rafmacalaba/datause-encoder-data huggingface dataset:
# **Career Placement of Skilled Migrants in the U.S. Labor Market: A Dynamic Approach**

## Ileana Cristina Neagu*

JEL codes: F22; H52; J24; O24 Keywords: _migration; assimilation; skilled occupation_

*Development Economics Research Group (DECRG), The World Bank. MC3-303, 1818 H Street, NW, Washington, DC 20433; E-mail: ineagu@worldbank.org

I would like to thank Çağlar Özden, Aaditya Mattoo, Frederic Docquier and Maurice Schiff without the help and mentorship of whom this paper would not have been possible, David McKenzie for valuable advice, participants of the seminar at the University of Louvain-laNeuve, Belgium, for helpful comments and suggestions, and members of the doctoral committee for patience. The views expressed in the paper are those of the author and should not be attributed to the World Bank, its Executive Directors or countries they represent.
Appendix outlines the P-Star model and develops the alternative measures of deviations from money market equilibrium.

The analysis focuses on the period January 1999 to November 2008, including the lags. A nationally representative CPI is available from 1997 but extending the sample further back in time is challenging: there were significant data revisions of the National Accounts and the CPI methodology in 2000. Data on the Euro exchange rate, which we prefer to use, is available from January 1999. The Ethiopia and Eritrea war during 1998-2000 created economic instability. The appendix describes the data sources, methods, and definitions of the variables used.

# _4.1 The Monetary Sector_

Modeling money demand in Ethiopia is less straightforward than in many other countries because of its small financial sector and heavy government regulation. The sector consists of 10 commercial banks and one development bank. The Commercial Bank of Ethiopia, a state-owned bank, dominates the market (IMF, 2007). It had a market share of over 70 percent in both deposits and loans in 2002, and these have only declined moderately since then (IMF, 2002; IMF, 2007). Moreover, the capital account of the Balance of Payments is closed, so domestic investors are not allowed to issue debt in international capital markets. Thus, the market structure is concentrated and there is limited competition.

Interest rates are partially liberalized: the National Bank of Ethiopia sets the minimum bank deposit rate while banks are free to set all lending rates and deposit rates beyond the minimum. The minimum interest rate was adjusted only twice between January 1999 and July 2008, and the averaged deposit rate only changed a few more times.

The banking system is characterized by excess liquidity and banks hold about twice as much reserves in the National Bank of Ethiopia as required (Saxegaard, 2006; IMF, 2008c). No interest is paid on excess reserves. As the capital account is closed, treasury bills may become attractive for banks, buying over 80 percent of the ones issued. Subsequently the Treasury bill rate is low, and it has been negative in real terms since mid-2002. In 2007/08, the real Treasury bill rate was -24 percent, and the 16
regressions include the full set of regressors and upazila and year fixed effects.

The first three columns of Table 3 report the fixed effects regression results for employment by all firms (using population census data) and the last three for employment by the large firms (using data from economic censuses). In order to facilitate comparison between the economic census data on large firms and the population census data on all firms, the employment categories are kept at a fairly aggregate level, distinguishing only between manufacturing and services.<sup>23</sup> The top panel reports the results for the STE sample and the bottom for the rural sample. The number of periods in the census panel data is two and economic census is three. This is because 1990 census data are used to define the initial characteristics of upazilas, and the economic census has an additional data point for 2006 which is a non-population census year.

# **Evidence on All Firms (Irrespective of Size)**

The evidence in the first three columns on all firms shows an interesting pattern: the estimated effect of a positive rainfall shock is positive and statistically significant on the manufacturing and services sectors, while the effect is negative on agricultural employment, although it is not statistically significant at the 10 percent level. This pattern of estimates is observed in both the rural and the STE samples, with the magnitude of the effect larger for manufacturing and smaller for services in the case of the rural sample. The negative effect on agriculture, although imprecisely estimated, suggests labor reallocation away from farming. Note that according to the theoretical analysis, given a labor endowment _L_<sup> ̄</sup> , a higher agricultural productivity results in labor reallocation away from agriculture when _Ewθ >_ 1. Given that the estimates of _Ewθ_ in the rural and STE samples are 1.82 and 1.47 respectively, the theory predicts a more robust negative effect on agricultural employment. One possible explanation is that the controls we use for changes on labor endowment over time are less than perfect, and thus the negative effect on agricultural employment is diluted by a growing labor force.

