rafmacalaba/datause-encoder-data huggingface dataset:
WPS3676

# **TRADE AND EMPLOYMENT: STYLIZED FACTS AND RESEARCH FINDINGS***

## Bernard Hoekman and L. Alan Winters<sup>**</sup>

**Abstract:** The substantial literature investigating the links between trade, trade policy, and labor market outcomes—both returns to labor and employment—has generated a number of stylized facts, but many open questions remain. This paper surveys the subset of the literature focusing on trade policy and integration into the world economy. Although in the longer run trade opportunities can have a major impact in creating more productive and higher paying jobs, this literature tends to take employment as given. A common finding is that much of the shorter run impacts of trade and reforms involve reallocation of labor or wage impacts _within_ sectors. This reflects a pattern of expansion of more productive firms—especially export-oriented or suppliers to exporters—and contraction/adjustment of less productive enterprises in sectors that become subject to greater import competition. Wage responses to trade and trade reforms are generally greater than employment impacts, but trade can only explain a small fraction of the general increase in wage inequality observed in both developed and developing countries in recent decades. A feature of the literature survey is that the focus is almost exclusively on industries producing goods. Given the importance of service industries as a source of employment and determinants of competitiveness, we argue that one priority area for future research is to study the employment effects of services trade and investment reforms.

## World Bank Policy Research Working Paper 3676, August 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* An earlier version of this paper was presented by Hoekman on January 30, 2005 at the IDRC/ECES expert group meeting on trade and employment held at the Egyptian Center for Economic Studies, Cairo. The authors would like to thank Ahmed Galal, Heba Handoussa, José Antonio Ocampo, and participants in the UN DESA Development Forum on “Integrating Economic and Social Policies to Achieve the UN Development Agenda,” held in New York on March 14-15, 2005 for comments on a previous draft.

**  Development Research Group, World Bank. This paper was written while Hoekman was visiting professor of economics at the Groupe d’Economie Mondiale, Institut d’Etudes Politiques, Paris.
community at large: 68% of students in teacher-training schools report that there is an active health club in their schools, compared to 5% in regular schools.

# <u>5.2 Knowledge, attitudes, and behavior of Students After Two years</u>

A self-administered, anonymous Knowledge, Attitudes and Practice (KAP) survey was conducted among students enrolled in grades 6, 7, and 8 in 2005. The survey was conducted more than two years after the teacher training and after two rounds of uniform distribution, but only a few months after the debates on condoms and essay competitions. Table 3 shows the results of OLS regressions for knowledge outcomes. Besides increasing by 5.7 percentage points (15%) the likelihood that boys mention abstinence as a means to prevent HIV infection (column 6), the teacher training program had no impact on practical knowledge. Participation in a debate on condoms increased knowledge about condoms among both girls and boys. Compared to other girls, girls in condoms debate schools are 7.9 percentage points (25%) more likely to mention condom use as a way to protect oneself from HIV infection (column 7); 6.7 percentage points (14%) more likely to think that condoms can prevent pregnancy (column 9); and 7.0 percentage points (13%) more likely to think that condoms can prevent HIV transmission (column 11). Compared to other boys, boys in condom debate schools are 6.1 percentage points (13%) more likely to mention condom use as a way to protect oneself from HIV infection (column 8) and 6.1 percentage points (9%) more likely to think that condoms can prevent pregnancy (column 10).

Table 4 shows the results of OLS regressions for a series of attitudinal outcomes. The teacher training program made girls more tolerant of people who are HIV positive: 18
correctly captured by the wealth quintiles in Appendix 3, Table 3.1.<sup>16</sup> Estimation results are, however, qualitatively similar where taken altogether, the wealth quintiles can correctly capture between 60 percent and 80 percent of the corresponding consumption quintiles. Notably, the correlation between asset indexes and household consumption is higher for the PCA wealth index than for the simple aggregation method (e.g., this correlation is 0.61 for Model 1 in 2012, but increases to 0.67 and 0.70 respectively for Models 2 and 3 in the same year).

