rafmacalaba/data-use-mentions-extended huggingface dataset:
# Appendix 2: The role of networks in explaining migrants’ occupational choices

To better understand the role of networks of co-national migrants in the occupational choices of migrants, we estimate the following regression using two different waves of the EU-LFS: Log(pct_migrant_2018) cok = α + β log(pct_migrant_2005)cok + ηc + Ωo + φk + ε cok (2) where log(pct_migrant_2018)cok is the logarithm of the percentage of migrants in 2018 in each ISCO 3-digit occupation _c_ that are from the region of origin _o_ in NUTS2 division of destination _k_ . Log(pct_migrant_2005) cok is the logarithm of the share of migrant population from region of origin _o_ in each occupation _c_ residing in NUTS2 division of residence _k_ in 2005. Ωo is a vector of regions of origin _o_ (native, EU15, NMS13, EFTA, Other European countries, MENA, SubSaharan Africa, Asia, Latin America and non-Europe OECD countries); ηc is a vector of dummy variables for each ISCO 3-digit occupation; and φk is a vector of dummy variables for each NUTS2 division of residence K. In order to avoid overlapping of migrants in the two periods, we restrict the sample of migrants in 2018 to those that arrived within the previous ten years.

Migrant networks are particularly strong among co-nationals in the same area of residence. We run the OLS regression in equation (2) in which the cell of analysis is the ratio of migrants from world region of origin _o_ to native in NUTS2 division of residence _k_ working in occupation _c_ . The results of this regression analysis show a strong path dependence in occupational choices of migrants even after controlling for occupation, region of origin and NUTS2 division of residence fixed effects (Table A1). Our preferred regression includes all occupations – even those without a single migrant – and origin region, host NUTS2 division and occupation fixed effects. The results (Column 2) show that a 1 percent increase in the share of co-national migrants in an occupation in 2005 is associated with a 0.13 percent
**Table 1. Size and Type Distribution of Firms and Employment, 2008 (shares)**

||**_Formal and legal_**|**_Mixed_**|**_Informal and legal_**|**_Informal and illegal_**|**_Total_**|
|---|---|---|---|---|---|
|**_Firms_**||||||
|1–5|1.22|3.65|65.84|19.29|90.00|
|6–10|0.69|1.54|1.10|2.43|5.77|
|11–50|0.84|1.20|0.55|0.87|3.46|
|50+|0.25|0.29|0.18|0.05|0.77|
|**Total**|3.00|6.69|67.67|22.64|100.00|
|**_Workers_**||||||
|1–5|0.83|2.65|25.49|11.80|40.77|
|6–10|1.17|2.56|1.73|3.90|9.36|
|11–50|3.98|5.45|2.50|3.49|15.42|
|50+|12.56|13.47|7.41|13.47|34.44|
|**Total**|18.54|24.14|37.12|20.20|100.00|

_Source:_ Author’s computations based on Economic Census data.

# **Misallocation is High and Persistent in Mexico**
<!-- Start of picture text --> Figure 7c: The Evolution of Access to Adequate Environment<br>Prevalence of Adequacy in Environment<br>81<br>71<br>49<br>45<br>38<br>36<br>32 31<br>17 14 16<br>10<br>5<br>0 1 2<br>BGD, HK BOL IDN NPL ZWE<br>BGD, IFPRI ETH KHM PER<br>Year 1 Year 2<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (HK, IFPRI); Bolivia 2003, 2008; Cambodia 2005, 2010;<br>Ethiopia 2000, 2011; Indonesia 2010; Nepal 2000, 2011; Peru 2005, 2012; Zimbabwe 2005, 2010.<br>Note: (1) Bolivia (2003 and 2008) do not include improved sanitation.<br>(2) Ethiopia, Nepal, Bolivia, and Indonesia do not include community sanitation.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text -->

**Figure 7d: The Evolution of Access to Adequate Health Services**

<!-- Start of picture text --> Prevalence of Adequacy in Health<br>78<br>42<br>35<br>27<br>21 22<br>18 18<br>16<br>13 13<br>8<br>4 5 4<br>0<br>BGD, HK BOL IDN NPL ZWE<br>BGD, IFPRI ETH KHM PER<br>Year 1 Year 2<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (HK, IFPRI); Bolivia 2003,
starting year is 1984, reflecting the earliest availability of all relevant data. Data on fiscal rules comes from the IMF’s Fiscal Rules dataset (Davoodi et al. 2022). Data on primary balances, as well as other data on macroeconomic variables is sourced from the IMF’s October 2024 World Economic Outlook (WEO) database.