The evidence from the population census data suggests that agricultural productivity growth leads to

> 23Census data do not provide employment information at disaggregate level for 2010.

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prices are significantly smaller than earlier estimates based on different stylization of complete or partial liberalization The results in scenarios I and II are based on the assumption that restraints on domestic support will be ineffective. In contrast, scenario IV (column 5) assumes that domestic support restraints will be effective. The benchmark simulation used 1989/93 protection levels for the period 1994/2002. Where protection is negative (in the case of import subsidies or export tax), no changes in protection rates were assumed. As expected, the price impacts from scenario IV show larger prices increases than in Scenario I and II.

The last column are from the FAO study (1995) based on a partial equilibrium model and excludes the impact of manufacturing liberalization. The analysis is based on the detailed data on agreed bound tariffs rather on stylized reductions used in other studies. However, the FAO study does not consider the extent of "slippage" due to the excessive tariffication and ineffective disciplines in domestic support. That is, the FAO study measures liberalization by the reduction in the bound tariff rate.

Overall, the price changes from several simulations shown in Table 2 suggest that world agricultural prices relative to the price of OECD manufacturing exports are unlikely to rise significantly as a result of the actual outcome of the Round. The estimates are significantly smaller compared with the price boom in food prices in 1995 following the completion of the Round. In utilizing these results, it is important to keep in mind the inherent difficulty in defining the true counterfactual rate of protection. Since protection rates are measured based on world prices, there is a question of establishing the representative world market prices at the end of the implementation period. Further, as shown by Anderson and Hayami (1986), protection rates in agriculture are determined by economic variables and influenced by the level of development of a country. If the empirical evidence of the upward trend in protection in some countries (e.g. East Asia and EU) are true and assumed to continue in the future, then the counterfactual rate of protection in the absence of the Round would be higher than those assumed in the analyses, and the price impacts would be larger. On the other hand, it can be argued that the recent fundamental reforms in many countries, partly motivated by factors independent of the Round, (e.g. CAP reforms in EU, liberalization in certain commodities in Japan and the Republic of Korea, and unilateral reforms during structural adjustments in developing countries) represent a structural change toward more world market integration and open economies. Since many of these reform began in early 1990s, the benchmark protection based on 1989-93 have captured these changes to 11
suggest that collateral registries can reduce information asymmetries between borrowers and lenders and improve access to finance.

One limitation of the paper is our relatively small sample of reformers, since we only have seven countries that introduced registries for movable collateral for which we are able to obtain pre and post reform data. Given the small sample of reformers, we are forced to treat all reforms homogenously, but as argued by Alvarez de la Campa et al. (2012) not all registries operate in the same way (e.g., some are less flexible in terms of the procedures to register assets, some charge higher fees than others, etc.). Surely, differences in the way registries operate might affect the extent to which the introduction of a registry increases access to finance. As more countries introduce collateral registries for movable assets and more firm surveys post reform become available, it will be important to further test the robustness of our results and to examine how differences in the functioning of collateral registries for movable assets affect their impact on access to finance.

16
in some specifications). Despite the consumption boom induced by demobilization cash transfers, we find that the DDR program had no significant indirect effect on shopkeepers. This surprising result may be due to an increasingly competitive environment following the return of demobilized combatants. Table S.9 in the appendix indeed shows that shop ownership increased for FNL households between 2006 and 2010, from 12% to 45% ( _p_ -value = .12). In contrast, we find that shop ownership of CNDD+ households declined between 2006 and 2010, from 56% to 30% ( _p_ -value = .29). These changes go in the direction consistent with our interpretation but are not significant at conventional levels. Increased competition could also explain why we observe a reduction in the price of bottled beers in collines characterized by a high density of demobilized FNL returns and a positive evolution in the price of bottled beers in collines with a high density of CNDD+ returns (table S.8 in the appendix). Finally, the impact on public sector employees is striking and should be emphasized. The relative situation of public employees has sharply improved in collines with a high proportion of CNDD+ returns. On the contrary, public employees living in collines with numerous FNL ex-combatants are relatively worse off. This is not surprising if we remember that the actual president of Burundi, Pierre Nkurunziza, is the former leader of the CNDD-FDD rebel group; public employees working in collines with numerous CNDD+ ex-combatants may have been favored following his election in 2005.

