rafmacalaba/datause-encoder-data huggingface dataset:
# **Table F2: Growth Models**

|||**G**|**rowth Model**|**s**|||||
|---|---|---|---|---|---|---|---|---|
||**Bangla**|**desh**|**Ethio**|**pia**|**Indo**|**nesia**|**Sri La**|**nka**|
||**coef**|**se**|**coef**|**se**|**coef**|**se**|**coef**|**se**|
|||**M**<br>|**ODEL 1: GE**<br>|**NDER O**<br>|**NLY**<br>||||
|mngr is male|0.013|(0.010)|0.085|(0.058)|0.008|(0.041)|-0.006|(0.012)|
|Observations|2,301||725||776||1,130||
|R2|0.000||0.009||0.000||0.001||
|Adjusted R2|0000||0008||-0001||-0000||
|<br>|.<br>**MO**<br>|**DEL 2: GE**<br>|.<br>**NDER, INI**<br>|**TIAL SIZE**<br>|.<br>**AND SE**<br>|**CTOR**<br>|.<br>||
|mngr is male|0.054***|(0.013)|0.075|(0.051)|0.038|(0.034)|-0.004|(0.011)|
|ln Lt-1|-0.047***|(0.011)|-0.132***|(0.029)|-0.030|(0.045)|-0.018*|(0.010)|
|services|-0.065***|(0.022)|-0.057|(0.046)|0.070|(0.053)|-0.019|(0.015)|
|trade|-0.096***|<br>(0.020)|-0.041|<br>(0.049)|0.080|<br>(0.055)|-0.013|<br>(0.014)|
|hh enterprise|-0.059***|(0.020)|||-0.082|(0.058)|-0.005|(0.012)|
|<br>Observations|2,301||724||776||1,128||
|R2|0.046||0.116||0.026||0.015||
|Adjusted R2|0.044||0.112||0.020||0.010||
|||**M**|<br>**ODEL 3: AL**|**L CONTR**|<br>**OLS**||||
|mngr is male|0.055***|(0.015)|0.077|(0.063)|0.017|(0.031)|-0.001|(0.012)|
|ln Lt-1|-0.046***|(0.012)|-0.159***|(0.034)|-0.041|(0.043)|-0.020*|(0.011)|
|services|-0.055***|<br>(0.020)|-0.071*|<br>(0.040)|0.044|<br>(0.060)|-0.026|<br>(0.018)|
|trade|-0.095***|(0.023)|-0.085|(0.057)|0.076|(0.063)|-0.018|(0.016)|
|hh enterprise|-0.041**|(0.019)|||-0.064|(0.070)|0.003|(0.012)|
|mngr age|0.001|(0.002)|0.013*|(0.008)|0.020*|(0.012)|-0.003**|(0.001)|
|mngr age<sup>2</sup>/100|-0.146|(0.212)|-0.012|(0.009)|-0.020*|(0.011)|0.006*|(0.003)|
|<br>mngr primary|0.004|<br>(0.013)|0.047|<br>(0.059)|-0.016|<br>(0.038)|-0.003|<br>(0.016)|
|mngr secondary|-0.004|(0.015)|0.151**|(0.075)|0.041|(0.042)|0.010|(0.018)|
|mngr tertiary|0.055*|(0.033)|||0.024|(0.075)|0.015|(0.024)|
|electricity usage|0.024*|(0.012)|0.063|(0.088)|0.126**|(0.038)|0.005|(0.012)|
|ln firm age|-0.014***|(0.005)|-0.017|(0.018)|0.022|(0.029)|0.000|(0.005)|
|<br>town|-0.018|<br>(0.011)|0.096*|<br>(0.058)|0.031|<br>(0.029)|||
|ln dist market|0.016*|(0.008)|-0.027|(0.022)|0.015|(0.014)|-0.009|(0.006)|
|credit institution|0.007|<br>(0.008)|0.001|<br>(0.042)|0.030|<br>(0.036)|0.009|<br>(0.010)|
|ln local wage|0.038**|(0.016)|-0.035|(0.048)|0.046*|(0.027)|0.031|(0.021)|
|<br>Observations|1,855||636||697||993||
|R2|0.064||0.170||0.121||0.045||
|Adjusted R2|0.056||0.152||0.100||0.030||

