rafmacalaba/datause-encoder-data huggingface dataset:
Gasparini, L., and Marchionni, M. (editors). (2015). Bridging gender gaps? The rise and deceleration of female labor force participation in Latin America. CEDLAS Center for Distributive, Labor and Social Studies, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, First edition, La Plata. PDF eBook ISBN: 978-950-34-1287-9.

Goldin, C. (1988). Marriage Bars: Discrimination against Married Women Workers, 1920's to 1950's, NBER, Working Paper No. 2747.

Goldin, C. (2006). The quiet revolution that transformed women's employment, education,and family. _The American Economic Review_ , Vol. 96, No. 2, pp. 1-21.

Goldin, C. (2014). “A Grand Gender Convergence: Its Last Chapter.” _The American Economic Review_ , Vol. 104, No. 4, pp. 1091-1119.

Goldin, C. (2020). Journey across a Century of Women. _The 2020 Martin S. Feldstein Lecture_ . _NBER Reporter,_ No. 3.

Goldin, C. (2021). _Career and Family: Women’s Century-Long Journey Towards Equity_ . Princeton: Princeton University Press.

Goldin, C. and Mitchell, J. (2017). The New Life Cycle of Women’s Employment: Disappearing Humps, Sagging Middles, Expanding Tops. _Journal of Economic Perspectives_ , Vol. 31, No. 1, pp. 161-182.

Gonzales, C.; Chandra, S. J.; Kochhar, K., and Newiak, M. (2015). “Fair Play: More Equal Laws Boost Female Labor Force Participation”, _IMF Staff Discussion Notes_ 2015/002, International Monetary Fund.

Gonzalez, N.I., and Daza, N.A. (2015). Determinantes y perfiles de la participación laboral en Colombia en el periodo 2002-2013. _Revista de Economía del Rosario_ , Vol. 18, No. 1, January-June, pp. 5-59.

Gronau R. (1973) The intrafamily allocation of time: The value of the housewives' time. _The American Economic Review,_ Vol. 63, No. 4, pp. 634-651.

Hastie, T., Tibshirani, R., and Friedman, J. (2001). _The Elements of Statistical Learning_ , New York: Springer Verlag, Inc.

Iregui-Bohórquez, A. M., Melo-Becerra, L. A., Ramírez-Giraldo, M. T., and Tribín-Uribe, A. M. (2021). _El camino hacia la igualdad de género en Colombia: Todavía hay mucho por hacer_ . Banco de la República, Bogotá.

31
**Table 5.8 Outstanding equity issues by infrastructure providers in focus countries, except South Africa**

As of end-2006 except as noted

|Domicile of issuer|Tally of<br>issues (by<br>domicile<br>country)|Securities exchange(s)|Market cap of<br>issues (US$ millions)|% share of all equity<br>issues in issuer’s<br>domicile country /b|
|---|---|---|---|---|
|_Transport infrastructure construction_|||||
|Côte d’Ivoire|1|BRVM|2|0.1|
|Nigeria|4|Nigeria SE|62|0.3|
|Sudan|1|Khartoum SE|0.01|0.0002|
|_Transport services_|||||
|Côte d’Ivoire|2|BRVM|67|5|
|Nigeria|3|Nigeria SE|26|0.1|
|Tanzania /a|1|Dar Es Salaam SE|20|0.8|
|Kenya /a|3|Nairobi SE, USE, DSE|2,173|64|
|Total transport|||2,349|4|
|_Telecoms service providers_|||||
|Nigeria|1|Nigeria SE|2|0.01|
|Senegal|1|BRVM|1,827|100|
|Sudan|1|Khartoum SE|2,302|50|
|Total telecoms|||4,131|6|
|_Electricity generation/power_|||||
|Côte d’Ivoire|1|BRVM|53|4|
|Kenya /a|4|Nairobi SE|1,235|11|
|Nigeria|1|Nigeria SE|15|0.06|
|Total electricity generation/power|||1,303|2|
|_Water & sanitation_|||||
|Côte d’Ivoire|1.0|BRVM|11|0.9|
|Nigeria|1.0|Nigeria SE|2|0.01|
|Total water and sanitation|||13|0.02|
|Total equity issues by infrastructure providers|||7,796.19|12|