An alternative explanation for our findings could be that collines hosting FNLs and CNDD+ ex-combatants were following different trends: a positive trend for FNL collines and a negative trend for CNDD+ collines. This explanation is unlikely for a few reasons. First, there is no evidence of strategic relocation of ex-combatants. The registers include data on village of origin and return. Comparing these for each of the ex-combatants, we find that around 88% have come back to their colline of origin, and 92% to their home municipality. Formally, our analysis of the relative influence of unobservables compared to observables suggests that our results are not driven by unobservable characteristics, such as the drivers of relocation (table S.6 in the appendix).

Second, when the proportion of CNDD+ demobilized soldiers is included in the analysis of Verwimp and Bundervoet (2009), the coefficient associated with these spillovers is positive and equal to 0.01 ( _p_ -value = .04) without province fixed effects and 0.006 ( _p_ -value = .13) with fixed effects (available upon request). Its size is hard to compare with that of our estimates as their sample is different. In 2007, they did not go to two of our three provinces (Bubanza and Bujumbura Mairie) because the situation was deemed too volatile. Moreover, we do not use the exact same specification and indicators. Nevertheless, this suggests that collines hosting numerous CNDD+ ex-combatants were following a positive trend between 1998 and 2007.

25
given more latitude during the term of their loan, in the case of a bad week or shock. Default at the end of the cycle, however, is unequivocally avoided.

We find that proportion of missed weeks is overall lower under individual liability (just as reported in Table 2), but is statistically significant for those with stronger social networks. If those with stronger social networks are given more latitude under group liability because others are able to vouch for their intent to repay the following week, the shift to individual liability reduces the frequency of missing payments since this luxury is no longer available. This is similar to Karlan (2005), which uses data from a group lending program in Peru and finds evidence that those with higher social capital are able to default on loans and remain in the lending circle, whereas those with lower social capital are typically forced out after default.

Eventual default, however, is lower under individual liability for those with stronger social networks. This may be an indication that those identified as having stronger social networks are more trustworthy, hence the shift to individual liability has no adverse effect on their likelihood of repaying. Those with weaker social networks, however, are in fact more likely to go into default on their loan under individual liability, relative to those who remain in group liability. An alternative hypothesis is that those with stronger social networks must repay their loan in both setups in order to protect their social networks. Those with weaker social networks have less to lose (less social collateral, in the model of Besley and Coate (1995)), and hence the shift to individual liability generates higher default.

# _E.  Changes in Social Networks_

Next, we examine the results of the follow-up social network survey. In Table 11 we show these results. As we have both baseline and follow-up data on social networks, we are able to employ a difference-in-difference empirical specification. We find only one social network 21
Policy Research Working Paper 9014

# **Abstract**

This paper analyzes the differential impact of migration on labor supply of the left-behind household members in Nepal, where international migration for employment, predominantly a male phenomenon, increased substantially between 2001 and 2011. Using the Nepal Living Standard Survey data, the paper extends the analysis by incorporating the impacts on the extensive and intensive margins. The study also answer the question: if they are not wage-employed, in what activities are the remaining household members engaging instead? The paper finds that, in response to out-migration of some family members, women realign their priorities and reallocate their time from market employment to self-employment and home production, possibly filling in the roles vacated by the migrants. In contrast, the income effect dominates the impact of migration on the left-behind men; that is, men value their leisure more because of the remittances from abroad and decrease their overall supply of labor. Additionally, the research finds significant heterogeneity in the supply of labor by age, skill, and household head status among the left-behind women, pointing toward intrahousehold bargaining.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at lphadera@worldbank.org.