Note: *** p<0.01, ** p<0.05, * p<0.1. Regressions are weighted, except for Sri Lanka where weights are not available. Standard errors are heteroscedasticity robust and clustered at the village level 66
primary source for these is the “Economic Freedom of the World (EFW)” database (see Gwartney and Lawson [2004]). This database has been developed under the auspices of the Fraser Institute in Canada with the aid of a worldwide network of economists and research institutes. In particular, we use indicators referring to hiring and firing practices, regulation of business activities and integrity of the legal system.

Despite other indicators available in the literature for developing and emerging economies (e.g., the World Bank Doing Business database), the EFW tracks changes in regulations over time and is thus more suitable for our analysis of job flows that have indeed been influenced by policy changes over the period covered by our data (see Table 11 for details on the regulatory variables).

**Table 11:** Institutional Variables, 1990s

|**OVERALL**|||||
|---|---|---|---|---|
|Variable|Mean|Std. Dev.|Min|Max|
|Hiring and Firing Practices|5.261|1.515|2.878|7.700|
|Law&Order adj. Hiring and Firing Practices|4.113|2.019|0.000|7.209|
|Business Regulations|3.490|1.389|1.100|5.900|
|Law&Order adj. Business Regulations|2.490|1.233|0.000|4.600|
|Law and Order|2.280|2.818|0.000|10.000|
|**EU & USA**|||||
|Hiring and Firing Practices|5.427|1.804|2.878|7.400|
|Law&Order adj. Hiring and Firing Practices|5.084|1.559|2.878|6.600|
|Business Regulations|3.074|1.682|1.100|5.600|
|Law&Order adj. Business Regulations|2.822|1.349|1.100|4.600|
|Law and Order|0.469|1.121|0.000|3.000|
|**LAC**|||||
|Hiring and Firing Restrictions|4.679|0.943|3.230|5.740|
|Law&Order adj. Hiring and Firing Restrictions|2.249|1.642|0.000|4.431|
|Business Regulations|4.206|1.297|2.617|5.900|
|Law&Order adj. Business Regulations|1.811|1.321|0.000|3.320|
|Law and Order|4.949|2.769|2.280|10.000|
|**TRANSITION**|||||
|Hiring and Firing Restrictions|5.696|1.705|3.586|7.700|
|Law&Order adj. Hiring and Firing Restrictions|4.742|1.846|3.079|7.209|
|Business Regulations|3.323|0.669|2.650|4.200|
|Law&Order adj. Business Regulations|2.757|0.716|1.776|3.486|
|Law and Order|1.763|1.119|0.637|3.300|

Source: Own calculations based on harmonized firm-level database and Gwartney and Lawson [2004].

The EFW indicator of hiring and firing restrictions is measured on a scale of 0 to 10, 30
_Fandly Planning Success Stofles in Bangladesh and India_ _18_ Table S. Cost-effectlveness ratios In 1985 US dollars in the Treatment and Compaison Areas under different models

|||_D_<br>_Model 11_|_eatment Ar_<br>_Model 1H_|_ea_<br>||_Compar_|_ison Area_|
|---|---|---|---|---|---|---|---|
|_Rado_|_Model_**_I_**|_Ad-_<br>_Cost-i_|_A4d-_<br>_Cost-2_|_Model_<br>_lIU_<br>_lIA1B_|_Model_<br>|_Model_<br>_1114_|_Model_<br>_IIIB_|
|Average cost<br>per contracep-<br>tive user|42|35|40|43|49|49|64|
|Average cost<br>per birth pre-<br>vented|180|158|173|191-<br>220*|171-<br>196*|240|298|

*Th loWer ad highor figus tpresm respectively te otmate when cot of govoenment wvlkes is not includod and is incudd.

government program develops a method mix with a larger role of reversible methods, the cost of services is likely to increase. The results of Model m suggests, however, that, at the local level, the delivery of all methods in the Treatment Area is more cost-effective than the limited service provision in the Comparison Area. Hence the cost per contraceptive user in the government program is not likely to increase significantly if it expands its services for providing reversible methods.

