rafmacalaba/datause-encoder-data huggingface dataset:
they should work in such a way that they are capable of explaining not only the main results—the diaspora-co-patenting relation—but also the differentiated effect of diaspora networks across different cultural dimensions, which is unlikely.

TABLE 3. Inventor Diaspora Effect between Culturally Closer/More Distant Countries

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||PPML|PPML|PPML|PPML|PPML|
|||C<br>|o-inventorshi<br>|p<br>||
|ln(Diaspora)|0.204***<br>(0.0247)|0.199***<br>(0.0229)|0.198***<br>(0.0230)|0.201***<br>(0.0230)|0.206***<br>(0.0249)|
|ln(Distance)|-0.263***|-0.270***|-0.267***|-0.275***|-0.262***|
||(0.0535)|(0.0517)|(0.0520)|(0.0560)|(0.0557)|
|Contiguity|0.213*|0.217*|0.233*|0.216*|0.250**|
||(0.127)|(0.128)|(0.127)|(0.127)|(0.127)|
|Common language|0.690***<br>(0.193)|0.613***<br>(0.132)|0.630***<br>(0.131)|0.628***<br>(0.131)|0.776***<br>(0.198)|
|Lang. similarity|0.508**|0.781***|0.528**|0.532**|0.858***|
||(0.227)|(0.273)|(0.224)|(0.224)|(0.285)|
|Colonial links|0.0502|0.0774|0.473*|0.0724|0.468*|
||(0.131)|(0.124)|(0.248)|(0.126)|(0.253)|
|Religion similarity|0.702***|0.676***|0.692***|0.782***|0.639**|
||(0.254)|(0.262)|(0.256)|(0.267)|(0.287)|
|ln(Dia.)*Com. language|-0.0122<br>(0.0223)||||-0.0302<br>(0.0241)|
|ln(Dia.)*Language sim.||-0.0829<br>(0.0522)|||-0.117*<br>(0.0606)|
|ln(Diaspora)*Colonial|||-0.106**<br>(0.0498)||-0.110**<br>(0.0494)|
|ln(Diaspora)*Religion||||-0.0526<br>(0.0708)|0.00141<br>(0.0758)|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Constant|-6.102|-5.847|-6.941|-5.701|-6.745|
||(6.019)|(6.000)|(5.985)|(6.080)|(6.044)|
|Observations|26,160|26,160|26,160|26,160|26,160|
|Pseudo R2|0.958|0.958|0.957|0.957|0.958|
|Sending FE|Yes|Yes|Yes|Yes|Yes|
|Receiving FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|LogLik|-18490.83|-18482.05|-18478.32|-18490.28|-18459.73|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia, and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

27
ism, and accepts the suspension of some freedoms in order to guarantee security; and (5) prioritizes law and order. We classified parties that did not include at least three of the stances from either list in their statutes as neither nor policy left-wing right-wing.

3. For parties for which official statutes were not available, we checked the government plan that candidates submit to the electoral authority before elections and— when available—searched them for the same policy stances as in the second step.<sup>22</sup> The majority of candidates are classified using individual-level information, rather than party affiliation: 40% are classified based on their government plans, 20% using social media posts, and 14% based on coalition composition (See Table A.4). This approach reduces the risk of over-relying on party ideology and helps capture local variations in candidate positioning, addressing the concern regarding the high number of centrist candidates.

Table A.4. Distribution of the Type of Source Used to Classified Candidates

|**Type of Source**|**Candidates**|**Percent**|
|---|---|---|
|Government Programs|562|41.82|
|Social Media|275|20.46|
|Other|243|18.08|
|Coalition Composition|192|14.29|
|News/Media|44|3.27|
|Blogs/Opinions|11|0.82|
|Unclassifed|10|0.74|
|Academic|7|0.52|
|**Total**|1,344|100.00|

_Note:_ This table provides an overview of the primary sources used to determine candidates’ political ideology.

