rafmacalaba/datause-encoder-data huggingface dataset:
# 6 Conclusion

This paper reviews the existing literature on the impact of migrant networks (diasporas) on the international migration patterns. In addition to size of the migration flows, we include the skill composition and concentration among these patterns we analyze and show that diasporas strongly influence all three. We first present a simple theoretical model that identifies the channels through which diasporas would influence migration patterns. These channels mainly operate through lowering of bilateral migration barriers via assimilation effects and family reunification programs. It identifies the key issues, namely the impact on size, selection and concentration of the migration flows. The paper also reviews the data and econometric hurdles that the researchers face in assessing the importance of networks. Among the key issues are the choice of individual micro vs aggregate macro approach, the definition of a network, the access to suitable data, and the adoption of econometric methods accounting for the main features of those data, such as wide prevalence of zeros.

The main results are illustrated with estimation results obtained using the Docquier-Lowell-Marfouk (DLM, 2009) data. Larger networks are shown to exert strong positive influence on the size of the international flows and lead to lower skill composition for a given corridor. We also show that diasporas also favour the concentration of the unskilled migrants with respect to the skilled ones. Destination and origin country specific factors are captured via fixed effects. All other bilateral variables, such as linguistic overlap, distance, colonial linkages have the expected signs and economically significant effects.

Naturally, there are many questions remain unanswered. One venue to consider is whether if these results hold for non-OECD destination countries and south-south migration. They require higher quality data that covers larger number of destination countries outside the OECD. Another key issue is separating the assimilation effect of diasporas from the visa effect which operates mainly through the family reunification programs. Such questions will require different types of dataset that combine aggregate data with household level data. In closing, diasporas are among the key determinants of migration patterns and we have only scratched the surface in identifying their effects.

15
school autonomy to improve the quality of public schools.

While these studies provide a positive impression of the impact of participation on project effectiveness, other studies are more ambiguous. Gugerty and Kremer (2000) use random assignments to evaluate the impact of a participatory program to provide agricultural inputs that was designed to build social capital among women in Western Kenya. They find that the costs of inputs were greater than the benefits (increased agricultural output).

Hoddinott and others (2001) look at the relationship between participation and project employment outcomes using data on public works programs from South Africa’s Western Cape Province and attempt to isolate the impact of de jure and de facto participation on each outcome using an instrumental variable strategy. Their instruments for participation are measures of community fractionalization (percentage of adults who are divorced, a measure of racial fractionalization, an index of political fractionalization). They find that participation has no effect on any of their outcome variables. When they interact the participation variable with project type, the effects of participation are somewhat stronger but still quite weak. However, it is unclear whether their instrument set is valid for the outcome variables they analyze, and their results are also based on a small set of projects.

In addition to impact evaluations, there are other types of studies that define the causal effects less clearly but that are nevertheless interesting. Katz and Sara (1997), who analyze the performance of water systems in a variety of countries, find that performance was markedly better in communities where households were able to make informed 29
**Figure 7. College Graduation Rates by Gender and Birth Cohort and Risk of Induction in Vietnam, by Birth Cohort**

<!-- Start of picture text --> 0.4 0.25<br>0.35<br>0.2<br>0.3<br>0.25 0.15<br>0.2<br>0.1<br>0.15<br>0.1<br>0.05<br>0.05<br>0 0<br>37 - 38 - 39 - 40 - 41 - 42 - 43 - 44 - 45 - 46 - 47 - 48 - 49 - 50 - 51 - 52 - 53 - 54 - 55 -<br>38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56<br>Birth Cohort<br>Males Females Induction risk<br>Risk of induction<br>College graduation rate<br><!-- End of picture text --> _Note_ : College graduation rates are calculated for individuals born between 1937 and 1956 from the 1983, 1985, 1987, 1988, 1990, 1991, 1992, 1994 and 1995 National Health Interview Surveys. The risk of induction in Vietnam is the average yearly number of inductions in Vietnam during the years when that birth cohort was aged 19-22, divided by the size of that birth cohort, calculated from data in Card and Lemieux [2000] and United States Department of Education [2000].