These results are broadly consistent with the empirical evidence offered in recent studies. Reviewing 17 studies that analyze 36 different data sets, Howe _et al._ (2009) observe a poor correlation between wealth indexes and household consumption. Filmer and Scott (2012) analyze survey data from 11 countries from four continents and find a similar result. Furthermore, Filmer and Scott (2012) also find that the correlation between wealth indexes and consumption is only stronger under certain, and more demanding, conditions such as urban settings, limited measurement errors, and a small share of individually consumed goods (e.g., food) in total expenditures.<sup>17</sup> The latter caveats can perhaps also be partly explained by the fact that, wealth indexes are a stock measure, rather than a flow measure as with household consumption.

# **III.2. Tracking Welfare over Time with Wealth Indexes**

_Illustrative Example 2_ As earlier discussed, since wealth indexes are biased estimates of household consumption levels, they appear likely to provide biased estimates of consumption trends as well. We provide

> 16 In this case, the poverty line can equal the 20th, 40th, or 60th (and so on) percentile of the wealth index distribution. 17 Some other practical issues with using wealth indexes to measure poverty are that it is unclear how to set the poverty line for wealth indexes (compared with the more established calorie-intake or cost-of-basic-needs with the consumption-based poverty line) or that asset ownership may also be affected by factors related to supply but not household consumption (e.g., inadequate water supply may result in less ownership of flush toilets or washing machines). An alternative approach is to collect data on a reduced set of consumption items that may offer strong correlation with the total consumption aggregate (Morris _et al._ , 2000). Another approach is to produce households’ ranks in the population with the number of consumption items they own, if we make the additional assumption that households place an order of importance on their consumption items when having to reduce their consumption expenditure (Deutsch, Silber, and Wang, 2017).

16
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For the first time, inspectors (a special arm of employment offices) would check to see if benefit recipients were in fact unemployed (among other things, by paying home visits to benefit recipients) and actively searching for a job.

# **3 Data**

## **3.1 Data sources**

The analysis is based on administrative records of unemployment spells for the recipients of unemployment benefits, as well as of their post-unemployment employment spells, for all unemployment spells that started during August 1, 1997 - July 31, 1998 and during the period January 1, 1998 - December 31, 1999 (with censoring on December 31, 2001). The following data sets were used:

- The data set on registered unemployed is from the National Employment Office of Slovenia. For each spell, it contains starting and ending dates of registered unemployment, destination of exit, and information on the receipt of unemployment insurance benefits (starting and ending date of the eligibility and actual ending date of the receipt). Personal and family characteristics of recipients are also included.

- The work history data set is from the Statistical Office of Slovenia. For all formal sector workers, the data includes information about their employment spells. The data are obtained from social insurance records and contain information on the starting and ending date, the type of appointment, occupation, and personal characteristics (gender, age, education).

- The workers’ earnings data set is from the Pension and Disability Fund and contains information on earnings associated with each employment spell of workers employed in the formal sector. For each year (or part of the employment spell within a year) the information collected includes the amount of earnings, the number of hours worked in regular time and overtime, and the starting and ending date of the earnings period.