To estimate the response of the primary balance after the adoption of fiscal rules, we employ the local projections approach following Jordà (2005). Local projections are commonly used in the literature to estimate the dynamic effects of macroeconomic shocks and policy reforms to relevant economic variables.<sup>10</sup> We use the following specification: where _∆hfi,t+h ≡ fi,t+h – fi,t-1_ represents the cumulative change in the primary balance (as a % of GDP) from time _t-1_ to _t+h_ . As we track the response of the primary balance for the first ten years after fiscal rule adoption, _H_ is set to 9. We only include countries with at least ten observations per projection horizon _h_ , ensuring a theoretical rolling window of at _i_ and _τt_ least twenty observations of the primary balance.<sup>11</sup> _μ_ control for country- and timefixed effects (for each projection of the primary balance, time-fixed effects are included _h εit+h_ is the error term. with leads equal to ), respectively, and Following Afonso and Jalles (2019), we set our fiscal rules indicator (FRit) equal to one in the year a fiscal rule is introduced and zero otherwise, modeling rule adoption as a 10 Jordà and Taylor (2024) provide a review of the methodology and examples of its use in the literature. In the literature on fiscal rules, local projections have been used to estimate the effect of _fiscal rule adoption_ on sovereign spreads (Afonso and Jalles 2019) and government borrowing in foreign currency (Apeti et al. 2024). Moreover, research has used local projections
%, respectively (Table 1).

# **6.2.2 Upgrading in Terms of Occupation**

As discussed earlier, it seems reasonable to assume that education levels in Venezuela and in Ecuador are comparable. However, this assessment was based on very limited data. This raises concerns about the realism of the previous simulation where we relied heavily on the comparability of the education levels across the two countries. This section performs an alternative simulation that avoids this issue altogether.

As documented earlier (Section 5.2), Venezuelan migrants in Ecuador have experienced substantial occupational downgrading, relative to their last occupations prior to emigrating from Venezuela. The EPEC data show that 12,255 Venezuelan migrants are employed as day laborers in Ecuador, one of the low-pay occupations. However, only

> 21We switch our geographic unit to Ecuador’s 24 provinces, as opposed to 221 cantons. This higher level of aggregation increases sample size and delivers more robust findings.

> 22We note that our simulation keeps relative wages across education levels constant. This is a reasonable assumption given that Venezuelan migrants are only around 2.9% of Ecuador’s employment.

18
- Deng, X., Huang, J., Rozelle, S., and Uchida, E. (2008). Growth, population and industrialization, and urban land expansion of China. _Journal of Urban Economics_ , 63(1):96–115.

- Dercon, S., Gilligan, D. O., Hoddinott, J., and Woldehanna, T. (2009). The impact of agricultural extension and roads on poverty and consumption growth in fifteen Ethiopian villages. _American Journal of Agricultural Economics_ , 91(4):1007.

- Doll, C. N., Muller, J.-P., and Morley, J. G. (2006). Mapping regional economic activity from night-time light satellite imagery. _Ecological Economics_ , 57(1):75–92.

- Donaldson, D. (2015). The gains from market integration. _Annual Review of Economics_ , 7.

- Donaldson, D. and Hornbeck, R. (2016). Railroads and american economic growth: A “market access” approach”. _The Quarterly Journal of Economics_ , 131(2):799.

- Donaldson, D. and Storeygard, A. (2016). The view from above: Applications of satellite data in economics. _Journal of Economic Perspectives_ , 30(4):171–98.