**Figure 8. Risk of Induction in Vietnam and Risk of Induction in Vietnam Multiplied by the Risk of being Killed in Action, by Birth Cohort**

<!-- Start of picture text --> 0.25 0.0045<br>0.004<br>0.2 0.0035<br>0.003<br>0.15<br>0.0025<br>0.002<br>0.1<br>0.0015<br>0.05 0.001<br>0.0005<br>0 0<br>37- 38- 39- 40- 41- 42- 43- 44- 45- 46- 47- 48- 49- 50- 51- 52- 53- 54- 55- 56-<br>38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57<br>Birth Cohort<br>Risk of induction Risk of induction* Risk killed in action<br>Risk of induction<br><!-- End of picture text --> _Note_ : The risk of induction in Vietnam is the average yearly number of inductions in Vietnam during the years when that birth cohort was aged 19-22, divided by the size of that birth cohort, calculated from data in Card and Lemieux [2000] and United States Department of Education [2000]. The risk of being killed in action is the ratio between the number of soldiers killed in action in Vietnam in a year and the level of troops during that year (see notes 26 and 27 for details and data sources).

30
WPS3784 poverty.<sup>4</sup> The analysis concentrates on coffee, the main exportable crop and the main vehicle for raising cash incomes in rural areas and for stimulating a switch from subsistence to more market oriented agriculture. Beyond coffee farmers, reductions in rural transport costs will benefit all producers and consumers in rural areas and will be integral to a policy that seeks to increase the role of markets and market oriented production in rural areas. Thus, the impact of policy and investment initiatives to improve access to rural transport will by far exceed the more narrow simulations on poverty that we investigate here.

As a small coffee exporter, Rwanda is a price taker in the world markets. Hence in the analysis below we take world prices as exogenous and assume that changes in coffee supply in Rwanda have no impact on the world price. For all coffee farmers, regardless of the quality of their crop, one of the key determinants of the producer price that they receive in Rwanda will be the transport cost.

Transport costs incurred by exporters both within Rwanda and on the main corridor routes to ports in neighboring countries are very high. It is estimated that the transport cost from the farm-gate in Rwanda to the port in Mombassa is about 80 percent of the producer price. Rural transport costs alone, that is, of transport from the farm gate to the capital, Kigali, is estimated at 40 percent of the farm gate price!<sup>5</sup> These costs represent an implicit tax on coffee (and other exportable) producers.

Porto (2005) shows how reducing trade costs in Moldova could have a substantial impact on poverty. We follow a similar approach simulating lower rural and international transport costs leading to higher prices for farmers, which encourage greater production and raise the incomes of rural farmers. Here, we look specifically at the effect of a 50 percent cut in rural transport costs on coffee farmers’ income. This is not unrealistic. Most of the roads in rural areas are unpaved. Vehicle operating costs are estimated to be

> 4 Access to rural transport is also a key element in providing access to other key services related to poverty and standards of living, such as, participation in education and access to health services. 5 World Bank (2005)

6
<u>Table 3. Mean hours per day, by age group, type of activity and sex</u>

|||<br>Unaid<br>Wo|<br>men|<br>Leisure<br>15-|<br>24|<br>Unaid<br>Me|n|Leisure|
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|
|Albania|0.72|4.05|11.86|7.31|2.11|0.38|12.38|9.11|
|Argentina|1.35|2.61|n.a.|0.02|3.33|0.91|n.a.|0.02|
|Bangladesh|1.42|4.90|14.09|4.62|4.71|1.05|12.87|5.35|
|Colombia|1.81|2.97|10.75|8.46|3.23|0.98|10.36|9.41|
|Ecuador|1.41|3.57|10.00|7.16|3.12|0.94|9.74|7.21|
|Ghana|1.74|4.82|12.39|6.00|1.56|3.00|11.82|8.51|
|Guatemala|2.09|5.78|11.23|3.57|5.35|0.94|11.24|4.52|
|Iraq|0.22|3.86|n.a.|7.42|3.40|1.14|n.a.|8.57|
|Mexico|2.17|4.07|n.a.|16.55|4.22|1.43|n.a.|16.12|
|Moldova|0.54|2.75|11.72|7.81|1.06|2.29|11.61|7.85|
|Mongolia|1.73|5.15|11.72|5.60|2.04|4.40|11.66|6.33|
|Pakistan|0.72|5.38|13.43|4.45|3.42|2.00|12.47|6.08|
|West Bank and Gaza|0.67|2.43|11.27|7.87|3.99|0.14|11.18|8.35|
|Peru|2.04|4.11|10.11|7.36|3.81|1.79|9.83|8.06|
|South Africa|1.20|3.92|12.46|6.67|1.83|2.12|12.29|8.12|
|Timor|0.46|2.12|n.a.|n.a.|1.07|1.26|n.a.|n.a.|
|Turkey|0.95|3.89|11.30|5.96|2.59|0.31|11.05|6.57|
|Uruguay|1.81|3.48|10.21|4.27|3.32|1.23|9.98|3.74|
|USA|2.12|1.64|11.41|7.75|2.90|1.07|10.94|8.50|