Source: National and regional securities exchanges. Note: BRVM = Bourse régionale des valeurs mobiliéres; SE = stock exchange; DSE = Dar Es Salaam Stock Exchange; USE = Uganda Stock Exchange.

a. For issues on Tanzania’s Dar Es Salaam Stock Exchange, the reference date is end-March 2007; for listings on the Nigerian Stock Exchange, the date is end-June 2006. b. For individual issues, the percentage share of all listed issues in all sectors on the relevant national exchange, with the exception of BRVMlisted stocks; for BRVM-listed stocks, the percentage share of all listed issues in all sectors for the country of domicile. For sector totals, the percentage share is given as a share of all listed corporate equity issues in focus countries, except South Africa, on June 30, 2006.

The majority of cross-border listings by African-domiciled firms have been carried out on the Johannesburg Stock Exchange and, to a lesser extent, the Namibian Stock Exchange (which has trading technology links and other ties with the Johannesburg Stock Exchange). These two exchanges had 22 and 58
and _access to credit_ are somewhat different while the use of MCA to estimate _housing_ , _assets_ and _basic utilities_ indices introduces another non-comparability issue.

Although multidimensional poverty measurement is criticized for its weak theoretical framework and inherent aggregation problems, this approach seems to be increasingly useful and even essential in poverty assessment, including gender analysis. Poverty measures based on income or consumption remain the most appropriate, but they are insufficient to capture the multidimensional aspects of poverty, especially in poor countries. Therefore, it becomes wise to strengthen the theoretical and empirical bases of the use of such a multidimensional approach. The measure suggested by Alkire and Foster (2007, 2011) is an interesting one because of its simplicity and compliance with several desirable properties. For the choice of dimensions and their weights, it may be appropriate to adopt a consensual approach involving worldwide stakeholders. Already, the MDGs provide a good starting point for such consensus.

32
|**_global outputmt_**|**0.941*****|**0.755*****|(0.877)<br>**0.885*****|(0.886)<br>**0.662*****|
|---|---|---|---|---|
||(0.155)|(0.162)|(0.167)|(0.171)|
|**Constant**|**0.818*****|**0.672*****|**0.862*****|**1.032*****|
||(0.034)|(0.017)|(0.081)|(0.095)|
|Observations|450,629|450,629|450,629|450,629|
|R-squared|0.429|0.429|0.429|0.429|
|Exporter-Time FE|Yes|Yes|Yes|Yes|
|Importer-Time FE|Yes|Yes|Yes|Yes|
|Exporter-Partner-Sector|Yes|Yes|Yes|Yes|
|Sector-Time FE|Yes|Yes|Yes|Yes|
|Cluster|Exporter-Partner|Exporter-Partner|Exporter-Partner|Exporter-Partner|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Note: Sector-time fixed effects control for unobserved effects affecting aggregated sectors over time (see footnote 10). The variable _global outputmt_ additionally controls for global sector-time trends.

The availability of production data for years before 2020 also allows us to replicate the analysis for February-June 2019, to assess if our findings are driven by the Covid-19 pandemic. The results, reported in Appendix Table 7, show that sectoral differences related to the possibility of working remotely or the level of dependence on domestically or foreign sourced imported inputs for production did not significantly influence the relationship between the growth of industrial production and bilateral trade in 2019. Similarly, a higher share of durable goods in a sector in partner countries did not significantly influence the link between the growth of industrial production and bilateral trade in the previous year. A significant positive coefficient capturing the shock on demand of final goods relative to intermediates suggests that GVCs are more resilient to negative demand shocks in years when there was no pandemic. The positive and significant coefficient of the competition effect also suggests that during non-pandemic times, the pro-competitive effect of production and trade matters. These results confirm that the differential impact of the supply and demand shocks across sectors is specific feature of the Covid-19 pandemic.