- Duranton, G. and Puga, D. (2015). Urban land use. In _Handbook of regional and urban economics_ , volume 5, pages 467–560. Elsevier.

- Elvidge, C., Hsu, F.-C., Baugh, K., and Ghosh, T. (2014). National trends in satellite-observed lighting. _Global Urban Monit. Assess. Earth Obs._ , page 91–118.

- Elvidge, C. D., Baugh, K., Zhizhin, M., and Chi Hsu, F. (2013). Why VIIRS data are superior to DMSP for mapping
42 percent. Schooling disruptions (schools were closed almost a full year in 2014) _cannot_ explain the low enrollment rates at the time of the baseline, because the question asks whether parents enrolled their children in school, and thus refers a pre-Ebola situation. Schools closed in early 2014 due to Ebola, thus affecting the end of the 2013-14 school year. In the 2014-15 school year, because of Ebola, schools did not open in early October as usual, but instead in late January 2015. The political turmoil due to the elections in 2015 also disrupted the 2015-16 school year, in which schools opened in November instead of early October, because of a teachers’ national strike.

# 1. School enrollment

School enrollment was extremely low, by any international comparison, at the time of the baseline survey in June-August 2014. We derive school enrollment by asking parents whether their child is currently enrolled and whether she went to school during the school year. As shown in the left panel of Figure 4, school enrollment at the baseline for all children aged 5-15 years was at 42 percent (41.1 and 43.1 percent respectively for treated and control villages).

Figure 4 shows that school enrollment increased significantly in all of Guinea between 2014 and 2018. By the time of the endline in 2018, school enrollment reached 70 percent. The left panel of Figure 3 suggests that school enrollment increased significantly more in treated villages. It reached almost 66 percent in control and 74 percent in treated villages. The left panel of Figure 4 alone strongly suggests that the program was successful in increasing school enrollment more in treated villages.

Table 3 confirms that compared to control villages, treated villages see a significantly stronger increase in enrollment. The first column of Table 3 presents equation 1, comparing treated to control villages. The effect of the treatment is an increase of 11.8 percentage points in the enrollment rate. The time trend indicates that between 2014 and 2018 the overall increase in enrollment across all villages (treated and control)
# **Bibliography**

- **[1]** Aghion, P. and P. Howitt, 1992.  “A Model of Growth Through Creative Destruction,” _Econometrica_ 60, 323-352.

- **[2]** Ahn, Sanghoon, 2001.  “Firm Dynamics and Productivity Growth: a Review of Micro Evidence From the OECD,” OECD Economics Department.

- **[3]** Ahn, Sanghoon, 2002.  “Competition, Innovation and Productivity Growth: a Review of Theory and Evidence,” OECD Economics Department Working Paper 317.

- **[4]** Alesina, Alberto, Silvia Ardagna, Giuseppe Nicoletti and Fabio Schiantarelli, 2003. “Regulation and Investment,” NBER Working Paper No.  9560.

- **[5]** Barro, Robert and Jong-Wha Lee, 2000.  “International Data on Educational Attainment: Updates and Implications,” NBER Working Paper No.  7911.

- **[6]** Bassanini, Andrea and Ekkehard Ernst, 2002.  “Labor Market Institutions, Product Market Regulations and Innovation: Cross Country Evidence,” OECD Economics Department Working Paper 316.

- **[7]** Baxter, Marianne and Robert King, 1999.  “Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series,” _Review of Economics and Statistics_ , November 1999, v. 81, issue 4, pp.  575-93

- **[8]** Bayoumi, Tamim, Douglas Laxton and Paolo Pesenti, 2004.  “Benefits and Spillovers of Greater Competition in Europe: a Macroeconomic Assessment,” Federal Reserve Bank of New York Staff Report 182.

- **[9]** Bergoeing, Rafael, Norman Loayza and Andrea Repetto, 2004.  “Slow Recoveries,” NBER Working Paper No.  10584.