|||Unaid<br>Wo|men|Leisure<br>25-|44|Unaid<br>Me|n|Leisure|
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|
|Albania|2.59|6.36|10.89|4.07|5.50|0.61|11.81|6.06|
|Argentina|2.98|4.40|n.a.|0.03|5.86|1.33|n.a.|0.02|
|Bangladesh|1.84|6.44|13.75|1.93|7.10|1.52|13.18|2.19|
|Colombia|3.76|4.59|9.98|5.78|6.82|1.18|9.69|6.20|
|Ecuador|3.33|6.96|9.69|3.59|7.03|1.55|9.39|3.67|
|Ghana|3.14|6.66|12.15|3.45|3.97|4.20|11.68|5.27|
|Guatemala|3.03|7.68|10.62|1.82|7.81|1.31|10.80|2.28|
|Iraq|0.75|4.99|n.a.|5.19|7.18|1.41|n.a.|4.98|
|Mexico|3.80|7.21|n.a.|12.26|7.96|1.89|n.a.|12.13|
|Moldova|3.26|5.39|10.78|3.28|4.56|2.85|10.98|4.61|
|Mongolia|3.37|6.89|11.27|2.54|4.13|5.87|11.42|3.06|
|Pakistan|0.59|7.78|12.86|2.74|5.54|2.65|12.19|3.59|
|West Bank and Gaza|1.06|4.24|10.81|5.37|6.12|0.60|11.06|5.88|
|Peru|3.64|6.65|9.65|3.60|7.53|2.28|9.46|4.34|
|South Africa|3.36|4.70|12.16|3.99|5.72|2.11|12.06|4.69|
|Timor|1.26|2.71|n.a.|n.a.|3.68|1.40|n.a.|n.a.|
|Turkey|0.99|5.98|10.70|4.48|4.73|0.49|10.56|4.26|
|Uruguay|5.34|8.30|9.31|1.88|8.54|2.93|8.90|1.69|
|USA|3.85|3.90|10.67|4.50|5.80|2.27|10.14|5.22|

30
|**Does not know whether paid tax**|-0.0337|-0.5255***|0.0549|-0.0149|0.1136|-0.0973|-0.6057**|-0.3462|0.0527|0.0359|0.4970**|-0.1551**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Moonlighting**|0.2581***|0.0479|0.3305***|0.1704**|0.0997|0.3115***|(omitted)|0.295|0.1782|0.4322***|0.0987|0.2103**|
|**Dissatisfied with her / his job**|-0.0771**|-0.0276|-0.0853|-0.0902*|-0.107|-0.071|-0.256|0.0739|-0.0648|-0.1042|-0.1177|-0.0681|
|**On-the-job training (baseline: no training)**|||||||||||||
|**Training of which apprenticeship**|-0.0994|0.2872|0.2352|-0.1044|-0.1474|0.2818*|0.3917|1.2057|0.3337|0.2764|-0.3269|-0.1473|
|**Mentoring**|-0.4067***|0.3651|(omitted)|-0.4783***|-0.4231*|(omitted)|(omitted)|(omitted)|0.0991|(omitted)|-0.9145**|-0.4374**|
|**Training in new technologies**|(omitted)|(omitted)|0.2657|(omitted)|(omitted)|0.3811**|(omitted)|(omitted)|(omitted)|0.2211|(omitted)|(omitted)|
|**Other type of training**|-0.1494**|0.217|0.1795|-0.1799**|-0.1146|0.2439|0.0869|0.3671|0.3544|0.1377|-0.3705|-0.1634*|
|**Union Membership**|-0.1494***|-0.4996**|-0.1696**|-0.1327**|-0.2089***|-0.1088*|-0.3896|-0.7352*|-0.0828|-0.1596*|-0.2347**|-0.0957|
|**Firm size (baseline: does not know the firm’s size)**|||||||||||||
|**5 workers or less**|-0.1940***|0.0358|-0.2565***|-0.1098|-0.1278|-0.2669***|-0.2985|0.5928|-0.2390*|-0.2303**|0.0111|-0.2599***|
|**From 5 to 9**|0.0399|0.4455|-0.1363|0.1630*|-0.0066|0.0147|-0.3247|1.0852**|-0.325|0.0044|0.3022|0.0159|
|**From 10 to 19 size_10t19**|0.0959**|0.1822|0.0727|0.1117|0.0165|0.0597|-0.2876|1.3500**|0.0138|0.1187|0.0485|-0.0308|
|**More than 20**|0.1187**|0.7575**|0.0975|0.1001|-0.0098|0.1255*|0.0111|1.7452**|-0.1852|0.2978***|0.1899|-0.0388|
|**Living in rural areas**|-0.1260***|-0.0874*|-0.1507***|-0.1178***|-0.1611***|-0.1064***|0.0129|-0.0926|-0.1209*|-0.1359***|-0.1941**|-0.1214***|
|**Constant**|5.5152***|6.1937***|5.3133***|5.4711***|6.1275***|4.9538***|5.0654**|5.5964***|4.9288***|5.4960***|6.7281***|5.0239***|
|**N**|1896|316|703|877|658|1238|129|187|240|463|289|588|