Finally, we used electoral roll information to calculate the municipality’s turnout and the

> 22For example: for the 2019 elections, Colombia’s electoral authority official website gathered information on all government plans https://wapp.registraduria.gov.co/electoral/Elecciones2019/infocandidatos2019.php

60
especially when females do not manage to engage in industries with high return and substantial entry barriers. Policy makers need to adopt policies that facilitate females' participation in industries that have high returns such as construction, transport and mining, but where socio-cultural factors limit female participation. For instance, female employees could be selectively targeted in public infrastructure investments such as road construction within their communities. Due to common social practices and cultural role assignments such as increased responsibilities bestowed upon women to care for children and elderly, encouraging female participation in national road projects might however be challenging. (2) Education is identied as one of the major drivers of participation in non-farm employment and self-employment. Policy measures that address the skill shortage among females could improve their chance of accessing the RNFE. Even short-term training to improve employability and business skills could help alleviate the skills and know-how challenges facing female-headed households. (3) Non-farm enterprise owners, both female and male, reported that they rely extensively on personal savings/incomes to start their enterprises. Only a handful of enterprise owners (less than 5%) were able to access loan from formal nancial sources such as credit/savings associations, micro-nance or banks to start their business. Policies that improve access to nance could help level the playing eld for poor female-headed households that have limited personal resources to start a lucrative non-farm enterprise that would require large initial capital. (4) Return analysis shows that female-headed households earn less, and endowment gaps explain only part of the return gaps. Policy measures that address gaps in human and nancial capital (outlined in points 2 & 3 above) could address some of the endowment gaps, and hence reduce the return dierential. However, most of the return gaps remain unexplained, and hence policy makers would also need to address gender based discrimination in the non-farm sector to reduce the gender gap in return.

29
_Table 4: Total fraction of aggregate potential harvest lost, by country-year_

|Country|Year|Fraction of total potential harvest<br>lost in shocks|
|---|---|---|
||2013-2014|15.14 %|
|Ethiopia|2015-2016|34.63 %|
||2018-2019|31.52 %|
|Malawi|2018-2019|46.53 %|
|Mli|2014|7.59 %|
|a|2017|8.22 %|
|Nier|2011|81.12 %|
|g|2014|29.24 %|
|Nigeria|2019|3.19 %|
|Pooled|2011-2019|28.57 %|

_Note: Sample weights are used to calculate estimates. Current prices are used to value losses and attainable harvest_ 25
Table 2: Conditional convergence 1970-2010, Market exchange rates

|||_D_|_ependent variab_|_le:_||
|---|---|---|---|---|---|
||1970-80s<br>(1)|10-year log <br>1980-90s<br>(2)|change in labor <br>1990-2000s<br>(3)|productivity<br>2000-2010s<br>(4)|Panel<br>(5)|
|**_All economies_**||||||
|Initial productivity|-0.101***|-0.121***|-0.116***|-0.136***|-0.124***|
|_Convergence rate (annual)_|_1.06%_<sup>_∗∗∗_</sup>|_1.29%_<sup>_∗∗∗_</sup>|_1.24%_|_1.46%_<sup>_∗∗∗_</sup>|_1.33%_<sup>_∗∗∗_</sup>|
|Schooling (years)|0.004|0.011|0.013|0.011|0.008|
|Economic complexity|0.081**|0.087*|0.066*|0.043|0.065***|
|Commodity exporter|-0.129*|-0.109**|-0.089|-0.020|-0.089***|
|Trade (% GDP)|0.076|0041|-0.024|-0.006|0.019|
|Investment (% GDP)|0.012|0.404*|0.682**|0.720**|0.077*|
|Law and Order|0.035*|0.036*|0.033|0.026|0.042***|
|Observations (All)|62|68|78|79|287|
|_Note:_|||_∗_p_<_|0.1; _∗∗_p_<_0.05;|_∗∗∗_p_<_0.01|

Note: Time-effects are used in the panel specification but country-fixed effects are not. Initial productivity is the average of log productivity over the 10-years in the preceding decade, measured in US dollars at 2010 prices and exchange rates. Productivity growth calculated as the change in average log-productivity between the two decades (10-year average growth). Productivity is assumed to grow at its average rate between 2010-8 for the final year of the decade, 2019. All conditioning variables are lagged decadal averages. Data availability of these covariates affects the sample size in each decade.

# **D Estimating convergence clubs: Commonalities in productivity levels**

The mixture model analysis allows the detection of convergence clubs using snapshots of the crosscountry distribution of productivity levels (Battisti and Parmeter, 2013; Henderson et al., 2008; Gr ̈un and Leisch, 2008). Countries fall into the same convergence club when their productivity levels gravitate toward the same long-term productivity level. In contrast, countries fall into distinct convergence clubs when their productivity levels are pulled toward different attraction points.