_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 be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
29 Table 5: Impact of US and Doha partial reform of cotton subsidies and tariffs on cotton production volume and real value of exports, by region (percent)

||Change in o<br>|utput volu|me (%)|Change in v<br>|alue of exp|orts (%)|
|---|---|---|---|---|---|---|
||Compliance<br>by US to<br>WTO DS<br>panel<sup>a</sup>|Doha<br>partial<br>reform|Full<br>reform|Compliance<br>by US to<br>WTO DS<br>panel<sup>a</sup>|Doha<br>partial<br>reform|Full<br>reform|
|**High-income countries**|**-5.3**|**-7.7**|**-20.4**|**-3.9**|**-6.6**|**-18.2**|
|Australia|8.6|8.3|25.0|11.8|12.3|38.1|
|United States|-9.7|-8.9|-24.6|-11.8|-9.7|-29.0|
|EU25|4.4|-21.7|-54.0|6.1|-18.5|-48.8|
|Japan|1.1|0.3|0.7|13.5|11.8|61.9|
|Korea-Taiwan|4.3|4.2|11.9|8.6|11.3|33.6|
|**Developing countries**|**1.5**|**2.1**|**5.7**|**8.5**|**12.8**|**46.3**|
|**_E. Europe & C. Asia_**|**_1.3_**|**_2.9_**|**_7.0_**|**_5.5_**|**_12.5_**|**_35.9_**|
|Turkey|0.9|1.5|1.9|7.1|16.4|36.6|
|Other ECA|1.6|4.1|10.0|5.3|12.2|35.0|
|**_East Asia_**|**_0.6_**|**_0.8_**|**_2.4_**|**_14.1_**|**_18.8_**|**_71.9_**|
|China|0.5|0.7|2.0|17.4|21.6|75.7|
|**_South Asia_**|**_1.1_**|**_1.4_**|**_1.7_**|**_9.5_**|**_13.7_**|**_54.7_**|
|Bangladesh|1.7|2.1|8.1|8.0|11.2|67.5|
|India|1.0|1.2|-0.6|9.0|13.2|31.1|
|Pakistan|1.2|1.6|4.7|11.7|16.5|60.6|
|**_M. East &North Africa_**|**_1.8_**|**_1.3_**|**_6.2_**|**_9.1_**|**_7.1_**|**_37.4_**|
|**_Sub-Saharan Africa_**|**_5.2_**|**_8.6_**|**_32.0_**|**_8.6_**|**_14.2_**|**_55.0_**|
|South Africa|6.1|7.9|19.4|11.3|14.5|46.5|
|Mozambique|3.8|9.9|19.0|4.9|12.9|29.2|
|<br>Zambia|1.0|2.2|3.8|2.8|6.1|10.8|
|Uganda|5.1|9.1|27.3|8.2|14.6|44.5|
|Other Sthn & E. Africa|3.9|6.5|21.1|8.2|13.4|45.6|
|Nigeria|5.2|6.7|22.7|10.8|13.7|47.4|
|Other Sub-Sah. Africa|5.8|9.8|38.8|8.7|14.7|59.6|
|**_Latin America & Car._**|**_3.5_**|**_4.3_**|**_11.0_**|**_15.3_**|**_15.7_**|**_54.0_**|
|Argentina|2.7|3.5|13.6|12.7|16.3|66.1|
|Brazil|2.2|3.3|9.8|11.0|16.7|57.6|
|Mexico|5.3|5.1|13.0|13.4|14.4|42.3|
|**World**|**-0.2**|**-0.3**|**-0.8**|**1.1**|**1.3**|**7.9**|

a Reduction of one-third in production subsidies (average 2000-2002) in U.S. alone Source: Authors’ GTAP model simulation results
# TABLE OF CONTENTCS