_Note: * p<.1; ** p<.05; *** p<.01. 1 For the definition of actual work experience adopted see the data section. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 27
employment decisions. However, other aspects of the business environment can affect these decisions as well, raising costs and risks associated with doing business or by creating barriers to competition. By affecting the overall growth prospects, issues from the quality of infrastructure, the strength of property rights, the nature and enforcement of business regulations and the overall openness in the management of public resources can all affect firms’ growth (World Bank, 2004). This paper looks at the joint and relative importance of a number of dimensions of the business environment on firms’ growth.

Unlike most existing studies which rely on variation in business environment conditions across countries, sectors or sub-national territories (states), this paper exploits firm level data on the business environment conditions faced by firms. Yet, there are a number of methodological issues that need to be addressed, including potential measurement error, omitted variable bias and endogeneity. To minimize the risk of the first two problems, the analysis is conducted with full set of sector-survey interaction dummies. In addition to controlling for some potentially important omitted variables, and measurement errors, they also control for survey differences and differences in demand conditions. In addition, incorporating multiple dimensions of the broader business environment simultaneously deals seriously with concerns of omitted variable bias of papers that include only a single dimension, such as labor regulations or finance.

To address potential endogeneity, this paper uses location-sector-size averages (minus individual firms’ own responses) of the business environment measures rather than individual firms’ own responses. The paper shows that there is a large potential degree of endogeneity in individual responses and that the results vary considerably if such endogeneity is not accounted for. The paper also makes the point that how these averages enter into the regressions matters greatly. Using the location-sector-size average does not necessarily address the endogeneity problem – if the averages used are based on current size. As objective conditions can be more challenging for smaller firms (e.g. smaller firms are less likely to access finance and more likely to pay bribes), assigning firms that grow substantially their current more favorable IC conditions, rather than those faced by firms of their initial size, can introduce a mechanical relationship between employment growth and many of the IC measures. Matching the averaged indicators to initial size is needed to avoid 3
Lebanon, Morocco Tunisia, the West Bank and Gaza, and Yemen. Five countries in the sample have two waves of data (2013 and 2019/2020), with the exception of Egypt and Yemen. Egypt that has three waves of data (2013, 2016 and 2019/2020). The two waves of data for Yemen are 2010 and 2013. Summary statistics are provided in table A1. Table A2 provides the survey and country composition of the sample.

The ES methodology has the advantage of deploying the same definition of the universe of inference, standard sampling methodology, standardized survey instrument, and a uniform methodology of implementation. The selection of firms in each country is done by stratified random sampling with three levels of stratification: sector, size, and location within the country. Sampling weights are used to correct for unequal probability of selection as well as for ineligibility and non-response. The data are largely collected using Computer-Assisted Personal Interviewing (CAPI) software. The CAPI software collects geo coordinates of the firm’s location that we use to match rainfall and temperature data with the firm-level data. To maintain anonymity of the respondents, the geo-codes are masked around a 2km radius.

Implementation of the surveys is consistent across countries. Formal training sessions of supervisors and enumerators ensure best practices are employed. Quality control checks guarantee the quality of the data throughout the data collection process. Consistency checks are employed for 10% and 50% batches of the data during the survey. Enterprise Surveys’ global methodology, sample design, and weights computation are available on the website http://www.enterprisesurveys.org. The data have been widely used in the literature (Besley & Mueller, 2018; Chauvet & Ehrhar, 2018; Hjort & Poulsen, 2019).