28
economies. This could be in particular the case for less developed countries which suffer from weak reporting capacities. We check both approaches.<sup>15</sup>

# **3 Empirical Strategy**

This section sets out the empirical strategy in a two‐step approach. First a so‐called services linkage will be developed following previous works in that the policy variable indicators used for our regressions are “weighted” with the input‐output coefficients. Then, in a second step, our baseline specification for the regressions is presented.

# **3.1 Services Linkage**

The empirical estimation strategy follows the one which is pioneered by Arnold _et al_ . (2011; 2015) and which has been used in all subsequent papers with the purpose of creating a so‐called services reform index. This means that one needs to interact the input‐output coefficient of services input reliance for each manufacturing and services sector with the sector‐specific regulatory barrier index in services for each country. This is an identification strategy that relies on the assumption that manufacturing as well as services industries which are more reliant on services inputs would be more affected by changes in regulation within each input services sector over time. This “weighted” average of services policy regulation that relies on services intensities is a more just approach to measure the effect of services regulation on TFP in contrast to an unweighted one.

For this reason, each of the country‐sector specific reform indicators is multiplied with the proportion of services sourced as inputs in country _c_ , from service sector _s_ , for downstream services and manufacturing firm _j_ , i.e. _φcjs_ , which is the input‐output coefficients as explained in the previous section. As a consequence, we apply the next formula: where _φcjs_ comes from the input‐output tables from the year 2010 reported at 2‐digit level in which the input shares are computed based on the total value‐added of inputs used, and where the services reform index refers to the country‐sector‐year specific regulatory indexes in services from the OECD’s NMR and the FDI indexes database. The fact that we chose our input‐output coefficients to be industry‐specific from these matrixes is because Arnold _et al_ . (2015) claim that deployment of inputs reliance measures at firm‐level may suffer from endogeneity issues in connection with the performance of the firm. Industry

> 15 On a more technical note, some regulatory variable indexes from the NMR or related to FDI do not show much variance over time and therefore regressions could pick up any variation of the input‐output coefficients for each country alone rather than the variation of input‐output coefficients interacted with the regulatory variable indexes. This provides us with an additional argument to use EU‐wide or one country‐specific (i.e. US) input‐output coefficients as explained in the main text of this section.

12
which such statements were based is summarized in Table 2, which is derived from three publications05 "- 14). The data are drawn from three sets of studies. The first is a series of national Knowledge, Attitude and Practice (KAP) surveys commissioned by NCDDP and conducted annually since 1984. The study universe covered Cairo and eight other govemorates (four in Lower Egypt and four in Upper Egypt). The other two studies are a 1986 follow-up of the Dakahlia 1980 control study and a 1988 follow-up of the Menoufia 1979-80 control study.

3
behaviors, which capture women’s ability to make decisions around their own mobility. There are four indicators to measure tolerance towards _violence against women_ and two proxies for violent behaviors. _Patriarchal gender norms_ include six indicators measuring the disagreement with statements around male dominance within the household. Four of the indicators on attitudes are combined into a single measure that differentiates women who disagree with all statements compared to those who agree with at least one of them. All indicators are assigned a 1 for a less traditional attitude/behavior and 0 otherwise. Only the more recent waves include questions about attitudes towards gender equality.