- **[10]** Berkowitz, Jeremy and Michelle J.  White, 2002.  “Bankruptcy and Small Firms' Access to Credit,” NBER Working Paper
, the growth of human settlements in the north more broadly, and the degradation of pastures in the north due to droughts. The death toll has been increasing, with 2,500 fatalities recorded in 2016 alone (International Crisis Group 2017).

Our data indicate that 25 percent of households in the North Central region experienced at least one conflict event between 2010 and 2017. The most common perpetrators of the events are reported to be pastoralists or nomads (45 percent), and the second most common perpetrators are insurgents (21 percent).<sup>6</sup> This indicates that the Boko Haram insurgency also has been active in the North Central region, but to a lesser degree than in the North East.

> 5 Appendix B provides more details on the spatial and time variation of conflict in the three regions with comparisons to external data sources.

> 6 The household survey data does not include nomadic households. Therefore, we only capture one side of the violence in the herder-farmer conflict—namely, the events where the perpetrator was a pastoralist and not the side where the perpetrator was a farmer .

7
I POLIcY RESEARCH WORKING PAPER 2477

# Summary findings

Geographic targeting is perhaps the most popular mechanism used to direct social programs to the poor in Latin America.

Schady empirically compares geographic targeting indicators available in Peru. He combines householdlevel information from the 1994 and 1997 Peru Living Standards Measurement Surveys and district-level information from the 1993 Peru Population and Housing Census.

He then conducts a series of simulations that estimate leakage rates; concentration curves; the impact of transfers on poverty as measured by the headcount index, poverty gap, and PI measures of the Foster-GreerThorbecke family; and nonparametric (kernel) densities when transfers are based on alternative indicators.

He concludes that there is substantial potential for geographic targeting in Peru. The differences in outcomes across geographic targeting indicators are small and not statistically significant.

This paper-a product of the Poverty Sector Unit, Latin America and the Caribbean Region-is part of a larger effort in the region to explore the potential of geographic targeting. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contactTania Gomez, room 18-102, telephone 202-473-2127, fax 202522-0054, email address tgomez@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The author may be contacted at nschady@worldbank.org. November 2000. (23 pages)

> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should he cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank. its Executive Directors, or the comntries they represent._

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In addition, the parameter estimate yields a long-run elasticity in the range of 0.290.39. This magnitude is similar to the benchmark results obtained in the earlier estimate. Given the interpolated nature of these sources, a too strong interpretation of the associated changes makes little sense. Insofar, the sign and significance of the variables are more important than their magnitude. All in all, employing alternative data on human capital confirms the earlier conclusions about the importance of education on growth.

# **_Population Instead of Labor Force Estimate_**

The regression in column 6 employs population data (15-64 years) instead of the labor force. Alternatively ILO labor force estimates could be used. The time series properties, however, are almost identical, and population statistics refer to a longer time period. In any case, the results are rather disappointing. That is, the significance of the coefficients and the overall fit of the model are poor. In order to ameliorate the estimate, the lag structure of the short-run capital coefficients was modified.

Human capital still enters positively but is only weakly significant. A puzzling finding is that long-run capital accumulation has now a negative impact on growth, which is a counterintuitive and implausible result. Overall, given the absence of fluctuations and considering the civil war, Guatemalan population data seems to be a poor proxy for labor as well.

# **6.3 Additional Explanatory Variables**

When the conditioning set of data in the regressions is modified, it is interesting to observe changes in the explanatory variables, such as schooling. For example, the production elasticities of human or physical capital could be larger than their factor shares because of presumed externalities. The benchmarks of the following analyzes are the results in section 5. When possible, the following paragraphs differentiate for the effect of primary, secondary and tertiary schooling.

# **_Quality-Adjustment Capital_**

Column 2 of Table 10 shows an OLS estimate for the period 1971-2002 when the capital stock is adjusted for quality. For comparative purposes,
found to have significant attenuating effects on performance differences. Given the importance of primary-school reading ability for later learning, these strong familybackground effects may have important consequences for the longer-run intergenerational mobility of Latin American societies.