# _Success and Its LImtaions_

The Matlab Project has demonstrated that a well-designed, consumer-oriented family planning and MCH programme can succeed in raising contraceptive prevalence and reduce child mortality substantially even in an environment economically and socially unfavorable to these developments. The experimental design of the Project which allowed special service inputs in the Treatment Area while keeping the Comparison Area with similar socioeconomic condition almost free from such inputs has been useful in establishing that the family planning success in the Treatment Area relative to the Comparison Area was not due to any difference in socioeconomic condition between the two Areas (except a little difference in Hindu-Muslim composition which would have negligible effect in contraceptive prevalence). The success can be fully attributed to the Project.

Absence of any difference between the Treatment and Comparison Areas in childbearing desire shows that the Project did not succeed in generating demand for small family, although the interpersonal communication aspect of the Project at the field-level was attuned toward this objective. However, it is reasonable to presume that the Project's supply aspects were able to crystallize the fragile demand for contraception among many ambivalent couples, so as to make them use contraception.

Since the higher contraceptive prevalence in the Treatment Area relative to the Comparison Area and Bangladesh was due more to higher prevalence of reversible methods (mainly injectables and IUDs) in the Treatment Area rather than permanent methods, it seems that
# **1. Introduction**

In developing countries, a large majority of small and medium firms operate in the informal sector (La Porta and Shleifer, 2014a). In Benin, informality is particularly high: in 2009, the national statistics agency estimated that the informal sector represented up to 70 percent of the GDP and 95 percent of employment (INSAE, (2009)). High level of informality may carry some costs for both governments and firms. Governments may face more difficulties to collect taxes and finance public services (Levy 2008). Informal firms may not be able to access bank financing, public contracts and government programs and may face more corruption or intimidation from the authorities. Formal firms may suffer from unfair competition by the informal sector (Farrell (2004)) and from higher taxes if all the tax burden is supported by the formal sector. Two competing and not mutually exclusive theories may explain why most firms remain informal (Perry et al. (2007)). The _exclusion_ hypothesis, often attributed to De Soto (1989), supports the view that administrative rigidities and direct costs of registration discourage firms from formalizing, whereas according to the _exit_ hypothesis (Levy (2008), Maloney (2004)) firms rationally choose to stay informal by comparing costs and benefits of formality. Direct and indirect (i.e. added taxes) costs of registration may be greater than potential benefits for most informal businesses which are small, have very low productivity, and employ managers with a low level of human capital (La Porta and Shleifer, 2014b).

Following the publication of the _Doing Business_ project of the World Bank, many countries around the world recently implemented a large number of business entry regulation reforms in order to reduce informality. For example, between 2004 and 2014, 100 of the 189 countries included in the _Doing Business_ project implemented a “one-stop shop” for business registration to reduce the number of steps and the time required to register a firm (World Bank (2015)). However, the literature suggests two important results: (1) easing entry regulation and providing information may not be sufficient to increase formalization (at least when formality also means tax registration) and (2) formalization alone may not be sufficient in itself to increase firms’ profits and stimulate growth.

A recent review of the literature by Bruhn and McKenzie (2014) suggests that reforms which only ease business registration have limited impact on formalization. In Mexico, Bruhn (2013) finds that a significant simplification of the registration process had a limited impact on informality. In Sri Lanka (de Mel et al. (2013)) and in Bangladesh (De Giorgi and Rahman 2
McKenzie, D. and C. Woodruff. (2017) "Business Practices in Small Firms in Developing Countries," _Management Science_ , 63(9): 2967-2981.

