populations wiU ensure continued population growth for many decades. Other countries in the region are lagging in fertility decline, and their populations will continue to grow at high rates. Infant and child mortality are lowest in countries where fertility has declined to low levels.

The PRE Working Paper Scries disseminates the findings of work under way in the Bank's Policy, Research, and Extcmal Affairs Complcx. An objective of the scrics is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the PRE Dissemination Ccnter
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**Table 3: Poland: General Government Expenditure 2014**

|Spending Component|Millions of<br>PLN|% of<br>GDP|Included in<br>analysis|<br>Amounts in<br>survey|Ratio of total<br>amounts in the<br>survey and external<br>statistics,%|
|---|---|---|---|---|---|
|Total expenditures|724,465|42.1||367,516|51%|
|Social Spending|447,164|26.0||367,516|82%|
|_Social Protection_|276,949|16.1||207,823|75%|
|Contributory social insurance benefits|216,511|12.6||192,439|89%|
|Old age|148,859|8.7|||0%|
|Gross retirement pension|133,049|7.7|Yes|145,682|109%|
|Gross pre-retirement pension|2,370|0.1|Yes|2,203|93%|
|Structural (farmer) pension|13,440|0.8|Yes|1,544|11%|
|Other pensions|67,652|3.9|||0%|
|Gross family pension|29,668|1.7|Yes|16,834|57%|
|Gross disability pension|20,277|1.2|Yes|15,581|77%|
|Gross maternity benefit|6,736|0.4|Yes|6,975|104%|
|Gross unemployment benefit|9,692|0.6|Yes|3,620|37%|
|Gross rehabilitation benefits|1,279|0.1|Yes|605|47%|
|Non-contributory (social assistance) benefits|14,656|0.9||15,384|105%|
|Social assistance|1,859|0.1|Yes|2,590|139%|
|Social pension|2,267|0.1|Yes|5,154|227%|
|Family benefits|5,152|0.3|Yes|3,517|68%|
|Child birth grant in monthly terms|279|0.0|Yes|4|1%|
|Nursing Benefit (zasiłek)|1,702|0.1|Yes|1,115|66%|
|Nursing Allowance (świadczenie)|929|0.1|Yes|2,010|216%|
|Alimony fund maintenance income|1,497|0.1|Yes|||
|Housing benefits|971|0.1|Yes|995|102%|
|Other|45,781|2.7|No|||
|_In-kind transfers_|170,216<br>|9.9||159,694|94%|
|_Education_|90,482|5.3|Yes|96,495|107%|
|Kindergarten subsidy|||Yes|6,772||
|Primary school subsidy|||Yes|32,879||
|Primary school (disability) subsidy|||Yes|4,011||
|Gymnasium|||Yes|16,778||
|Gymnasion (disability)|61,243|3.6|Yes|1,361||
|High school|||Yes|5,354||
|High school (disability)|||Yes|621||
|Vocational school|||Yes|7,629||
|Vocational school (disability)|||Yes|1,688||
|Tertiary schools|14,477|0.8|Yes|19,403|134%|
|Other education|14,761|0.9|No|||
|_Healthcare_|79,734|4.6|Yes|63,199|79%|
|Health insurance fund|61,653|3.6|No|||
|Other health|18,081|1.1||||
|_Other social spending_||||||
|Other expenditure(non-social)|277,301|16.1||||

Sources: Eurostat, GUS, and own estimates based on HBS 2014.

9