Comparing the evidence for Argentina and Colombia with the set of comparison countries, the general pattern of results in terms of effects of student background and school characteristics is rather consistent across countries. But benchmarking the different estimated parameters across the countries also reveals characteristic national patterns. In particular, the effects of family background, measured by the number of books at home and by parental education, are relatively strong in Argentina and relatively weak in Colombia. In this sense, the fact that the intergenerational transmission of skills is particularly strong in Argentina can be viewed as one important underlying factor behind the relatively large variation in student performance observed in Argentina, particularly in the comparison within Latin America to Colombia.

Another result specific to Argentina is that once the family-background factors are held constant, no significant performance difference is found between rural and urban areas. A specific result for Colombia is that it is the only country where boys do not score significantly worse than girls. Finally, a result that distinguishes the two Latin American countries from all comparison countries is that they do not show a significant performance difference between parental occupations.

The results presented here using the data from the PIRLS student achievement test provide useful descriptive features of the different primary-school systems. However, the limitations of such retrospective studies, particularly when it comes to causal inferences, are well known (cf., e.g., Glewwe 2002; Todd and Wolpin 2003; Glewwe and Kremer 2005). The need for further research on the underlying behavioral relationships, preferably based on quasiexperimental or truly experimental setups which are able to exploit exogenous variations in important family and school characteristics, is beyond doubt.

22
often are not consistent or comparable across economies. The Global Findex indicator on account penetration lends itself most easily to comparison. While the results are broadly consistent with those of earlier efforts, the correlation is imperfect and in a few cases there are nontrivial discrepancies.

These differences are likely to stem from three important variations in user-side data on the use of financial services. First, the definition of an account varies across surveys and respondents are often prompted in different ways. The Global Findex survey defines an account as an individual or joint account at a formal financial institution (a bank, credit union, cooperative, post office, or microfinance institution) and notes in the question text that an account can be used to save money, to make or receive payments, or to receive wages and remittances. It also includes those who report having a debit or ATM card. Other surveys may list an array of institutions (formal or semiformal) or products (savings account, checking account, pension scheme, Islamic loan) that are specific to the economy or region, while still others may simply ask, “do you have a bank account?” Second, there are important differences in the unit of measurement across surveys. While the Global Findex account penetration indicator refers to individual or joint account ownership, many earlier surveys measured account penetration

**5**

MEASURING FINANCIAL INCLUSION
. Therefore, we use a vintage of the national accounts data that is different, even for the same years and countries. Similarly, survey data is sometimes revised, or could also be removed. Deflators – both for surveys and national accounts are also often revised – but are not a concern in our assessment of gaps levels, as we rely on survey and national accounts data in the current values.

In terms of methodology, in our analysis we use weights that differ from those used in Deaton. Our preferred weighting scheme is to give each country equal weight within each sub-sample for which we provide estimates. Ravallion uses only one observation per country and thus also give each country equal weight. Deaton uses two weighting schemes. The first is unweighted – giving each observation equal weight regardless of the number of observations per country, and a second uses population weights.

Given the differences in both the time periods covered, data sources, and weighting methods, it is useful to try to understand the source of the differences. To this end, we examine our estimates by decade. About three-quarters of our observations are from the period after 2000, a period not covered by either of the previous studies. Thus, our overall estimates are heavily weighted towards a time period not covered by earlier studies. The HHS-NAS gap estimated separately for each decade (see Table 1) reveal much smaller gaps for the periods prior to 2000 also in our data. Comparing survey and HFCE means, we estimate a gap of about -15% in the 1980s and 1990s and about -25% for the 2000s and 2010s, a difference which is statistically significant (t=2.38 when comparing 1990s and 2000s samples). Our point estimates of around -15% for 1980s and 1990s are similar to the corresponding estimates of gaps of -12% and -17% for Deaton and Ravallion, respectively. The fact that our sample is heavily weighted towards more recent years than previous studies, contribute to our overall finding of larger gaps