Table 1. Attitudes and behaviors indicators

|**Sphere**|**Component**|**Attitude or behavior question**|**Attitude or behavior**<br>**nontraditional if response**<br>**is**|**Surveys**|**Abbreviation**|
|---|---|---|---|---|---|
|Reproductive<br>health|Attitude|Do you approve or disapprove that<br>couples use a method to prevent<br>pregnancy?|Approve|All|App contra|
||Behavior|Main decision maker for the use of<br>contraception|Sole decision maker for<br>contraception|2010, 2015|Use dec contra|
||Attitude|Women’s most important role is to<br>care for the household and to cook|Disagree|2015|D Wcare hh|
|Economic<br>||Main or shared decision on how to<br>spend money|Respondent works &<br>decides how to spend<br>money|All|Decide money|
|opportunities|Behavior|Who cleans the house, prepare food,<br>clean bathroom, wash clothes, buy<br>food/supermarket, pay bills, take care<br>of sick(chores x=1-8)|Respondent & partner,<br>partner more, partner<br>alone, neither|2015|Share chores|
||Attitude|It is normal that men do not allow<br>their wives togo out|Disagree|2015|D rest mobility|
|Mobility|Behavior|Who has final say on visits to family or<br>relatives|No one, decision not made,<br>respondent, or respondent<br>&partner|2010, 2015|Say visits|
||Attitude|When men are mad it is better not to<br>tempt them|Disagree|2015|D tempt men|
|Violence against<br>|<br>|Women that stay in a relation after<br>beingbeaten is because theylike it|Disagree|2015|D Wbeat & stay|
|women||Has friends who abuse their wives|No|2015|No abu friend|
||Behavior|Would call the attention (or has done<br>it)of a friend who abuses a woman|Yes|2015|Call abu friend|
|||Men always have the last word on<br>household decisions|Disagree|2015|M last word|
|||Families with a man have less<br>problems|Disagree|2015|M less prob|
||Attitude|Men are head of households|Disagree|2015|M heads|
|Patriarchal<br>norms||A good wife always obeys her husband|Disagree|2015|Wife obeys|
|||Disagreement with all patriarchal<br>statements|Disagree with all|2015|No patriarchy|
||Behavior|Who has final say on making large<br>household purchases and own health<br>care|Respondent alone or<br>respondent & husband|2010, 2015|Say imp<br>decisions|

12
the world.”<sup>19</sup> For example, In Dar es Salaam, an estimated 70 percent of all travel is done on foot and roughly 20 percent is by minibus.

In African cities, residents lack access to efficient and affordable transit systems and are thus forced to live close to the city center. In contrast, cities like Barcelona, London, Washington DC and Paris all have extensive and proficient transport systems. Barcelona has a population of 1.6 million, similar in size to Dar es Salaam’s 1.4 million, but in Barcelona 33 percent of commuters rely on public transportation, while in Dar es Salaam only 5 percent of its residents use public transit. As a result, the gradients in Figure 2 show African cities have a steeper decline in population density than the European cities in Figure 2 with well‐developed transport infrastructure. While all the cities seem to follow a monocentric model, in Africa density drops off more dramatically than in European and Asian cities.

In a smartly planned well‐functioning monocentric city, one would expect to see a high peak in population density near the city center, followed by a gradual decline moving away from the city center. A strong and far reaching urban transit system allows residents to efficiently and affordably reach jobs and amenities that might be many miles from their homes. This gives residents the freedom to live in the periphery of the city and still benefit from the job opportunities in the urban core. In European cities a high proportion of central and peripheral land is dedicated to transport, in contrast African cities tend to have a much lower share of land used for transport with a rapid decline near the edges of the city. The land cover charts shown in Figure 3 provide a visualization of how the share in land cover changes as one moves to either the north or south of the city center. In Paris, roughly 16% of the land within 10kms of the CBD is dedicated to paved roads. In urban planning literature Paris is often held up as an example of a city with a sustainable and effective urban form.<sup>20</sup> With a prominent core and high land use density, Paris also benefits from high accessibility through a well‐developed road network. In contrast, a city like Dar es Salaam devotes only 7% of its land within 10kms to paved roads, and beyond 12kms the amount of paved roads drops off rapidly. This dramatic drop is consistently observed in almost all the Africa cities examined in this study, and is suggestive of low peripheral connectivity and limited accessibility of the city’s core.

> 19 UNEP (2013) Share the Road: Design Guidelines for Non Motorised Transport in Africa.

> 20 Rodger L. Kemp and Carl J. Stephani. Global Models of Urban Planning: Best Practices Outside the United States. Originally published as “Paris: A New Blue Print” Urban Land Vol. 69 No. 3⁄4 (March/April 2010), by the Urban Land Institute, Washington DC.

8
# **3.3** **<u>Children’s Education and Reduction in Gender Disparity</u>**

Human capital investment is another cornerstone of the success of microcredit programs. Earlier studies show that microcredit borrowing, especially by women, has a positive effect on children’s schooling (Pitt and Khandker 1998). Earlier findings also suggest that microcredit improves health and nutrition (Pitt, Khandker, Chowdhury, and Millimet 2003) and empowers women (Pitt, Khandker, and Cartwright 2006), thereby playing a pivotal role in improving social and human development. The question is whether these benefits are sustained over time.