The data sources provide exceptionally rich and high quality information. First, they provide a complete coverage - all registered unemployed in the selected period 7
<!-- Start of picture text --> Government Domestic/Foreign Debt Government area ceo Debi<br>Selecied Developing Countries Selected Developing Countries<br>circa 1990 and 1995 1995 2004<br>: 6<br>Equal Ratio<br>* * .<br>Equal Featio 5 1995 & 20 eC<br>1990 &<br>}a ;ple<br>i, Cari,<br>A 1 a<br>x<br>+ +<br>o-* ‘ aL<br>Domestic/Foreign circa 1990 Domestic/Foreign 1995<br><!-- End of picture text -->
returns to education measured in permanent household income do not differ significantly across farm and nonfarm households in rural China during the study period is robust to inclusion of number of children in the household as a control (see online appendix Table A.5). _The evidence on returns to education when put together with the evidence on complementarity discussed earlier in Table 1 provide a theoretically consistent explanation: a lack of difference in returns to education across farm and non-farm occupations leads to separability between father’s education and nonfarm occupation in determining son’s schooling in rural China._ Also, the magnitude of _θ_ 2 is likely to be low in rural China during the relevant period (the children who went to school during 1970s-1990s) which would reduce the impact of any emerging advantage in favor of nonfarm households in returns to education. Recall that _θ_ 2 is the efficiency of parental investment, determined primarily by the supply side of the education market such as availability of high-quality private schools. In China, the availability of private schools was limited; in 1996, only 4 percent of the schools in China were private (Kwong (1996)). Most of the private schools in rural areas in the 1990s were primary schools with limited facilities and equipment, and they catered to children from the low-income households. At the secondary level, the private schools primarily met the demand by the students who were unsuccessful in the admission test given after grade 9 to screen for the senior secondary public schools (Lin (1999)). This implies that, in contrast to many other countries, any quality advantage in education in rural China is associated with better quality public schools. While local financing and various types of fees increasingly played a role in public schools after the fiscal decentralization, it is unlikely to create a significant impact on the magnitude of _θ_ 2 for the following reason: the share of private expenditure remained small compared to the public expenditure during the 1980s and 1990s; for example, tuition and other fees paid by the parents amount to only 4.42 percent of total educational expenditure in 1991 and 10.72 percent in 1995 (see Table 7.2 in Hannum et al. (2008)).

# <u>Rural India</u>

For the estimates of IGRCs across farm and nonfarm households in India to be consistent with the extended Becker-Tomes model of section (2), the returns to schooling in nonfarm households need to be _higher_ than that in the farming households in the parental generation. Table 4 (panel B) reports the estimates of household-level returns to education in rural India using household expenditure data from the NSS 1993 survey (the employment and 24
using data from the base year, we only require an explicit calibration of four parameters. Namely: the DNWR parameter _δ_ , the growth in world nominal GDP in dollars _γ_ , the inverse elasticity of mobility across sectors _ν_ , and the trade elasticity 1 _− σ_ . We normalize _δ_ to one, indicating that nominal wages cannot fall, without loss of generality. We then set _γ_ to 4%, in line with the relatively high nominal growth rate observed in the post-pandemic period, but we explore the robustness of our results to different values. Finally, we obtain _ν_ directly from RUV, and we take _σ_ from the trade literature.

Our model-based analysis shows a temporary yet long-lasting decrease in U.S. labor force participation due to the shock. During the high trade cost period, engaging in the home production sector (which provides a constant utility flow) becomes more appealing, resulting in a decline in labor force participation. Once the shock dissipates, there is negative pressure on nominal wages as the economy adjusts to the lower trade costs, which in the presence of DNWR can generate unemployment. The manufacturing sector, where increase the most the trade cost the most unem- wages during high period, experiences ployment when the shock dissipates.

The impact of the trade cost shock on the labor market varies by sector. There is a temporary employment increase in manufacturing, a sector that is very tradable and where the United States is a net importer. By contrast, the service and agriculture sectors experience temporary employment reductions. We also find that the effect of the shock varies by state. States with larger service sectors (such as Alaska, Nevada, and Hawaii) tend to experience a more significant decline in labor force participation than those with larger manufacturing sectors (such as Ohio, Pennsylvania, and Wisconsin), but additional factors such as the exposure to other countries and states also play an important role in determining the extent of the impact.