# **_2.1.2 Precipitation Shocks_**

To measure precipitation shocks, we use re-analysis data from the European Copernicus program. This is the Earth Observation component of the European Union’s space program. Through the ERA5-Land data platform they make data on precipitation available at the hourly level on a 0.1◦×0.1◦ grid for the planet from 6
|Competitiveness and Enterprise Development Project(CEDP)|Moses K. Kibirige|
|---|---|
|Competitiveness and Enterprise Development Project(CEDP)|Moses K. Kibirige|
|Competitiveness and Enterprise Development Project(CEDP)|Moses K. Kibirige|
|Competitiveness and Enterprise Development Project(CEDP)|Moses K. Kibirige|
|Competitiveness and Enterprise Development Project(CEDP)|Moses K. Kibirige|
|Competitiveness and Enterprise Development Project(CEDP)|Moses K. Kibirige|
|Competitiveness and Enterprise Development Project(CEDP)|Moses K. Kibirige|
|Management and Protection of KeyBiodiversityAreas in Belize|Maja Murisic|
|Management and Protection of KeyBiodiversityAreas in Belize|Maja Murisic|
|Management and Protection of KeyBiodiversityAreas in Belize|Maja Murisic|
|Management and Protection of KeyBiodiversityAreas in Belize|Maja Murisic|
|Management and Protection of KeyBiodiversityAreas in Belize|Maja Murisic|
|Management and Protection of KeyBiodiversityAreas in Belize|Maja Murisic|
|Management and Protection of KeyBiodiversityAreas in Belize|Maja Murisic|
|Management and Protection of KeyBiodiversityAreas in Belize|Maja Murisic|
|Management and Protection of KeyBiodiversityAreas in Belize|Maja Murisic|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|ENVIRONMENTAL SERVICES PROJECT|Drite Dade|
|Private Sector Development and Competitiveness Project|Jean Mabi Mulumba|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Lao PDR Second Trade Development FacilityProject|Mombert Hoppe|
|Jordan Public Procurement Reform Support|Sepehr Fotovat Ahmadi|
|Jordan Public Procurement Reform Support|Sepehr Fotovat Ahmadi|
|Jordan Public Procurement Reform Support|Sepehr Fotovat Ahmadi|
|IN Karnataka Urban Water SupplyModernization Project|Andreas Rohde|
|IN Karnataka Urban Water SupplyModernization Project|Andreas Rohde|
<!-- Start of picture text --> Enrollmen in Higher Education and Financial Aid<br>High School graduation Cohorts 2017, 2018 and 2019<br>1<br>=<br>8<br>=<br>Cc<br>iT)° ><br>6<br>= 6<br>oO<br>S<br>me}<br>Wu<br>3} 95% Cl<br>= 4 ; ;<br>ma - — Migrants - Aid<br>< Migrants - No Aid<br>National - No Aid<br>. National - Aid<br>2<br>-100 -50 0 50 100<br>Cut-Off Financial Aid<br><!-- End of picture text -->
# ACKNOWLEDGEMENTS

A number of persons provided us with important information for this work or assistcd us in preparing it in other ways. The Estimates and Projections Section of the U.N. Population Division has been continually supportive in pro'.ridin- us with data, even before their publication as U.N. assessments. Partly as a result, nonessendia. ..lferences between these projections and the biannual U.N. assessments have beep minimized. We are grateful to the former acting section chief, Shunichi Inoue, and to his continually accommodating staff, as well as to the head of the division, Jean-Claude Chasteland, and hope that the collaboration will continue.

World Bank country economists, population specialists, and the few demographers in the operating divisions have also provided us with recent population estimates, bringing to our attention government reports we might not otherwise see.

This exercise was carried out using a personal compu._r program, ProjPC-II, written by Kenneth Hill of Johns Hopkins University.

Both the Population, Health, and Nutrition Division and the Population and Human Resources Department have been supportive of this work, despite the time required. We are grateful to the division chief, Dr. Anthony R. Measham, and the department director, Ann 0. Hamilton.

We also wish to acknowledge the interest of the Bank's International Economics Socioeconomic Data Division (IECSE), which not only utilizes our figures--for such important Bank purposes as estimating per capita GNP--and incorporates them into the databases it maintains and publications it distributes but also helps coordinate communications with country economists. Sulekha Patel of that division has been particularly helpful in the past year.

In producing the manuscript, Inga Schweiger was responsible for some typing and coordinating the paper flow.

Rodolfo A. Bulatao Eduard Bos Patience W. Stephens My T. Vu