In terms of trends in the rate of children’s school enrollment over time, we find that, for both participants and non-participants in microcredit programs, there is incremental growth in schooling investment for both boys and girls. However, as earlier research has shown, the schooling gains are higher for girls than for boys among microcredit program participants. Among participants, the school participation rate for girls (ages 5–18) was 71 percent in 2010/11, about 20 percent higher than in 1991/92; while the corresponding rate for boys was 69.6 percent in 2010/11, 14.7 percent higher than in 1991/92. Among non-participants, by contrast, the school participation rate for girls was similar to that of boys in 1991/92; however, boys gained 5.8 percent more than girls over time (70.1 percent versus 64.3 percent) (Table 4). Therefore, we can say that microcredit expansion has helped to reduce gender disparity in rural Bangladesh, at least in terms of children’s education. This finding is consistent with earlier ones (e.g., Pitt et al. 2006), which showed that microcredit, by helping to improve women’s empowerment, has in turn contributed to the social upliftment of girls in society.

# **3.4** **<u>Intergenerational Mobility</u>**

At an aggregate level, our long panel data also shows intergenerational mobility in terms of human capital investment. Overall, by 2010/11, 69.7 percent of boys and 69.9 percent of girls were 11
Table 6. Dynamic and Declining Products in Egypt's Exports: 198042 to 1990-92.

||1980-82 Aver|age Exports|1990-92 Ave|rage Exports|Compound<br>|
|---|---|---|---|---|---|
|Description (SITC)|Value ($ 000)|Share of Total|Value ($ 000)|Share of Total|Growth<br>Rate|
|DYNAMIC PRODUCTS||||||
|Iron and Steel Shapes (673)|181||32,573|1.2|68.1|
|Glassware (665)|52||6,768|0.2|62.7<br>|
|Chemicals, nes (599)|203||21,185|0.8|59.2|
|Articles of Plastic (893)|125||7,860|0.3|51.4|
|Plumbing and Lighting Equipment (812)|167||9,453|0.3|49.7|
|Meat Fresh and Frozen (011)|226||12,504|0.4|49.4|
|Cereal Preparations (048)|100||4,220|0.1|45.5|
|Rubber Articles (629)|59||2,491|0.1|45.3|
|Iron and Steel Tubes (678)|286||10,546|0.4|43.4|
|Stone, Sand and Gravel (273)|130||3,857|0.1|40.3|
|Leather Manufactures (612)|206||5,892|0.2|39.9|
|Cheese and Curd (024)|150||3,863|0.1|38.4|
|Food Preparations, nes (099)|484||10,897|0.4|36.5|
|Manufactured Fertilizers (561)|1.165<br>||25,544<br>|0.9<br>|36.2<br>|
|Structures and Parts (691)|252||4.973|0.2|34.7|
|Inorganic Chemicals (514)|58||989|0.0<br>|32.7<br>|
|Furniture (821)|2,702|0.1|40,974|1.5|31.2|
|Metal Manufactures, nes (698)|581||8,600|0.3|30.9|
|Wood Manufactures, nes (632)|268||3.686|0.1|30.0|
|FreFish<br>(031)|870||11,478|0.4|29.4|
|Cement and Building Products (661)|310||3,534|0.1|27.6|
|DECIINING PRODUCTS||||||
|Live Animals (011)|21,347|0.7|17,256|0.6|-2.1|
|Petroleum Products (332)|264,736|8.6|212,159|7.5|-2.2|
|Oil Seeds and Nuts (221)|7,787|0.3|5,832|0.2|-2.8|
|Essential Oils (551)|10,982|0.4|7,928|0.3|-3.2|
|Preserved Fruit (053)|6,795|0.2|3,720|0.1|-5.8|
|Sugar and lloney (061)|18,370|0.6|9.751|0.3|-6.1|
|Crude Petroleum (331)|1,746,086|56.7|818.362|29.1|-7.3|
|Tobacco Manufactures (122)|2.498|0.1|923||-9.5|
|Cotton (263)|431,453|140<br>|130,811<br>|4.6|-11.2<br>|
|Crude Fertilizers (271)|2,349|0.1|513||-14.1|
|Non-Ferrous Metal Scrap (284)|25,368|0.8|586<br>|||-31.4|

Source: United Nations Series D Trade Tapes.

**4-.**