The impact of the shock on labor force participation varies internationally and depends on size and trade openness. On the one hand, countries like China, the United States, Brazil, and India, which are relatively large and less reliant on international trade, experience a smaller decrease in labor force participation. On the other hand, small and open countries like Ireland, Estonia, the Slovakia Republic, and Cyprus, which rely heavily on foreign intermediate inputs and importing/exporting, experience a more signifi3
# **References**

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Attewell, Paul, and Juan Battle. 1999. "Home Computers and School Performance." _The Information Society_ 15(1): 1-10. <u>https://doi.org/10.1080/019722499128628</u> Angrist, Joshua and Victor Lavy. 2002. “New Evidence on Classroom Computers and Pupil Learning.” _The Economic Journal_ , _112_ (482): 735-765. https://doi.org/10.1111/1468-0297.00068 <mark>Banerjee, Abhijit V., Shawn Cole, Esther Duflo, and Leigh Linden. 2007. “Remedying Education: Evidence from Two Randomized Experiments in India.”</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>,</mark> _<mark>122</mark>_ <mark>(3): 1235-1264. https://doi.org/10.1162/qjec.122.3.1235</mark> <mark>Banerjee, Abhijit V., Rukmini Banerji, James Berry, Esther Duflo, Harini Kannan, Shobhini Mukherji, Marc Shotland, and Michael Walton. 2016. “Mainstreaming an Effective Intervention: Evidence from Randomized Evaluations of “Teaching at the Right Level” in India.” NBER Working Paper No. 22746. National Bureau of Economic Research. https://doi.org/10.3386/w22746</mark> Banerjee, Abhijit V., Rukmini Banerji, Esther Duflo, Rachel Glennerster, and Stuti Khemani. 2010. “Pitfalls of Participatory Programs: Evidence from a Randomized Evaluation in Education in India.” _American Economic Journal: Economic Policy_ 2(1): 1–30. <u>http://www.aeaweb.org/articles.php?doi=10.1257/pol.2.1.1</u> Barrera-Osorio, Felipe and Leigh L. Linden. 2009. “The Use and Misuse of Computers in Education: Evidence from a Randomized Experiment in Colombia.” Impact Evaluation Series, Policy Research Working Paper No. WPS4836. Washington, DC: World Bank. <u>http://documents.worldbank.org/curated/en/346301468022433230/The-use-and-misuse-of-computers-ineducation-evidence-from-a-randomized-experiment-in-Colombia</u> Beatty, Amanda, and Lant Pritchett. 2012. "From Schooling Goals to Learning Goals _._ " CGD Policy Paper 012. Washington, DC: Center for Global Development. <u>http://www.cgdev.org/content/publications/detail/1426531</u> Beuermann, Diether W., Julian Cristia, Santiago Cueto, Ofer Malamud, and Yyannu Cruz-Aguayo. 2015. “One Laptop per Child at Home: Short-term Impacts from a Randomized Experiment in Peru.” _American Economic Journal: Applied Economics_ , 7(2): 53-80. <u>http://dx.doi.org/10.1257/app.20130267</u> Bold, Tessa, Deon Filmer, Gayle Martin, Ezequiel Molina, Brian Stacy, Christophe Rockmore, Jakob Svensson, and Waly Wane. 2017. “Enrollment without Learning: Teacher Effort, Knowledge, and Skill in Primary Schools in Africa.” _Journal of Economic Perspectives_ , _31_ (4), 185-204. <u>https://doi.org/10.1257/jep.31.4.185</u> Bulman, George, and Robert W. Fairlie. 2016. “Technology and Education: Computers, Software, and the Internet.” _Handbook of the Economics of Education_ 5: 239-280. https://doi.org/10.1016/B978-0-444- <u>63459-7.00005-1</u> Butler, Declan. 2007. “The Race to Wire up the Poor.” _Nature_ 447(6-7). https://doi.org/10.1038/447006a 13
**Table** **<u>3. Predicted production increases by halving the port waiting time at Dar es Salaams</u>**

||Increase i<br>Coffee|n prod<br>Tea|uction va<br>Cotton|lue ($ milli<br>Tobacco|on)<br>Total|% of GDP<br>(2010)<br>|
|---|---|---|---|---|---|---|
|Burundi|17.1|0.5|0.4|0.3|18.3|<br>0.90|
|Kenya|0.0|0.0|0.0|0.0|0.0|<br>0.00|
|Rwanda|12.4|1.4|0.0|1.0|14.8|<br>0.26|
|Tanzania|13.1|2.7|72.2|29.8|117.7|<br>0.51|
|Uganda|0.9|0.2|0.2|0.2|1.4|<br>0.01|
|Total|43.4|4.8|72.8|31.2|152.2|<br>0.19|