# **_Focusing on the periods of lockdowns and progressive opening_**

Figure 1 shows the contrast in the behavior of mobility and trade during the period February to April, when lockdown policies were imposed, and for the period April to June when lockdown policies were progressively reversed. The next robustness test examines these two periods separately (Tables 4 and 5). Both tables report the results using the specification that is comparable to Table 2 which covers the full period. Regression results show that, while the direction and significance of the previous findings hold, several variables changed their magnitude. Between February and April 2020, the possibility of remote work mitigated the negative trade effect of a supply shock in exporting countries more strongly, as shown 21
Dinkelman and Mariotti 2016, Kusunose and Rignall 2018). Others make use of a variety of nonexperimental methodologies, including controls for observables (Adams (1998)), selection correction methods (Barham and Boucher (1998)), matching (McKenzie et al 2010), instrumental variables (Brown and Leeves (2007), McKenzie and Rapoport (2007), Yang (2008), Macours and Vakis (2010)), panel data techniques (Beegle, De Weerdt, and Dercon (2011)), and natural policy experiments such as discontinuities in eligibility for migration (Clemens and Tiongson, 2017) and migration lotteries (Clemens 2013, Gibson, McKenzie, and Stillman 2011, Mergo 2016). Our study is among the very few that overcomes the selection problem by exploiting a randomized lottery program. Even among these few, our paper is the only one that estimates the impact of migration in the context of low-skilled temporary migration; others focus on permanent migration to a developed country often among highskilled workers.

Some studies use designed or natural policy experiments to study specific aspects of the migration experience, such as remittance cost (Aycinena et al. 2010), or employer monopsony power (Naidu et al. 2014). There is also a large literature on the broader socio-demographic effects of migration on children, health and family (Alcarez et al. (2012), Edwards and Ureta 2003; Yang 2008, Giannelli and Mangiavacchi 2010), which is well summarized by Antman (2012). Our results contribute to this literature as well.

The remainder of the paper is structured as follows: Section 2 describes the context of international migration in Bangladesh and the G2G migration lottery program; Section 3 describes the data collection and sampling strategy employed in this paper and outlines the empirical strategy; Section 4 discusses the impact of the lottery and migration on a host of outcomes; and Section 5 concludes.

# 2. Context

Bangladesh has a large outflow of workers temporarily migrating abroad for low-skilled work. Data from the Bureau of Manpower, Employment, and Training (BMET) show that the annual outflow of low-skilled workers from Bangladesh has increased from about 0.2 million workers in 2000 to well over 0.5 million in recent years ( **Figure 1** ). The outflow increased to a record high of 1 million workers in 2017, with over half of them going to Saudi Arabia. Historically, Saudi Arabia (30 percent), the United Arab Emirates (19 percent), Oman (12 percent), Malaysia (9 percent), Qatar (6 percent), and Singapore (6 percent) have been the major destination countries for low-skilled Bangladeshi workers.

6
# Table 8b: District-industry-year regressions for log share of financial loans for young <u>plants</u>