Tanaka, M. (2019) “Exporting Sweatshops? Evidence from Myanmar” forthcoming in _Review of Economics and Statistics_ Walker, F. (1887) “The Source of Business Profits.” _Quarterly Journal of Economics_ . 1(3): 265-288 Wittenberg, Martin (2010) “An introduction to maximum entropy and minimum cross-entropy using Stata” in _The Stata Journal_ , vol. 10(3): 315-330.

26
(2) and (4) show that using only _within_ country pair variations and controlling for aggregate time windows factors, an increase in GVC participation is significantly related to an increase in GDP co-movement at business cycle frequency.

**Table. 9.** Global Value Chain: Robustness

|||c<br>|orr GDP<sup>_HP_</sup><br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ln(_gvce_)|**0.029**<sup>_∗∗∗_</sup>|0.013|||
||(0.002)|(0.015)|||
|ln(_gvc_broad_)|||**0.027**<sup>_∗∗∗_</sup>|**0.036**<sup>_∗∗_</sup>|
||||(0.002)|(0.016)|
|Country-Pair FE|No|Yes|No|Yes|
|Time Window FE|No|Yes|No|Yes|
|_N_|17,210|17,210|17,224|17,224|
|R<sup>2</sup>|0.046|0.176|0.043|0.176|

_Notes:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. In parenthesis: std. deviation. Data are from 1990 to 2009, with two time-windows of 10 years.

In table 10, we use the decomposition of both gross exports and value added exports to investigate the role of three different parts of the export flows: the final goods absorbed by consumers in the destination country, the intermediate goods that are used in the destination country for production and absorbed in this very same country, and finally the intermediates that are re-exported further. For all filtering method featured in table 10 (Hodrick-Prescott, Baxter-King or simple first difference), a consistent message emerges from our analysis: for both gross and value added flows, the only part of total trade that is strongly and significantly related to GDP synchronization is trade in intermediate inputs that are _not_ re-exported further.<sup>23</sup> The different results between imported input used for domestic production and imported input that are re-exported further is interesting because it sheds light on the difference between two concept: “cross-country produiction”, and “Global Value Chains”. Since GDP is the sum of domestic value-added produced in a country, its fluctuations are tightly linked

> 23Notice that the results are not significant when taking into account only the sub-sample with country-pairs with at least one high income country. However, the point estimates are still positive for intermediate inputs.

22
association between China’s WTO accession and its potential impacts on trade flows – and thus economic outcomes – across African countries.

Figures 2a and 2b, in Appendix I, depict the patterns of the main metals (aluminum, copper, iron ore, and steel), minerals (coal, chromite, and manganese), and petroleum production in Africa in the 1996–2012 period. Reported in constant 2000 levels, both figures indicate substantial rises in the production of metals, minerals and petroleum after 2001. Similarly, Figures 3a, 3b, and 3c, also in Appendix I, show that the patterns of Chinese imports for the same commodities during 1996–2013 and distinctively mirror the African production levels.

Table 2: Average product trade flows shares between China and Africa, 1990–2014

|Periods|199|0-95|1996|-2001|2002|-2007|2008|-2014|
|---|---|---|---|---|---|---|---|---|
|Product|Exports<br>(%)|Imports<br>(%)|Exports<br>(%)|Imports<br>(%)|Exports<br>(%)|Imports<br>(%)|Exports<br>(%)|Imports<br>(%)|
|Agricultural|17.12|0.65|25.40|0.33|10.35|0.48|6.07|0.57|
|Chemical|1.62|7.06|2.39|12.33|2.21|7.26|1.37|8.08|
|Food|29.81|12.97|9.32|8.06|3.23|4.76|3.35|3.17|
|Fuel|4.00|2.50|31.21|2.00|56.84|1.15|39.43|0.50|
|Machinery|24.20|20.65|3.18|28.98|1.65|38.53|1.29|46.96|
|Manufactures|28.01|82.47|10.31|88.19|11.73|91.20|9.12|93.66|
|Ores and Metals|20.74|1.21|23.36|1.09|17.40|1.22|41.69|1.46|
|Textiles|15.95|21.85|6.57|15.95|5.02|16.06|2.34|8.99|
|Total|17.54|18.63|13.97|19.62|13.55|20.08|13.08|20.42|