||EXECUTIVE SUMMARY.... . . . . . . . . . . . . . .|. . . .|.|i|
|---|---|---|---|---|
|I.|INTRODUCTIOND||||
||-<br>Sectoral ID inthe<br>Bank<br>.1||||
||-<br>Scope and Plan of Paper<br>... . . . . . . . . .|. . . .|.|3|
|II.|FREQUENCY, SCOPE AND DESIGN OF ID COMPONENTS . . . .|. . . .|. .|4|
||-<br>Evolution of ID Work<br>... . . . . . . . . . . .|. .<br>.|.|4|
||-<br>Institutional Analysis<br>... . . . . . . . . . .|. .<br>.|.|5|
||-<br>Design Features<br>.... . . . . . . . . . . . .|. . . .|.|9|
|III.|PERFORMANCE OF ID COMPONENTS ... . . . . . . . . . .|. . . .|.|14|
||-<br>Patterns of Performance<br>.<br>.<br>.<br>I. . . . . . .|. . . .|. .|14|
||-<br>Emerging Issues<br>.... . . . .<br>... . . . . .|. . . .|.|18|
|IV.|VARYING ID PERFORMANCE: UNDERLYING FACTORS .<br>. . .|. . . .|. .|28|
||-<br>Influence of the Macroeconomic Environment .<br>.|. . . .|. .|28|
||-<br>Impactof Design .... . . . . . . . . . . . .|. . . .|.|29|
||-<br>Implementation Problems<br>... . . . . . . . . .|. . . .|.|29|
||-<br>An In-Depth Analysis of OED Audits<br>. . . . . .|. . . .|. .|30|
|V.|CONCLUSIONS<br>.... . . . . . . . . . . . . . . . . .|. . . .|.|35|
||-<br>RecentTrends<br>.... . . . . . . . . . . . . .|. . . .|.|35|
||-<br>Emerging Challenges<br>... . . . . . . . . . . .|. . . .|.|36|
|VI.|POLICY IMPLICATIONS<br>.... . . . . . . . . . . . . .|. . . .|.|37|
||-<br>Strengthening the Bank's ID Capability . . . .|. . . .|. .|37|
||-<br>Improving the Match between Sector Strategy and|Project|ID|38|
||-<br>Institutional Analysis . . . . . . . . . . . .|. . . .|. .|41|
||-<br>Augmenting Sustainability<br>.<br>.<br>. . . . . . . <br>APPENDIX I .... . . . . . . . . . . . . . . . . . .|. . . . <br> . . . .|. .<br>.|42<br>46|

- _Assistance in the preperation of this paper was provided by 'Stuart Bell, Paige Eaves and Leila_ Zlaoui. _Comments on an earlier draft of this paper were gratefully received frcm_

_Arturo Israel, Mary Shirley, Ashok Subramanian and participants In a seminar held to discuss the paper._
robustness, we re-estimate the final specification (i.e., using (1) the RISE index; (2) the IV based on affinity through United Nations General Assembly voting; and (3) the IV moving average of 5 years) without large hydropower.

We remove hydropower by replacing REC and REO (“Renewable energy consumption” and “Renewable electricity output”) variables with the shares of “Modern Energy Generation over Total Generation” and “Modern Energy Capacity over Total Installed Capacity” as our dependent variables. We refer to the new five dependent variables as the “altered dependent variables.” Results are little changed. Although some regions experienced improvements compared to the results of Table 7 (Appendix Table A.11), the energy policy packages still had a minimal or negative impact on the energy mix, while second stage results still clustered together (Appendix Figure A.7).

Figure A.8 and Table A.12 in the Appendix replicate the main results using the altered dependent variables. The median and mean results are slightly different.

The means of types related to the legal framework, attributes of financial and regulatory incentives, and network connection and use hover close to zero instead of having immediate negative effects. The means of planning for expansion and attributes of financial and regulatory incentives still increase consistently over time, though the means of all remaining three energy policy packages peak in the second period. The energy policy package that deals with counterparty risk remains the one with the highest mean closest to implementation, and it goes down over time.

Medians are negative or close to zero for all energy policy packages, except for attributes of financial and regulatory incentives, which increases over time, and for counterparty risk, which peaks after five years and becomes negative after seven years.

28