|||<br>Pa|nel estimation|<br>with district-indu|<br>stry,state-year|and industry-yea|<br>r FE||
|---|---|---|---|---|---|---|---|---|
|||Using district||Using state-|Adding||Misallocation|Misallocation|
||Baseline<br>estimation|<br>covariates<br>instead of FE|Dropping<br>weights|<br>industry-year<br>FE|<br>employment<br>control|Using<br>balanced panel|without industry<br>aggregation step|using OLS<br>TFP metric|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||A. Using l|and and labor mi|sallocation in o|rganized sector|||
|Land misallocation index|-0.064|0.108++|-0.033|-0.079|-0.076|-0.082|-0.006|-0.003|
||(0.083)|(0.049)|(0.081)|(0.104)|(0.080)|(0.085)|(0.092)|(0.090)|
|Labor misallocation index|0.025|-0.039|0.009|0.059|0.083|0.092|0.043|0.005|
||(0.076)|(0.063)|(0.078)|(0.103)|(0.078)|(0.080)|(0.091)|(0.094)|
|Log employment in|||||-0.425+++||||
|district-industry-year|||||(0.059)||||
|Observations|5231|5231|5231|5231|5231|3189|5231|5231|
|Adjusted R-squared|0.216|0.139|0.247|0.216|0.255|0.193|0.216|0.216|
|<br>|||B. Using land a|nd labor misallo|cation in total m|anufacturing sect|or||
|Land misallocation index|-0.090|0.070|-0.081|-0.073|-0.040|-0.098|-0.086|-0.090|
||(0.093)|(0.052)|(0.091)|(0.122)|(0.091)|(0.101)|(0.102)|(0.100)|
|Labor misallocation index|0.107|0.000|0.097|0.123|0.088|0.116|0.057|0.002|
||(0.087)|(0.058)|(0.090)|(0.116)|(0.089)|(0.089)|(0.094)|(0.097)|
|Log employment in|||||-0.421+++||||
|district-industry-year|||||(0.058)||||
|Observations|5231|5231|5231|5231|5231|3189|5231|5231|
|Adjusted R-squared|0.217|0.139|0.248|0.217|0.255|0.193|0.216|0.217|

Notes: See Table 6a.
-12-

**Table 2. Prudential Regulatory Requirements for Commercial Banks and Finance Companies, 1990**

||**Commercial Banks**|**Finance Companies**|
|---|---|---|
|Limits on ownership|o<br>Shareholding to one person limited to 5<br>percent but nominee shareholding is<br>permitted.|*<br>Shareholdings lirnited to<br>10<br>percent of shares outstanding.|
|Level of minimum capital adequacy<br>requirements|e<br>8 percent of risk assets for on-balance<br>sheet items; however number of<br>exemptions apply which effectively<br>reduce minimum capital adequacy ratio<br>-<br>considerably.<br>*<br>20 percent in relation to amount of<br>avals, acceptance bills, and loan<br>guarantees outstanding.|*<br>6 percent of risk assets for balance<br>sheet risks.<br>.<br>25 percent for off-balance sheet<br>contingent liabilities.|
|Loan classification requirements<br>(number of days before loan is<br>classified as nonperformiing)|*<br>180 (uncollateralized).<br>*<br>360 (collateralized).|*<br>180 (uncollateralized).<br>.<br>360 (collateralized).|
|Provisioning requirements for loans<br>classified as nonperforming|*<br>50 percent on doubtful.<br>*<br>0 on substandard.|.<br>50 percent on doubtful.<br>*<br>0 on substandard.|
|Limit on Risk Exposure:<br>- Liquidity Requirement|*<br>7 percent of deposits.|*<br>NA|
|- Foreign Exposure Limit|*<br>Open foreign exchange position limited<br>to 20 percent of capital.|*<br>NA|
|- Single Exposure Limit|*<br>25 percent of bank's capital fund.<br>*<br>50 percent for contingent exposures.<br>**__________________________________**|.<br>30 percent of fimance company's<br>capital fund.<br>.<br>40 percent including contingent<br>liabilities.|
|- Loans to Insiders|*****<br>Loans to directors prohibited.|.<br>NA|