* These include raw materials. ** Includes transport equipment. _Notes:_

(1) Exports refers to products’ shares exported to China.

(2) Imports refers to products’ shares imported from China.

Source: Authors’ calculations using World Integrated Trade Solutions, (WITS) Table 2 contextualizes the production and imports patterns described above. A few important facts become noticeable when we decompose the patterns. On the one hand, Africa imported more manufactured goods from China (import shares range from 82.47 percent during 1990–1995 to 91.20 percent and 93.66 percent during 2002–2007 and 2008–2014, respectively), suggesting that Africa has been a growing consumer market for Chinese industrial products. A similar dominant pattern is noticeable for Africa’s imports of chemicals, machinery, and textiles, albeit in relatively small magnitudes.

On the other hand, exports from Africa to China were predominantly agricultural products (including raw materials), food items, fuel products, and ores and metals. The export shares of agricultural products have dropped steadily, from an average of 17.12 percent during 1990–1995 to 10.35 percent and 6.07 percent during 2002–2007 and 2008–2014, respectively. A similar pattern is visible for food items: export shares plummeted from 29.81 percent to 3.23 percent 7
Looking at the proportions of households receiving remittances by caste (bottom part of Table 1), Dalit households have the highest probability of receiving money from outside Nepal (25 percent), while the incidence of external remittances is much lower among Newars and Terai Janjatis. At the same time, only 10 percent of Dalit households receive remittances from Nepal. This might suggest that poor job opportunities at home prompt Dalit households to concentrate their job search efforts abroad.

Individual profiles constructed using NLSS data reveal that almost all international migrants are male (97 percent) aged 15 to 44 years, and either sons or husbands of the person receiving remittances. Brothers represent about 10 percent of the total number of donors. In 1995, 85 percent of Nepali migrants worked in India, and the rest were spread among Malaysia (11 percent), Bhutan, and Hong Kong. As of 2004, international migrants were living in 10 countries: 65 percent worked in India, 18 percent in Arab countries, and about 2 percent in United Kingdom, while some migrants lived as far away as Japan and the United States. Remittances from abroad constituted 76 percent of the total amount of remittances received in Nepal in 2004. The largest share of international remittances came from Saudi Arabia, Qatar, and the United Arab Emirates (35 percent), followed by 30 percent from India, 17 percent from other Asian countries, and the remainder from United Kingdom, United States, and other countries.

The correlations between household income and the incidence and amount of remittances are shown in Figure 2. The main difficulty in illustrating this relationship is that current income is endogenous to the remittances. We attempt to address this problem by constructing a two-year-lagged asset index to proxy for pre-migration income.<sup>5</sup> Overall, the incidence of remittances (or migration) is higher among (asset) poor households. It reaches 44 percent for the poorest households in Nepal and declines monotonically to about 10 percent for the richest households. The correlation between the amount of remittances and household wealth goes in the opposite direction. Households with the highest lagged asset index receive significantly larger amounts of money from working migrants than do poor households. These results, however, could indicate that

> 5 The lagged asset index was constructed based on the estimated cash value of the flow of services provided by the durable goods. In our calculations, we included only durable assets purchased by households at least two years prior the date of the survey (2001 and older). In justifying the exogeneity of the lagged asset index, the fact that the major increase in work migration in Nepal was initiated by the reforms of the administrative system of 2001 was taken into consideration (see Section 3).