Source: Authors’ calculation based on the SPTobit estimation results.

|**Table 4. Pr**|**edicted p**<br>Increase<br>|**roducti**<br>in prod<br>|**on incre**<br>uction va<br>|**ases by ha**<br>lue ($ milli<br>|**lving th**<br>on)<br>|**e port waiting time at Dar es Salaams**<br>% of GDP<br><br>|
|---|---|---|---|---|---|---|
||Coffee|Tea|Cotton|Tobacco|Total|(2010)<br>|
|Burundi|14.3|0.4|0.3|0.2|15.3|<br>0.75|
|Kenya|109.8|181.9|128.9|19.1|439.6|<br>1.37|
|Rwanda|10.9|1.3|0.0|0.9|13.0|<br>0.23|
|Tanzania|12.4|1.7|69.7|20.3|104.1|<br>0.45|
|Uganda|167.4|2.9|13.4|4.8|188.4|<br>1.10|
|Total|314.8|188.1|212.3|45.3|760.4|<br>0.95|

Source: Authors’ calculation based on the SPTobit estimation results.

# **V. CONCLUSION**

Africa has great potential for agriculture. However, the potential has not been fully explored yet. It is important to accelerate agricultural growth further, because Africa is currently importing significant amounts of food from abroad, though Africa can feed itself from the agro-ecological point of view.

The literature suggests a lot of constraints, from fertilizer use to public infrastructure availability. The current paper focuses on the relationship between major crop production and transport connectivity in the East Africa Community. Unlike the existing literature, the paper generates new agricultural production data for each crop at the detailed spatial resolution. In addition, spatial data is also developed to measure transport connectivity from each production location to the destination, either domestic market or port to export crops.
Figure 5: First Order Marginal Dominance Comparison by Household Composition <!-- Start of picture text --> 120 1989<br>100 . -- _ 1<br>80<br>80 =____ 0-5<br>60 e'des......... ,<br>300 . - ._. All households<br>20<br>0<br>0.25 0.5 0.75 1 1.25 1.5<br>90 19<br>80 119<br>70<br>60 0 ___<br>50 --- de--<br>40 ------ .All households~<br>30<br>20<br>10<br>0<br>0.25 0.5 0.75 1 1.25 1.5<br>300 -<br>250 . ..... . ~~~ .. ~~~~~ .. ~ ~~... 1991<br>2500<br>0-<br>150 I---- - elders<br>100 - .~~~~~~~~~ - -. -. ~~~~~~---- Alhouseholds<br>500<br>50 I<br>0.25 0.5 0.75 1 1.25 1.5<br><!-- End of picture text --> Note: Alternative poverty lines represent fractions of JS6 198 (in 1991 dollars, which is the third decile of 1989 percapita expenditure).

34
<!-- Start of picture text --> Employment in agriculture - Tunisia vs MENA<br>(% of total employment) (modeled ILO estimate)<br>28.00<br>26.00<br>.<br>24.00 ne<br>22.00 ——<br>20.00 Ts<br>18.00 eee<br>16.00 ie<br>—<br>14.00<br>12.00<br>10.00<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>——Tunisia —- —-—-MENA<br><!-- End of picture text --> Employment in agriculture in Tunisia - Female vs Male (% of total employment) (modeled ILO estimate) 30.00 25.00 20.00 TSS eS LL 15.00 aa 10.00 5.00 0.00 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 ——Female — — —Male