**_Market Discipline._** In Thailand, market discipline was not only hampered by a partial implicit guarantee on financial system deposits-a legacy of the resolution of the 1983-87 financial crisis (see below)-but also by loose financial accounting and disclosure. Furthermore, the role of a limited number of families in the ownership of both financial and nonfinancial institutions limited the scope for market oversight. Indeed, each of the major banks was associated, through cross ownership and control, with a variety of nonfinancial companies as well as with at least one, and usually more than one, finance company. It has been estimated that ten families as of end 1987 controlled 46.2 percent of the market capitalization of all listed firms, of which 39.6 percent were in financial institutions, 60.9 percent in nonfinancial companies.<sup>21</sup> Weaknesses in the governance of financial institutions may encourage lending to risky sectors or unviable projects. Moreover, a bank's relationship with enterprises which are part of its industrial financial group may not be conducted at arms-length and fair market prices.

- **21** Figures cited after Claessens et al (1999).
# Table 5

_Overarching Aspects – 5 largest regression coefficients_

|Overarching Aspects variable|Program impact<br>on student<br>learning|
|---|---|
|Program provides textbooks|0.355**<br>(0.128)|
|Program provides other reading materials (flashcards, word banks, primers)|0.159*<br>(0.087)|
|Participation has implications for promotion or points towards promotion or<br>salary implications|0.143**<br>(0.066)|
|Targeting by years of experience|0.136<br>(0.198)|
|Program provides storybooks|0.129<br>(0.094)|
|Observations|26|
|Standard errors inparentheses. ***p<0.01, **p<0.05, *p<0.1.||

31
# **Decline in Standard of Living**

By 1950 (the end of the colonization period), the "Third World" economies had a much lower standard of living than did developed countries. Bairoch (1993) attributes this difference to a century and a half of Industrial Revolution experienced by the developed countries that boosted their standard of living fivefold. In his estimation, the average "Third World" country in the 1950s had nearly the same standard of living as it did in 1800, or at least no better than 10-20 percent higher. In terms of real income per capita122 it was five to six times lower than that of developed countries. Labor productivity in the agricultural sector of "Third World"' countries in 1950 was seven times lower than that in Western countries and 30-40 percent below that of developed countries in 1800.123 The patterns found by Bairoch are supported by data compiled by Maddison (2001) as shown in Table 1.14 below.

## Table 1.14

|LevelsofGDPPe<br>|rCapitainEu<br>_____________<br>(|ropeanColo<br>1990Interna|nialPowers<br>tionaldolar|andFormer<br>s)|Colonies,1|500-1998|
|---|---|---|---|---|---|---|
||**1500**|**1700**|**1820**|**1913**|**1950**|**1998**|
|Britaina|762|1,405|2,121|5,150|6,907|18,714|
|France|727|986|1,230|3,485|5,270|19,558|
|Italy|1,100|1,100|1,117|2,564|3,502|17,759|
|Netherlands|754|2,110|1,821|4,049|5,996|20,224|
|China<br>|600|600|600|552|439|3,117|
|India|550|550|533|673|619|1,746|
|Indonesia|565|580|612|904|840|3,070|
|**'**ReferstoEngland,Scotl<br>Source:Maddison(2001:|andandiWalesfor1<br>Table2-22a,90)|500-1913.Nohr|hemIrelandis inc|ludedfor1950 a|nd 1998.||

This table shows that GDP per capita for India and Indonesia increased in the early colonial period but declined toward its end - between 1913 and 1950. China's per capita GDP, on the other hand, dropped between 1820 and 1913.

Maddison also developed the following tables (1.15 and 1.16) for Indonesia and India, respectively, as "a crude measure of the burden of colonial rule and the colonialist gain."124 25
Table 5 shows the truncated regressions (on the dependent variable) to verify the changes in coefficients with bias adjustment using WLS. OLS estimates produce positive but insignificant coefficients for the firm-level data dummy variable and for the dummy indicating paper focusing on innovation impacts measurement.