9
In sum, the fi ndings in this section show that there is a very robust positive association between trade (either exports or imports) and technological innovations. Minority foreign participations are also a better channel for technology diffusion than majority foreign participations. Furthermore, majority foreign-owned fi rms are signi fi cantly less likely to innovate technologically than domestic-owned fi rms, especially in low-tech industries. Although we do not analyze technological spillovers to domestic fi rms, this fi nding is consistent with minority foreign participations bringing greater bene fi ts to the domestic economy than majority foreign participations. These patterns do not seem to be driven by innovation inputs such as R&D activities or the use of licensed technology that directly affect technological innovation.

# 4. Conclusion

This paper uses a fi rm-level dataset for a large number of developing countries to provide new evidence on the importance of technology diffusion through trade and FDI for technological innovations. Unlike most previous studies, we are able to identify whether fi rms have recently adopted new technology, which is arguably a better measure of a technological innovation for the fi rm than its R&D activities or its use of patents. Our fi ndings show a very strong positive correlation between trade and technological innovations at the fi rm level. Importers and exporters are 4.3 and 7.3 percentage points more likely to adopt new technology than fi rms that do not engage in each of these activities, respectively. While these effects may be biased due to the selection of the more competitive fi rms (and hence more prone to adopt new technologies) into these activities, our evidence suggests that this problem is not too severe. We also fi nd strong evidence that, all else constant, majority foreign-owned fi rms are signi fi cantly less likely to engage in technological innovations than domestic-owned fi rms with similar managerial education and access to fi nance operating in the same region and industry. This result is particularly strong in low-tech industries. We interpret this fi nding as evidence that the technology transferred from multinational parents to majority-owned subsidiaries is more mature than that transferred to minority-owned subsidiaries. This fi nding supports the idea that equity joint ventures transfer a more dynamic technology to local fi rms, which importers do not differ systematically in the use of licenses from abroad.

20
**Figure 7. Brain drain and remittances**

<!-- Start of picture text --> cM T+cM cL T+cL cn co c<br><!-- End of picture text --> In other words, for a bene...cial brain drain to obtain through remittances, the transfer received by each remaining resident must be relatively high so that a large share of the population gains access to education. This do not seem to portray the evidence from remittance data available in developing countries. Although remittances are generally positively correlated with donors’ incomes, meaning that skilled emigrants are presumably important remitters, the results from household surveys are mixed. At an aggregate level, Faini (2002) shows that migrants’ remittances decrease with the proportion of skilled individuals among the emigrants and concludes that ”this result suggests that the negative impact of the brain drain cannot be counterbalanced by higher remittances”. This does not imply that remittances by skilled migrants are negligible, especially if the proportion of temporary migrants increases; for example, Kangasniemi et al (2004) show that nearly half (45%) of Indian medical doctors working in the UK remit income to their home country and that remitters transfer on average 16% of their income.

Instead of sending remittances to relatives at home, migrants may return after they have accumulated savings abroad and use such savings for promoting investment projects (generally in small businesses). There is much evidence that low-skill workers migrate with the aim of accumulating enough savings so as to access to selfemployment and entrepreneurship (e.g., Mesnard, 2004, and Mesnard and Ravallion (2001) for Tunisia, Dustmann and Kirchkamp (2002) for Turkey, Ilahi (1999) for Pakistan, Woodru¤ and Zenteno (2001) for Mexico, or McCormick and Wahba (2001) for Egypt). The latter study o¤ers additional insights in that it shows that in the case of literate migrants, both the amount of savings and the migration duration have a signi...cant positive e¤ect on the probability of entrepreneurship upon return, while the ...rst proposition only holds true for illiterate migrants; this suggests that skillacquisition may be more important for relatively educated migrants than the need to overcome liquidity constraints. In terms of our notations, increases in the stock of physical capital through repatriated savings and acquisition of entrepreneurial skills can be modeled as a negative shock on the risk premium, 1⁄4; which rises the stock 26