These are two important variables because the former refers to estimating impacts with the proper measurement unit (firm) and the latter conveys innovation activities where firms invest subsidized resources. The statistically insignificant coefficient in both variables would suggest that both factors do not contribute to enhance heterogeneity between papers and thus these factors become irrelevant for the meta-analysis perspective. However, once the weights are estimated for each paper, minimizing sources of biases given their characteristics, the coefficient of both variables becomes statistically significant at least 95 percent. The negative sign of the variables that also turn statistically significant (years of program implementation, program investment amount, and peer review publication) suggest a high degree of heterogeneity, in spite of the truncation of the model.

Table 5. Truncated Regression (Minimum Correlation)

||Study<br>Correlation|Study<br>Correlation|
|---|---|---|
||(1)|(2)|
|Firm Level Data|0.0356|0.299***|
|s.e.|(0.07)|(0.10)|
|Identification 2 stage|-0.053|-0.0486*|
|s.e.|(0.05)|(0.03)|
|Identification Dif in Dif|-0.0273|-0.0176|
|s.e.|(0.09)|(0.05)|
|Identification PSM|-0.0873|-0.0547**|
|s.e.|(0.05)|(0.03)|
|Identification RD|-0.105|-0.0348|
|s.e.|(0.11)|(0.06)|
|Identification FE/RE|0.0611|0.0198|
|s.e.|(0.05)|(0.02)|
|Identification IV|-0.0144|-0.00475|
|s.e.|(0.06)|(0.03)|
|Dummy small business|||
|support|-0.00493|-0.0244|
|s.e.|(0.05)|(0.03)|
|Dummy SME|0.0327|0.0155|
|s.e.|(0.04)|(0.02)|
|Log years program|-0.0567|-0.0416**|

25
**<u>Table IV.11    Sources of Credit for Cocoa Producers: CRMG Survey Results</u>**

||**Total Avg.**|
|---|---|
|**Cocoa Producers**<br>**Receiving credit (Total)**|41%|
|**Source of credit:**||
|**Exporter**|14%|
|**Intermediary**|7%|
|**Cooperative**|1%|
|**Agricultural Bank**|3%|
|**Money lender**|1%|
|**Commercial Bank**|9%|
|**Other**|14%|

CRMG Survey (World Bank, 2002a)

# _Environmental externalities_

Cocoa production can have positive environmental impacts, but the industry’s environmental impacts are not viewed as being as important as are those of coffee. Major producing areas are located outside important watersheds. Since cocoa is a perennial tree crop, often grown under shaded conditions, it reduces soil erosion and helps maintain forested areas. Since cocoa is located on gentler slopes on level land, it does not have an equivalent impact on preventing erosion as does coffee. Outside of erosion control, organic cocoa production tends to be more environmentally benign than coffee production. Post-harvest processing of coffee uses considerable quantities of water, compared to cocoa post-harvest processes – either fermented or unfermented.

There is some overlap of cocoa and coffee production in the mountainous areas of Central Region. In these areas, coffee is grown on hillsides at higher elevations and cocoa at lower elevations. Environmental impacts of coffee and cocoa production on soil erosion, leaching of agri-chemicals and water borne wastes, are all linked to watershed quality. Production and postharvest activities in the two industries should be managed in a way that considers the impacts of these activities on watershed quality.

# **E. Structure of Marketing and Exports**

The chain of post-harvest activities begins on the cocoa farms, moving on to actors such as intermediaries, exporters, and domestic processors. Since we have already devoted considerable attention to producers and post-harvest practices, we begin this section with intermediaries.

# **E.1 Cocoa Intermediaries**

Intermediaries purchase cocoa beans from producers at the farmgate or nearby villages. Some buy cocoa beans and sell them to exporters, while a larger share of intermediaries act as purchasing agents for exporters. Large commercial exporters use intermediaries as local purchase agents. Approximately 1000 agents currently work as intermediaries in the Dominican Republic. Exporters also buy directly from the larger producers and from associations of producers, and some producers deliver their cocoa beans directly to the exporters Intermediaries can be categorized into four general groupings: 63